Master of Electrical Engineering

Course code: MC-ELECENG

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Domestic students

domestic

International students

international

Duration

3 years full time / 6 years part time

2 years full time (or part time equivalent) with relevant prior qualifications

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Mode (Location)

On campus (Parkville)

Intake

March, July

Key dates

Fees

Commonwealth Supported Places (CSPs) available

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Entry schemes

Access Melbourne is available

Learn more

How to apply

Enquire

Duration

3 years full time

2 years full time with relevant prior qualifications

Check entry points

Mode (Location)

On campus (Parkville)

Intake

March, July

Key dates

Fees

AUD $62,976 (2026 indicative first year fee)

Learn more

English language requirements

IELTS 6.5: with no band less than 6.0

View full entry requirements

CRICOS code

106106J

How to apply

Enquire

Overview

The Master of Electrical Engineering is a 2–3 year degree (full-time) depending on your prior study.

Course structure

First year

In your first year (or equivalent) you’ll complete foundation engineering subjects – tailored to students from a non-engineering background. If you’ve completed the Electrical Systems major in the Bachelor of Science at the University of Melbourne, plus the required maths and science subjects, you’ll receive credit for these foundation engineering subjects and start in second year. If you’ve completed another science or engineering degree, you may be eligible for advanced standing.

Second and third year

In the second and third year (or equivalent) you’ll focus on your chosen engineering discipline. You will be guided in acquiring core skills in electronics, control, signal processing, communications and power systems. Learn from leading experts in power systems, energy-efficient telecommunications systems and sensor networks that monitor the environment.

You’ll undertake an industry, design or research project and gain the skills and knowledge to practice as a professional engineer.

Choose your specialisation

As a Master of Electrical Engineering student, you can pursue your career goals and interests through one of five specialisations, or you can choose not to specialise if you’d prefer.

Artificial Intelligence

Develop expertise in the design, implementation and analysis of systems that learn, plan and reason.

Autonomous Systems

Study the fundamental principles of electrical engineering, focusing on the areas that underpin modern autonomous systems in everything from robotics to UAVs – control systems, signal processing and optimisation.

Business

Study tailored business subjects developed in partnership with the Melbourne Business School, covering how economics, marketing and finance relate to engineering.

Electronics and Embedded Systems

Study the fundamental principles in electronic engineering, with an emphasis on designing and building electronic and opto-electronic systems for applications in modern communications, computing, instrumentation and sensing.

Low-Carbon Power Systems

Develop expertise in the operation, planning and design of low-carbon power systems and learn about energy markets with deep penetration of renewables, distributed energy resources and smart grid technologies.

Learn more about FEIT specialisations

Industry, design and research subjects

Internship subject

Enhance your skills and build work experience through our academically credited Internship subject. Run over 10–15 weeks, you could intern at a biotechnology, aerostructures, oil and gas, automation, technical consulting, power solutions or computing devices organisation.

Creating Innovative Engineering subject

Work on a real-world innovation challenge with an industry mentor through our Creating Innovative Engineering subject.

Engineering Capstone subject

Work alongside our world-leading engineering researchers in our Engineering Capstone Subject. Access an industry-partnered project, or pursue your own exploratory research. You’ll have the opportunity to present the findings to the public at our annual engineering showcase, the Endeavour Engineering and IT Exhibition .

Handbook entries

Master of Electrical Engineering

Sample course plan

View some sample course plans to help you select subjects that will meet the requirements for this coursework.

Semester 1 entry: Artificial Intelligence

Accordion
Year 1 100 pts
Semester 1 · 50 pts	Foundations of Electrical Networks – core – ELEN20005 – 12.5 pts Intro. to Numerical Computation in C – core – COMP20005 – 12.5 pts Engineering Mathematics – core – MAST20029 – 12.5 pts Digital Systems – core – ELEN20006 – 12.5 pts
Semester 2 · 50 pts	Electrical Network Analysis and Design – core – ELEN30009 – 12.5 pts Electrical Device Modelling – core – ELEN30011 – 12.5 pts Signals and Systems – core – ELEN30012 – 12.5 pts Electronic System Implementation – core – ELEN30013 – 12.5 pts

Accordion

Year 1

100 pts

Semester 1 · 50 pts

Foundations of Electrical Networks – core – ELEN20005 – 12.5 pts
Intro. to Numerical Computation in C – core – COMP20005 – 12.5 pts
Engineering Mathematics – core – MAST20029 – 12.5 pts
Digital Systems – core – ELEN20006 – 12.5 pts

Semester 2 · 50 pts

Electrical Network Analysis and Design – core – ELEN30009 – 12.5 pts
Electrical Device Modelling – core – ELEN30011 – 12.5 pts
Signals and Systems – core – ELEN30012 – 12.5 pts
Electronic System Implementation – core – ELEN30013 – 12.5 pts

Accordion
Year 2 100 pts
Semester 1 · 50 pts	Electronic Circuit Design – core – ELEN90056 – 12.5 pts Probability and Random Models – core – ELEN90054 – 12.5 pts Interdisciplinary Design for Engineers – core – ENGR90051 – 12.5 pts Introduction to Power Engineering – core – ELEN90074 – 12.5 pts
Semester 2 · 50 pts	Signal Processing – core – ELEN90058 – 12.5 pts Communication Systems – core – ELEN90057 – 12.5 pts Embedded System Design – core – ELEN90066 – 12.5 pts Control Systems – core – ELEN90055 – 12.5 pts

Accordion

Year 2

100 pts

Semester 1 · 50 pts

Electronic Circuit Design – core – ELEN90056 – 12.5 pts
Probability and Random Models – core – ELEN90054 – 12.5 pts
Interdisciplinary Design for Engineers – core – ENGR90051 – 12.5 pts
Introduction to Power Engineering – core – ELEN90074 – 12.5 pts

Semester 2 · 50 pts

Signal Processing – core – ELEN90058 – 12.5 pts
Communication Systems – core – ELEN90057 – 12.5 pts
Embedded System Design – core – ELEN90066 – 12.5 pts
Control Systems – core – ELEN90055 – 12.5 pts

Accordion
Year 3 100 pts
Semester 1 · 50 pts	System Optimisation & Machine Learning – core – ELEN90088 – 12.5 pts Modelling and Analysis for AI – core – ELEN90097 – 12.5 pts elective – 12.5 pts Engineering Capstone Project Part 1 – capstone – ENGR90037 – 12.5 pts
Semester 2 · 50 pts	Applied Deep Learning for Engineers – core – ELEN90099 – 12.5 pts Reinforcement Learning for Engineering – core – ELEN90098 – 12.5 pts elective – 12.5 pts Engineering Capstone Project Part 2 – capstone – ENGR90038 – 12.5 pts

Accordion

Year 3

100 pts

Semester 1 · 50 pts

System Optimisation & Machine Learning – core – ELEN90088 – 12.5 pts
Modelling and Analysis for AI – core – ELEN90097 – 12.5 pts
elective – 12.5 pts
Engineering Capstone Project Part 1 – capstone – ENGR90037 – 12.5 pts

Semester 2 · 50 pts

Applied Deep Learning for Engineers – core – ELEN90099 – 12.5 pts
Reinforcement Learning for Engineering – core – ELEN90098 – 12.5 pts
elective – 12.5 pts
Engineering Capstone Project Part 2 – capstone – ENGR90038 – 12.5 pts

Semester 1 entry: Autonomous Systems

Accordion
Year 1 100 pts
Semester 1 · 50 pts	Foundations of Electrical Networks – core – ELEN20005 – 12.5 pts Intro. to Numerical Computation in C – core – COMP20005 – 12.5 pts Engineering Mathematics – core – MAST20029 – 12.5 pts Digital Systems – core – ELEN20006 – 12.5 pts
Semester 2 · 50 pts	Electrical Network Analysis and Design – core – ELEN30009 – 12.5 pts Electrical Device Modelling – core – ELEN30011 – 12.5 pts Signals and Systems – core – ELEN30012 – 12.5 pts Electronic System Implementation – core – ELEN30013 – 12.5 pts

Accordion

Year 1

100 pts

Semester 1 · 50 pts

Foundations of Electrical Networks – core – ELEN20005 – 12.5 pts
Intro. to Numerical Computation in C – core – COMP20005 – 12.5 pts
Engineering Mathematics – core – MAST20029 – 12.5 pts
Digital Systems – core – ELEN20006 – 12.5 pts

Semester 2 · 50 pts

Electrical Network Analysis and Design – core – ELEN30009 – 12.5 pts
Electrical Device Modelling – core – ELEN30011 – 12.5 pts
Signals and Systems – core – ELEN30012 – 12.5 pts
Electronic System Implementation – core – ELEN30013 – 12.5 pts

Accordion
Year 2 100 pts
Semester 1 · 50 pts	Electronic Circuit Design – core – ELEN90056 – 12.5 pts Probability and Random Models – core – ELEN90054 – 12.5 pts Interdisciplinary Design for Engineers – core – ENGR90051 – 12.5 pts Introduction to Power Engineering – core – ELEN90074 – 12.5 pts
Semester 2 · 50 pts	Signal Processing – core – ELEN90058 – 12.5 pts Communication Systems – core – ELEN90057 – 12.5 pts Embedded System Design – core – ELEN90066 – 12.5 pts Control Systems – core – ELEN90055 – 12.5 pts

Accordion

Year 2

100 pts

Semester 1 · 50 pts

Electronic Circuit Design – core – ELEN90056 – 12.5 pts
Probability and Random Models – core – ELEN90054 – 12.5 pts
Interdisciplinary Design for Engineers – core – ENGR90051 – 12.5 pts
Introduction to Power Engineering – core – ELEN90074 – 12.5 pts

Semester 2 · 50 pts

Signal Processing – core – ELEN90058 – 12.5 pts
Communication Systems – core – ELEN90057 – 12.5 pts
Embedded System Design – core – ELEN90066 – 12.5 pts
Control Systems – core – ELEN90055 – 12.5 pts

Accordion
Year 3 100 pts
Semester 1 · 50 pts	Advanced Signal Processing – core – ELEN90052 – 12.5 pts Autonomous Systems Clinic – core – ELEN90090 – 12.5 pts elective – 12.5 pts Engineering Capstone Project Part 1 – capstone – ENGR90037 – 12.5 pts
Semester 2 · 50 pts	Advanced Control Systems – core – ELEN90064 – 12.5 pts AI for Robotics – core – ELEN90095 – 12.5 pts elective – 12.5 pts Engineering Capstone Project Part 2 – capstone – ENGR90038 – 12.5 pts

Accordion

Year 3

100 pts

Semester 1 · 50 pts

Advanced Signal Processing – core – ELEN90052 – 12.5 pts
Autonomous Systems Clinic – core – ELEN90090 – 12.5 pts
elective – 12.5 pts
Engineering Capstone Project Part 1 – capstone – ENGR90037 – 12.5 pts

Semester 2 · 50 pts

Advanced Control Systems – core – ELEN90064 – 12.5 pts
AI for Robotics – core – ELEN90095 – 12.5 pts
elective – 12.5 pts
Engineering Capstone Project Part 2 – capstone – ENGR90038 – 12.5 pts

Semester 1 entry: Business

Accordion
Year 1 100 pts
Semester 1 · 50 pts	Foundations of Electrical Networks – core – ELEN20005 – 12.5 pts Intro. to Numerical Computation in C – core – COMP20005 – 12.5 pts Engineering Mathematics – core – MAST20029 – 12.5 pts Digital Systems – core – ELEN20006 – 12.5 pts
Semester 2 · 50 pts	Electrical Network Analysis and Design – core – ELEN30009 – 12.5 pts Electrical Device Modelling – core – ELEN30011 – 12.5 pts Signals and Systems – core – ELEN30012 – 12.5 pts Electronic System Implementation – core – ELEN30013 – 12.5 pts

Accordion

Year 1

100 pts

Semester 1 · 50 pts

Foundations of Electrical Networks – core – ELEN20005 – 12.5 pts
Intro. to Numerical Computation in C – core – COMP20005 – 12.5 pts
Engineering Mathematics – core – MAST20029 – 12.5 pts
Digital Systems – core – ELEN20006 – 12.5 pts

Semester 2 · 50 pts

Electrical Network Analysis and Design – core – ELEN30009 – 12.5 pts
Electrical Device Modelling – core – ELEN30011 – 12.5 pts
Signals and Systems – core – ELEN30012 – 12.5 pts
Electronic System Implementation – core – ELEN30013 – 12.5 pts

Accordion
Year 2 100 pts
Semester 1 · 50 pts	Electronic Circuit Design – core – ELEN90056 – 12.5 pts Probability and Random Models – core – ELEN90054 – 12.5 pts Interdisciplinary Design for Engineers – core – ENGR90051 – 12.5 pts Introduction to Power Engineering – core – ELEN90074 – 12.5 pts
Semester 2 · 50 pts	Signal Processing – core – ELEN90058 – 12.5 pts Communication Systems – core – ELEN90057 – 12.5 pts Embedded System Design – core – ELEN90066 – 12.5 pts Control Systems – core – ELEN90055 – 12.5 pts

Accordion

Year 2

100 pts

Semester 1 · 50 pts

Electronic Circuit Design – core – ELEN90056 – 12.5 pts
Probability and Random Models – core – ELEN90054 – 12.5 pts
Interdisciplinary Design for Engineers – core – ENGR90051 – 12.5 pts
Introduction to Power Engineering – core – ELEN90074 – 12.5 pts

Semester 2 · 50 pts

Signal Processing – core – ELEN90058 – 12.5 pts
Communication Systems – core – ELEN90057 – 12.5 pts
Embedded System Design – core – ELEN90066 – 12.5 pts
Control Systems – core – ELEN90055 – 12.5 pts

Accordion
Year 3 100 pts
Semester 1 · 50 pts	Economic Analysis for Engineers – core – ENGM90011 – 12.5 pts Strategy Execution for Engineers – core – ENGM90013 – 12.5 pts elective – 12.5 pts Engineering Capstone Project Part 1 – capstone – ENGR90037 – 12.5 pts
Semester 2 · 50 pts	Engineering Contracts and Procurement – core – ENGM90006 – 12.5 pts Marketing Management for Engineers – core – ENGM90012 – 12.5 pts elective – 12.5 pts Engineering Capstone Project Part 2 – capstone – ENGR90038 – 12.5 pts

Accordion

Year 3

100 pts

Semester 1 · 50 pts

Economic Analysis for Engineers – core – ENGM90011 – 12.5 pts
Strategy Execution for Engineers – core – ENGM90013 – 12.5 pts
elective – 12.5 pts
Engineering Capstone Project Part 1 – capstone – ENGR90037 – 12.5 pts

Semester 2 · 50 pts

Engineering Contracts and Procurement – core – ENGM90006 – 12.5 pts
Marketing Management for Engineers – core – ENGM90012 – 12.5 pts
elective – 12.5 pts
Engineering Capstone Project Part 2 – capstone – ENGR90038 – 12.5 pts

Semester 1 entry: Electronics and Embedded Systems

Accordion
Year 1 100 pts
Semester 1 · 50 pts	Foundations of Electrical Networks – core – ELEN20005 – 12.5 pts Intro. to Numerical Computation in C – core – COMP20005 – 12.5 pts Engineering Mathematics – core – MAST20029 – 12.5 pts Digital Systems – core – ELEN20006 – 12.5 pts
Semester 2 · 50 pts	Electrical Network Analysis and Design – core – ELEN30009 – 12.5 pts Electrical Device Modelling – core – ELEN30011 – 12.5 pts Signals and Systems – core – ELEN30012 – 12.5 pts Electronic System Implementation – core – ELEN30013 – 12.5 pts

Accordion

Year 1

100 pts

Semester 1 · 50 pts

Foundations of Electrical Networks – core – ELEN20005 – 12.5 pts
Intro. to Numerical Computation in C – core – COMP20005 – 12.5 pts
Engineering Mathematics – core – MAST20029 – 12.5 pts
Digital Systems – core – ELEN20006 – 12.5 pts

Semester 2 · 50 pts

Electrical Network Analysis and Design – core – ELEN30009 – 12.5 pts
Electrical Device Modelling – core – ELEN30011 – 12.5 pts
Signals and Systems – core – ELEN30012 – 12.5 pts
Electronic System Implementation – core – ELEN30013 – 12.5 pts

Accordion
Year 2 100 pts
Semester 1 · 50 pts	Electronic Circuit Design – core – ELEN90056 – 12.5 pts Probability and Random Models – core – ELEN90054 – 12.5 pts Interdisciplinary Design for Engineers – core – ENGR90051 – 12.5 pts Introduction to Power Engineering – core – ELEN90074 – 12.5 pts
Semester 2 · 50 pts	Signal Processing – core – ELEN90058 – 12.5 pts Communication Systems – core – ELEN90057 – 12.5 pts Embedded System Design – core – ELEN90066 – 12.5 pts Control Systems – core – ELEN90055 – 12.5 pts

Accordion

Year 2

100 pts

Semester 1 · 50 pts

Electronic Circuit Design – core – ELEN90056 – 12.5 pts
Probability and Random Models – core – ELEN90054 – 12.5 pts
Interdisciplinary Design for Engineers – core – ENGR90051 – 12.5 pts
Introduction to Power Engineering – core – ELEN90074 – 12.5 pts

Semester 2 · 50 pts

Signal Processing – core – ELEN90058 – 12.5 pts
Communication Systems – core – ELEN90057 – 12.5 pts
Embedded System Design – core – ELEN90066 – 12.5 pts
Control Systems – core – ELEN90055 – 12.5 pts

Accordion
Year 3 100 pts
Semester 1 · 50 pts	Microprocessor Design Clinic – core – ELEN90093 – 12.5 pts elective – 12.5 pts elective – 12.5 pts Engineering Capstone Project Part 1 – capstone – ENGR90037 – 12.5 pts
Semester 2 · 50 pts	Electronic System Design – core – ELEN90053 – 12.5 pts High Speed Electronics – core – ELEN90062 – 12.5 pts Semiconductor Devices – core – ELEN90091 – 12.5 pts Engineering Capstone Project Part 2 – capstone – ENGR90038 – 12.5 pts

Accordion

Year 3

100 pts

Semester 1 · 50 pts

Microprocessor Design Clinic – core – ELEN90093 – 12.5 pts
elective – 12.5 pts
elective – 12.5 pts
Engineering Capstone Project Part 1 – capstone – ENGR90037 – 12.5 pts

Semester 2 · 50 pts

Electronic System Design – core – ELEN90053 – 12.5 pts
High Speed Electronics – core – ELEN90062 – 12.5 pts
Semiconductor Devices – core – ELEN90091 – 12.5 pts
Engineering Capstone Project Part 2 – capstone – ENGR90038 – 12.5 pts

Semester 1 entry: Low-Carbon Power Systems

Accordion
Year 1 100 pts
Semester 1 · 50 pts	Foundations of Electrical Networks – core – ELEN20005 – 12.5 pts Intro. to Numerical Computation in C – core – COMP20005 – 12.5 pts Engineering Mathematics – core – MAST20029 – 12.5 pts Digital Systems – core – ELEN20006 – 12.5 pts
Semester 2 · 50 pts	Electrical Network Analysis and Design – core – ELEN30009 – 12.5 pts Electrical Device Modelling – core – ELEN30011 – 12.5 pts Signals and Systems – core – ELEN30012 – 12.5 pts Electronic System Implementation – core – ELEN30013 – 12.5 pts

Accordion

Year 1

100 pts

Semester 1 · 50 pts

Foundations of Electrical Networks – core – ELEN20005 – 12.5 pts
Intro. to Numerical Computation in C – core – COMP20005 – 12.5 pts
Engineering Mathematics – core – MAST20029 – 12.5 pts
Digital Systems – core – ELEN20006 – 12.5 pts

Semester 2 · 50 pts

Electrical Network Analysis and Design – core – ELEN30009 – 12.5 pts
Electrical Device Modelling – core – ELEN30011 – 12.5 pts
Signals and Systems – core – ELEN30012 – 12.5 pts
Electronic System Implementation – core – ELEN30013 – 12.5 pts

Accordion
Year 2 100 pts
Semester 1 · 50 pts	Electronic Circuit Design – core – ELEN90056 – 12.5 pts Probability and Random Models – core – ELEN90054 – 12.5 pts Interdisciplinary Design for Engineers – core – ENGR90051 – 12.5 pts Introduction to Power Engineering – core – ELEN90074 – 12.5 pts
Semester 2 · 50 pts	Signal Processing – core – ELEN90058 – 12.5 pts Communication Systems – core – ELEN90057 – 12.5 pts Embedded System Design – core – ELEN90066 – 12.5 pts Control Systems – core – ELEN90055 – 12.5 pts

Accordion

Year 2

100 pts

Semester 1 · 50 pts

Electronic Circuit Design – core – ELEN90056 – 12.5 pts
Probability and Random Models – core – ELEN90054 – 12.5 pts
Interdisciplinary Design for Engineers – core – ENGR90051 – 12.5 pts
Introduction to Power Engineering – core – ELEN90074 – 12.5 pts

Semester 2 · 50 pts

Signal Processing – core – ELEN90058 – 12.5 pts
Communication Systems – core – ELEN90057 – 12.5 pts
Embedded System Design – core – ELEN90066 – 12.5 pts
Control Systems – core – ELEN90055 – 12.5 pts

Accordion
Year 3 100 pts
Semester 1 · 50 pts	Grid Integration of Renewables – core – ELEN90077 – 12.5 pts Power System Analysis – core – ELEN90060 – 12.5 pts elective – 12.5 pts Engineering Capstone Project Part 1 – capstone – ENGR90037 – 12.5 pts
Semester 2 · 50 pts	Power Electronics – core – ELEN90075 – 12.5 pts Low-carbon Grids: Operation & Economics – core – ELEN90092 – 12.5 pts elective – 12.5 pts Engineering Capstone Project Part 2 – capstone – ENGR90038 – 12.5 pts

Accordion

Year 3

100 pts

Semester 1 · 50 pts

Grid Integration of Renewables – core – ELEN90077 – 12.5 pts
Power System Analysis – core – ELEN90060 – 12.5 pts
elective – 12.5 pts
Engineering Capstone Project Part 1 – capstone – ENGR90037 – 12.5 pts

Semester 2 · 50 pts

Power Electronics – core – ELEN90075 – 12.5 pts
Low-carbon Grids: Operation & Economics – core – ELEN90092 – 12.5 pts
elective – 12.5 pts
Engineering Capstone Project Part 2 – capstone – ENGR90038 – 12.5 pts

Semester 1 entry: no specialisation

Accordion
Year 1 100 pts
Semester 1 · 50 pts	Intro. to Numerical Computation in C – core – COMP20005 – 12.5 pts Foundations of Electrical Networks – core – ELEN20005 – 12.5 pts Digital Systems – core – ELEN20006 – 12.5 pts Engineering Mathematics – core – MAST20029 – 12.5 pts
Semester 2 · 50 pts	Electrical Network Analysis and Design – core – ELEN30009 – 12.5 pts Electrical Device Modelling – core – ELEN30011 – 12.5 pts Signals and Systems – core – ELEN30012 – 12.5 pts Electronic System Implementation – core – ELEN30013 – 12.5 pts

Accordion

Year 1

100 pts

Semester 1 · 50 pts

Intro. to Numerical Computation in C – core – COMP20005 – 12.5 pts
Foundations of Electrical Networks – core – ELEN20005 – 12.5 pts
Digital Systems – core – ELEN20006 – 12.5 pts
Engineering Mathematics – core – MAST20029 – 12.5 pts

Semester 2 · 50 pts

Electrical Network Analysis and Design – core – ELEN30009 – 12.5 pts
Electrical Device Modelling – core – ELEN30011 – 12.5 pts
Signals and Systems – core – ELEN30012 – 12.5 pts
Electronic System Implementation – core – ELEN30013 – 12.5 pts

Accordion
Year 2 100 pts
Semester 1 · 50 pts	Probability and Random Models – core – ELEN90054 – 12.5 pts Electronic Circuit Design – core – ELEN90056 – 12.5 pts Introduction to Power Engineering – core – ELEN90074 – 12.5 pts Interdisciplinary Design for Engineers – core – ENGR90051 – 12.5 pts
Semester 2 · 50 pts	Communication Systems – core – ELEN90057 – 12.5 pts Embedded System Design – core – ELEN90066 – 12.5 pts Signal Processing – core – ELEN90058 – 12.5 pts Control Systems – core – ELEN90055 – 12.5 pts

Accordion

Year 2

100 pts

Semester 1 · 50 pts

Probability and Random Models – core – ELEN90054 – 12.5 pts
Electronic Circuit Design – core – ELEN90056 – 12.5 pts
Introduction to Power Engineering – core – ELEN90074 – 12.5 pts
Interdisciplinary Design for Engineers – core – ENGR90051 – 12.5 pts

Semester 2 · 50 pts

Communication Systems – core – ELEN90057 – 12.5 pts
Embedded System Design – core – ELEN90066 – 12.5 pts
Signal Processing – core – ELEN90058 – 12.5 pts
Control Systems – core – ELEN90055 – 12.5 pts

Accordion
Year 3 100 pts
Semester 1 · 50 pts	Engineering Capstone Project Part 1 – capstone – ENGR90037 – 12.5 pts elective – 12.5 pts elective – 12.5 pts elective – 12.5 pts
Semester 2 · 50 pts	Engineering Capstone Project Part 2 – capstone – ENGR90038 – 12.5 pts elective – 12.5 pts elective – 12.5 pts elective – 12.5 pts

Accordion

Year 3

100 pts

Semester 1 · 50 pts

Engineering Capstone Project Part 1 – capstone – ENGR90037 – 12.5 pts
elective – 12.5 pts
elective – 12.5 pts
elective – 12.5 pts

Semester 2 · 50 pts

Engineering Capstone Project Part 2 – capstone – ENGR90038 – 12.5 pts
elective – 12.5 pts
elective – 12.5 pts
elective – 12.5 pts

Explore this course

Explore the subjects you could choose as part of this degree.

Artificial Intelligence specialisation

Core

Year 1

Students must complete 100 points of Year 1 compulsory subjects.

Accordion
Intro. to Numerical Computation in C · 12.5 pts	AIMS Many engineering disciplines make use of numerical solutions to computational problems. In this subject students will be introduced to the key elements of programming in a high level language, and will then use that skill to explore methods for solving numerical problems in a range of discipline areas. INDICATIVE CONTENT Algorithmic problem solving Fundamental data types: numbers and characters Approximation and errors in numerical computation Fundamental program structures: sequencing, selection, repetition, functions Simple data storage structures, variables, arrays, and structures Roots of equations and of linear algebraic equations Curve fitting and splines Interpolation and extrapolation Numerical differentiation and integration View detailed information in the Handbook
Foundations of Electrical Networks · 12.5 pts	INDICATIVE CONTENT Foundations of Electrical Networks develops an understanding of fundamental modelling techniques for the analysis of systems that involve electrical phenomena. This includes networks models of “flow-drop” one-port elements in steady state (DC and AC), electrical power systems, simple RC and RL transient analysis, and networks involving ideal and non-ideal operational amplifiers. It forms the foundation of many engineering subjects exploring fundamental concepts in electrical and electronic engineering. The subject will cover key electrical engineering topics in the areas of: Electrical phenomena – charge, current, electrical potential, conservation of energy and charge, the generation, storage, transport and dissipation of electrical power. Network models – networks of “flow-drop” one-port elements, Kirchoff’s laws, standard current-voltage models for one-ports (independent sources, resistors, capacitors, inductors, transducers, diodes), analysis of static networks, properties of linear time-invariant (LTI) one-ports and impedance functions, diodes, transformers, steady-state (DC and AC) analysis of LTI networks via mesh and node techniques, equivalent circuits, and transient analysis of simple circuits; Electrical power systems – overview of power generation and transmission, analysis of single-phase and balanced three-phase AC power systems. Analysis and design of networks involving ideal and non-ideal operational amplifiers. This material will be complemented by exposure to software tools for the simulation of electrical and electronic systems and the opportunity to develop basic electrical engineering laboratory skills using a prototyping breadboard, digital multimeter, function generator, DC power supply, and oscilloscope. Please view this video for further information: Foundations of Electrical Networks View detailed information in the Handbook
Digital Systems · 12.5 pts	AIMS This subject develops a fundamental understanding of concepts used in the analysis, design and building of digital systems. Such systems form the information and communication technologies (ICT) that underpin modern society. This subject provides a foundation for subsequent subjects, including ELEN30013 Electronic System Implementation, ELEN90066 Embedded System Design and ELEN90061 Communication Networks. INDICATIVE CONTENT Topics include: Digital systems - quantifying and encoding information, digital data processing, design process abstractions; Combinational logic – timing contracts, acyclic networks, switching algebra, logic synthesis; Sequential logic – cyclic networks and finite-state machines, metastability, microcode; These topics will be complemented by exposure to the hardware description language such as Verilog and the use of engineering design automation tools and configurable logic devices (e.g. FPGAs) in the laboratory. Please view this video for further information: Digital Systems View detailed information in the Handbook
Electrical Network Analysis and Design · 12.5 pts	AIMS This subject develops a fundamental understanding of linear time-invariant network models for the analysis and design of electrical and electronic systems. Such models arise in the study of systems ranging from large-scale power grids to tiny radio frequency signal amplifiers. This subject is one of four subjects that define the Electrical Systems Major in the Bachelor of Science and it is a core requirement for the Master of Engineering (Electrical). It provides a foundation for various subsequent subjects, including ELEN30013 Electronic System Implementation, ELEN90066 Embedded System Design, and ELEN30012 Signal and Systems. INDICATIVE CONTENT Topics include: Transient and frequency domain analysis of linear time-invariant (LTI) models – linearity, time-invariance, impulse response and convolution, oscillations and damping, the Laplace transform and transfer functions, frequency response and bode plots, lumped versus distributed parameter transfer functions, poles, zeros, and resonance, stability of circuits, modelling and simulation with simulation tools; Electrical network models – one-port elements, impedance functions, two-port elements, dependent sources, matrix representations of two-ports, driving point impedances and network functions, ladder and lattice networks, passive versus active networks, multi-stage modelling and design, and multi-port generalisations; Analysis and design of networks involving ideal and non-ideal operational amplifiers with emphasis on the design of active filters and broadband circuits with specific frequency characteristics; Circuits and networks for managing voltage and power requirements for common electronic circuits. These topics will be complemented by tutorials and workshops designed to develop skills in design and modelling of electronic circuits through software tools and building, testing, and verification of electronic circuits. Please view this video for further information: Electrical Network Analysis and Design View detailed information in the Handbook
Electrical Device Modelling · 12.5 pts	AIM This subject develops the theoretical and practical tools required to understand, construct, validate and apply models of standard electrical and electronic devices. In particular, students will study the theoretical and practical development of models for devices such as resistors, capacitors, inductors, transformers, motors, batteries, diodes, transistors, and transmission lines. In doing so, students will gain exposure to a variety of fundamental fields in physics, including electromagnetism, semiconductor materials and quantum electronics. This material will be complemented by exposure to experiment design and measurement techniques in the laboratory, the application of models from device manufacturers, and the use of electronic circuit simulation software. INDICATIVE CONTENT Topics include: Vector calculus for device modelling, Maxwell’s equations, physics of conductors and insulators, passive device models (including for resistors, capacitors and inductors), lumped and distributed circuit models for wired interconnections (including treatment of signal integrity and termination strategies), semiconductors and quantum electronics, static and dynamic models for p-n junctions diodes and bipolar junction transistors. View detailed information in the Handbook
Signals and Systems · 12.5 pts	AIMS The aim of this subject is twofold: firstly, to develop an understanding of the fundamental tools and concepts used in the analysis of signals and the analysis and design of linear time-invariant systems path in continuous–time and discrete-time; secondly, to develop an understanding of their application in a broad range of areas, including electrical networks, telecommunications, signal-processing and automatic control. The subject formally introduces the fundamental mathematical techniques that underpin the analysis and design of electrical networks, telecommunication systems, signal-processing systems and automatic control systems. Such systems lie at the heart of the electrical engineering technologies that underpin modern society. This subject is one of four Level 3 subjects that define the Electrical Engineering Systems Major in the Bachelor of Science. . It provides the foundation for various subsequent subjects, including ELEN90057 Communication Systems, ELEN90058 Signal Processing and ELEN90055 Control Systems. INDICATIVE CONTENT Topics include: Signals – continuously and discretely indexed signals, important signal types, frequency-domain analysis (Fourier, Laplace and Z transforms), nonlinear transformations and harmonics, sampling; Systems – viewing differential / difference equations as systems that process signals, the notions of input, output and internal signals, block diagrams (series, parallel and feedback connections), properties of input-output models (causality, delay, stability, gain, shift-invariance, linearity), transient and steady state behaviour; Linear time-invariant systems – continuous and discrete impulse response; convolution operation, transfer functions and frequency response, time-domain interpretation of stable and unstable poles and zeros, state-space models (construction from high-order ODEs, canonical forms, state transformations and stability), and the discretisation of models for systems of continuously indexed signals. This material is complemented by exposure to the use of MATLAB for computation and simulation and examples from diverse areas including electrical engineering, biology, population dynamics and economics. View detailed information in the Handbook
Electronic System Implementation · 12.5 pts	AIMSThis subject provides students with hands-on electronic skills to gain basic competencies in design and implementation of simple circuits. Students will design with a range of standard electrical and electronic devices, basic circuit construction methods and electrical measurement techniques to test and verify the function of electronic systems. This subject is one of four subjects that define the Electrical Systems Major in the Bachelor of Science and it is a core requirement for the Master of Engineering (Electrical) and the Master of Engineering (Electrical with Business). This includes hands-on experience with: • Operation and selection of electrical and electronic devices used in various electronic circuits; • Common electronic circuit realisations to meet the most commonly required signal processing and conditioning applications; • Programmable digital circuits and microprocessor programming; • Circuit design and simulation tools; • Printed circuit board layout, circuit assembly, and soldering techniques; • Test and Measurement equipment and methods; • Managing design issues and requirements. Students will complete electronic circuit implementation projects in small groups and be required to prepare technical documentation and present project outcomes. INDICATIVE CONTENT • Devices such as resistors, capacitors, inductors, switches, transducers, motors, diodes, transistors, op-amps, voltage regulators, comparators, oscillators, timers, A/D and D/A converters, microprocessors and controllers; • Circuit functions and techniques such as buffering, referencing, signal conditioning, filtering, bridges, detection, waveform generation, and pulse-width modulation; • Microprocessor programming, the role of assembly and high-level languages, assemblers, compilers and debuggers; • PCB layout, circuit assembly, and soldering techniques; • Test and Measurement methods and working with common equipment such as multimeters and oscilloscopes. View detailed information in the Handbook
Engineering Mathematics · 12.5 pts	This subject introduces important mathematical methods required in engineering such as manipulating vector differential operators, computing multiple integrals and using integral theorems. A range of ordinary and partial differential equations are solved by a variety of methods and their solution behaviour is interpreted. The subject also introduces series including the concepts of convergence and divergence. Topics include: Vector calculus, including Gauss’ and Stokes’ Theorems; systems of homogeneous ordinary differential equations, including phase plane and linearisation for nonlinear systems; Laplace transforms; series, including Taylor series and power series; Fourier series and Fourier integrals; second order partial differential equations and separation of variables. View detailed information in the Handbook

Year 2

Students must complete 100 points of Year 2 compulsory subjects.

Accordion
Probability and Random Models · 12.5 pts	AIMS This subject provides an introduction to probability theory, random variables, random vectors, decision tests, and stochastic processes. Uncertainty is inevitable in real engineering systems, and the laws of probability offer a powerful way to evaluate uncertainty, to predict and to make decisions according to well-defined, quantitative principles. The material covered is important in fields such as communications, data networks, signal processing and electronics. This subject is a core requirement in the Master of Engineering (Electrical, Mechanical and Mechatronics). INDICATIVE CONTENT Topics include: Foundations – combinatorial analysis, axioms of probability, independence, conditional probability, Bayes’ rule; Random variables (rv’s)– definition; cumulative distribution, probability mass and probability density functions; expectation and variance; functions of an rv; important distributions and their properties and uses; Multiple random variables – joint cumulative distribution, probability mass and probability density functions; independent rv’s; correlation and covariance; conditional distributions and expectation; functions of several rv’s; jointly Gaussian rv’s; random vectors; Sums, inequalities and limit theorems – sums of rv’s, moment generating function; Markov and Chebychev inequalities; weak and strong laws of large numbers; the Central Limit Theorem; Decision testing - maximum likelihood, maximum a posterior, minimum cost and Neyman-Pearson rules; basic minimum mean-square error estimation; Stochastic processes – mean and autocorrelation functions, strict and wide-sense stationarity; ergodicity; important processes and their properties and uses; Introduction to Markov chains. This material is complemented by exposure to examples from electrical engineering and software tools (e.g. MATLAB) for computation and simulations. View detailed information in the Handbook
Control Systems · 12.5 pts	AIMS This subject provides an introduction to automatic control systems, with an emphasis on classical techniques for the analysis and design of feedback interconnections. The main challenge in automatic control is to achieve desired performance in the presence of uncertainty about the system dynamics and the operating environment. Feedback control is one way to deal with modelling uncertainty in the design of engineering systems. This subject is a core requirement in the Master of Engineering (Electrical, Electrical with Business, Mechanical, Mechanical with Business and Mechatronics). INDICATIVE CONTENT Topics include: * Modelling for control, linearization, relationships between time and frequency domain models of linear time-invariant dynamical systems, and the structure, stability, performance, and robustness of feedback interconnections; * Frequency-domain analysis and design, Nyquist and Bode plots, gain and phase margins, loop-shaping with proportional, integral, lead, and lag compensators, loop delays, and fundamental limitations in design; and * Actuator constraints and anti-windup compensation. This material is complemented by the use of software tools (e.g. MATLAB/Simulink) for computation and simulation, and exposure to control system hardware in the laboratory. View detailed information in the Handbook
Electronic Circuit Design · 12.5 pts	AIMS This subject provides an in-depth coverage of transistor (MOSFET and BJT) devices and their use in common circuits. In particular, students will study topics including: transistor operating modes and switching; principles of CMOS circuits; transistor biasing; current-source/emitter-amplifiers; low-frequency response; followers; class B amplifiers; current limiting; current sources and mirrors; differential pairs; feedback in amplifiers and stability; operational amplifiers; operational amplifier circuits; and voltage regulation. This material will be complemented by exposure to circuit simulation software tools and the opportunity to further develop circuit construction/test skills in the laboratory. INDICATIVE CONTENT Design-focused field-effect and bipolar elementary transistor models, and design of elementary amplifier stages and biasing circuits. Static and dynamic behaviour of amplifier circuits including frequency response, feedback and stability, slew-rate and clipping. Operational amplifiers and opamp based circuits; voltage regulators, references and voltage converters. Verification of electronic circuits using simulation and constructing them in the laboratory. Please view this video for further information: Electronic Circuit Design View detailed information in the Handbook
Communication Systems · 12.5 pts	AIMS This subject provides an introduction to the analysis and design of telecommunication signals and systems, in the presence of uncertainty. The emphasis is on understanding the basic concepts that underpin the physical layer of modern communication systems. INDICATIVE CONTENT Topics to be covered include: Introduction to communication systems including historical developments and comparisons between analogue and digital communications. Review of assumed knowledge from linear algebra, signals and systems and probability and random processes. The sampling theorem, analog-to-digital conversion, complex baseband representation of passband signals, filtering of random processes, power spectral density, bandwidth of random signals, additive white Gaussian noise (AWGN), signal-to-noise ratio. Communication over baseband AWGN channels including modulation techniques (pulse amplitude modulation, orthogonal modulation), signal space representation, optimal detectors, matched filters, error probability calculations and bandwidth / power trade-off. Communication over passband AWGN channels including modulation techniques (phase shift keying, quadrature amplitude modulation and frequency shift keying), optimal coherent detectors, noncoherent detectors and error probability calculations. Communication over linear time-invariant channels including concepts of distortion, inter-symbol interference, pulse shaping, Nyquist’s criterion, equalization, sequence detection and the Viterbi algorithm. Synchronization including carrier, symbol and frame synchronization. View detailed information in the Handbook
Signal Processing · 12.5 pts	AIMS This subject provides an introduction to the fundamental theory of time domain and frequency domain representation of discrete time signals and linear time invariant dynamical systems, and how this theory is used to analyse and design digital signal processing systems and algorithms. Topics include: Applications of signal processing techniques; Sampling of analog signals, anti-aliasing filters; Frequency-domain analysis of signals and systems, Discrete Time Fourier Transform, Discrete Fourier Transform, Fast Fourier Transform; Digital filters, low-pass, high-pass, band-pass, stop-band and all pass filters. Phase and group delay, FIR and IIR filters; Design of digital FIR and IIR filters; Multi-rate signal processing, with a focus on up-sampling, down-sampling, and sampling rate conversion; Simple non-parametric methods for spectral estimation. This fundamental material will be complemented by exposure to MATLAB tools for signal analysis and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory. INDICATIVE CONTENT Sampling of continuous time signals, Design of anti-aliasing filters, Time and frequency representation of discrete time signals and discrete time linear time invariant systems, Discrete Time Fourier Transform and z-transform and their properties, Low order lowpass, highpass, bandpass, bandstop filters, All-pass filter, Design of IIR filters using the bilinear transformation, Design of FIR filters with linear phase using windowing techniques and the Parks McClelland method, Discrete Time Fourier transform and its properties, Fast Fourier Transform, The use of the DFT in implementation of linear filtering algorithms, Up-sampling and down-sampling, multistage and computationally efficient implementations of up-samplers and down-samplers, Energy and power spectra for deterministic signals. View detailed information in the Handbook
Embedded System Design · 12.5 pts	AIMS This subject provides a practical introduction to the basics of modelling, analysis, and design of microprocessor-based embedded systems. Students will learn how to integrate computation with physical processes to meet a desired specification within the context of a design project. The project work will expose students to the various stages in an engineering project (design, implementation, testing and documentation) and a range of embedded system concepts. INDICATIVE CONTENT Topics covered may include: digital computer and microprocessor architectures, modelling of dynamic behaviours, control, models of computation, operating systems concepts, multi-tasking, resource management and real-time behaviours, interfacing with the physical world, analysis and verification, safety, reliability, and security and privacy. This material will be complemented by exposure to standard software tools including compilers and debuggers, finite state machine design and analysis software, and simulation tools. The subject will include a level of industry engagement, to provide broader examples of engineering projects, through guest lectures. View detailed information in the Handbook
Introduction to Power Engineering · 12.5 pts	AIMS To develop a solid foundation for the study of systems that involve the generation, transport, and conversion of electric power. INDICATIVE CONTENT Physical principles of electromagnetism, magnetic circuits, energy storage, loss mechanisms, electromechanical energy conversion. Modelling of transmission lines, transformers, motors and generators (synchronous and asynchronous), and other loads. Circuit theory for power system analysis, three phase-phase circuits, power flow and maximum power transfer, per-unit system. Please view this video for further information: Introduction to Power Engineering View detailed information in the Handbook
Interdisciplinary Design for Engineers · 12.5 pts	In this subject, students will actively engage in an interdisciplinary, collaborative and project-based learning environment, offering insights into the professional nature of engineering work. Through a real-world project, students will gain hands-on design experience addressing a complex challenge. The project will require students to integrate discipline knowledge and apply professional skills like teamwork and communication. Students will experience the entire engineering design process, covering problem definition, ideation, concept development, analysis, prototyping, testing and iteration. The project provides practical experience, equipping students with tools and methods to address complex challenges. Students are expected to integrate diverse perspectives, considering factors like stakeholders, sustainability (including environmental and social issues), safety, feasibility, and technical and ethical considerations. View detailed information in the Handbook

Capstone

Year 3

Students must complete 25 points of Year 3 compulsory capstone project subjects.

Accordion
Engineering Capstone Project Part 1 · 12.5 pts	The subject involves undertaking a substantial group project (typically in groups of three students) requiring an independent investigation on an approved topic in advanced engineering design and / or research. Each project is carried out under the supervision of a member of academic staff and where appropriate an industry partner. The emphasis of the project can be associated with either: A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the engineering science disciplines, often as an adjunct to existing academic research initiatives. It is expected that the Capstone Project will incorporate findings associated with both well-defined professional practice and research principles and will provide students with the opportunity to integrate technical knowledge and generic skills gained in earlier years. The project component of this subject is supplemented by a lecture course dealing with project management tools and practices. Please note: Students enrolled in the suite of Master of Engineering programs must be within the final 112.5 points of their degree to enrol. Students enrolled in the Master of Industrial Engineering must be within the final 100 points of their degree to enrol. Students are to take Engineering Capstone Project Part 1 and then subsequently continue with Engineering Capstone Project Part 2 in the following semester. Upon successful completion of this project, students will receive 25 points credit. View detailed information in the Handbook
Engineering Capstone Project Part 2 · 12.5 pts	Please refer to ENGR90037 Engineering Capstone Project Part 1 for this information. View detailed information in the Handbook

Accordion

Engineering Capstone Project Part 1 · 12.5 pts

The subject involves undertaking a substantial group project (typically in groups of three students) requiring an independent investigation on an approved topic in advanced engineering design and / or research. Each project is carried out under the supervision of a member of academic staff and where appropriate an industry partner.

The emphasis of the project can be associated with either:

A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or
A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the engineering science disciplines, often as an adjunct to existing academic research initiatives.

It is expected that the Capstone Project will incorporate findings associated with both well-defined professional practice and research principles and will provide students with the opportunity to integrate technical knowledge and generic skills gained in earlier years.

The project component of this subject is supplemented by a lecture course dealing with project management tools and practices.

Please note:

Students enrolled in the suite of Master of Engineering programs must be within the final 112.5 points of their degree to enrol.

Students enrolled in the Master of Industrial Engineering must be within the final 100 points of their degree to enrol.

Students are to take Engineering Capstone Project Part 1 and then subsequently continue with Engineering Capstone Project Part 2 in the following semester. Upon successful completion of this project, students will receive 25 points credit.

View detailed information in the Handbook

Engineering Capstone Project Part 2 · 12.5 pts

Please refer to ENGR90037 Engineering Capstone Project Part 1 for this information.

View detailed information in the Handbook

Specialisation

Year 3

Students must complete 50 credit points of Year 3 core specialisation subjects.

Accordion
System Optimisation & Machine Learning · 12.5 pts	This subject introduces the basic principles, analysis methods, and applications of optimisation and machine learning to engineering systems; encompassing fundamental concepts and practical algorithms. It covers the fundamentals of continuous optimisation followed by machine learning basics for engineering applications. The concepts and methods discussed are illustrated in multiple application areas including Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks.The concepts taught in this subject will allow a better understanding of continuous optimisation and machine learning for systems engineering. INDICATIVE CONTENT Topics covered may include: Fundamentals of continuous optimisation: convex sets and functions; local vs global solutions, constrained optimisation and Lagrange multipliers; linear, quadratic, and nonlinear programming Basics of machine learning encompassing supervised and unsupervised learning: binary classification, linear and nonlinear regression, kernel methods, and clustering. Specific machine learning methods such as Support Vector Machines (SVMs), Neural Networks (NNs), k-means clustering, and reinforcement learning. Applications to Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks. View detailed information in the Handbook
Modelling and Analysis for AI · 12.5 pts	This subject builds up the fundamentals for modelling dynamical systems, with a key focus on the aspects and decisions of modelling that are relevant for the application of AI and data-intensive learning methods. The discussion and evaluation of modelling methods focuses on how model fidelity influences simulation-to-real transfer; how modelling and simulation decisions influence computation time required for training and validation; and how discrete-time models introduce complexity when representing continuous-time engineering systems. Subsequently, it introduces the basic principles and engineering applications of programming and data structures in a condensed form with a project-centric pedagogy. It covers the fundamentals of databases and data structures, basic algorithms, scientific programming, and classic AI problem solving. It will focus specifically on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how programming and databases play a role in modern engineering and cyber-physical systems. INDICATIVE CONTENT Topics covered may include: Models for engineering systems in multiple disciplines, including analysis of what makes the models amenable to AI and data-intensive learning methods. Principles for simulating dynamic systems that are most relevant for the use of AI methods and to address these principles with existing software tools. Scientific programming for modelling using Python programming language and libraries such as scipy and numpy. Engineering data structures and time series data and their storage in SQL and noSQL databases. Example engineering applications will be taught via projects in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. View detailed information in the Handbook
Reinforcement Learning for Engineering · 12.5 pts	The key focus of this subject is the design and implementation of decision-making policies for enabling a dynamical system to behave autonomously and achieve a desired objective. This subject covers both model-based and model-free learning methods, with a focus on evaluating, contrasting, and combining methods. The influence of noisy sensor data on performance, and the trade-offs between exploration and exploitation during a learning phase, will also be covered. The examples used in this subject range across existing and emerging decision-making methods, and their application to consumer and industrial engineering systems. INDICATIVE CONTENT Topics covered may include: Reinforcement learning fundamentals such as principle of optimality, Bellman equation, value and policy iteration. Temporal-difference learning, Q-learning, Deep Q-learning, Actor critic methods and hybrid approaches in engineering context. Model based vs model free approaches, multi-agent RL and their engineering applications. RL methods for Cyber-physical resilience and security such as fuzzing methods. View detailed information in the Handbook
Applied Deep Learning for Engineers · 12.5 pts	This subject covers a modern deep learning approach to engineering using a project-centric pedagogy. Building upon system optimisation and machine learning fundamentals presented in ELEN90088, the subject will present advanced deep learning architectures to address long-standing engineering challenges such as system complexity, curse of dimensionality, and modelling gap. Subject will specifically focus on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how advanced deep learning frameworks can be applied to modern engineering and cyber-physical systems. INDICATIVE CONTENT Topics covered may include: Latent spaces, auto encoder architectures. Advanced deep learning architectures, auto-differentiation, physics-inspired neural networks. Sequential data analysis and predictive models such as transformers. Generative models such as GANs and GPT variants. Other advanced topics such as meta parameter optimisation, Markov Chain Monte Carlo sampling. Distributed machine learning, federated learning, graph neural networks. Cyber-physical security of modern engineering systems, including data-based anomaly and threat detection and prediction. Subject projects will focus on engineering applications in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. View detailed information in the Handbook

Electives

Electrical Engineering Electives (Group A)

Students must complete 25 points of Year 3 elective subjects.

Accordion
Introduction to Optimisation · 12.5 pts	AIMS This subject provides a rigorous introduction to numerical nonlinear optimization, as used across all of science and particularly in engineering design. There is an emphasis on both the theory and application of optimization techniques, with a focus on solving unconstrained and constrained nonlinear programmes. This subject is intended for graduate and research higher-degree students in engineering. INDICATIVE CONTENT Topics include: Algorithms for unconstrained optimization Algorithms for constrained optimization Convex sets and functions Convex optimization problems Duality theory Computational complexity Approximation algorithms and penalty methods. View detailed information in the Handbook
Advanced Communication Systems · 12.5 pts	AIMS The aim of this subject is to develop a thorough understanding of the main concepts, techniques and performance criteria used in the analysis and design of digital communication systems and wireless networks. Such systems and networks lie at the heart of the information and communication technologies (ICT) that underpin modern society and are very much part of the Internet of Things which involves machine to machine communication. INDICATIVE CONTENT This subject provides an in-depth treatment of the main concepts and techniques used in the analysis and design of digital communication systems and wireless networks. Topics include: Source coding; entropy, Shannon source coding bound, data compression techniques; Channel modelling, modulation over time-varying fading channels, time and frequency diversity, energy and spectral efficiency, multiple carrier modulation including orthogonal frequency division multiplexing (OFDM) modulation, phase noise characteristics and its impact on single-carrier and multicarrier systems, spatial multiplexing for multiple access protocols, multiple antenna technologies (MIMO systems), cellular networks; Channel coding for error control: mutual information, channel capacity, Shannon channel coding bound, channel coding concepts, block codes; convolutional / trellis codes; introduction to LDPC codes, turbo codes, polar codes. Examples include short, medium and long range communication systems such as bluetooth, cellular and satellite communication systems. View detailed information in the Handbook
Advanced Signal Processing · 12.5 pts	AIMS This subject provides an in-depth introduction to statistical signal processing. INDICATIVE CONTENT Students will study a selection of the following topics: Applications of statistical signal processing; A review of stochastic signals and systems fundamentals – random processes, white noise, stationarity, auto- and cross-correlation functions, spectral- and cross-spectral densities, properties of linear time-invariant systems excited by white noise; Parameter estimation - least squares and its properties, recursive least squares and least mean squares, optimisation-based methods, maximum likelihood methods; Kalman, Wiener and Markov filtering; Power spectrum estimation. This material will be complemented with the use of software tools (e.g. MATLAB) for computation and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory. View detailed information in the Handbook
Electronic System Design · 12.5 pts	AIMS This subject will explore the design of various electrical and electronic systems and provide students with a range of common and practical design techniques and circuits in the context of a guided laboratory based project. INDICATIVE CONTENT Subject may cover specific concepts surrounding the design and implementation of: Design process; Design for manufacture and assembly; Advanced PCB design; Oscillators; Phase-locked loops and frequency synthesis; Base-band signalling schemes and clock recovery; Mixers and logarithmic amplification; Automatic gain control; Filters; Synchronous detection; High-speed analog-digital conversion; High-frequency amplification; Low noise amplifiers; Power supply design; Batteries, battery charging systems, and management; Test and measurement; Sensors. View detailed information in the Handbook
Lightwave Systems · 12.5 pts	AIMS Lightwave systems are fundamentally changing the way we communicate through broadband communications, helping clinicians to perform a range of medical procedures and diagnosis supported by advanced biomedical instrumentation, and even in the way we live in our homes through sophisticated interactive televisions and security systems. This subject will explore the physical principles and issues that arise in the design of lightwave systems often found in those key industry sectors. Students will study topics from: transmission of light over wave guides; production of light by lasers; light modulation; conversion of light signals to electrical signals; optical multiplexing and demultiplexing; light amplification; dispersion and dispersion compensation; optical nonlinearities; modulation and advanced detection schemes. This material will be complemented by exposure to lightwave systems and measurement techniques in the laboratory. INDICATIVE CONTENT This subject will explore the physical principles governing the generation, modulation, amplification, guiding, transmission, multiplexing, demultiplexing and detection of light and issues that arise in the design of lightwave systems such as transmission impairments, noise. Students learn selected examples of lightwave systems and methods for design, modelling and testing of simple lightwave systems. View detailed information in the Handbook
Power System Analysis · 12.5 pts	AIMS This subject provides an insight into the fundamental elements to analyse electrical power transmission and distribution systems, with both analytical and simulation tools for analysis of operations of these systems. Problems related to power flow and use of Newton-Raphson and other algorithms such as backward-forward sweep will be discussed. Fault calculation and analysis, symmetrical components, and analytical methods for solving symmetrical (balanced) faults will be covered. Principles, concepts and problems related to power system dynamics and control, particularly for frequency and voltage regulation, will be discussed and analysed in detail. Finally, small-signal, transient, voltage and frequency stability will be introduced and exemplified. Focus will be put on real-world examples, particularly to prepare the student for the ongoing transition towards a low-carbon grid dominated by renewables and distributed energy resources. INDICATIVE CONTENT Power flow calculations, Newton-Raphson, Gauss-Seidel and backward-forward sweep methods; Fault calculations, balanced and unbalanced, symmetrical components, fundamentals of protection; Frequency regulation and frequency stability, Voltage regulation in transmission and distribution networks, including use of flexible AC transmission systems (FACTS); Voltage stability, small-signal stability, transient stability; Computer simulations. View detailed information in the Handbook
Communication Networks · 12.5 pts	AIMS This subject introduces the basic principles, analysis, and design of communication networks. It covers relevant analytical methods, the layered network architecture of the Internet, and a multitude of network protocols. Analytical tools from queueing, optimisation, and graph theories are used to develop an in-depth understanding of basic principles and the role they play in network design. Specifically, queueing and graph theories are emphasised as methodological frameworks for communication network delay and structure analysis. The concepts taught in this subject lead to a better understanding of the Internet as well as modern communication paradigms such as Software-Defined Networks, Machine-to-Machine communication, Internet of Things, and social networks. INDICATIVE CONTENT Topics covered may include: The layered network architecture with a focus on physical-layer multiple access (TDM, FDM, WDM), link layer protocols and medium access control (MAC), network layer topologies, least-cost routing algorithms and protocols, transport layer protocols and the principles and techniques of practical reliable transport; LAN protocols, Ethernet, Wi-Fi, and serial communications; The Internet's network layer including the Internet Protocol (IP) and routing protocols including an introduction to BGP and the operation of forwarding tables in routers and shortest prefix routing; The Internet's transport layer protocols UDP and TCP, including the flow and congestion control algorithms; Network security, application layer, cloud and fog computing, Machine-to-Machine communication, and Internet of Things; Queuing theory: basics, birth-death processes, M/M/x and Markovian queues, networks of queues; Basics of graph theory and social network analysis relevant to communication networks. View detailed information in the Handbook
High Speed Electronics · 12.5 pts	AIMS The aim of the subject is to provide theoretical and practical treatment of high-speed electronics. Through the subject, students will grasp the fundamental properties and models of high-speed signals and interconnects, acquire high-speed digital design skills with a focus on the modelling, analysis, design and application of high speed transistors, logic gates and modern logic families, and master the high-speed analogue design capability including the design of oscillators and filters for RF applications. The students will be exposed to the state-of-the-art technologies that are shaping the fast evolving semiconductor industry. INDICATIVE CONTENT The topics include: Fundamental properties of analogue systems; Smith charts: principles and applications; High-speed analogue circuits: voltage control oscillators, matching networks, and low noise amplifiers; Bipolar junction transistors: device, switching, and logic; CMOS: device, switching and logic; High-speed signalling consideration: power dissipation, heat, signal propagation, and termination. View detailed information in the Handbook
Advanced Control Systems · 12.5 pts	AIMS This subject provides an introduction to modern control theory with a particular focus on design of advanced control laws via state-space methods and optimal control. The role of feedback in control design will be reinforced within this context, alongside the role of optimisation techniques in control system synthesis. INDICATIVE CONTENT Topics include: State-space models - first-order vector differential/difference equations; Lyapunov stability; linearisation; discretisation; Kalman decomposition (observable, detectable, reachable and stabilisable subspaces); state-feedback and pole placement; output-feedback and observer design in both continuous-time and discrete-time. Optimal control - dynamic programming; linear quadratic regulation in both continuous-time and discrete-time. Model predictive control in discrete-time; moving-horizon with constraints. View detailed information in the Handbook
Power Electronics · 12.5 pts	AIMS The aim of this subject is to understand the fundamental concepts and basic theory involved in modelling and analysis of the power electronic components that comprise power electronic devices such as power supplies, inverters, converters and their control systems. It is expected that at the end of this subject the student has a sound understanding of the physical concepts and mathematical models behind each of the basic components and of their functionality within a system, such as a high voltage DC transmission system. Furthermore this subject seeks to combine the fields of electronics, semiconductor devices, power system operation, power system measurement and control. It is expected that through this subject the students are exposed to examples of real electrical engineering systems where the three disciplines of electronics, power systems and control come together. INDICATIVE CONTENT Topics covered in this subject include: introduction to power semiconductor switches; discussion on the role of power electronics in the operation of electric power systems; models of power semiconductor devices and circuit components, including diodes, Thyristors, IGBT, Snubber circuits. Also basic concepts of single- and three-phase diode bridge rectifiers; single- and three-phase converters and inverters; operation and design of DC-AC inverters with emphases on switch-mode inverters, i.e. single- and three-phase inverters. Finally, the acquired knowledge of power electronic devices is applied to wind and PV solar systems where the design of voltage source converters and associated control loops are used to interface the wind/solar system with the power grid. View detailed information in the Handbook
Grid Integration of Renewables · 12.5 pts	AIMS This subject develops a foundation for pursuing electrical engineering oriented research in the area of sustainable energy systems. This subject aims to introduce the concepts behind smart grids, future low-carbon energy networks, sustainable electricity systems as well as the main renewable and low-carbon generation technologies. The subject will introduce students to tools and techniques so that distributed energy resources (e.g. distributed renewable generation, storage, electric vehicles, demand response, etc.) may be integrated effectively into the power system in the context of both traditional grids and future smart grids. INDICATIVE CONTENT This subject will cover the following topics: Distributed low-carbon technologies Introduction to distribution networks Introduction to distributed low-carbon technologies (wind energy, photovoltaic systems, electric vehicles, electric heating, storage) Wind Energy: impacts and challenges Photovoltaic systems: impacts and challenges Electric vehicles: impacts and challenges Electric heat pumps and electric heating: impacts and challenges Storage: impacts and challenges Smart Distribution and Smart Transmission Networks Distributed low-carbon technologies and active network management Towards Smart Grids Smart grids - Transmission and Distribution perspectives Smart Transmission: HVDC and FACTS, dynamic line rating, post-contingency security, special protection schemes The role of future Distribution System Operators Low-carbon Electricity System Towards low-carbon networks: relationship between sustainability and smart grids Introduction to low-carbon thermal generation (nuclear, Carbon Capture and Storage, Concentrated Solar Power, biomass, etc.) Utility-scale renewable technologies: wind farms; solar farms; other large-scale renewables; utility-scale batteries System-level operational challenges and solutions for renewables integration: variability and uncertainty; low-inertia operation; low system-strength operation; minimum load issues; DER visibility; indistinct events; general stability issues; flexibility System-level planning challenges and solutions for renewables integration: system adequacy and reliability; capacity credit of renewables and storage; extreme weather events and resilience; role of transmission Sector coupling and multi-energy systems: decarbonisation of gas, heating and transport; role of hydrogen Distributed energy systems: new technical and commercial architectures for two-sided systems and markets; demand response; aggregators and virtual power plants; distributed energy markets; peer-to-peer trading; local energy communities; microgrids View detailed information in the Handbook
System Optimisation & Machine Learning · 12.5 pts	This subject introduces the basic principles, analysis methods, and applications of optimisation and machine learning to engineering systems; encompassing fundamental concepts and practical algorithms. It covers the fundamentals of continuous optimisation followed by machine learning basics for engineering applications. The concepts and methods discussed are illustrated in multiple application areas including Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks.The concepts taught in this subject will allow a better understanding of continuous optimisation and machine learning for systems engineering. INDICATIVE CONTENT Topics covered may include: Fundamentals of continuous optimisation: convex sets and functions; local vs global solutions, constrained optimisation and Lagrange multipliers; linear, quadratic, and nonlinear programming Basics of machine learning encompassing supervised and unsupervised learning: binary classification, linear and nonlinear regression, kernel methods, and clustering. Specific machine learning methods such as Support Vector Machines (SVMs), Neural Networks (NNs), k-means clustering, and reinforcement learning. Applications to Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks. View detailed information in the Handbook
Communication Design Clinic · 12.5 pts	Students work collaboratively in small groups to implement and optimize components in a modern communication system or network with the goal of supporting a targeted application. To meet this goal students will need to: determine system requirements based on the target application and additional constraints; propose and evaluate multiple solutions through theoretical analysis and detailed simulations; implement, integrate, verify, and iterate on their selected solutions. Lectures will cast content from prerequisite subjects into the context at hand and cover additional topics relevant to the task. Each student group is expected to demonstrate initiative and independence while pursuing the goal of designing and optimizing their communication system or network, with a key focus being that students learn through hands-on experience. Students will receive early exposure to advanced topics critical to modern communication systems, such as: source and channel coding, multicarrier modulation, multiantenna transmission, and network architectures and protocols. Successful completion of the project will require the student to draw upon knowledge, understanding, and skills learned in prerequisite subjects, which may include: Communication Systems – analog-to-digital conversion, signal-to-noise ratio, modulation and demodulation, bandwidth/power trade-off, error probability calculations, distortion, inter-symbol interference, pulse shaping, equalization, sequence detection, and synchronization. Signal Processing - design and implementation of digital filters (low-, high-, band-, all- pass filters); ARMA systems; up-sampling and down-sampling. Embedded System Design – system-level programming, operating systems concepts, real-time issues, and standard software tools. Additional topics required for the assigned project may also be covered, such as: ideation, prototyping, and design practices; analog RF components; software packages for modelling and implementation; and the use of test & measurement equipment. View detailed information in the Handbook
Autonomous Systems Clinic · 12.5 pts	AIMS: Students work collaboratively in small groups to engineer an autonomous system that performs a specified task. This includes carrying out steps such as: task analysis; proposing multiple solutions; feasibility analysis through prototyping and computer-aided design; detailed design, construction, and testing of the chosen solution; and demonstrating the solution in a proving ground. The lectures will cast content from the pre-requisite subjects into the context of the task at hand, as well as covering additional topics relevant to the task. Each student group is expected to demonstrate initiative and independence while pursuing the goal of designing and building their autonomous system, with a focus of the subject being that students learn through hands-on experience, implementation, and verification. INDICATIVE CONTENT: Successful completion of the project requires the student to draw upon knowledge, understanding, and skills learned in the prerequisite subjects, namely: • Embedded System Design - including topics such as: finite, extended, and hierarchical state machines; modelling cyber-physical systems; scheduling, multi-tasking, and real-time issues; interfacing to the analogue world. • Control Systems - including topics such as: modelling; linearisation; feedback interconnections; proportional, integral, derivative (PID) control; actuator constraint considerations. • Signal Processing - including topics such as: design and implementation of digital filters (low-, high-, band-, all- pass filters); ARMA systems; up-sampling and down-sampling. Additional topics, specific to the task as hand, will be covered, such as: ideation, prototyping, and design practices; image processing and computer vision tools; software introductions; safety and failure analysis. A range of materials, components, and fabrication facilities are provided, from which the students are expected to utilise a subset for designing and building their autonomous system, such as: electric motors, range sensors, camera, voltage converters, compute power, sheet wood, soldering stations, laser wood cutting, 3D printing. The task to be performed is motivated by a real-world application of autonomous systems, such as: operating in hazardous environments or performing repetitive tasks. Please view this video for further information: Autonomous Systems Clinic View detailed information in the Handbook
Semiconductor Devices · 12.5 pts	This subject serves as an introduction to semiconductor devices. It describes the fundamentals, theory, material and physical properties of semiconductor devices. The following topics will be covered. Fundamentals: Crystal properties and of the growth of bulk crystals and of epitaxial layers. Physical concepts related to atoms and electrons. These concepts may include the photoelectric effect, the Bohr model, quantum mechanics, and the periodic table. Energy bands and charge carriers in semiconductors: Bonding forces and energy bands in solids, charge carriers in semiconductors, carrier concentrations, the drift of carriers in electric and magnetic fields, and the Fermi level. Excess carriers in semiconductors: Optical absorption, luminescence, carrier lifetime and photoconductivity, and the diffusion of carriers. Junctions: Fabrication of pn junctions, equilibrium conditions, forward and reverse biased junctions in steady state, reverse bias breakdown, transient and AC conditions, metal-semiconductor junctions and heterojunctions. In the next part of the subject PN junction diodes: Junction diodes, tunnel diodes, photodiodes, and light-emitting diodes and lasers. Bipolar junction transistors (BJTs): Amplification and switching, fundamentals of BJT operation, BJT fabrication, minority carrier distributions and terminal currents, generalised biasing, switching, the frequency limitations of transistors, and heterojunction bipolar transistors. Field effect transistors (FETs): Topics may include junction FETs, the metal semiconductor FET and the metal-insulator-semiconductor FET. Additional topics (if time permits): Integrated circuits, pnpn switching devices, and microwave devices. View detailed information in the Handbook
Low-carbon Grids: Operation & Economics · 12.5 pts	This subject introduces the student to foundational aspects of economic, secure and reliable operation of low-carbon power systems and electricity markets with large shares of variable and uncertain renewable energy sources. The underlying framework is the so-called “affordability-sustainability-security” energy trilemma, which seeks to strike a delicate balance among: the desire to operate power systems at low cost (“affordability”); the desire to meet specific environmental targets (“sustainability”); and the need to “keep the lights on” (“security”). In order for the energy trilemma to be analysed in the context of a competitive market environment, the subject will provide the student with fundamentals of economics, operation of electricity markets, optimal bidding strategies of different market stakeholders, economics of transmission and distribution networks, and role of new technologies and commercial entities such as storage and aggregators. Different aspects of power system security will be analysed, from system-level requirements and constraints to provision of security services from market stakeholders. Basic concepts of optimization, including linear, quadratic, and mixed integer linear programming, will also be taught to provide the student with the tools required to understand and model current and future power system and energy market operation. View detailed information in the Handbook
Microprocessor Design Clinic · 12.5 pts	Students in this subject will be introduced to computer architectures, microprocessors, microcontrollers, operating systems, compilers and software design. The proposed course will cover a broad range of topics necessary to make students knowledgeable in the art of microprocessor design including advanced concepts such as in line and out of order execution and execution unit resource optimisation. Students in this course will learn to design execution units, arithmetic logic units, memory hierarchies and learn strategies for cache sizing. As part of this, students will become proficient in microcode and instruction set design, multi-processor and multi core theory and design, including new design methodologies such as chiplet design. Upon completion, students will be familiar with the specification and synthesis of microprocessor systems using high level generator languages such as Chisel and Scala. The course will also introduce students to compiler and linker design, enhancements to instruction sets, c-language and the theory of operating systems. View detailed information in the Handbook
Large Data Methods & Applications · 12.5 pts	This course provides an introduction to an important contemporary statistical toolset for applications including data science, machine learning, signal processing, financial engineering, biomedical engineering, communication systems and other high-dimensional statistical applications. The course will cover topics including introduction to random matrix theory models in engineering; eigenvalue distributions; finite-dimensional and large-dimensional techniques, covariance estimation, principal component analysis and spectral clustering. These topics will be supplemented by applications across a range of traditional and emerging domains involving big data sets. View detailed information in the Handbook
Directed Studies · 12.5 pts	AIMS Directed studies provide the students with broader experience in addition to the regular class based learning. The directed studies can be conducted in the forms of: Industrial internship or research placements in the department’s research groups based on availability. This is only open to students who have completed a minimum of one semester of study and who have achieved an average of H2A or above in their prior subjects; Individually arranged supervised study of current research topics with staff members associated with the Department of Electrical and Electronic Engineering. INDICATIVE CONTENT The examples of the research topics are: Cloud Computing, Content Distribution and Information Logistics; Internet Services Energy Star Rating; Energy Efficiency of Future Modulation Formats; Low-Energy Fibre Access Networks; Video Coding for Energy Efficient Telecommunications; Fundamental Limits of Electronics and Photonics; Broadband fibre wireless networks and systems; Optimal design of few-mode fibres. View detailed information in the Handbook
AI for Robotics · 12.5 pts	AIMS: This subject focuses on the software and algorithms (i.e., artificial intelligence) that enable robotic systems to move autonomously through their environment and perform tasks. The key focus of this subject is the foundations of robotic systems that use software to move autonomously through their environment. This subject focus on the software & algorithms that enable the robot to perform tasks autonomously. Hence, this subject focused on artificial intelligence (AI) software & algorithms for robotics. The first main aim of the subject is to provide a foundation of the feedback loop that is core to all AI-enabled robots, namely: sensors measure the world around the robot; AI algorithms decide what action to take; the robot enacts that action by moving its joint or wheels; and the loop repeats endlessly. The second main aim of the subject is to provide implementation experience with cutting edge AI algorithm applicable to consumer and industrial robotics, where we consider both model-based method and reinforcement-learning methods. INDICATIVE CONTENT: Topics covered are at the intersection of automatic control and artificial intelligence, including: Cyber-physical feedback system formulation, such as: black-box and grey-box modelling, stability and robustness safety requirements, hierarchical and network control architectures. Safety and convergence guarantees for model-based methods, such as: learning models from data; adaptive control schemes; stability and robustness of PID and MPC control approaches. Connections between optimal control and reinforcement learning formulations for robotics. Reinforcement learning for robotics, such as: actor-critic methods, on-policy versus off-policy learning, sample efficiency, transferring simulation-based learning to real-world robots View detailed information in the Handbook
Hardware Accelerated Computing · 12.5 pts	Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include: Review of combinational and sequential digital logic FPGA architectures and fundamentals Hardware description languages (Verilog/VHDL) and hardware design flows High-level synthesis and OpenCL The use of parallelism, locality, and precision in hardware accelerators Host-accelerator interactions and hardware-software co-design Optimisation of hardware designs with respect to throughput, latency, energy, and area Accelerator design for selected applications such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application View detailed information in the Handbook
Electrical Engineering Research Project · 25 pts	This subject is for students to undertake a substantial individual research project on an approved topic over the semester, requiring independent investigation with a chosen supervisor either from a university (research institute) or from an industry partner. Note: the student is responsible for contacting the potential supervisor for the project. This subject can also be taken by Master of Electrical Engineering outgoing exchange students for research projects carried out in an overseas university. If the project is to be carried out within the EEE Department, the student is encouraged to take ELEN90011 (Directed Studies) if possible. The emphasis of the project can be associated with either A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, often as an adjunct to existing academic research initiatives. It is expected that the project will incorporate findings associated with both well-defined professional practice and research principles. View detailed information in the Handbook
Applied Deep Learning for Engineers · 12.5 pts	This subject covers a modern deep learning approach to engineering using a project-centric pedagogy. Building upon system optimisation and machine learning fundamentals presented in ELEN90088, the subject will present advanced deep learning architectures to address long-standing engineering challenges such as system complexity, curse of dimensionality, and modelling gap. Subject will specifically focus on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how advanced deep learning frameworks can be applied to modern engineering and cyber-physical systems. INDICATIVE CONTENT Topics covered may include: Latent spaces, auto encoder architectures. Advanced deep learning architectures, auto-differentiation, physics-inspired neural networks. Sequential data analysis and predictive models such as transformers. Generative models such as GANs and GPT variants. Other advanced topics such as meta parameter optimisation, Markov Chain Monte Carlo sampling. Distributed machine learning, federated learning, graph neural networks. Cyber-physical security of modern engineering systems, including data-based anomaly and threat detection and prediction. Subject projects will focus on engineering applications in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. View detailed information in the Handbook
Modelling and Analysis for AI · 12.5 pts	This subject builds up the fundamentals for modelling dynamical systems, with a key focus on the aspects and decisions of modelling that are relevant for the application of AI and data-intensive learning methods. The discussion and evaluation of modelling methods focuses on how model fidelity influences simulation-to-real transfer; how modelling and simulation decisions influence computation time required for training and validation; and how discrete-time models introduce complexity when representing continuous-time engineering systems. Subsequently, it introduces the basic principles and engineering applications of programming and data structures in a condensed form with a project-centric pedagogy. It covers the fundamentals of databases and data structures, basic algorithms, scientific programming, and classic AI problem solving. It will focus specifically on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how programming and databases play a role in modern engineering and cyber-physical systems. INDICATIVE CONTENT Topics covered may include: Models for engineering systems in multiple disciplines, including analysis of what makes the models amenable to AI and data-intensive learning methods. Principles for simulating dynamic systems that are most relevant for the use of AI methods and to address these principles with existing software tools. Scientific programming for modelling using Python programming language and libraries such as scipy and numpy. Engineering data structures and time series data and their storage in SQL and noSQL databases. Example engineering applications will be taught via projects in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. View detailed information in the Handbook
Reinforcement Learning for Engineering · 12.5 pts	The key focus of this subject is the design and implementation of decision-making policies for enabling a dynamical system to behave autonomously and achieve a desired objective. This subject covers both model-based and model-free learning methods, with a focus on evaluating, contrasting, and combining methods. The influence of noisy sensor data on performance, and the trade-offs between exploration and exploitation during a learning phase, will also be covered. The examples used in this subject range across existing and emerging decision-making methods, and their application to consumer and industrial engineering systems. INDICATIVE CONTENT Topics covered may include: Reinforcement learning fundamentals such as principle of optimality, Bellman equation, value and policy iteration. Temporal-difference learning, Q-learning, Deep Q-learning, Actor critic methods and hybrid approaches in engineering context. Model based vs model free approaches, multi-agent RL and their engineering applications. RL methods for Cyber-physical resilience and security such as fuzzing methods. View detailed information in the Handbook

Approved Electives (Group B)

Students must complete 25 points of Year 3 elective subjects.

Accordion
Optimisation for Industry · 12.5 pts	The use of mathematical optimisation is widespread in business, where it is a key analytical tool for managing and planning business operations. It is also required in many industrial processes and is useful to government and community organizations. This subject will expose students to operations research techniques as used in industry. A heavy emphasis will be placed on the modelling process that turns an industrial problem into a mathematical formulation. The focus will then be on how to solve the resulting mathematical problem with mixed-integer programming techniques. View detailed information in the Handbook
Distributed Systems · 12.5 pts	AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. View detailed information in the Handbook
Advanced Motion Control · 12.5 pts	AIMS This subject is intended to give students an overview of the present state-of-the-art in industrial motion control and the likely future trends in control design. Students will be exposed to and have practical experience in the design and implementation of advanced controllers for various motion control problems. Advanced modelling and control topics will include system identification, modelling and compensation of friction and other disturbances, industrial servo loops, model-based and model-free controller design, and adaptive control. Applications will be drawn from industrial, medical and transport automation (eg robots, machine tools, production machines, laboratory automation, automotive and aerospace by-wire systems). INDICATIVE CONTENT Advanced modelling and control topics will include system identification, modelling and compensation of friction and other disturbances, industrial servo loops, model-based and model-free controller design, and adaptive control. Applications will be drawn from industrial, medical and transport automation (eg robots, machine tools, production machines, laboratory automation, automotive and aerospace by-wire systems). View detailed information in the Handbook
Leadership for Innovation · 12.5 pts	This subject, which is offered to students who have completed ENGR90034 Creating Innovative Engineering (CIE), will give participants core leadership skills for managing professionals engaged in innovation and other ambiguous project-based work. The subject teaches leadership at three levels (12 hours each). The first level, taught intensively before the start of the semester, will enable you to learn basic management theory that allows you to bridge from the skills and theory taught in CIE to the level needed to start mentoring a team in CIE or another subject. The second level, taught as four three-hour workshops during the semester, will focus on key thematic issues in the leadership of innovative teams. The third level, taught in twelve one-hour sessions, will focus on specific leadership skills. These include facilitation, coaching, mentoring, conflict resolution, etc. Students will apply the theory and skills to the mentoring of a student project team in CIE or another subject within the University. You will apply what you are learning, and develop skills, by mentoring an industry-sponsored project within CIE or a project within another subject. CIE mentors will also need to manage their relationship with the external sponsor of the project. View detailed information in the Handbook
Global Business Practicum · 12.5 pts	This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. View detailed information in the Handbook
Engineering Entrepreneurship · 12.5 pts	AIMS This subject is available as an elective in many of the Faculty of Engineering and IT Masters programs. It is aimed both at students who have immediate entrepreneurial intentions and at students who may be considering starting their own business at some point in their careers. The subject is designed to introduce all participants to their potential as entrepreneurs. By developing their own enterprise proposal within small groups, students will learn and demonstrate various processes by which successful new ventures move from idea to launch. INDICATIVE CONTENT Business modelling, opportunity analysis, value creation, financial management, sources of finance, creativity, innovation, entrepreneurial behaviour, successful engineering entrepreneurs. TEACHING METHOD The teaching method is based around a structured process of mini-lectures, class exercises, and active hands-on learning by doing. Intensive field research and minimum viable product development are very important to the subject. Learning is further enhanced through meetings with the lecturer and review by peers. View detailed information in the Handbook
Internship · 25 pts	AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship View detailed information in the Handbook
Nuclear Engineering · 12.5 pts	This subject provides an introduction to nuclear science and engineering. It presents the properties of atomic nuclei, radioactivity, nuclear reactions, and selected topics in thermodynamics as required for the analysis of power systems based on nuclear fission. The working principles of nuclear reactors and nuclear power plants are discussed, focusing on pressurised-water reactor systems. Indicative content: Introduction to nuclear physics Thermodynamics of nuclear power plants Nuclear power generation View detailed information in the Handbook
Radiation Protection · 12.5 pts	Nuclear technology involves the risk of exposure to ionising radiation, with potentially harmful effects on human health. This subject equips students with the necessary knowledge and skills to understand this risk and to manage it by applying established methods of radiation protection. Indicative content: Effects of ionising radiation on human health Methods of radiation detection and measurement Principles and methods of radiation protection Radiation shielding View detailed information in the Handbook
Engineering of Nuclear Systems · 12.5 pts	This subject presents nuclear reactor theory and its applications to reactor operation. It examines reactor response to control actions, feedback effects, and the intermediate and long-term effects on reactivity due to fission product poisoning and fuel burnup. Furthermore, it covers the fundamentals of thermal and hydraulic analysis of pressurised-water reactors. Indicative content: Nuclear reactor theory and engineering Reactor dynamics and control Effects of fuel burnup and the long-term evolution of the reactor core properties Heat generation and heat transfer from fuel to coolant Thermal design of nuclear reactors View detailed information in the Handbook
Nuclear Safety, Security and Safeguards · 12.5 pts	Safety, security and safeguards are critical requirements in the operation of nuclear facilities. This subject presents the safety aspects and safety assessment methods of nuclear power plants. Nuclear security and safeguards are discussed in the context of the nuclear fuel cycle. Indicative content: Nuclear fuel cycle Fundamentals of nuclear safety Safety systems and safety features of nuclear reactors Probabilistic safety assessment Nuclear security and safeguards View detailed information in the Handbook
Design Innovation and Leadership · 12.5 pts	A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects.  In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge.  Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication.  In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need?   How will you know if you need to change?  How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information.  The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. View detailed information in the Handbook

Autonomous Systems specialisation

Core

Year 1

Students must complete 100 points of Year 1 compulsory subjects.

Accordion
Intro. to Numerical Computation in C · 12.5 pts	AIMS Many engineering disciplines make use of numerical solutions to computational problems. In this subject students will be introduced to the key elements of programming in a high level language, and will then use that skill to explore methods for solving numerical problems in a range of discipline areas. INDICATIVE CONTENT Algorithmic problem solving Fundamental data types: numbers and characters Approximation and errors in numerical computation Fundamental program structures: sequencing, selection, repetition, functions Simple data storage structures, variables, arrays, and structures Roots of equations and of linear algebraic equations Curve fitting and splines Interpolation and extrapolation Numerical differentiation and integration View detailed information in the Handbook
Foundations of Electrical Networks · 12.5 pts	INDICATIVE CONTENT Foundations of Electrical Networks develops an understanding of fundamental modelling techniques for the analysis of systems that involve electrical phenomena. This includes networks models of “flow-drop” one-port elements in steady state (DC and AC), electrical power systems, simple RC and RL transient analysis, and networks involving ideal and non-ideal operational amplifiers. It forms the foundation of many engineering subjects exploring fundamental concepts in electrical and electronic engineering. The subject will cover key electrical engineering topics in the areas of: Electrical phenomena – charge, current, electrical potential, conservation of energy and charge, the generation, storage, transport and dissipation of electrical power. Network models – networks of “flow-drop” one-port elements, Kirchoff’s laws, standard current-voltage models for one-ports (independent sources, resistors, capacitors, inductors, transducers, diodes), analysis of static networks, properties of linear time-invariant (LTI) one-ports and impedance functions, diodes, transformers, steady-state (DC and AC) analysis of LTI networks via mesh and node techniques, equivalent circuits, and transient analysis of simple circuits; Electrical power systems – overview of power generation and transmission, analysis of single-phase and balanced three-phase AC power systems. Analysis and design of networks involving ideal and non-ideal operational amplifiers. This material will be complemented by exposure to software tools for the simulation of electrical and electronic systems and the opportunity to develop basic electrical engineering laboratory skills using a prototyping breadboard, digital multimeter, function generator, DC power supply, and oscilloscope. Please view this video for further information: Foundations of Electrical Networks View detailed information in the Handbook
Digital Systems · 12.5 pts	AIMS This subject develops a fundamental understanding of concepts used in the analysis, design and building of digital systems. Such systems form the information and communication technologies (ICT) that underpin modern society. This subject provides a foundation for subsequent subjects, including ELEN30013 Electronic System Implementation, ELEN90066 Embedded System Design and ELEN90061 Communication Networks. INDICATIVE CONTENT Topics include: Digital systems - quantifying and encoding information, digital data processing, design process abstractions; Combinational logic – timing contracts, acyclic networks, switching algebra, logic synthesis; Sequential logic – cyclic networks and finite-state machines, metastability, microcode; These topics will be complemented by exposure to the hardware description language such as Verilog and the use of engineering design automation tools and configurable logic devices (e.g. FPGAs) in the laboratory. Please view this video for further information: Digital Systems View detailed information in the Handbook
Electrical Network Analysis and Design · 12.5 pts	AIMS This subject develops a fundamental understanding of linear time-invariant network models for the analysis and design of electrical and electronic systems. Such models arise in the study of systems ranging from large-scale power grids to tiny radio frequency signal amplifiers. This subject is one of four subjects that define the Electrical Systems Major in the Bachelor of Science and it is a core requirement for the Master of Engineering (Electrical). It provides a foundation for various subsequent subjects, including ELEN30013 Electronic System Implementation, ELEN90066 Embedded System Design, and ELEN30012 Signal and Systems. INDICATIVE CONTENT Topics include: Transient and frequency domain analysis of linear time-invariant (LTI) models – linearity, time-invariance, impulse response and convolution, oscillations and damping, the Laplace transform and transfer functions, frequency response and bode plots, lumped versus distributed parameter transfer functions, poles, zeros, and resonance, stability of circuits, modelling and simulation with simulation tools; Electrical network models – one-port elements, impedance functions, two-port elements, dependent sources, matrix representations of two-ports, driving point impedances and network functions, ladder and lattice networks, passive versus active networks, multi-stage modelling and design, and multi-port generalisations; Analysis and design of networks involving ideal and non-ideal operational amplifiers with emphasis on the design of active filters and broadband circuits with specific frequency characteristics; Circuits and networks for managing voltage and power requirements for common electronic circuits. These topics will be complemented by tutorials and workshops designed to develop skills in design and modelling of electronic circuits through software tools and building, testing, and verification of electronic circuits. Please view this video for further information: Electrical Network Analysis and Design View detailed information in the Handbook
Electrical Device Modelling · 12.5 pts	AIM This subject develops the theoretical and practical tools required to understand, construct, validate and apply models of standard electrical and electronic devices. In particular, students will study the theoretical and practical development of models for devices such as resistors, capacitors, inductors, transformers, motors, batteries, diodes, transistors, and transmission lines. In doing so, students will gain exposure to a variety of fundamental fields in physics, including electromagnetism, semiconductor materials and quantum electronics. This material will be complemented by exposure to experiment design and measurement techniques in the laboratory, the application of models from device manufacturers, and the use of electronic circuit simulation software. INDICATIVE CONTENT Topics include: Vector calculus for device modelling, Maxwell’s equations, physics of conductors and insulators, passive device models (including for resistors, capacitors and inductors), lumped and distributed circuit models for wired interconnections (including treatment of signal integrity and termination strategies), semiconductors and quantum electronics, static and dynamic models for p-n junctions diodes and bipolar junction transistors. View detailed information in the Handbook
Signals and Systems · 12.5 pts	AIMS The aim of this subject is twofold: firstly, to develop an understanding of the fundamental tools and concepts used in the analysis of signals and the analysis and design of linear time-invariant systems path in continuous–time and discrete-time; secondly, to develop an understanding of their application in a broad range of areas, including electrical networks, telecommunications, signal-processing and automatic control. The subject formally introduces the fundamental mathematical techniques that underpin the analysis and design of electrical networks, telecommunication systems, signal-processing systems and automatic control systems. Such systems lie at the heart of the electrical engineering technologies that underpin modern society. This subject is one of four Level 3 subjects that define the Electrical Engineering Systems Major in the Bachelor of Science. . It provides the foundation for various subsequent subjects, including ELEN90057 Communication Systems, ELEN90058 Signal Processing and ELEN90055 Control Systems. INDICATIVE CONTENT Topics include: Signals – continuously and discretely indexed signals, important signal types, frequency-domain analysis (Fourier, Laplace and Z transforms), nonlinear transformations and harmonics, sampling; Systems – viewing differential / difference equations as systems that process signals, the notions of input, output and internal signals, block diagrams (series, parallel and feedback connections), properties of input-output models (causality, delay, stability, gain, shift-invariance, linearity), transient and steady state behaviour; Linear time-invariant systems – continuous and discrete impulse response; convolution operation, transfer functions and frequency response, time-domain interpretation of stable and unstable poles and zeros, state-space models (construction from high-order ODEs, canonical forms, state transformations and stability), and the discretisation of models for systems of continuously indexed signals. This material is complemented by exposure to the use of MATLAB for computation and simulation and examples from diverse areas including electrical engineering, biology, population dynamics and economics. View detailed information in the Handbook
Electronic System Implementation · 12.5 pts	AIMSThis subject provides students with hands-on electronic skills to gain basic competencies in design and implementation of simple circuits. Students will design with a range of standard electrical and electronic devices, basic circuit construction methods and electrical measurement techniques to test and verify the function of electronic systems. This subject is one of four subjects that define the Electrical Systems Major in the Bachelor of Science and it is a core requirement for the Master of Engineering (Electrical) and the Master of Engineering (Electrical with Business). This includes hands-on experience with: • Operation and selection of electrical and electronic devices used in various electronic circuits; • Common electronic circuit realisations to meet the most commonly required signal processing and conditioning applications; • Programmable digital circuits and microprocessor programming; • Circuit design and simulation tools; • Printed circuit board layout, circuit assembly, and soldering techniques; • Test and Measurement equipment and methods; • Managing design issues and requirements. Students will complete electronic circuit implementation projects in small groups and be required to prepare technical documentation and present project outcomes. INDICATIVE CONTENT • Devices such as resistors, capacitors, inductors, switches, transducers, motors, diodes, transistors, op-amps, voltage regulators, comparators, oscillators, timers, A/D and D/A converters, microprocessors and controllers; • Circuit functions and techniques such as buffering, referencing, signal conditioning, filtering, bridges, detection, waveform generation, and pulse-width modulation; • Microprocessor programming, the role of assembly and high-level languages, assemblers, compilers and debuggers; • PCB layout, circuit assembly, and soldering techniques; • Test and Measurement methods and working with common equipment such as multimeters and oscilloscopes. View detailed information in the Handbook
Engineering Mathematics · 12.5 pts	This subject introduces important mathematical methods required in engineering such as manipulating vector differential operators, computing multiple integrals and using integral theorems. A range of ordinary and partial differential equations are solved by a variety of methods and their solution behaviour is interpreted. The subject also introduces series including the concepts of convergence and divergence. Topics include: Vector calculus, including Gauss’ and Stokes’ Theorems; systems of homogeneous ordinary differential equations, including phase plane and linearisation for nonlinear systems; Laplace transforms; series, including Taylor series and power series; Fourier series and Fourier integrals; second order partial differential equations and separation of variables. View detailed information in the Handbook

Year 2

Students must complete 100 points of Year 2 compulsory subjects.

Accordion
Probability and Random Models · 12.5 pts	AIMS This subject provides an introduction to probability theory, random variables, random vectors, decision tests, and stochastic processes. Uncertainty is inevitable in real engineering systems, and the laws of probability offer a powerful way to evaluate uncertainty, to predict and to make decisions according to well-defined, quantitative principles. The material covered is important in fields such as communications, data networks, signal processing and electronics. This subject is a core requirement in the Master of Engineering (Electrical, Mechanical and Mechatronics). INDICATIVE CONTENT Topics include: Foundations – combinatorial analysis, axioms of probability, independence, conditional probability, Bayes’ rule; Random variables (rv’s)– definition; cumulative distribution, probability mass and probability density functions; expectation and variance; functions of an rv; important distributions and their properties and uses; Multiple random variables – joint cumulative distribution, probability mass and probability density functions; independent rv’s; correlation and covariance; conditional distributions and expectation; functions of several rv’s; jointly Gaussian rv’s; random vectors; Sums, inequalities and limit theorems – sums of rv’s, moment generating function; Markov and Chebychev inequalities; weak and strong laws of large numbers; the Central Limit Theorem; Decision testing - maximum likelihood, maximum a posterior, minimum cost and Neyman-Pearson rules; basic minimum mean-square error estimation; Stochastic processes – mean and autocorrelation functions, strict and wide-sense stationarity; ergodicity; important processes and their properties and uses; Introduction to Markov chains. This material is complemented by exposure to examples from electrical engineering and software tools (e.g. MATLAB) for computation and simulations. View detailed information in the Handbook
Control Systems · 12.5 pts	AIMS This subject provides an introduction to automatic control systems, with an emphasis on classical techniques for the analysis and design of feedback interconnections. The main challenge in automatic control is to achieve desired performance in the presence of uncertainty about the system dynamics and the operating environment. Feedback control is one way to deal with modelling uncertainty in the design of engineering systems. This subject is a core requirement in the Master of Engineering (Electrical, Electrical with Business, Mechanical, Mechanical with Business and Mechatronics). INDICATIVE CONTENT Topics include: * Modelling for control, linearization, relationships between time and frequency domain models of linear time-invariant dynamical systems, and the structure, stability, performance, and robustness of feedback interconnections; * Frequency-domain analysis and design, Nyquist and Bode plots, gain and phase margins, loop-shaping with proportional, integral, lead, and lag compensators, loop delays, and fundamental limitations in design; and * Actuator constraints and anti-windup compensation. This material is complemented by the use of software tools (e.g. MATLAB/Simulink) for computation and simulation, and exposure to control system hardware in the laboratory. View detailed information in the Handbook
Electronic Circuit Design · 12.5 pts	AIMS This subject provides an in-depth coverage of transistor (MOSFET and BJT) devices and their use in common circuits. In particular, students will study topics including: transistor operating modes and switching; principles of CMOS circuits; transistor biasing; current-source/emitter-amplifiers; low-frequency response; followers; class B amplifiers; current limiting; current sources and mirrors; differential pairs; feedback in amplifiers and stability; operational amplifiers; operational amplifier circuits; and voltage regulation. This material will be complemented by exposure to circuit simulation software tools and the opportunity to further develop circuit construction/test skills in the laboratory. INDICATIVE CONTENT Design-focused field-effect and bipolar elementary transistor models, and design of elementary amplifier stages and biasing circuits. Static and dynamic behaviour of amplifier circuits including frequency response, feedback and stability, slew-rate and clipping. Operational amplifiers and opamp based circuits; voltage regulators, references and voltage converters. Verification of electronic circuits using simulation and constructing them in the laboratory. Please view this video for further information: Electronic Circuit Design View detailed information in the Handbook
Communication Systems · 12.5 pts	AIMS This subject provides an introduction to the analysis and design of telecommunication signals and systems, in the presence of uncertainty. The emphasis is on understanding the basic concepts that underpin the physical layer of modern communication systems. INDICATIVE CONTENT Topics to be covered include: Introduction to communication systems including historical developments and comparisons between analogue and digital communications. Review of assumed knowledge from linear algebra, signals and systems and probability and random processes. The sampling theorem, analog-to-digital conversion, complex baseband representation of passband signals, filtering of random processes, power spectral density, bandwidth of random signals, additive white Gaussian noise (AWGN), signal-to-noise ratio. Communication over baseband AWGN channels including modulation techniques (pulse amplitude modulation, orthogonal modulation), signal space representation, optimal detectors, matched filters, error probability calculations and bandwidth / power trade-off. Communication over passband AWGN channels including modulation techniques (phase shift keying, quadrature amplitude modulation and frequency shift keying), optimal coherent detectors, noncoherent detectors and error probability calculations. Communication over linear time-invariant channels including concepts of distortion, inter-symbol interference, pulse shaping, Nyquist’s criterion, equalization, sequence detection and the Viterbi algorithm. Synchronization including carrier, symbol and frame synchronization. View detailed information in the Handbook
Signal Processing · 12.5 pts	AIMS This subject provides an introduction to the fundamental theory of time domain and frequency domain representation of discrete time signals and linear time invariant dynamical systems, and how this theory is used to analyse and design digital signal processing systems and algorithms. Topics include: Applications of signal processing techniques; Sampling of analog signals, anti-aliasing filters; Frequency-domain analysis of signals and systems, Discrete Time Fourier Transform, Discrete Fourier Transform, Fast Fourier Transform; Digital filters, low-pass, high-pass, band-pass, stop-band and all pass filters. Phase and group delay, FIR and IIR filters; Design of digital FIR and IIR filters; Multi-rate signal processing, with a focus on up-sampling, down-sampling, and sampling rate conversion; Simple non-parametric methods for spectral estimation. This fundamental material will be complemented by exposure to MATLAB tools for signal analysis and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory. INDICATIVE CONTENT Sampling of continuous time signals, Design of anti-aliasing filters, Time and frequency representation of discrete time signals and discrete time linear time invariant systems, Discrete Time Fourier Transform and z-transform and their properties, Low order lowpass, highpass, bandpass, bandstop filters, All-pass filter, Design of IIR filters using the bilinear transformation, Design of FIR filters with linear phase using windowing techniques and the Parks McClelland method, Discrete Time Fourier transform and its properties, Fast Fourier Transform, The use of the DFT in implementation of linear filtering algorithms, Up-sampling and down-sampling, multistage and computationally efficient implementations of up-samplers and down-samplers, Energy and power spectra for deterministic signals. View detailed information in the Handbook
Embedded System Design · 12.5 pts	AIMS This subject provides a practical introduction to the basics of modelling, analysis, and design of microprocessor-based embedded systems. Students will learn how to integrate computation with physical processes to meet a desired specification within the context of a design project. The project work will expose students to the various stages in an engineering project (design, implementation, testing and documentation) and a range of embedded system concepts. INDICATIVE CONTENT Topics covered may include: digital computer and microprocessor architectures, modelling of dynamic behaviours, control, models of computation, operating systems concepts, multi-tasking, resource management and real-time behaviours, interfacing with the physical world, analysis and verification, safety, reliability, and security and privacy. This material will be complemented by exposure to standard software tools including compilers and debuggers, finite state machine design and analysis software, and simulation tools. The subject will include a level of industry engagement, to provide broader examples of engineering projects, through guest lectures. View detailed information in the Handbook
Introduction to Power Engineering · 12.5 pts	AIMS To develop a solid foundation for the study of systems that involve the generation, transport, and conversion of electric power. INDICATIVE CONTENT Physical principles of electromagnetism, magnetic circuits, energy storage, loss mechanisms, electromechanical energy conversion. Modelling of transmission lines, transformers, motors and generators (synchronous and asynchronous), and other loads. Circuit theory for power system analysis, three phase-phase circuits, power flow and maximum power transfer, per-unit system. Please view this video for further information: Introduction to Power Engineering View detailed information in the Handbook
Interdisciplinary Design for Engineers · 12.5 pts	In this subject, students will actively engage in an interdisciplinary, collaborative and project-based learning environment, offering insights into the professional nature of engineering work. Through a real-world project, students will gain hands-on design experience addressing a complex challenge. The project will require students to integrate discipline knowledge and apply professional skills like teamwork and communication. Students will experience the entire engineering design process, covering problem definition, ideation, concept development, analysis, prototyping, testing and iteration. The project provides practical experience, equipping students with tools and methods to address complex challenges. Students are expected to integrate diverse perspectives, considering factors like stakeholders, sustainability (including environmental and social issues), safety, feasibility, and technical and ethical considerations. View detailed information in the Handbook

Capstone

Year 3

Students must complete 25 points of Year 3 compulsory capstone project subjects.

Accordion
Engineering Capstone Project Part 1 · 12.5 pts	The subject involves undertaking a substantial group project (typically in groups of three students) requiring an independent investigation on an approved topic in advanced engineering design and / or research. Each project is carried out under the supervision of a member of academic staff and where appropriate an industry partner. The emphasis of the project can be associated with either: A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the engineering science disciplines, often as an adjunct to existing academic research initiatives. It is expected that the Capstone Project will incorporate findings associated with both well-defined professional practice and research principles and will provide students with the opportunity to integrate technical knowledge and generic skills gained in earlier years. The project component of this subject is supplemented by a lecture course dealing with project management tools and practices. Please note: Students enrolled in the suite of Master of Engineering programs must be within the final 112.5 points of their degree to enrol. Students enrolled in the Master of Industrial Engineering must be within the final 100 points of their degree to enrol. Students are to take Engineering Capstone Project Part 1 and then subsequently continue with Engineering Capstone Project Part 2 in the following semester. Upon successful completion of this project, students will receive 25 points credit. View detailed information in the Handbook
Engineering Capstone Project Part 2 · 12.5 pts	Please refer to ENGR90037 Engineering Capstone Project Part 1 for this information. View detailed information in the Handbook

Accordion

Engineering Capstone Project Part 1 · 12.5 pts

The emphasis of the project can be associated with either:

A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or
A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the engineering science disciplines, often as an adjunct to existing academic research initiatives.

The project component of this subject is supplemented by a lecture course dealing with project management tools and practices.

Please note:

Students enrolled in the suite of Master of Engineering programs must be within the final 112.5 points of their degree to enrol.

Students enrolled in the Master of Industrial Engineering must be within the final 100 points of their degree to enrol.

View detailed information in the Handbook

Engineering Capstone Project Part 2 · 12.5 pts

Please refer to ENGR90037 Engineering Capstone Project Part 1 for this information.

View detailed information in the Handbook

Specialisation

Year 3

Students must complete 50 credit points of Year 3 core specialisation subjects.

Accordion
Advanced Signal Processing · 12.5 pts	AIMS This subject provides an in-depth introduction to statistical signal processing. INDICATIVE CONTENT Students will study a selection of the following topics: Applications of statistical signal processing; A review of stochastic signals and systems fundamentals – random processes, white noise, stationarity, auto- and cross-correlation functions, spectral- and cross-spectral densities, properties of linear time-invariant systems excited by white noise; Parameter estimation - least squares and its properties, recursive least squares and least mean squares, optimisation-based methods, maximum likelihood methods; Kalman, Wiener and Markov filtering; Power spectrum estimation. This material will be complemented with the use of software tools (e.g. MATLAB) for computation and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory. View detailed information in the Handbook
Autonomous Systems Clinic · 12.5 pts	AIMS: Students work collaboratively in small groups to engineer an autonomous system that performs a specified task. This includes carrying out steps such as: task analysis; proposing multiple solutions; feasibility analysis through prototyping and computer-aided design; detailed design, construction, and testing of the chosen solution; and demonstrating the solution in a proving ground. The lectures will cast content from the pre-requisite subjects into the context of the task at hand, as well as covering additional topics relevant to the task. Each student group is expected to demonstrate initiative and independence while pursuing the goal of designing and building their autonomous system, with a focus of the subject being that students learn through hands-on experience, implementation, and verification. INDICATIVE CONTENT: Successful completion of the project requires the student to draw upon knowledge, understanding, and skills learned in the prerequisite subjects, namely: • Embedded System Design - including topics such as: finite, extended, and hierarchical state machines; modelling cyber-physical systems; scheduling, multi-tasking, and real-time issues; interfacing to the analogue world. • Control Systems - including topics such as: modelling; linearisation; feedback interconnections; proportional, integral, derivative (PID) control; actuator constraint considerations. • Signal Processing - including topics such as: design and implementation of digital filters (low-, high-, band-, all- pass filters); ARMA systems; up-sampling and down-sampling. Additional topics, specific to the task as hand, will be covered, such as: ideation, prototyping, and design practices; image processing and computer vision tools; software introductions; safety and failure analysis. A range of materials, components, and fabrication facilities are provided, from which the students are expected to utilise a subset for designing and building their autonomous system, such as: electric motors, range sensors, camera, voltage converters, compute power, sheet wood, soldering stations, laser wood cutting, 3D printing. The task to be performed is motivated by a real-world application of autonomous systems, such as: operating in hazardous environments or performing repetitive tasks. Please view this video for further information: Autonomous Systems Clinic View detailed information in the Handbook
Advanced Control Systems · 12.5 pts	AIMS This subject provides an introduction to modern control theory with a particular focus on design of advanced control laws via state-space methods and optimal control. The role of feedback in control design will be reinforced within this context, alongside the role of optimisation techniques in control system synthesis. INDICATIVE CONTENT Topics include: State-space models - first-order vector differential/difference equations; Lyapunov stability; linearisation; discretisation; Kalman decomposition (observable, detectable, reachable and stabilisable subspaces); state-feedback and pole placement; output-feedback and observer design in both continuous-time and discrete-time. Optimal control - dynamic programming; linear quadratic regulation in both continuous-time and discrete-time. Model predictive control in discrete-time; moving-horizon with constraints. View detailed information in the Handbook
AI for Robotics · 12.5 pts	AIMS: This subject focuses on the software and algorithms (i.e., artificial intelligence) that enable robotic systems to move autonomously through their environment and perform tasks. The key focus of this subject is the foundations of robotic systems that use software to move autonomously through their environment. This subject focus on the software & algorithms that enable the robot to perform tasks autonomously. Hence, this subject focused on artificial intelligence (AI) software & algorithms for robotics. The first main aim of the subject is to provide a foundation of the feedback loop that is core to all AI-enabled robots, namely: sensors measure the world around the robot; AI algorithms decide what action to take; the robot enacts that action by moving its joint or wheels; and the loop repeats endlessly. The second main aim of the subject is to provide implementation experience with cutting edge AI algorithm applicable to consumer and industrial robotics, where we consider both model-based method and reinforcement-learning methods. INDICATIVE CONTENT: Topics covered are at the intersection of automatic control and artificial intelligence, including: Cyber-physical feedback system formulation, such as: black-box and grey-box modelling, stability and robustness safety requirements, hierarchical and network control architectures. Safety and convergence guarantees for model-based methods, such as: learning models from data; adaptive control schemes; stability and robustness of PID and MPC control approaches. Connections between optimal control and reinforcement learning formulations for robotics. Reinforcement learning for robotics, such as: actor-critic methods, on-policy versus off-policy learning, sample efficiency, transferring simulation-based learning to real-world robots View detailed information in the Handbook

Electives

Electrical Engineering Electives (Group A)

Students must complete 25 points of Year 3 elective subjects.

Accordion
Introduction to Optimisation · 12.5 pts	AIMS This subject provides a rigorous introduction to numerical nonlinear optimization, as used across all of science and particularly in engineering design. There is an emphasis on both the theory and application of optimization techniques, with a focus on solving unconstrained and constrained nonlinear programmes. This subject is intended for graduate and research higher-degree students in engineering. INDICATIVE CONTENT Topics include: Algorithms for unconstrained optimization Algorithms for constrained optimization Convex sets and functions Convex optimization problems Duality theory Computational complexity Approximation algorithms and penalty methods. View detailed information in the Handbook
Advanced Communication Systems · 12.5 pts	AIMS The aim of this subject is to develop a thorough understanding of the main concepts, techniques and performance criteria used in the analysis and design of digital communication systems and wireless networks. Such systems and networks lie at the heart of the information and communication technologies (ICT) that underpin modern society and are very much part of the Internet of Things which involves machine to machine communication. INDICATIVE CONTENT This subject provides an in-depth treatment of the main concepts and techniques used in the analysis and design of digital communication systems and wireless networks. Topics include: Source coding; entropy, Shannon source coding bound, data compression techniques; Channel modelling, modulation over time-varying fading channels, time and frequency diversity, energy and spectral efficiency, multiple carrier modulation including orthogonal frequency division multiplexing (OFDM) modulation, phase noise characteristics and its impact on single-carrier and multicarrier systems, spatial multiplexing for multiple access protocols, multiple antenna technologies (MIMO systems), cellular networks; Channel coding for error control: mutual information, channel capacity, Shannon channel coding bound, channel coding concepts, block codes; convolutional / trellis codes; introduction to LDPC codes, turbo codes, polar codes. Examples include short, medium and long range communication systems such as bluetooth, cellular and satellite communication systems. View detailed information in the Handbook
Advanced Signal Processing · 12.5 pts	AIMS This subject provides an in-depth introduction to statistical signal processing. INDICATIVE CONTENT Students will study a selection of the following topics: Applications of statistical signal processing; A review of stochastic signals and systems fundamentals – random processes, white noise, stationarity, auto- and cross-correlation functions, spectral- and cross-spectral densities, properties of linear time-invariant systems excited by white noise; Parameter estimation - least squares and its properties, recursive least squares and least mean squares, optimisation-based methods, maximum likelihood methods; Kalman, Wiener and Markov filtering; Power spectrum estimation. This material will be complemented with the use of software tools (e.g. MATLAB) for computation and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory. View detailed information in the Handbook
Electronic System Design · 12.5 pts	AIMS This subject will explore the design of various electrical and electronic systems and provide students with a range of common and practical design techniques and circuits in the context of a guided laboratory based project. INDICATIVE CONTENT Subject may cover specific concepts surrounding the design and implementation of: Design process; Design for manufacture and assembly; Advanced PCB design; Oscillators; Phase-locked loops and frequency synthesis; Base-band signalling schemes and clock recovery; Mixers and logarithmic amplification; Automatic gain control; Filters; Synchronous detection; High-speed analog-digital conversion; High-frequency amplification; Low noise amplifiers; Power supply design; Batteries, battery charging systems, and management; Test and measurement; Sensors. View detailed information in the Handbook
Lightwave Systems · 12.5 pts	AIMS Lightwave systems are fundamentally changing the way we communicate through broadband communications, helping clinicians to perform a range of medical procedures and diagnosis supported by advanced biomedical instrumentation, and even in the way we live in our homes through sophisticated interactive televisions and security systems. This subject will explore the physical principles and issues that arise in the design of lightwave systems often found in those key industry sectors. Students will study topics from: transmission of light over wave guides; production of light by lasers; light modulation; conversion of light signals to electrical signals; optical multiplexing and demultiplexing; light amplification; dispersion and dispersion compensation; optical nonlinearities; modulation and advanced detection schemes. This material will be complemented by exposure to lightwave systems and measurement techniques in the laboratory. INDICATIVE CONTENT This subject will explore the physical principles governing the generation, modulation, amplification, guiding, transmission, multiplexing, demultiplexing and detection of light and issues that arise in the design of lightwave systems such as transmission impairments, noise. Students learn selected examples of lightwave systems and methods for design, modelling and testing of simple lightwave systems. View detailed information in the Handbook
Power System Analysis · 12.5 pts	AIMS This subject provides an insight into the fundamental elements to analyse electrical power transmission and distribution systems, with both analytical and simulation tools for analysis of operations of these systems. Problems related to power flow and use of Newton-Raphson and other algorithms such as backward-forward sweep will be discussed. Fault calculation and analysis, symmetrical components, and analytical methods for solving symmetrical (balanced) faults will be covered. Principles, concepts and problems related to power system dynamics and control, particularly for frequency and voltage regulation, will be discussed and analysed in detail. Finally, small-signal, transient, voltage and frequency stability will be introduced and exemplified. Focus will be put on real-world examples, particularly to prepare the student for the ongoing transition towards a low-carbon grid dominated by renewables and distributed energy resources. INDICATIVE CONTENT Power flow calculations, Newton-Raphson, Gauss-Seidel and backward-forward sweep methods; Fault calculations, balanced and unbalanced, symmetrical components, fundamentals of protection; Frequency regulation and frequency stability, Voltage regulation in transmission and distribution networks, including use of flexible AC transmission systems (FACTS); Voltage stability, small-signal stability, transient stability; Computer simulations. View detailed information in the Handbook
Communication Networks · 12.5 pts	AIMS This subject introduces the basic principles, analysis, and design of communication networks. It covers relevant analytical methods, the layered network architecture of the Internet, and a multitude of network protocols. Analytical tools from queueing, optimisation, and graph theories are used to develop an in-depth understanding of basic principles and the role they play in network design. Specifically, queueing and graph theories are emphasised as methodological frameworks for communication network delay and structure analysis. The concepts taught in this subject lead to a better understanding of the Internet as well as modern communication paradigms such as Software-Defined Networks, Machine-to-Machine communication, Internet of Things, and social networks. INDICATIVE CONTENT Topics covered may include: The layered network architecture with a focus on physical-layer multiple access (TDM, FDM, WDM), link layer protocols and medium access control (MAC), network layer topologies, least-cost routing algorithms and protocols, transport layer protocols and the principles and techniques of practical reliable transport; LAN protocols, Ethernet, Wi-Fi, and serial communications; The Internet's network layer including the Internet Protocol (IP) and routing protocols including an introduction to BGP and the operation of forwarding tables in routers and shortest prefix routing; The Internet's transport layer protocols UDP and TCP, including the flow and congestion control algorithms; Network security, application layer, cloud and fog computing, Machine-to-Machine communication, and Internet of Things; Queuing theory: basics, birth-death processes, M/M/x and Markovian queues, networks of queues; Basics of graph theory and social network analysis relevant to communication networks. View detailed information in the Handbook
High Speed Electronics · 12.5 pts	AIMS The aim of the subject is to provide theoretical and practical treatment of high-speed electronics. Through the subject, students will grasp the fundamental properties and models of high-speed signals and interconnects, acquire high-speed digital design skills with a focus on the modelling, analysis, design and application of high speed transistors, logic gates and modern logic families, and master the high-speed analogue design capability including the design of oscillators and filters for RF applications. The students will be exposed to the state-of-the-art technologies that are shaping the fast evolving semiconductor industry. INDICATIVE CONTENT The topics include: Fundamental properties of analogue systems; Smith charts: principles and applications; High-speed analogue circuits: voltage control oscillators, matching networks, and low noise amplifiers; Bipolar junction transistors: device, switching, and logic; CMOS: device, switching and logic; High-speed signalling consideration: power dissipation, heat, signal propagation, and termination. View detailed information in the Handbook
Advanced Control Systems · 12.5 pts	AIMS This subject provides an introduction to modern control theory with a particular focus on design of advanced control laws via state-space methods and optimal control. The role of feedback in control design will be reinforced within this context, alongside the role of optimisation techniques in control system synthesis. INDICATIVE CONTENT Topics include: State-space models - first-order vector differential/difference equations; Lyapunov stability; linearisation; discretisation; Kalman decomposition (observable, detectable, reachable and stabilisable subspaces); state-feedback and pole placement; output-feedback and observer design in both continuous-time and discrete-time. Optimal control - dynamic programming; linear quadratic regulation in both continuous-time and discrete-time. Model predictive control in discrete-time; moving-horizon with constraints. View detailed information in the Handbook
Power Electronics · 12.5 pts	AIMS The aim of this subject is to understand the fundamental concepts and basic theory involved in modelling and analysis of the power electronic components that comprise power electronic devices such as power supplies, inverters, converters and their control systems. It is expected that at the end of this subject the student has a sound understanding of the physical concepts and mathematical models behind each of the basic components and of their functionality within a system, such as a high voltage DC transmission system. Furthermore this subject seeks to combine the fields of electronics, semiconductor devices, power system operation, power system measurement and control. It is expected that through this subject the students are exposed to examples of real electrical engineering systems where the three disciplines of electronics, power systems and control come together. INDICATIVE CONTENT Topics covered in this subject include: introduction to power semiconductor switches; discussion on the role of power electronics in the operation of electric power systems; models of power semiconductor devices and circuit components, including diodes, Thyristors, IGBT, Snubber circuits. Also basic concepts of single- and three-phase diode bridge rectifiers; single- and three-phase converters and inverters; operation and design of DC-AC inverters with emphases on switch-mode inverters, i.e. single- and three-phase inverters. Finally, the acquired knowledge of power electronic devices is applied to wind and PV solar systems where the design of voltage source converters and associated control loops are used to interface the wind/solar system with the power grid. View detailed information in the Handbook
Grid Integration of Renewables · 12.5 pts	AIMS This subject develops a foundation for pursuing electrical engineering oriented research in the area of sustainable energy systems. This subject aims to introduce the concepts behind smart grids, future low-carbon energy networks, sustainable electricity systems as well as the main renewable and low-carbon generation technologies. The subject will introduce students to tools and techniques so that distributed energy resources (e.g. distributed renewable generation, storage, electric vehicles, demand response, etc.) may be integrated effectively into the power system in the context of both traditional grids and future smart grids. INDICATIVE CONTENT This subject will cover the following topics: Distributed low-carbon technologies Introduction to distribution networks Introduction to distributed low-carbon technologies (wind energy, photovoltaic systems, electric vehicles, electric heating, storage) Wind Energy: impacts and challenges Photovoltaic systems: impacts and challenges Electric vehicles: impacts and challenges Electric heat pumps and electric heating: impacts and challenges Storage: impacts and challenges Smart Distribution and Smart Transmission Networks Distributed low-carbon technologies and active network management Towards Smart Grids Smart grids - Transmission and Distribution perspectives Smart Transmission: HVDC and FACTS, dynamic line rating, post-contingency security, special protection schemes The role of future Distribution System Operators Low-carbon Electricity System Towards low-carbon networks: relationship between sustainability and smart grids Introduction to low-carbon thermal generation (nuclear, Carbon Capture and Storage, Concentrated Solar Power, biomass, etc.) Utility-scale renewable technologies: wind farms; solar farms; other large-scale renewables; utility-scale batteries System-level operational challenges and solutions for renewables integration: variability and uncertainty; low-inertia operation; low system-strength operation; minimum load issues; DER visibility; indistinct events; general stability issues; flexibility System-level planning challenges and solutions for renewables integration: system adequacy and reliability; capacity credit of renewables and storage; extreme weather events and resilience; role of transmission Sector coupling and multi-energy systems: decarbonisation of gas, heating and transport; role of hydrogen Distributed energy systems: new technical and commercial architectures for two-sided systems and markets; demand response; aggregators and virtual power plants; distributed energy markets; peer-to-peer trading; local energy communities; microgrids View detailed information in the Handbook
System Optimisation & Machine Learning · 12.5 pts	This subject introduces the basic principles, analysis methods, and applications of optimisation and machine learning to engineering systems; encompassing fundamental concepts and practical algorithms. It covers the fundamentals of continuous optimisation followed by machine learning basics for engineering applications. The concepts and methods discussed are illustrated in multiple application areas including Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks.The concepts taught in this subject will allow a better understanding of continuous optimisation and machine learning for systems engineering. INDICATIVE CONTENT Topics covered may include: Fundamentals of continuous optimisation: convex sets and functions; local vs global solutions, constrained optimisation and Lagrange multipliers; linear, quadratic, and nonlinear programming Basics of machine learning encompassing supervised and unsupervised learning: binary classification, linear and nonlinear regression, kernel methods, and clustering. Specific machine learning methods such as Support Vector Machines (SVMs), Neural Networks (NNs), k-means clustering, and reinforcement learning. Applications to Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks. View detailed information in the Handbook
Communication Design Clinic · 12.5 pts	Students work collaboratively in small groups to implement and optimize components in a modern communication system or network with the goal of supporting a targeted application. To meet this goal students will need to: determine system requirements based on the target application and additional constraints; propose and evaluate multiple solutions through theoretical analysis and detailed simulations; implement, integrate, verify, and iterate on their selected solutions. Lectures will cast content from prerequisite subjects into the context at hand and cover additional topics relevant to the task. Each student group is expected to demonstrate initiative and independence while pursuing the goal of designing and optimizing their communication system or network, with a key focus being that students learn through hands-on experience. Students will receive early exposure to advanced topics critical to modern communication systems, such as: source and channel coding, multicarrier modulation, multiantenna transmission, and network architectures and protocols. Successful completion of the project will require the student to draw upon knowledge, understanding, and skills learned in prerequisite subjects, which may include: Communication Systems – analog-to-digital conversion, signal-to-noise ratio, modulation and demodulation, bandwidth/power trade-off, error probability calculations, distortion, inter-symbol interference, pulse shaping, equalization, sequence detection, and synchronization. Signal Processing - design and implementation of digital filters (low-, high-, band-, all- pass filters); ARMA systems; up-sampling and down-sampling. Embedded System Design – system-level programming, operating systems concepts, real-time issues, and standard software tools. Additional topics required for the assigned project may also be covered, such as: ideation, prototyping, and design practices; analog RF components; software packages for modelling and implementation; and the use of test & measurement equipment. View detailed information in the Handbook
Autonomous Systems Clinic · 12.5 pts	AIMS: Students work collaboratively in small groups to engineer an autonomous system that performs a specified task. This includes carrying out steps such as: task analysis; proposing multiple solutions; feasibility analysis through prototyping and computer-aided design; detailed design, construction, and testing of the chosen solution; and demonstrating the solution in a proving ground. The lectures will cast content from the pre-requisite subjects into the context of the task at hand, as well as covering additional topics relevant to the task. Each student group is expected to demonstrate initiative and independence while pursuing the goal of designing and building their autonomous system, with a focus of the subject being that students learn through hands-on experience, implementation, and verification. INDICATIVE CONTENT: Successful completion of the project requires the student to draw upon knowledge, understanding, and skills learned in the prerequisite subjects, namely: • Embedded System Design - including topics such as: finite, extended, and hierarchical state machines; modelling cyber-physical systems; scheduling, multi-tasking, and real-time issues; interfacing to the analogue world. • Control Systems - including topics such as: modelling; linearisation; feedback interconnections; proportional, integral, derivative (PID) control; actuator constraint considerations. • Signal Processing - including topics such as: design and implementation of digital filters (low-, high-, band-, all- pass filters); ARMA systems; up-sampling and down-sampling. Additional topics, specific to the task as hand, will be covered, such as: ideation, prototyping, and design practices; image processing and computer vision tools; software introductions; safety and failure analysis. A range of materials, components, and fabrication facilities are provided, from which the students are expected to utilise a subset for designing and building their autonomous system, such as: electric motors, range sensors, camera, voltage converters, compute power, sheet wood, soldering stations, laser wood cutting, 3D printing. The task to be performed is motivated by a real-world application of autonomous systems, such as: operating in hazardous environments or performing repetitive tasks. Please view this video for further information: Autonomous Systems Clinic View detailed information in the Handbook
Semiconductor Devices · 12.5 pts	This subject serves as an introduction to semiconductor devices. It describes the fundamentals, theory, material and physical properties of semiconductor devices. The following topics will be covered. Fundamentals: Crystal properties and of the growth of bulk crystals and of epitaxial layers. Physical concepts related to atoms and electrons. These concepts may include the photoelectric effect, the Bohr model, quantum mechanics, and the periodic table. Energy bands and charge carriers in semiconductors: Bonding forces and energy bands in solids, charge carriers in semiconductors, carrier concentrations, the drift of carriers in electric and magnetic fields, and the Fermi level. Excess carriers in semiconductors: Optical absorption, luminescence, carrier lifetime and photoconductivity, and the diffusion of carriers. Junctions: Fabrication of pn junctions, equilibrium conditions, forward and reverse biased junctions in steady state, reverse bias breakdown, transient and AC conditions, metal-semiconductor junctions and heterojunctions. In the next part of the subject PN junction diodes: Junction diodes, tunnel diodes, photodiodes, and light-emitting diodes and lasers. Bipolar junction transistors (BJTs): Amplification and switching, fundamentals of BJT operation, BJT fabrication, minority carrier distributions and terminal currents, generalised biasing, switching, the frequency limitations of transistors, and heterojunction bipolar transistors. Field effect transistors (FETs): Topics may include junction FETs, the metal semiconductor FET and the metal-insulator-semiconductor FET. Additional topics (if time permits): Integrated circuits, pnpn switching devices, and microwave devices. View detailed information in the Handbook
Low-carbon Grids: Operation & Economics · 12.5 pts	This subject introduces the student to foundational aspects of economic, secure and reliable operation of low-carbon power systems and electricity markets with large shares of variable and uncertain renewable energy sources. The underlying framework is the so-called “affordability-sustainability-security” energy trilemma, which seeks to strike a delicate balance among: the desire to operate power systems at low cost (“affordability”); the desire to meet specific environmental targets (“sustainability”); and the need to “keep the lights on” (“security”). In order for the energy trilemma to be analysed in the context of a competitive market environment, the subject will provide the student with fundamentals of economics, operation of electricity markets, optimal bidding strategies of different market stakeholders, economics of transmission and distribution networks, and role of new technologies and commercial entities such as storage and aggregators. Different aspects of power system security will be analysed, from system-level requirements and constraints to provision of security services from market stakeholders. Basic concepts of optimization, including linear, quadratic, and mixed integer linear programming, will also be taught to provide the student with the tools required to understand and model current and future power system and energy market operation. View detailed information in the Handbook
Microprocessor Design Clinic · 12.5 pts	Students in this subject will be introduced to computer architectures, microprocessors, microcontrollers, operating systems, compilers and software design. The proposed course will cover a broad range of topics necessary to make students knowledgeable in the art of microprocessor design including advanced concepts such as in line and out of order execution and execution unit resource optimisation. Students in this course will learn to design execution units, arithmetic logic units, memory hierarchies and learn strategies for cache sizing. As part of this, students will become proficient in microcode and instruction set design, multi-processor and multi core theory and design, including new design methodologies such as chiplet design. Upon completion, students will be familiar with the specification and synthesis of microprocessor systems using high level generator languages such as Chisel and Scala. The course will also introduce students to compiler and linker design, enhancements to instruction sets, c-language and the theory of operating systems. View detailed information in the Handbook
Large Data Methods & Applications · 12.5 pts	This course provides an introduction to an important contemporary statistical toolset for applications including data science, machine learning, signal processing, financial engineering, biomedical engineering, communication systems and other high-dimensional statistical applications. The course will cover topics including introduction to random matrix theory models in engineering; eigenvalue distributions; finite-dimensional and large-dimensional techniques, covariance estimation, principal component analysis and spectral clustering. These topics will be supplemented by applications across a range of traditional and emerging domains involving big data sets. View detailed information in the Handbook
Directed Studies · 12.5 pts	AIMS Directed studies provide the students with broader experience in addition to the regular class based learning. The directed studies can be conducted in the forms of: Industrial internship or research placements in the department’s research groups based on availability. This is only open to students who have completed a minimum of one semester of study and who have achieved an average of H2A or above in their prior subjects; Individually arranged supervised study of current research topics with staff members associated with the Department of Electrical and Electronic Engineering. INDICATIVE CONTENT The examples of the research topics are: Cloud Computing, Content Distribution and Information Logistics; Internet Services Energy Star Rating; Energy Efficiency of Future Modulation Formats; Low-Energy Fibre Access Networks; Video Coding for Energy Efficient Telecommunications; Fundamental Limits of Electronics and Photonics; Broadband fibre wireless networks and systems; Optimal design of few-mode fibres. View detailed information in the Handbook
AI for Robotics · 12.5 pts	AIMS: This subject focuses on the software and algorithms (i.e., artificial intelligence) that enable robotic systems to move autonomously through their environment and perform tasks. The key focus of this subject is the foundations of robotic systems that use software to move autonomously through their environment. This subject focus on the software & algorithms that enable the robot to perform tasks autonomously. Hence, this subject focused on artificial intelligence (AI) software & algorithms for robotics. The first main aim of the subject is to provide a foundation of the feedback loop that is core to all AI-enabled robots, namely: sensors measure the world around the robot; AI algorithms decide what action to take; the robot enacts that action by moving its joint or wheels; and the loop repeats endlessly. The second main aim of the subject is to provide implementation experience with cutting edge AI algorithm applicable to consumer and industrial robotics, where we consider both model-based method and reinforcement-learning methods. INDICATIVE CONTENT: Topics covered are at the intersection of automatic control and artificial intelligence, including: Cyber-physical feedback system formulation, such as: black-box and grey-box modelling, stability and robustness safety requirements, hierarchical and network control architectures. Safety and convergence guarantees for model-based methods, such as: learning models from data; adaptive control schemes; stability and robustness of PID and MPC control approaches. Connections between optimal control and reinforcement learning formulations for robotics. Reinforcement learning for robotics, such as: actor-critic methods, on-policy versus off-policy learning, sample efficiency, transferring simulation-based learning to real-world robots View detailed information in the Handbook
Hardware Accelerated Computing · 12.5 pts	Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include: Review of combinational and sequential digital logic FPGA architectures and fundamentals Hardware description languages (Verilog/VHDL) and hardware design flows High-level synthesis and OpenCL The use of parallelism, locality, and precision in hardware accelerators Host-accelerator interactions and hardware-software co-design Optimisation of hardware designs with respect to throughput, latency, energy, and area Accelerator design for selected applications such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application View detailed information in the Handbook
Electrical Engineering Research Project · 25 pts	This subject is for students to undertake a substantial individual research project on an approved topic over the semester, requiring independent investigation with a chosen supervisor either from a university (research institute) or from an industry partner. Note: the student is responsible for contacting the potential supervisor for the project. This subject can also be taken by Master of Electrical Engineering outgoing exchange students for research projects carried out in an overseas university. If the project is to be carried out within the EEE Department, the student is encouraged to take ELEN90011 (Directed Studies) if possible. The emphasis of the project can be associated with either A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, often as an adjunct to existing academic research initiatives. It is expected that the project will incorporate findings associated with both well-defined professional practice and research principles. View detailed information in the Handbook
Applied Deep Learning for Engineers · 12.5 pts	This subject covers a modern deep learning approach to engineering using a project-centric pedagogy. Building upon system optimisation and machine learning fundamentals presented in ELEN90088, the subject will present advanced deep learning architectures to address long-standing engineering challenges such as system complexity, curse of dimensionality, and modelling gap. Subject will specifically focus on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how advanced deep learning frameworks can be applied to modern engineering and cyber-physical systems. INDICATIVE CONTENT Topics covered may include: Latent spaces, auto encoder architectures. Advanced deep learning architectures, auto-differentiation, physics-inspired neural networks. Sequential data analysis and predictive models such as transformers. Generative models such as GANs and GPT variants. Other advanced topics such as meta parameter optimisation, Markov Chain Monte Carlo sampling. Distributed machine learning, federated learning, graph neural networks. Cyber-physical security of modern engineering systems, including data-based anomaly and threat detection and prediction. Subject projects will focus on engineering applications in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. View detailed information in the Handbook
Modelling and Analysis for AI · 12.5 pts	This subject builds up the fundamentals for modelling dynamical systems, with a key focus on the aspects and decisions of modelling that are relevant for the application of AI and data-intensive learning methods. The discussion and evaluation of modelling methods focuses on how model fidelity influences simulation-to-real transfer; how modelling and simulation decisions influence computation time required for training and validation; and how discrete-time models introduce complexity when representing continuous-time engineering systems. Subsequently, it introduces the basic principles and engineering applications of programming and data structures in a condensed form with a project-centric pedagogy. It covers the fundamentals of databases and data structures, basic algorithms, scientific programming, and classic AI problem solving. It will focus specifically on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how programming and databases play a role in modern engineering and cyber-physical systems. INDICATIVE CONTENT Topics covered may include: Models for engineering systems in multiple disciplines, including analysis of what makes the models amenable to AI and data-intensive learning methods. Principles for simulating dynamic systems that are most relevant for the use of AI methods and to address these principles with existing software tools. Scientific programming for modelling using Python programming language and libraries such as scipy and numpy. Engineering data structures and time series data and their storage in SQL and noSQL databases. Example engineering applications will be taught via projects in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. View detailed information in the Handbook
Reinforcement Learning for Engineering · 12.5 pts	The key focus of this subject is the design and implementation of decision-making policies for enabling a dynamical system to behave autonomously and achieve a desired objective. This subject covers both model-based and model-free learning methods, with a focus on evaluating, contrasting, and combining methods. The influence of noisy sensor data on performance, and the trade-offs between exploration and exploitation during a learning phase, will also be covered. The examples used in this subject range across existing and emerging decision-making methods, and their application to consumer and industrial engineering systems. INDICATIVE CONTENT Topics covered may include: Reinforcement learning fundamentals such as principle of optimality, Bellman equation, value and policy iteration. Temporal-difference learning, Q-learning, Deep Q-learning, Actor critic methods and hybrid approaches in engineering context. Model based vs model free approaches, multi-agent RL and their engineering applications. RL methods for Cyber-physical resilience and security such as fuzzing methods. View detailed information in the Handbook

Approved Electives (Group B)

Students must complete 25 points of Year 3 elective subjects.

Accordion
Optimisation for Industry · 12.5 pts	The use of mathematical optimisation is widespread in business, where it is a key analytical tool for managing and planning business operations. It is also required in many industrial processes and is useful to government and community organizations. This subject will expose students to operations research techniques as used in industry. A heavy emphasis will be placed on the modelling process that turns an industrial problem into a mathematical formulation. The focus will then be on how to solve the resulting mathematical problem with mixed-integer programming techniques. View detailed information in the Handbook
Distributed Systems · 12.5 pts	AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. View detailed information in the Handbook
Advanced Motion Control · 12.5 pts	AIMS This subject is intended to give students an overview of the present state-of-the-art in industrial motion control and the likely future trends in control design. Students will be exposed to and have practical experience in the design and implementation of advanced controllers for various motion control problems. Advanced modelling and control topics will include system identification, modelling and compensation of friction and other disturbances, industrial servo loops, model-based and model-free controller design, and adaptive control. Applications will be drawn from industrial, medical and transport automation (eg robots, machine tools, production machines, laboratory automation, automotive and aerospace by-wire systems). INDICATIVE CONTENT Advanced modelling and control topics will include system identification, modelling and compensation of friction and other disturbances, industrial servo loops, model-based and model-free controller design, and adaptive control. Applications will be drawn from industrial, medical and transport automation (eg robots, machine tools, production machines, laboratory automation, automotive and aerospace by-wire systems). View detailed information in the Handbook
Leadership for Innovation · 12.5 pts	This subject, which is offered to students who have completed ENGR90034 Creating Innovative Engineering (CIE), will give participants core leadership skills for managing professionals engaged in innovation and other ambiguous project-based work. The subject teaches leadership at three levels (12 hours each). The first level, taught intensively before the start of the semester, will enable you to learn basic management theory that allows you to bridge from the skills and theory taught in CIE to the level needed to start mentoring a team in CIE or another subject. The second level, taught as four three-hour workshops during the semester, will focus on key thematic issues in the leadership of innovative teams. The third level, taught in twelve one-hour sessions, will focus on specific leadership skills. These include facilitation, coaching, mentoring, conflict resolution, etc. Students will apply the theory and skills to the mentoring of a student project team in CIE or another subject within the University. You will apply what you are learning, and develop skills, by mentoring an industry-sponsored project within CIE or a project within another subject. CIE mentors will also need to manage their relationship with the external sponsor of the project. View detailed information in the Handbook
Global Business Practicum · 12.5 pts	This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. View detailed information in the Handbook
Engineering Entrepreneurship · 12.5 pts	AIMS This subject is available as an elective in many of the Faculty of Engineering and IT Masters programs. It is aimed both at students who have immediate entrepreneurial intentions and at students who may be considering starting their own business at some point in their careers. The subject is designed to introduce all participants to their potential as entrepreneurs. By developing their own enterprise proposal within small groups, students will learn and demonstrate various processes by which successful new ventures move from idea to launch. INDICATIVE CONTENT Business modelling, opportunity analysis, value creation, financial management, sources of finance, creativity, innovation, entrepreneurial behaviour, successful engineering entrepreneurs. TEACHING METHOD The teaching method is based around a structured process of mini-lectures, class exercises, and active hands-on learning by doing. Intensive field research and minimum viable product development are very important to the subject. Learning is further enhanced through meetings with the lecturer and review by peers. View detailed information in the Handbook
Internship · 25 pts	AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship View detailed information in the Handbook
Nuclear Engineering · 12.5 pts	This subject provides an introduction to nuclear science and engineering. It presents the properties of atomic nuclei, radioactivity, nuclear reactions, and selected topics in thermodynamics as required for the analysis of power systems based on nuclear fission. The working principles of nuclear reactors and nuclear power plants are discussed, focusing on pressurised-water reactor systems. Indicative content: Introduction to nuclear physics Thermodynamics of nuclear power plants Nuclear power generation View detailed information in the Handbook
Radiation Protection · 12.5 pts	Nuclear technology involves the risk of exposure to ionising radiation, with potentially harmful effects on human health. This subject equips students with the necessary knowledge and skills to understand this risk and to manage it by applying established methods of radiation protection. Indicative content: Effects of ionising radiation on human health Methods of radiation detection and measurement Principles and methods of radiation protection Radiation shielding View detailed information in the Handbook
Engineering of Nuclear Systems · 12.5 pts	This subject presents nuclear reactor theory and its applications to reactor operation. It examines reactor response to control actions, feedback effects, and the intermediate and long-term effects on reactivity due to fission product poisoning and fuel burnup. Furthermore, it covers the fundamentals of thermal and hydraulic analysis of pressurised-water reactors. Indicative content: Nuclear reactor theory and engineering Reactor dynamics and control Effects of fuel burnup and the long-term evolution of the reactor core properties Heat generation and heat transfer from fuel to coolant Thermal design of nuclear reactors View detailed information in the Handbook
Nuclear Safety, Security and Safeguards · 12.5 pts	Safety, security and safeguards are critical requirements in the operation of nuclear facilities. This subject presents the safety aspects and safety assessment methods of nuclear power plants. Nuclear security and safeguards are discussed in the context of the nuclear fuel cycle. Indicative content: Nuclear fuel cycle Fundamentals of nuclear safety Safety systems and safety features of nuclear reactors Probabilistic safety assessment Nuclear security and safeguards View detailed information in the Handbook
Design Innovation and Leadership · 12.5 pts	A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects.  In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge.  Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication.  In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change?  How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information.  The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. View detailed information in the Handbook

Business specialisation

Core

Year 1

Students must complete 100 points of Year 1 compulsory subjects.

Accordion
Intro. to Numerical Computation in C · 12.5 pts	AIMS Many engineering disciplines make use of numerical solutions to computational problems. In this subject students will be introduced to the key elements of programming in a high level language, and will then use that skill to explore methods for solving numerical problems in a range of discipline areas. INDICATIVE CONTENT Algorithmic problem solving Fundamental data types: numbers and characters Approximation and errors in numerical computation Fundamental program structures: sequencing, selection, repetition, functions Simple data storage structures, variables, arrays, and structures Roots of equations and of linear algebraic equations Curve fitting and splines Interpolation and extrapolation Numerical differentiation and integration View detailed information in the Handbook
Foundations of Electrical Networks · 12.5 pts	INDICATIVE CONTENT Foundations of Electrical Networks develops an understanding of fundamental modelling techniques for the analysis of systems that involve electrical phenomena. This includes networks models of “flow-drop” one-port elements in steady state (DC and AC), electrical power systems, simple RC and RL transient analysis, and networks involving ideal and non-ideal operational amplifiers. It forms the foundation of many engineering subjects exploring fundamental concepts in electrical and electronic engineering. The subject will cover key electrical engineering topics in the areas of: Electrical phenomena – charge, current, electrical potential, conservation of energy and charge, the generation, storage, transport and dissipation of electrical power. Network models – networks of “flow-drop” one-port elements, Kirchoff’s laws, standard current-voltage models for one-ports (independent sources, resistors, capacitors, inductors, transducers, diodes), analysis of static networks, properties of linear time-invariant (LTI) one-ports and impedance functions, diodes, transformers, steady-state (DC and AC) analysis of LTI networks via mesh and node techniques, equivalent circuits, and transient analysis of simple circuits; Electrical power systems – overview of power generation and transmission, analysis of single-phase and balanced three-phase AC power systems. Analysis and design of networks involving ideal and non-ideal operational amplifiers. This material will be complemented by exposure to software tools for the simulation of electrical and electronic systems and the opportunity to develop basic electrical engineering laboratory skills using a prototyping breadboard, digital multimeter, function generator, DC power supply, and oscilloscope. Please view this video for further information: Foundations of Electrical Networks View detailed information in the Handbook
Digital Systems · 12.5 pts	AIMS This subject develops a fundamental understanding of concepts used in the analysis, design and building of digital systems. Such systems form the information and communication technologies (ICT) that underpin modern society. This subject provides a foundation for subsequent subjects, including ELEN30013 Electronic System Implementation, ELEN90066 Embedded System Design and ELEN90061 Communication Networks. INDICATIVE CONTENT Topics include: Digital systems - quantifying and encoding information, digital data processing, design process abstractions; Combinational logic – timing contracts, acyclic networks, switching algebra, logic synthesis; Sequential logic – cyclic networks and finite-state machines, metastability, microcode; These topics will be complemented by exposure to the hardware description language such as Verilog and the use of engineering design automation tools and configurable logic devices (e.g. FPGAs) in the laboratory. Please view this video for further information: Digital Systems View detailed information in the Handbook
Electrical Network Analysis and Design · 12.5 pts	AIMS This subject develops a fundamental understanding of linear time-invariant network models for the analysis and design of electrical and electronic systems. Such models arise in the study of systems ranging from large-scale power grids to tiny radio frequency signal amplifiers. This subject is one of four subjects that define the Electrical Systems Major in the Bachelor of Science and it is a core requirement for the Master of Engineering (Electrical). It provides a foundation for various subsequent subjects, including ELEN30013 Electronic System Implementation, ELEN90066 Embedded System Design, and ELEN30012 Signal and Systems. INDICATIVE CONTENT Topics include: Transient and frequency domain analysis of linear time-invariant (LTI) models – linearity, time-invariance, impulse response and convolution, oscillations and damping, the Laplace transform and transfer functions, frequency response and bode plots, lumped versus distributed parameter transfer functions, poles, zeros, and resonance, stability of circuits, modelling and simulation with simulation tools; Electrical network models – one-port elements, impedance functions, two-port elements, dependent sources, matrix representations of two-ports, driving point impedances and network functions, ladder and lattice networks, passive versus active networks, multi-stage modelling and design, and multi-port generalisations; Analysis and design of networks involving ideal and non-ideal operational amplifiers with emphasis on the design of active filters and broadband circuits with specific frequency characteristics; Circuits and networks for managing voltage and power requirements for common electronic circuits. These topics will be complemented by tutorials and workshops designed to develop skills in design and modelling of electronic circuits through software tools and building, testing, and verification of electronic circuits. Please view this video for further information: Electrical Network Analysis and Design View detailed information in the Handbook
Electrical Device Modelling · 12.5 pts	AIM This subject develops the theoretical and practical tools required to understand, construct, validate and apply models of standard electrical and electronic devices. In particular, students will study the theoretical and practical development of models for devices such as resistors, capacitors, inductors, transformers, motors, batteries, diodes, transistors, and transmission lines. In doing so, students will gain exposure to a variety of fundamental fields in physics, including electromagnetism, semiconductor materials and quantum electronics. This material will be complemented by exposure to experiment design and measurement techniques in the laboratory, the application of models from device manufacturers, and the use of electronic circuit simulation software. INDICATIVE CONTENT Topics include: Vector calculus for device modelling, Maxwell’s equations, physics of conductors and insulators, passive device models (including for resistors, capacitors and inductors), lumped and distributed circuit models for wired interconnections (including treatment of signal integrity and termination strategies), semiconductors and quantum electronics, static and dynamic models for p-n junctions diodes and bipolar junction transistors. View detailed information in the Handbook
Signals and Systems · 12.5 pts	AIMS The aim of this subject is twofold: firstly, to develop an understanding of the fundamental tools and concepts used in the analysis of signals and the analysis and design of linear time-invariant systems path in continuous–time and discrete-time; secondly, to develop an understanding of their application in a broad range of areas, including electrical networks, telecommunications, signal-processing and automatic control. The subject formally introduces the fundamental mathematical techniques that underpin the analysis and design of electrical networks, telecommunication systems, signal-processing systems and automatic control systems. Such systems lie at the heart of the electrical engineering technologies that underpin modern society. This subject is one of four Level 3 subjects that define the Electrical Engineering Systems Major in the Bachelor of Science. . It provides the foundation for various subsequent subjects, including ELEN90057 Communication Systems, ELEN90058 Signal Processing and ELEN90055 Control Systems. INDICATIVE CONTENT Topics include: Signals – continuously and discretely indexed signals, important signal types, frequency-domain analysis (Fourier, Laplace and Z transforms), nonlinear transformations and harmonics, sampling; Systems – viewing differential / difference equations as systems that process signals, the notions of input, output and internal signals, block diagrams (series, parallel and feedback connections), properties of input-output models (causality, delay, stability, gain, shift-invariance, linearity), transient and steady state behaviour; Linear time-invariant systems – continuous and discrete impulse response; convolution operation, transfer functions and frequency response, time-domain interpretation of stable and unstable poles and zeros, state-space models (construction from high-order ODEs, canonical forms, state transformations and stability), and the discretisation of models for systems of continuously indexed signals. This material is complemented by exposure to the use of MATLAB for computation and simulation and examples from diverse areas including electrical engineering, biology, population dynamics and economics. View detailed information in the Handbook
Electronic System Implementation · 12.5 pts	AIMSThis subject provides students with hands-on electronic skills to gain basic competencies in design and implementation of simple circuits. Students will design with a range of standard electrical and electronic devices, basic circuit construction methods and electrical measurement techniques to test and verify the function of electronic systems. This subject is one of four subjects that define the Electrical Systems Major in the Bachelor of Science and it is a core requirement for the Master of Engineering (Electrical) and the Master of Engineering (Electrical with Business). This includes hands-on experience with: • Operation and selection of electrical and electronic devices used in various electronic circuits; • Common electronic circuit realisations to meet the most commonly required signal processing and conditioning applications; • Programmable digital circuits and microprocessor programming; • Circuit design and simulation tools; • Printed circuit board layout, circuit assembly, and soldering techniques; • Test and Measurement equipment and methods; • Managing design issues and requirements. Students will complete electronic circuit implementation projects in small groups and be required to prepare technical documentation and present project outcomes. INDICATIVE CONTENT • Devices such as resistors, capacitors, inductors, switches, transducers, motors, diodes, transistors, op-amps, voltage regulators, comparators, oscillators, timers, A/D and D/A converters, microprocessors and controllers; • Circuit functions and techniques such as buffering, referencing, signal conditioning, filtering, bridges, detection, waveform generation, and pulse-width modulation; • Microprocessor programming, the role of assembly and high-level languages, assemblers, compilers and debuggers; • PCB layout, circuit assembly, and soldering techniques; • Test and Measurement methods and working with common equipment such as multimeters and oscilloscopes. View detailed information in the Handbook
Engineering Mathematics · 12.5 pts	This subject introduces important mathematical methods required in engineering such as manipulating vector differential operators, computing multiple integrals and using integral theorems. A range of ordinary and partial differential equations are solved by a variety of methods and their solution behaviour is interpreted. The subject also introduces series including the concepts of convergence and divergence. Topics include: Vector calculus, including Gauss’ and Stokes’ Theorems; systems of homogeneous ordinary differential equations, including phase plane and linearisation for nonlinear systems; Laplace transforms; series, including Taylor series and power series; Fourier series and Fourier integrals; second order partial differential equations and separation of variables. View detailed information in the Handbook

Year 2

Students must complete 100 points of Year 2 compulsory subjects.

Accordion
Probability and Random Models · 12.5 pts	AIMS This subject provides an introduction to probability theory, random variables, random vectors, decision tests, and stochastic processes. Uncertainty is inevitable in real engineering systems, and the laws of probability offer a powerful way to evaluate uncertainty, to predict and to make decisions according to well-defined, quantitative principles. The material covered is important in fields such as communications, data networks, signal processing and electronics. This subject is a core requirement in the Master of Engineering (Electrical, Mechanical and Mechatronics). INDICATIVE CONTENT Topics include: Foundations – combinatorial analysis, axioms of probability, independence, conditional probability, Bayes’ rule; Random variables (rv’s)– definition; cumulative distribution, probability mass and probability density functions; expectation and variance; functions of an rv; important distributions and their properties and uses; Multiple random variables – joint cumulative distribution, probability mass and probability density functions; independent rv’s; correlation and covariance; conditional distributions and expectation; functions of several rv’s; jointly Gaussian rv’s; random vectors; Sums, inequalities and limit theorems – sums of rv’s, moment generating function; Markov and Chebychev inequalities; weak and strong laws of large numbers; the Central Limit Theorem; Decision testing - maximum likelihood, maximum a posterior, minimum cost and Neyman-Pearson rules; basic minimum mean-square error estimation; Stochastic processes – mean and autocorrelation functions, strict and wide-sense stationarity; ergodicity; important processes and their properties and uses; Introduction to Markov chains. This material is complemented by exposure to examples from electrical engineering and software tools (e.g. MATLAB) for computation and simulations. View detailed information in the Handbook
Control Systems · 12.5 pts	AIMS This subject provides an introduction to automatic control systems, with an emphasis on classical techniques for the analysis and design of feedback interconnections. The main challenge in automatic control is to achieve desired performance in the presence of uncertainty about the system dynamics and the operating environment. Feedback control is one way to deal with modelling uncertainty in the design of engineering systems. This subject is a core requirement in the Master of Engineering (Electrical, Electrical with Business, Mechanical, Mechanical with Business and Mechatronics). INDICATIVE CONTENT Topics include: * Modelling for control, linearization, relationships between time and frequency domain models of linear time-invariant dynamical systems, and the structure, stability, performance, and robustness of feedback interconnections; * Frequency-domain analysis and design, Nyquist and Bode plots, gain and phase margins, loop-shaping with proportional, integral, lead, and lag compensators, loop delays, and fundamental limitations in design; and * Actuator constraints and anti-windup compensation. This material is complemented by the use of software tools (e.g. MATLAB/Simulink) for computation and simulation, and exposure to control system hardware in the laboratory. View detailed information in the Handbook
Electronic Circuit Design · 12.5 pts	AIMS This subject provides an in-depth coverage of transistor (MOSFET and BJT) devices and their use in common circuits. In particular, students will study topics including: transistor operating modes and switching; principles of CMOS circuits; transistor biasing; current-source/emitter-amplifiers; low-frequency response; followers; class B amplifiers; current limiting; current sources and mirrors; differential pairs; feedback in amplifiers and stability; operational amplifiers; operational amplifier circuits; and voltage regulation. This material will be complemented by exposure to circuit simulation software tools and the opportunity to further develop circuit construction/test skills in the laboratory. INDICATIVE CONTENT Design-focused field-effect and bipolar elementary transistor models, and design of elementary amplifier stages and biasing circuits. Static and dynamic behaviour of amplifier circuits including frequency response, feedback and stability, slew-rate and clipping. Operational amplifiers and opamp based circuits; voltage regulators, references and voltage converters. Verification of electronic circuits using simulation and constructing them in the laboratory. Please view this video for further information: Electronic Circuit Design View detailed information in the Handbook
Communication Systems · 12.5 pts	AIMS This subject provides an introduction to the analysis and design of telecommunication signals and systems, in the presence of uncertainty. The emphasis is on understanding the basic concepts that underpin the physical layer of modern communication systems. INDICATIVE CONTENT Topics to be covered include: Introduction to communication systems including historical developments and comparisons between analogue and digital communications. Review of assumed knowledge from linear algebra, signals and systems and probability and random processes. The sampling theorem, analog-to-digital conversion, complex baseband representation of passband signals, filtering of random processes, power spectral density, bandwidth of random signals, additive white Gaussian noise (AWGN), signal-to-noise ratio. Communication over baseband AWGN channels including modulation techniques (pulse amplitude modulation, orthogonal modulation), signal space representation, optimal detectors, matched filters, error probability calculations and bandwidth / power trade-off. Communication over passband AWGN channels including modulation techniques (phase shift keying, quadrature amplitude modulation and frequency shift keying), optimal coherent detectors, noncoherent detectors and error probability calculations. Communication over linear time-invariant channels including concepts of distortion, inter-symbol interference, pulse shaping, Nyquist’s criterion, equalization, sequence detection and the Viterbi algorithm. Synchronization including carrier, symbol and frame synchronization. View detailed information in the Handbook
Signal Processing · 12.5 pts	AIMS This subject provides an introduction to the fundamental theory of time domain and frequency domain representation of discrete time signals and linear time invariant dynamical systems, and how this theory is used to analyse and design digital signal processing systems and algorithms. Topics include: Applications of signal processing techniques; Sampling of analog signals, anti-aliasing filters; Frequency-domain analysis of signals and systems, Discrete Time Fourier Transform, Discrete Fourier Transform, Fast Fourier Transform; Digital filters, low-pass, high-pass, band-pass, stop-band and all pass filters. Phase and group delay, FIR and IIR filters; Design of digital FIR and IIR filters; Multi-rate signal processing, with a focus on up-sampling, down-sampling, and sampling rate conversion; Simple non-parametric methods for spectral estimation. This fundamental material will be complemented by exposure to MATLAB tools for signal analysis and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory. INDICATIVE CONTENT Sampling of continuous time signals, Design of anti-aliasing filters, Time and frequency representation of discrete time signals and discrete time linear time invariant systems, Discrete Time Fourier Transform and z-transform and their properties, Low order lowpass, highpass, bandpass, bandstop filters, All-pass filter, Design of IIR filters using the bilinear transformation, Design of FIR filters with linear phase using windowing techniques and the Parks McClelland method, Discrete Time Fourier transform and its properties, Fast Fourier Transform, The use of the DFT in implementation of linear filtering algorithms, Up-sampling and down-sampling, multistage and computationally efficient implementations of up-samplers and down-samplers, Energy and power spectra for deterministic signals. View detailed information in the Handbook
Embedded System Design · 12.5 pts	AIMS This subject provides a practical introduction to the basics of modelling, analysis, and design of microprocessor-based embedded systems. Students will learn how to integrate computation with physical processes to meet a desired specification within the context of a design project. The project work will expose students to the various stages in an engineering project (design, implementation, testing and documentation) and a range of embedded system concepts. INDICATIVE CONTENT Topics covered may include: digital computer and microprocessor architectures, modelling of dynamic behaviours, control, models of computation, operating systems concepts, multi-tasking, resource management and real-time behaviours, interfacing with the physical world, analysis and verification, safety, reliability, and security and privacy. This material will be complemented by exposure to standard software tools including compilers and debuggers, finite state machine design and analysis software, and simulation tools. The subject will include a level of industry engagement, to provide broader examples of engineering projects, through guest lectures. View detailed information in the Handbook
Introduction to Power Engineering · 12.5 pts	AIMS To develop a solid foundation for the study of systems that involve the generation, transport, and conversion of electric power. INDICATIVE CONTENT Physical principles of electromagnetism, magnetic circuits, energy storage, loss mechanisms, electromechanical energy conversion. Modelling of transmission lines, transformers, motors and generators (synchronous and asynchronous), and other loads. Circuit theory for power system analysis, three phase-phase circuits, power flow and maximum power transfer, per-unit system. Please view this video for further information: Introduction to Power Engineering View detailed information in the Handbook
Interdisciplinary Design for Engineers · 12.5 pts	In this subject, students will actively engage in an interdisciplinary, collaborative and project-based learning environment, offering insights into the professional nature of engineering work. Through a real-world project, students will gain hands-on design experience addressing a complex challenge. The project will require students to integrate discipline knowledge and apply professional skills like teamwork and communication. Students will experience the entire engineering design process, covering problem definition, ideation, concept development, analysis, prototyping, testing and iteration. The project provides practical experience, equipping students with tools and methods to address complex challenges. Students are expected to integrate diverse perspectives, considering factors like stakeholders, sustainability (including environmental and social issues), safety, feasibility, and technical and ethical considerations. View detailed information in the Handbook

Capstone

Year 3

Students must complete 25 points of Year 3 compulsory capstone project subjects.

Accordion
Engineering Capstone Project Part 1 · 12.5 pts	The subject involves undertaking a substantial group project (typically in groups of three students) requiring an independent investigation on an approved topic in advanced engineering design and / or research. Each project is carried out under the supervision of a member of academic staff and where appropriate an industry partner. The emphasis of the project can be associated with either: A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the engineering science disciplines, often as an adjunct to existing academic research initiatives. It is expected that the Capstone Project will incorporate findings associated with both well-defined professional practice and research principles and will provide students with the opportunity to integrate technical knowledge and generic skills gained in earlier years. The project component of this subject is supplemented by a lecture course dealing with project management tools and practices. Please note: Students enrolled in the suite of Master of Engineering programs must be within the final 112.5 points of their degree to enrol. Students enrolled in the Master of Industrial Engineering must be within the final 100 points of their degree to enrol. Students are to take Engineering Capstone Project Part 1 and then subsequently continue with Engineering Capstone Project Part 2 in the following semester. Upon successful completion of this project, students will receive 25 points credit. View detailed information in the Handbook
Engineering Capstone Project Part 2 · 12.5 pts	Please refer to ENGR90037 Engineering Capstone Project Part 1 for this information. View detailed information in the Handbook

Accordion

Engineering Capstone Project Part 1 · 12.5 pts

The emphasis of the project can be associated with either:

A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or
A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the engineering science disciplines, often as an adjunct to existing academic research initiatives.

The project component of this subject is supplemented by a lecture course dealing with project management tools and practices.

Please note:

Students enrolled in the suite of Master of Engineering programs must be within the final 112.5 points of their degree to enrol.

Students enrolled in the Master of Industrial Engineering must be within the final 100 points of their degree to enrol.

View detailed information in the Handbook

Engineering Capstone Project Part 2 · 12.5 pts

Please refer to ENGR90037 Engineering Capstone Project Part 1 for this information.

View detailed information in the Handbook

Specialisation

Year 3

Students must complete 50 credit points of Year 3 core specialisation subjects.

Accordion
Engineering Contracts and Procurement · 12.5 pts	AIMS Students will learn how to structure and work with engineering contracts to deliver and procure engineering outcomes in this subject. Students will develop a working knowledge of contract administration and gain an understanding of commercial aspects of engineering. All engineers interface commercially with engineering contracts throughout their careers, and thus the application of the subject content is broad. Those seeking to work as a contractor and as a contract administrator will find a direct application of this subject’s content. Students will learn how to use procurement and contracts to develop successful engineering projects. This includes administration of the contracts and understanding the business environment where these contracts are agreed. These skills will be useful to students in their future work and apply to a wide range of engineering disciplines. INDICATIVE CONTENT Management of engineering projects. This includes the role and responsibilities of corporate managers, market analysis, structuring of procurement options, development of contractual terms and conditions and the pricing of work. Estimating and tendering engineering works via work breakdown structures, work method statements, risk identification and tendering principles. The study material also covers contract administration and project control functions and techniques including time and money negotiations and cash flow management. View detailed information in the Handbook
Marketing Management for Engineers · 12.5 pts	This subject prepares graduate engineers to practice basic marketing in the engineering profession where there is a mutual need and reliance upon their training and skills in both engineering and marketing to satisfy the needs, wants and demands of the market, internally within the organisation, and through the entire supply chain in a sustainable manner. This subject provides an introduction to the basic concepts of marketing, marketing management and marketing engineering. Some of the principal topics include: what is marketing engineering; differences between engineering and consumer products; designing and managing engineering services; sales engineer and managing sales force; online marketing and the internet of things; business-to-business markets; business-to-government markets; company orientation; corporate division and strategic planning; market positioning, segmentation and targeting; marketing mix (product, pricing, place and promotion); marketing plan and strategies; SWOT analysis, understand the legal, economic, sociocultural, natural and technological environments; distribution channels; communications, models and simulations; decision tools; databases and data mining, forecasting; theory and evidence-based decision making; etc. The principles of sustainability will apply throughout the subject. View detailed information in the Handbook
Economic Analysis for Engineers · 12.5 pts	This subject seeks to - Build a thorough understanding of the theoretical and conceptual basis upon which the practice of financial project analysis is built and its application to engineering Satisfy the practical needs of the engineering manager toward making informed financial decisions when involved in an engineering project Incorporate critical decision-making tools that engineering managers can bring to the task of making informed financial decisions. View detailed information in the Handbook
Strategy Execution for Engineers · 12.5 pts	In fiercely competitive global and dynamic environments, companies face increasing pressures to exceed customer expectations along multiple performance measures, such as cost, quality, flexibility and innovativeness. To outperform their competitors, many firms make the mistake of mimicking their rivals, rather than focusing on developing the organizational capabilities that competitors will find difficult to match over the long term. And although operations are at the core of a firm’s value adding activities, few firms have sought to build a sustainable competitive advantage around these capabilities. As such, this subject emphasises the critical nature of Operations Management as an essential part of a competent engineer’s portfolio of knowledge and skills. Operations deals with the design, management and continuous improvement of business processes. It aims at providing some of the core concepts in operations that are essential for leveraging a firm’s operational capabilities to achieve sustainable competitive advantage. This course provides a logical and rigorous approach to plan and control process structure and managerial levers to achieve desired business process performance. View detailed information in the Handbook

Electives

Electrical Engineering Electives (Group A)

Students must complete 25 points of Year 3 elective subjects.

Accordion
Introduction to Optimisation · 12.5 pts	AIMS This subject provides a rigorous introduction to numerical nonlinear optimization, as used across all of science and particularly in engineering design. There is an emphasis on both the theory and application of optimization techniques, with a focus on solving unconstrained and constrained nonlinear programmes. This subject is intended for graduate and research higher-degree students in engineering. INDICATIVE CONTENT Topics include: Algorithms for unconstrained optimization Algorithms for constrained optimization Convex sets and functions Convex optimization problems Duality theory Computational complexity Approximation algorithms and penalty methods. View detailed information in the Handbook
Advanced Communication Systems · 12.5 pts	AIMS The aim of this subject is to develop a thorough understanding of the main concepts, techniques and performance criteria used in the analysis and design of digital communication systems and wireless networks. Such systems and networks lie at the heart of the information and communication technologies (ICT) that underpin modern society and are very much part of the Internet of Things which involves machine to machine communication. INDICATIVE CONTENT This subject provides an in-depth treatment of the main concepts and techniques used in the analysis and design of digital communication systems and wireless networks. Topics include: Source coding; entropy, Shannon source coding bound, data compression techniques; Channel modelling, modulation over time-varying fading channels, time and frequency diversity, energy and spectral efficiency, multiple carrier modulation including orthogonal frequency division multiplexing (OFDM) modulation, phase noise characteristics and its impact on single-carrier and multicarrier systems, spatial multiplexing for multiple access protocols, multiple antenna technologies (MIMO systems), cellular networks; Channel coding for error control: mutual information, channel capacity, Shannon channel coding bound, channel coding concepts, block codes; convolutional / trellis codes; introduction to LDPC codes, turbo codes, polar codes. Examples include short, medium and long range communication systems such as bluetooth, cellular and satellite communication systems. View detailed information in the Handbook
Advanced Signal Processing · 12.5 pts	AIMS This subject provides an in-depth introduction to statistical signal processing. INDICATIVE CONTENT Students will study a selection of the following topics: Applications of statistical signal processing; A review of stochastic signals and systems fundamentals – random processes, white noise, stationarity, auto- and cross-correlation functions, spectral- and cross-spectral densities, properties of linear time-invariant systems excited by white noise; Parameter estimation - least squares and its properties, recursive least squares and least mean squares, optimisation-based methods, maximum likelihood methods; Kalman, Wiener and Markov filtering; Power spectrum estimation. This material will be complemented with the use of software tools (e.g. MATLAB) for computation and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory. View detailed information in the Handbook
Electronic System Design · 12.5 pts	AIMS This subject will explore the design of various electrical and electronic systems and provide students with a range of common and practical design techniques and circuits in the context of a guided laboratory based project. INDICATIVE CONTENT Subject may cover specific concepts surrounding the design and implementation of: Design process; Design for manufacture and assembly; Advanced PCB design; Oscillators; Phase-locked loops and frequency synthesis; Base-band signalling schemes and clock recovery; Mixers and logarithmic amplification; Automatic gain control; Filters; Synchronous detection; High-speed analog-digital conversion; High-frequency amplification; Low noise amplifiers; Power supply design; Batteries, battery charging systems, and management; Test and measurement; Sensors. View detailed information in the Handbook
Lightwave Systems · 12.5 pts	AIMS Lightwave systems are fundamentally changing the way we communicate through broadband communications, helping clinicians to perform a range of medical procedures and diagnosis supported by advanced biomedical instrumentation, and even in the way we live in our homes through sophisticated interactive televisions and security systems. This subject will explore the physical principles and issues that arise in the design of lightwave systems often found in those key industry sectors. Students will study topics from: transmission of light over wave guides; production of light by lasers; light modulation; conversion of light signals to electrical signals; optical multiplexing and demultiplexing; light amplification; dispersion and dispersion compensation; optical nonlinearities; modulation and advanced detection schemes. This material will be complemented by exposure to lightwave systems and measurement techniques in the laboratory. INDICATIVE CONTENT This subject will explore the physical principles governing the generation, modulation, amplification, guiding, transmission, multiplexing, demultiplexing and detection of light and issues that arise in the design of lightwave systems such as transmission impairments, noise. Students learn selected examples of lightwave systems and methods for design, modelling and testing of simple lightwave systems. View detailed information in the Handbook
Power System Analysis · 12.5 pts	AIMS This subject provides an insight into the fundamental elements to analyse electrical power transmission and distribution systems, with both analytical and simulation tools for analysis of operations of these systems. Problems related to power flow and use of Newton-Raphson and other algorithms such as backward-forward sweep will be discussed. Fault calculation and analysis, symmetrical components, and analytical methods for solving symmetrical (balanced) faults will be covered. Principles, concepts and problems related to power system dynamics and control, particularly for frequency and voltage regulation, will be discussed and analysed in detail. Finally, small-signal, transient, voltage and frequency stability will be introduced and exemplified. Focus will be put on real-world examples, particularly to prepare the student for the ongoing transition towards a low-carbon grid dominated by renewables and distributed energy resources. INDICATIVE CONTENT Power flow calculations, Newton-Raphson, Gauss-Seidel and backward-forward sweep methods; Fault calculations, balanced and unbalanced, symmetrical components, fundamentals of protection; Frequency regulation and frequency stability, Voltage regulation in transmission and distribution networks, including use of flexible AC transmission systems (FACTS); Voltage stability, small-signal stability, transient stability; Computer simulations. View detailed information in the Handbook
Communication Networks · 12.5 pts	AIMS This subject introduces the basic principles, analysis, and design of communication networks. It covers relevant analytical methods, the layered network architecture of the Internet, and a multitude of network protocols. Analytical tools from queueing, optimisation, and graph theories are used to develop an in-depth understanding of basic principles and the role they play in network design. Specifically, queueing and graph theories are emphasised as methodological frameworks for communication network delay and structure analysis. The concepts taught in this subject lead to a better understanding of the Internet as well as modern communication paradigms such as Software-Defined Networks, Machine-to-Machine communication, Internet of Things, and social networks. INDICATIVE CONTENT Topics covered may include: The layered network architecture with a focus on physical-layer multiple access (TDM, FDM, WDM), link layer protocols and medium access control (MAC), network layer topologies, least-cost routing algorithms and protocols, transport layer protocols and the principles and techniques of practical reliable transport; LAN protocols, Ethernet, Wi-Fi, and serial communications; The Internet's network layer including the Internet Protocol (IP) and routing protocols including an introduction to BGP and the operation of forwarding tables in routers and shortest prefix routing; The Internet's transport layer protocols UDP and TCP, including the flow and congestion control algorithms; Network security, application layer, cloud and fog computing, Machine-to-Machine communication, and Internet of Things; Queuing theory: basics, birth-death processes, M/M/x and Markovian queues, networks of queues; Basics of graph theory and social network analysis relevant to communication networks. View detailed information in the Handbook
High Speed Electronics · 12.5 pts	AIMS The aim of the subject is to provide theoretical and practical treatment of high-speed electronics. Through the subject, students will grasp the fundamental properties and models of high-speed signals and interconnects, acquire high-speed digital design skills with a focus on the modelling, analysis, design and application of high speed transistors, logic gates and modern logic families, and master the high-speed analogue design capability including the design of oscillators and filters for RF applications. The students will be exposed to the state-of-the-art technologies that are shaping the fast evolving semiconductor industry. INDICATIVE CONTENT The topics include: Fundamental properties of analogue systems; Smith charts: principles and applications; High-speed analogue circuits: voltage control oscillators, matching networks, and low noise amplifiers; Bipolar junction transistors: device, switching, and logic; CMOS: device, switching and logic; High-speed signalling consideration: power dissipation, heat, signal propagation, and termination. View detailed information in the Handbook
Advanced Control Systems · 12.5 pts	AIMS This subject provides an introduction to modern control theory with a particular focus on design of advanced control laws via state-space methods and optimal control. The role of feedback in control design will be reinforced within this context, alongside the role of optimisation techniques in control system synthesis. INDICATIVE CONTENT Topics include: State-space models - first-order vector differential/difference equations; Lyapunov stability; linearisation; discretisation; Kalman decomposition (observable, detectable, reachable and stabilisable subspaces); state-feedback and pole placement; output-feedback and observer design in both continuous-time and discrete-time. Optimal control - dynamic programming; linear quadratic regulation in both continuous-time and discrete-time. Model predictive control in discrete-time; moving-horizon with constraints. View detailed information in the Handbook
Power Electronics · 12.5 pts	AIMS The aim of this subject is to understand the fundamental concepts and basic theory involved in modelling and analysis of the power electronic components that comprise power electronic devices such as power supplies, inverters, converters and their control systems. It is expected that at the end of this subject the student has a sound understanding of the physical concepts and mathematical models behind each of the basic components and of their functionality within a system, such as a high voltage DC transmission system. Furthermore this subject seeks to combine the fields of electronics, semiconductor devices, power system operation, power system measurement and control. It is expected that through this subject the students are exposed to examples of real electrical engineering systems where the three disciplines of electronics, power systems and control come together. INDICATIVE CONTENT Topics covered in this subject include: introduction to power semiconductor switches; discussion on the role of power electronics in the operation of electric power systems; models of power semiconductor devices and circuit components, including diodes, Thyristors, IGBT, Snubber circuits. Also basic concepts of single- and three-phase diode bridge rectifiers; single- and three-phase converters and inverters; operation and design of DC-AC inverters with emphases on switch-mode inverters, i.e. single- and three-phase inverters. Finally, the acquired knowledge of power electronic devices is applied to wind and PV solar systems where the design of voltage source converters and associated control loops are used to interface the wind/solar system with the power grid. View detailed information in the Handbook
Grid Integration of Renewables · 12.5 pts	AIMS This subject develops a foundation for pursuing electrical engineering oriented research in the area of sustainable energy systems. This subject aims to introduce the concepts behind smart grids, future low-carbon energy networks, sustainable electricity systems as well as the main renewable and low-carbon generation technologies. The subject will introduce students to tools and techniques so that distributed energy resources (e.g. distributed renewable generation, storage, electric vehicles, demand response, etc.) may be integrated effectively into the power system in the context of both traditional grids and future smart grids. INDICATIVE CONTENT This subject will cover the following topics: Distributed low-carbon technologies Introduction to distribution networks Introduction to distributed low-carbon technologies (wind energy, photovoltaic systems, electric vehicles, electric heating, storage) Wind Energy: impacts and challenges Photovoltaic systems: impacts and challenges Electric vehicles: impacts and challenges Electric heat pumps and electric heating: impacts and challenges Storage: impacts and challenges Smart Distribution and Smart Transmission Networks Distributed low-carbon technologies and active network management Towards Smart Grids Smart grids - Transmission and Distribution perspectives Smart Transmission: HVDC and FACTS, dynamic line rating, post-contingency security, special protection schemes The role of future Distribution System Operators Low-carbon Electricity System Towards low-carbon networks: relationship between sustainability and smart grids Introduction to low-carbon thermal generation (nuclear, Carbon Capture and Storage, Concentrated Solar Power, biomass, etc.) Utility-scale renewable technologies: wind farms; solar farms; other large-scale renewables; utility-scale batteries System-level operational challenges and solutions for renewables integration: variability and uncertainty; low-inertia operation; low system-strength operation; minimum load issues; DER visibility; indistinct events; general stability issues; flexibility System-level planning challenges and solutions for renewables integration: system adequacy and reliability; capacity credit of renewables and storage; extreme weather events and resilience; role of transmission Sector coupling and multi-energy systems: decarbonisation of gas, heating and transport; role of hydrogen Distributed energy systems: new technical and commercial architectures for two-sided systems and markets; demand response; aggregators and virtual power plants; distributed energy markets; peer-to-peer trading; local energy communities; microgrids View detailed information in the Handbook
System Optimisation & Machine Learning · 12.5 pts	This subject introduces the basic principles, analysis methods, and applications of optimisation and machine learning to engineering systems; encompassing fundamental concepts and practical algorithms. It covers the fundamentals of continuous optimisation followed by machine learning basics for engineering applications. The concepts and methods discussed are illustrated in multiple application areas including Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks.The concepts taught in this subject will allow a better understanding of continuous optimisation and machine learning for systems engineering. INDICATIVE CONTENT Topics covered may include: Fundamentals of continuous optimisation: convex sets and functions; local vs global solutions, constrained optimisation and Lagrange multipliers; linear, quadratic, and nonlinear programming Basics of machine learning encompassing supervised and unsupervised learning: binary classification, linear and nonlinear regression, kernel methods, and clustering. Specific machine learning methods such as Support Vector Machines (SVMs), Neural Networks (NNs), k-means clustering, and reinforcement learning. Applications to Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks. View detailed information in the Handbook
Communication Design Clinic · 12.5 pts	Students work collaboratively in small groups to implement and optimize components in a modern communication system or network with the goal of supporting a targeted application. To meet this goal students will need to: determine system requirements based on the target application and additional constraints; propose and evaluate multiple solutions through theoretical analysis and detailed simulations; implement, integrate, verify, and iterate on their selected solutions. Lectures will cast content from prerequisite subjects into the context at hand and cover additional topics relevant to the task. Each student group is expected to demonstrate initiative and independence while pursuing the goal of designing and optimizing their communication system or network, with a key focus being that students learn through hands-on experience. Students will receive early exposure to advanced topics critical to modern communication systems, such as: source and channel coding, multicarrier modulation, multiantenna transmission, and network architectures and protocols. Successful completion of the project will require the student to draw upon knowledge, understanding, and skills learned in prerequisite subjects, which may include: Communication Systems – analog-to-digital conversion, signal-to-noise ratio, modulation and demodulation, bandwidth/power trade-off, error probability calculations, distortion, inter-symbol interference, pulse shaping, equalization, sequence detection, and synchronization. Signal Processing - design and implementation of digital filters (low-, high-, band-, all- pass filters); ARMA systems; up-sampling and down-sampling. Embedded System Design – system-level programming, operating systems concepts, real-time issues, and standard software tools. Additional topics required for the assigned project may also be covered, such as: ideation, prototyping, and design practices; analog RF components; software packages for modelling and implementation; and the use of test & measurement equipment. View detailed information in the Handbook
Autonomous Systems Clinic · 12.5 pts	AIMS: Students work collaboratively in small groups to engineer an autonomous system that performs a specified task. This includes carrying out steps such as: task analysis; proposing multiple solutions; feasibility analysis through prototyping and computer-aided design; detailed design, construction, and testing of the chosen solution; and demonstrating the solution in a proving ground. The lectures will cast content from the pre-requisite subjects into the context of the task at hand, as well as covering additional topics relevant to the task. Each student group is expected to demonstrate initiative and independence while pursuing the goal of designing and building their autonomous system, with a focus of the subject being that students learn through hands-on experience, implementation, and verification. INDICATIVE CONTENT: Successful completion of the project requires the student to draw upon knowledge, understanding, and skills learned in the prerequisite subjects, namely: • Embedded System Design - including topics such as: finite, extended, and hierarchical state machines; modelling cyber-physical systems; scheduling, multi-tasking, and real-time issues; interfacing to the analogue world. • Control Systems - including topics such as: modelling; linearisation; feedback interconnections; proportional, integral, derivative (PID) control; actuator constraint considerations. • Signal Processing - including topics such as: design and implementation of digital filters (low-, high-, band-, all- pass filters); ARMA systems; up-sampling and down-sampling. Additional topics, specific to the task as hand, will be covered, such as: ideation, prototyping, and design practices; image processing and computer vision tools; software introductions; safety and failure analysis. A range of materials, components, and fabrication facilities are provided, from which the students are expected to utilise a subset for designing and building their autonomous system, such as: electric motors, range sensors, camera, voltage converters, compute power, sheet wood, soldering stations, laser wood cutting, 3D printing. The task to be performed is motivated by a real-world application of autonomous systems, such as: operating in hazardous environments or performing repetitive tasks. Please view this video for further information: Autonomous Systems Clinic View detailed information in the Handbook
Semiconductor Devices · 12.5 pts	This subject serves as an introduction to semiconductor devices. It describes the fundamentals, theory, material and physical properties of semiconductor devices. The following topics will be covered. Fundamentals: Crystal properties and of the growth of bulk crystals and of epitaxial layers. Physical concepts related to atoms and electrons. These concepts may include the photoelectric effect, the Bohr model, quantum mechanics, and the periodic table. Energy bands and charge carriers in semiconductors: Bonding forces and energy bands in solids, charge carriers in semiconductors, carrier concentrations, the drift of carriers in electric and magnetic fields, and the Fermi level. Excess carriers in semiconductors: Optical absorption, luminescence, carrier lifetime and photoconductivity, and the diffusion of carriers. Junctions: Fabrication of pn junctions, equilibrium conditions, forward and reverse biased junctions in steady state, reverse bias breakdown, transient and AC conditions, metal-semiconductor junctions and heterojunctions. In the next part of the subject PN junction diodes: Junction diodes, tunnel diodes, photodiodes, and light-emitting diodes and lasers. Bipolar junction transistors (BJTs): Amplification and switching, fundamentals of BJT operation, BJT fabrication, minority carrier distributions and terminal currents, generalised biasing, switching, the frequency limitations of transistors, and heterojunction bipolar transistors. Field effect transistors (FETs): Topics may include junction FETs, the metal semiconductor FET and the metal-insulator-semiconductor FET. Additional topics (if time permits): Integrated circuits, pnpn switching devices, and microwave devices. View detailed information in the Handbook
Low-carbon Grids: Operation & Economics · 12.5 pts	This subject introduces the student to foundational aspects of economic, secure and reliable operation of low-carbon power systems and electricity markets with large shares of variable and uncertain renewable energy sources. The underlying framework is the so-called “affordability-sustainability-security” energy trilemma, which seeks to strike a delicate balance among: the desire to operate power systems at low cost (“affordability”); the desire to meet specific environmental targets (“sustainability”); and the need to “keep the lights on” (“security”). In order for the energy trilemma to be analysed in the context of a competitive market environment, the subject will provide the student with fundamentals of economics, operation of electricity markets, optimal bidding strategies of different market stakeholders, economics of transmission and distribution networks, and role of new technologies and commercial entities such as storage and aggregators. Different aspects of power system security will be analysed, from system-level requirements and constraints to provision of security services from market stakeholders. Basic concepts of optimization, including linear, quadratic, and mixed integer linear programming, will also be taught to provide the student with the tools required to understand and model current and future power system and energy market operation. View detailed information in the Handbook
Microprocessor Design Clinic · 12.5 pts	Students in this subject will be introduced to computer architectures, microprocessors, microcontrollers, operating systems, compilers and software design. The proposed course will cover a broad range of topics necessary to make students knowledgeable in the art of microprocessor design including advanced concepts such as in line and out of order execution and execution unit resource optimisation. Students in this course will learn to design execution units, arithmetic logic units, memory hierarchies and learn strategies for cache sizing. As part of this, students will become proficient in microcode and instruction set design, multi-processor and multi core theory and design, including new design methodologies such as chiplet design. Upon completion, students will be familiar with the specification and synthesis of microprocessor systems using high level generator languages such as Chisel and Scala. The course will also introduce students to compiler and linker design, enhancements to instruction sets, c-language and the theory of operating systems. View detailed information in the Handbook
Large Data Methods & Applications · 12.5 pts	This course provides an introduction to an important contemporary statistical toolset for applications including data science, machine learning, signal processing, financial engineering, biomedical engineering, communication systems and other high-dimensional statistical applications. The course will cover topics including introduction to random matrix theory models in engineering; eigenvalue distributions; finite-dimensional and large-dimensional techniques, covariance estimation, principal component analysis and spectral clustering. These topics will be supplemented by applications across a range of traditional and emerging domains involving big data sets. View detailed information in the Handbook
Directed Studies · 12.5 pts	AIMS Directed studies provide the students with broader experience in addition to the regular class based learning. The directed studies can be conducted in the forms of: Industrial internship or research placements in the department’s research groups based on availability. This is only open to students who have completed a minimum of one semester of study and who have achieved an average of H2A or above in their prior subjects; Individually arranged supervised study of current research topics with staff members associated with the Department of Electrical and Electronic Engineering. INDICATIVE CONTENT The examples of the research topics are: Cloud Computing, Content Distribution and Information Logistics; Internet Services Energy Star Rating; Energy Efficiency of Future Modulation Formats; Low-Energy Fibre Access Networks; Video Coding for Energy Efficient Telecommunications; Fundamental Limits of Electronics and Photonics; Broadband fibre wireless networks and systems; Optimal design of few-mode fibres. View detailed information in the Handbook
AI for Robotics · 12.5 pts	AIMS: This subject focuses on the software and algorithms (i.e., artificial intelligence) that enable robotic systems to move autonomously through their environment and perform tasks. The key focus of this subject is the foundations of robotic systems that use software to move autonomously through their environment. This subject focus on the software & algorithms that enable the robot to perform tasks autonomously. Hence, this subject focused on artificial intelligence (AI) software & algorithms for robotics. The first main aim of the subject is to provide a foundation of the feedback loop that is core to all AI-enabled robots, namely: sensors measure the world around the robot; AI algorithms decide what action to take; the robot enacts that action by moving its joint or wheels; and the loop repeats endlessly. The second main aim of the subject is to provide implementation experience with cutting edge AI algorithm applicable to consumer and industrial robotics, where we consider both model-based method and reinforcement-learning methods. INDICATIVE CONTENT: Topics covered are at the intersection of automatic control and artificial intelligence, including: Cyber-physical feedback system formulation, such as: black-box and grey-box modelling, stability and robustness safety requirements, hierarchical and network control architectures. Safety and convergence guarantees for model-based methods, such as: learning models from data; adaptive control schemes; stability and robustness of PID and MPC control approaches. Connections between optimal control and reinforcement learning formulations for robotics. Reinforcement learning for robotics, such as: actor-critic methods, on-policy versus off-policy learning, sample efficiency, transferring simulation-based learning to real-world robots View detailed information in the Handbook
Hardware Accelerated Computing · 12.5 pts	Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include: Review of combinational and sequential digital logic FPGA architectures and fundamentals Hardware description languages (Verilog/VHDL) and hardware design flows High-level synthesis and OpenCL The use of parallelism, locality, and precision in hardware accelerators Host-accelerator interactions and hardware-software co-design Optimisation of hardware designs with respect to throughput, latency, energy, and area Accelerator design for selected applications such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application View detailed information in the Handbook
Electrical Engineering Research Project · 25 pts	This subject is for students to undertake a substantial individual research project on an approved topic over the semester, requiring independent investigation with a chosen supervisor either from a university (research institute) or from an industry partner. Note: the student is responsible for contacting the potential supervisor for the project. This subject can also be taken by Master of Electrical Engineering outgoing exchange students for research projects carried out in an overseas university. If the project is to be carried out within the EEE Department, the student is encouraged to take ELEN90011 (Directed Studies) if possible. The emphasis of the project can be associated with either A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, often as an adjunct to existing academic research initiatives. It is expected that the project will incorporate findings associated with both well-defined professional practice and research principles. View detailed information in the Handbook
Applied Deep Learning for Engineers · 12.5 pts	This subject covers a modern deep learning approach to engineering using a project-centric pedagogy. Building upon system optimisation and machine learning fundamentals presented in ELEN90088, the subject will present advanced deep learning architectures to address long-standing engineering challenges such as system complexity, curse of dimensionality, and modelling gap. Subject will specifically focus on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how advanced deep learning frameworks can be applied to modern engineering and cyber-physical systems. INDICATIVE CONTENT Topics covered may include: Latent spaces, auto encoder architectures. Advanced deep learning architectures, auto-differentiation, physics-inspired neural networks. Sequential data analysis and predictive models such as transformers. Generative models such as GANs and GPT variants. Other advanced topics such as meta parameter optimisation, Markov Chain Monte Carlo sampling. Distributed machine learning, federated learning, graph neural networks. Cyber-physical security of modern engineering systems, including data-based anomaly and threat detection and prediction. Subject projects will focus on engineering applications in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. View detailed information in the Handbook
Modelling and Analysis for AI · 12.5 pts	This subject builds up the fundamentals for modelling dynamical systems, with a key focus on the aspects and decisions of modelling that are relevant for the application of AI and data-intensive learning methods. The discussion and evaluation of modelling methods focuses on how model fidelity influences simulation-to-real transfer; how modelling and simulation decisions influence computation time required for training and validation; and how discrete-time models introduce complexity when representing continuous-time engineering systems. Subsequently, it introduces the basic principles and engineering applications of programming and data structures in a condensed form with a project-centric pedagogy. It covers the fundamentals of databases and data structures, basic algorithms, scientific programming, and classic AI problem solving. It will focus specifically on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how programming and databases play a role in modern engineering and cyber-physical systems. INDICATIVE CONTENT Topics covered may include: Models for engineering systems in multiple disciplines, including analysis of what makes the models amenable to AI and data-intensive learning methods. Principles for simulating dynamic systems that are most relevant for the use of AI methods and to address these principles with existing software tools. Scientific programming for modelling using Python programming language and libraries such as scipy and numpy. Engineering data structures and time series data and their storage in SQL and noSQL databases. Example engineering applications will be taught via projects in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. View detailed information in the Handbook
Reinforcement Learning for Engineering · 12.5 pts	The key focus of this subject is the design and implementation of decision-making policies for enabling a dynamical system to behave autonomously and achieve a desired objective. This subject covers both model-based and model-free learning methods, with a focus on evaluating, contrasting, and combining methods. The influence of noisy sensor data on performance, and the trade-offs between exploration and exploitation during a learning phase, will also be covered. The examples used in this subject range across existing and emerging decision-making methods, and their application to consumer and industrial engineering systems. INDICATIVE CONTENT Topics covered may include: Reinforcement learning fundamentals such as principle of optimality, Bellman equation, value and policy iteration. Temporal-difference learning, Q-learning, Deep Q-learning, Actor critic methods and hybrid approaches in engineering context. Model based vs model free approaches, multi-agent RL and their engineering applications. RL methods for Cyber-physical resilience and security such as fuzzing methods. View detailed information in the Handbook

Electronics and Embedded Systems specialisation

Core

Year 1

Students must complete 100 points of Year 1 compulsory subjects.

Accordion
Intro. to Numerical Computation in C · 12.5 pts	AIMS Many engineering disciplines make use of numerical solutions to computational problems. In this subject students will be introduced to the key elements of programming in a high level language, and will then use that skill to explore methods for solving numerical problems in a range of discipline areas. INDICATIVE CONTENT Algorithmic problem solving Fundamental data types: numbers and characters Approximation and errors in numerical computation Fundamental program structures: sequencing, selection, repetition, functions Simple data storage structures, variables, arrays, and structures Roots of equations and of linear algebraic equations Curve fitting and splines Interpolation and extrapolation Numerical differentiation and integration View detailed information in the Handbook
Foundations of Electrical Networks · 12.5 pts	INDICATIVE CONTENT Foundations of Electrical Networks develops an understanding of fundamental modelling techniques for the analysis of systems that involve electrical phenomena. This includes networks models of “flow-drop” one-port elements in steady state (DC and AC), electrical power systems, simple RC and RL transient analysis, and networks involving ideal and non-ideal operational amplifiers. It forms the foundation of many engineering subjects exploring fundamental concepts in electrical and electronic engineering. The subject will cover key electrical engineering topics in the areas of: Electrical phenomena – charge, current, electrical potential, conservation of energy and charge, the generation, storage, transport and dissipation of electrical power. Network models – networks of “flow-drop” one-port elements, Kirchoff’s laws, standard current-voltage models for one-ports (independent sources, resistors, capacitors, inductors, transducers, diodes), analysis of static networks, properties of linear time-invariant (LTI) one-ports and impedance functions, diodes, transformers, steady-state (DC and AC) analysis of LTI networks via mesh and node techniques, equivalent circuits, and transient analysis of simple circuits; Electrical power systems – overview of power generation and transmission, analysis of single-phase and balanced three-phase AC power systems. Analysis and design of networks involving ideal and non-ideal operational amplifiers. This material will be complemented by exposure to software tools for the simulation of electrical and electronic systems and the opportunity to develop basic electrical engineering laboratory skills using a prototyping breadboard, digital multimeter, function generator, DC power supply, and oscilloscope. Please view this video for further information: Foundations of Electrical Networks View detailed information in the Handbook
Digital Systems · 12.5 pts	AIMS This subject develops a fundamental understanding of concepts used in the analysis, design and building of digital systems. Such systems form the information and communication technologies (ICT) that underpin modern society. This subject provides a foundation for subsequent subjects, including ELEN30013 Electronic System Implementation, ELEN90066 Embedded System Design and ELEN90061 Communication Networks. INDICATIVE CONTENT Topics include: Digital systems - quantifying and encoding information, digital data processing, design process abstractions; Combinational logic – timing contracts, acyclic networks, switching algebra, logic synthesis; Sequential logic – cyclic networks and finite-state machines, metastability, microcode; These topics will be complemented by exposure to the hardware description language such as Verilog and the use of engineering design automation tools and configurable logic devices (e.g. FPGAs) in the laboratory. Please view this video for further information: Digital Systems View detailed information in the Handbook
Electrical Network Analysis and Design · 12.5 pts	AIMS This subject develops a fundamental understanding of linear time-invariant network models for the analysis and design of electrical and electronic systems. Such models arise in the study of systems ranging from large-scale power grids to tiny radio frequency signal amplifiers. This subject is one of four subjects that define the Electrical Systems Major in the Bachelor of Science and it is a core requirement for the Master of Engineering (Electrical). It provides a foundation for various subsequent subjects, including ELEN30013 Electronic System Implementation, ELEN90066 Embedded System Design, and ELEN30012 Signal and Systems. INDICATIVE CONTENT Topics include: Transient and frequency domain analysis of linear time-invariant (LTI) models – linearity, time-invariance, impulse response and convolution, oscillations and damping, the Laplace transform and transfer functions, frequency response and bode plots, lumped versus distributed parameter transfer functions, poles, zeros, and resonance, stability of circuits, modelling and simulation with simulation tools; Electrical network models – one-port elements, impedance functions, two-port elements, dependent sources, matrix representations of two-ports, driving point impedances and network functions, ladder and lattice networks, passive versus active networks, multi-stage modelling and design, and multi-port generalisations; Analysis and design of networks involving ideal and non-ideal operational amplifiers with emphasis on the design of active filters and broadband circuits with specific frequency characteristics; Circuits and networks for managing voltage and power requirements for common electronic circuits. These topics will be complemented by tutorials and workshops designed to develop skills in design and modelling of electronic circuits through software tools and building, testing, and verification of electronic circuits. Please view this video for further information: Electrical Network Analysis and Design View detailed information in the Handbook
Electrical Device Modelling · 12.5 pts	AIM This subject develops the theoretical and practical tools required to understand, construct, validate and apply models of standard electrical and electronic devices. In particular, students will study the theoretical and practical development of models for devices such as resistors, capacitors, inductors, transformers, motors, batteries, diodes, transistors, and transmission lines. In doing so, students will gain exposure to a variety of fundamental fields in physics, including electromagnetism, semiconductor materials and quantum electronics. This material will be complemented by exposure to experiment design and measurement techniques in the laboratory, the application of models from device manufacturers, and the use of electronic circuit simulation software. INDICATIVE CONTENT Topics include: Vector calculus for device modelling, Maxwell’s equations, physics of conductors and insulators, passive device models (including for resistors, capacitors and inductors), lumped and distributed circuit models for wired interconnections (including treatment of signal integrity and termination strategies), semiconductors and quantum electronics, static and dynamic models for p-n junctions diodes and bipolar junction transistors. View detailed information in the Handbook
Signals and Systems · 12.5 pts	AIMS The aim of this subject is twofold: firstly, to develop an understanding of the fundamental tools and concepts used in the analysis of signals and the analysis and design of linear time-invariant systems path in continuous–time and discrete-time; secondly, to develop an understanding of their application in a broad range of areas, including electrical networks, telecommunications, signal-processing and automatic control. The subject formally introduces the fundamental mathematical techniques that underpin the analysis and design of electrical networks, telecommunication systems, signal-processing systems and automatic control systems. Such systems lie at the heart of the electrical engineering technologies that underpin modern society. This subject is one of four Level 3 subjects that define the Electrical Engineering Systems Major in the Bachelor of Science. . It provides the foundation for various subsequent subjects, including ELEN90057 Communication Systems, ELEN90058 Signal Processing and ELEN90055 Control Systems. INDICATIVE CONTENT Topics include: Signals – continuously and discretely indexed signals, important signal types, frequency-domain analysis (Fourier, Laplace and Z transforms), nonlinear transformations and harmonics, sampling; Systems – viewing differential / difference equations as systems that process signals, the notions of input, output and internal signals, block diagrams (series, parallel and feedback connections), properties of input-output models (causality, delay, stability, gain, shift-invariance, linearity), transient and steady state behaviour; Linear time-invariant systems – continuous and discrete impulse response; convolution operation, transfer functions and frequency response, time-domain interpretation of stable and unstable poles and zeros, state-space models (construction from high-order ODEs, canonical forms, state transformations and stability), and the discretisation of models for systems of continuously indexed signals. This material is complemented by exposure to the use of MATLAB for computation and simulation and examples from diverse areas including electrical engineering, biology, population dynamics and economics. View detailed information in the Handbook
Electronic System Implementation · 12.5 pts	AIMSThis subject provides students with hands-on electronic skills to gain basic competencies in design and implementation of simple circuits. Students will design with a range of standard electrical and electronic devices, basic circuit construction methods and electrical measurement techniques to test and verify the function of electronic systems. This subject is one of four subjects that define the Electrical Systems Major in the Bachelor of Science and it is a core requirement for the Master of Engineering (Electrical) and the Master of Engineering (Electrical with Business). This includes hands-on experience with: • Operation and selection of electrical and electronic devices used in various electronic circuits; • Common electronic circuit realisations to meet the most commonly required signal processing and conditioning applications; • Programmable digital circuits and microprocessor programming; • Circuit design and simulation tools; • Printed circuit board layout, circuit assembly, and soldering techniques; • Test and Measurement equipment and methods; • Managing design issues and requirements. Students will complete electronic circuit implementation projects in small groups and be required to prepare technical documentation and present project outcomes. INDICATIVE CONTENT • Devices such as resistors, capacitors, inductors, switches, transducers, motors, diodes, transistors, op-amps, voltage regulators, comparators, oscillators, timers, A/D and D/A converters, microprocessors and controllers; • Circuit functions and techniques such as buffering, referencing, signal conditioning, filtering, bridges, detection, waveform generation, and pulse-width modulation; • Microprocessor programming, the role of assembly and high-level languages, assemblers, compilers and debuggers; • PCB layout, circuit assembly, and soldering techniques; • Test and Measurement methods and working with common equipment such as multimeters and oscilloscopes. View detailed information in the Handbook
Engineering Mathematics · 12.5 pts	This subject introduces important mathematical methods required in engineering such as manipulating vector differential operators, computing multiple integrals and using integral theorems. A range of ordinary and partial differential equations are solved by a variety of methods and their solution behaviour is interpreted. The subject also introduces series including the concepts of convergence and divergence. Topics include: Vector calculus, including Gauss’ and Stokes’ Theorems; systems of homogeneous ordinary differential equations, including phase plane and linearisation for nonlinear systems; Laplace transforms; series, including Taylor series and power series; Fourier series and Fourier integrals; second order partial differential equations and separation of variables. View detailed information in the Handbook

Year 2

Students must complete 100 points of Year 2 compulsory subjects.

Accordion
Probability and Random Models · 12.5 pts	AIMS This subject provides an introduction to probability theory, random variables, random vectors, decision tests, and stochastic processes. Uncertainty is inevitable in real engineering systems, and the laws of probability offer a powerful way to evaluate uncertainty, to predict and to make decisions according to well-defined, quantitative principles. The material covered is important in fields such as communications, data networks, signal processing and electronics. This subject is a core requirement in the Master of Engineering (Electrical, Mechanical and Mechatronics). INDICATIVE CONTENT Topics include: Foundations – combinatorial analysis, axioms of probability, independence, conditional probability, Bayes’ rule; Random variables (rv’s)– definition; cumulative distribution, probability mass and probability density functions; expectation and variance; functions of an rv; important distributions and their properties and uses; Multiple random variables – joint cumulative distribution, probability mass and probability density functions; independent rv’s; correlation and covariance; conditional distributions and expectation; functions of several rv’s; jointly Gaussian rv’s; random vectors; Sums, inequalities and limit theorems – sums of rv’s, moment generating function; Markov and Chebychev inequalities; weak and strong laws of large numbers; the Central Limit Theorem; Decision testing - maximum likelihood, maximum a posterior, minimum cost and Neyman-Pearson rules; basic minimum mean-square error estimation; Stochastic processes – mean and autocorrelation functions, strict and wide-sense stationarity; ergodicity; important processes and their properties and uses; Introduction to Markov chains. This material is complemented by exposure to examples from electrical engineering and software tools (e.g. MATLAB) for computation and simulations. View detailed information in the Handbook
Control Systems · 12.5 pts	AIMS This subject provides an introduction to automatic control systems, with an emphasis on classical techniques for the analysis and design of feedback interconnections. The main challenge in automatic control is to achieve desired performance in the presence of uncertainty about the system dynamics and the operating environment. Feedback control is one way to deal with modelling uncertainty in the design of engineering systems. This subject is a core requirement in the Master of Engineering (Electrical, Electrical with Business, Mechanical, Mechanical with Business and Mechatronics). INDICATIVE CONTENT Topics include: * Modelling for control, linearization, relationships between time and frequency domain models of linear time-invariant dynamical systems, and the structure, stability, performance, and robustness of feedback interconnections; * Frequency-domain analysis and design, Nyquist and Bode plots, gain and phase margins, loop-shaping with proportional, integral, lead, and lag compensators, loop delays, and fundamental limitations in design; and * Actuator constraints and anti-windup compensation. This material is complemented by the use of software tools (e.g. MATLAB/Simulink) for computation and simulation, and exposure to control system hardware in the laboratory. View detailed information in the Handbook
Electronic Circuit Design · 12.5 pts	AIMS This subject provides an in-depth coverage of transistor (MOSFET and BJT) devices and their use in common circuits. In particular, students will study topics including: transistor operating modes and switching; principles of CMOS circuits; transistor biasing; current-source/emitter-amplifiers; low-frequency response; followers; class B amplifiers; current limiting; current sources and mirrors; differential pairs; feedback in amplifiers and stability; operational amplifiers; operational amplifier circuits; and voltage regulation. This material will be complemented by exposure to circuit simulation software tools and the opportunity to further develop circuit construction/test skills in the laboratory. INDICATIVE CONTENT Design-focused field-effect and bipolar elementary transistor models, and design of elementary amplifier stages and biasing circuits. Static and dynamic behaviour of amplifier circuits including frequency response, feedback and stability, slew-rate and clipping. Operational amplifiers and opamp based circuits; voltage regulators, references and voltage converters. Verification of electronic circuits using simulation and constructing them in the laboratory. Please view this video for further information: Electronic Circuit Design View detailed information in the Handbook
Communication Systems · 12.5 pts	AIMS This subject provides an introduction to the analysis and design of telecommunication signals and systems, in the presence of uncertainty. The emphasis is on understanding the basic concepts that underpin the physical layer of modern communication systems. INDICATIVE CONTENT Topics to be covered include: Introduction to communication systems including historical developments and comparisons between analogue and digital communications. Review of assumed knowledge from linear algebra, signals and systems and probability and random processes. The sampling theorem, analog-to-digital conversion, complex baseband representation of passband signals, filtering of random processes, power spectral density, bandwidth of random signals, additive white Gaussian noise (AWGN), signal-to-noise ratio. Communication over baseband AWGN channels including modulation techniques (pulse amplitude modulation, orthogonal modulation), signal space representation, optimal detectors, matched filters, error probability calculations and bandwidth / power trade-off. Communication over passband AWGN channels including modulation techniques (phase shift keying, quadrature amplitude modulation and frequency shift keying), optimal coherent detectors, noncoherent detectors and error probability calculations. Communication over linear time-invariant channels including concepts of distortion, inter-symbol interference, pulse shaping, Nyquist’s criterion, equalization, sequence detection and the Viterbi algorithm. Synchronization including carrier, symbol and frame synchronization. View detailed information in the Handbook
Signal Processing · 12.5 pts	AIMS This subject provides an introduction to the fundamental theory of time domain and frequency domain representation of discrete time signals and linear time invariant dynamical systems, and how this theory is used to analyse and design digital signal processing systems and algorithms. Topics include: Applications of signal processing techniques; Sampling of analog signals, anti-aliasing filters; Frequency-domain analysis of signals and systems, Discrete Time Fourier Transform, Discrete Fourier Transform, Fast Fourier Transform; Digital filters, low-pass, high-pass, band-pass, stop-band and all pass filters. Phase and group delay, FIR and IIR filters; Design of digital FIR and IIR filters; Multi-rate signal processing, with a focus on up-sampling, down-sampling, and sampling rate conversion; Simple non-parametric methods for spectral estimation. This fundamental material will be complemented by exposure to MATLAB tools for signal analysis and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory. INDICATIVE CONTENT Sampling of continuous time signals, Design of anti-aliasing filters, Time and frequency representation of discrete time signals and discrete time linear time invariant systems, Discrete Time Fourier Transform and z-transform and their properties, Low order lowpass, highpass, bandpass, bandstop filters, All-pass filter, Design of IIR filters using the bilinear transformation, Design of FIR filters with linear phase using windowing techniques and the Parks McClelland method, Discrete Time Fourier transform and its properties, Fast Fourier Transform, The use of the DFT in implementation of linear filtering algorithms, Up-sampling and down-sampling, multistage and computationally efficient implementations of up-samplers and down-samplers, Energy and power spectra for deterministic signals. View detailed information in the Handbook
Embedded System Design · 12.5 pts	AIMS This subject provides a practical introduction to the basics of modelling, analysis, and design of microprocessor-based embedded systems. Students will learn how to integrate computation with physical processes to meet a desired specification within the context of a design project. The project work will expose students to the various stages in an engineering project (design, implementation, testing and documentation) and a range of embedded system concepts. INDICATIVE CONTENT Topics covered may include: digital computer and microprocessor architectures, modelling of dynamic behaviours, control, models of computation, operating systems concepts, multi-tasking, resource management and real-time behaviours, interfacing with the physical world, analysis and verification, safety, reliability, and security and privacy. This material will be complemented by exposure to standard software tools including compilers and debuggers, finite state machine design and analysis software, and simulation tools. The subject will include a level of industry engagement, to provide broader examples of engineering projects, through guest lectures. View detailed information in the Handbook
Introduction to Power Engineering · 12.5 pts	AIMS To develop a solid foundation for the study of systems that involve the generation, transport, and conversion of electric power. INDICATIVE CONTENT Physical principles of electromagnetism, magnetic circuits, energy storage, loss mechanisms, electromechanical energy conversion. Modelling of transmission lines, transformers, motors and generators (synchronous and asynchronous), and other loads. Circuit theory for power system analysis, three phase-phase circuits, power flow and maximum power transfer, per-unit system. Please view this video for further information: Introduction to Power Engineering View detailed information in the Handbook
Interdisciplinary Design for Engineers · 12.5 pts	In this subject, students will actively engage in an interdisciplinary, collaborative and project-based learning environment, offering insights into the professional nature of engineering work. Through a real-world project, students will gain hands-on design experience addressing a complex challenge. The project will require students to integrate discipline knowledge and apply professional skills like teamwork and communication. Students will experience the entire engineering design process, covering problem definition, ideation, concept development, analysis, prototyping, testing and iteration. The project provides practical experience, equipping students with tools and methods to address complex challenges. Students are expected to integrate diverse perspectives, considering factors like stakeholders, sustainability (including environmental and social issues), safety, feasibility, and technical and ethical considerations. View detailed information in the Handbook

Capstone

Year 3

Students must complete 25 points of Year 3 compulsory capstone project subjects.

Accordion
Engineering Capstone Project Part 1 · 12.5 pts	The subject involves undertaking a substantial group project (typically in groups of three students) requiring an independent investigation on an approved topic in advanced engineering design and / or research. Each project is carried out under the supervision of a member of academic staff and where appropriate an industry partner. The emphasis of the project can be associated with either: A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the engineering science disciplines, often as an adjunct to existing academic research initiatives. It is expected that the Capstone Project will incorporate findings associated with both well-defined professional practice and research principles and will provide students with the opportunity to integrate technical knowledge and generic skills gained in earlier years. The project component of this subject is supplemented by a lecture course dealing with project management tools and practices. Please note: Students enrolled in the suite of Master of Engineering programs must be within the final 112.5 points of their degree to enrol. Students enrolled in the Master of Industrial Engineering must be within the final 100 points of their degree to enrol. Students are to take Engineering Capstone Project Part 1 and then subsequently continue with Engineering Capstone Project Part 2 in the following semester. Upon successful completion of this project, students will receive 25 points credit. View detailed information in the Handbook
Engineering Capstone Project Part 2 · 12.5 pts	Please refer to ENGR90037 Engineering Capstone Project Part 1 for this information. View detailed information in the Handbook

Accordion

Engineering Capstone Project Part 1 · 12.5 pts

The emphasis of the project can be associated with either:

A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or
A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the engineering science disciplines, often as an adjunct to existing academic research initiatives.

The project component of this subject is supplemented by a lecture course dealing with project management tools and practices.

Please note:

Students enrolled in the suite of Master of Engineering programs must be within the final 112.5 points of their degree to enrol.

Students enrolled in the Master of Industrial Engineering must be within the final 100 points of their degree to enrol.

View detailed information in the Handbook

Engineering Capstone Project Part 2 · 12.5 pts

Please refer to ENGR90037 Engineering Capstone Project Part 1 for this information.

View detailed information in the Handbook

Specialisation

Year 3

Students must complete 50 credit points of Year 3 core specialisation subjects.

Accordion
Semiconductor Devices · 12.5 pts	This subject serves as an introduction to semiconductor devices. It describes the fundamentals, theory, material and physical properties of semiconductor devices. The following topics will be covered. Fundamentals: Crystal properties and of the growth of bulk crystals and of epitaxial layers. Physical concepts related to atoms and electrons. These concepts may include the photoelectric effect, the Bohr model, quantum mechanics, and the periodic table. Energy bands and charge carriers in semiconductors: Bonding forces and energy bands in solids, charge carriers in semiconductors, carrier concentrations, the drift of carriers in electric and magnetic fields, and the Fermi level. Excess carriers in semiconductors: Optical absorption, luminescence, carrier lifetime and photoconductivity, and the diffusion of carriers. Junctions: Fabrication of pn junctions, equilibrium conditions, forward and reverse biased junctions in steady state, reverse bias breakdown, transient and AC conditions, metal-semiconductor junctions and heterojunctions. In the next part of the subject PN junction diodes: Junction diodes, tunnel diodes, photodiodes, and light-emitting diodes and lasers. Bipolar junction transistors (BJTs): Amplification and switching, fundamentals of BJT operation, BJT fabrication, minority carrier distributions and terminal currents, generalised biasing, switching, the frequency limitations of transistors, and heterojunction bipolar transistors. Field effect transistors (FETs): Topics may include junction FETs, the metal semiconductor FET and the metal-insulator-semiconductor FET. Additional topics (if time permits): Integrated circuits, pnpn switching devices, and microwave devices. View detailed information in the Handbook
Microprocessor Design Clinic · 12.5 pts	Students in this subject will be introduced to computer architectures, microprocessors, microcontrollers, operating systems, compilers and software design. The proposed course will cover a broad range of topics necessary to make students knowledgeable in the art of microprocessor design including advanced concepts such as in line and out of order execution and execution unit resource optimisation. Students in this course will learn to design execution units, arithmetic logic units, memory hierarchies and learn strategies for cache sizing. As part of this, students will become proficient in microcode and instruction set design, multi-processor and multi core theory and design, including new design methodologies such as chiplet design. Upon completion, students will be familiar with the specification and synthesis of microprocessor systems using high level generator languages such as Chisel and Scala. The course will also introduce students to compiler and linker design, enhancements to instruction sets, c-language and the theory of operating systems. View detailed information in the Handbook
Electronic System Design · 12.5 pts	AIMS This subject will explore the design of various electrical and electronic systems and provide students with a range of common and practical design techniques and circuits in the context of a guided laboratory based project. INDICATIVE CONTENT Subject may cover specific concepts surrounding the design and implementation of: Design process; Design for manufacture and assembly; Advanced PCB design; Oscillators; Phase-locked loops and frequency synthesis; Base-band signalling schemes and clock recovery; Mixers and logarithmic amplification; Automatic gain control; Filters; Synchronous detection; High-speed analog-digital conversion; High-frequency amplification; Low noise amplifiers; Power supply design; Batteries, battery charging systems, and management; Test and measurement; Sensors. View detailed information in the Handbook
High Speed Electronics · 12.5 pts	AIMS The aim of the subject is to provide theoretical and practical treatment of high-speed electronics. Through the subject, students will grasp the fundamental properties and models of high-speed signals and interconnects, acquire high-speed digital design skills with a focus on the modelling, analysis, design and application of high speed transistors, logic gates and modern logic families, and master the high-speed analogue design capability including the design of oscillators and filters for RF applications. The students will be exposed to the state-of-the-art technologies that are shaping the fast evolving semiconductor industry. INDICATIVE CONTENT The topics include: Fundamental properties of analogue systems; Smith charts: principles and applications; High-speed analogue circuits: voltage control oscillators, matching networks, and low noise amplifiers; Bipolar junction transistors: device, switching, and logic; CMOS: device, switching and logic; High-speed signalling consideration: power dissipation, heat, signal propagation, and termination. View detailed information in the Handbook

Electives

Electrical Engineering Electives (Group A)

Students must complete 25 points of Year 3 elective subjects.

Accordion
Introduction to Optimisation · 12.5 pts	AIMS This subject provides a rigorous introduction to numerical nonlinear optimization, as used across all of science and particularly in engineering design. There is an emphasis on both the theory and application of optimization techniques, with a focus on solving unconstrained and constrained nonlinear programmes. This subject is intended for graduate and research higher-degree students in engineering. INDICATIVE CONTENT Topics include: Algorithms for unconstrained optimization Algorithms for constrained optimization Convex sets and functions Convex optimization problems Duality theory Computational complexity Approximation algorithms and penalty methods. View detailed information in the Handbook
Advanced Communication Systems · 12.5 pts	AIMS The aim of this subject is to develop a thorough understanding of the main concepts, techniques and performance criteria used in the analysis and design of digital communication systems and wireless networks. Such systems and networks lie at the heart of the information and communication technologies (ICT) that underpin modern society and are very much part of the Internet of Things which involves machine to machine communication. INDICATIVE CONTENT This subject provides an in-depth treatment of the main concepts and techniques used in the analysis and design of digital communication systems and wireless networks. Topics include: Source coding; entropy, Shannon source coding bound, data compression techniques; Channel modelling, modulation over time-varying fading channels, time and frequency diversity, energy and spectral efficiency, multiple carrier modulation including orthogonal frequency division multiplexing (OFDM) modulation, phase noise characteristics and its impact on single-carrier and multicarrier systems, spatial multiplexing for multiple access protocols, multiple antenna technologies (MIMO systems), cellular networks; Channel coding for error control: mutual information, channel capacity, Shannon channel coding bound, channel coding concepts, block codes; convolutional / trellis codes; introduction to LDPC codes, turbo codes, polar codes. Examples include short, medium and long range communication systems such as bluetooth, cellular and satellite communication systems. View detailed information in the Handbook
Advanced Signal Processing · 12.5 pts	AIMS This subject provides an in-depth introduction to statistical signal processing. INDICATIVE CONTENT Students will study a selection of the following topics: Applications of statistical signal processing; A review of stochastic signals and systems fundamentals – random processes, white noise, stationarity, auto- and cross-correlation functions, spectral- and cross-spectral densities, properties of linear time-invariant systems excited by white noise; Parameter estimation - least squares and its properties, recursive least squares and least mean squares, optimisation-based methods, maximum likelihood methods; Kalman, Wiener and Markov filtering; Power spectrum estimation. This material will be complemented with the use of software tools (e.g. MATLAB) for computation and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory. View detailed information in the Handbook
Electronic System Design · 12.5 pts	AIMS This subject will explore the design of various electrical and electronic systems and provide students with a range of common and practical design techniques and circuits in the context of a guided laboratory based project. INDICATIVE CONTENT Subject may cover specific concepts surrounding the design and implementation of: Design process; Design for manufacture and assembly; Advanced PCB design; Oscillators; Phase-locked loops and frequency synthesis; Base-band signalling schemes and clock recovery; Mixers and logarithmic amplification; Automatic gain control; Filters; Synchronous detection; High-speed analog-digital conversion; High-frequency amplification; Low noise amplifiers; Power supply design; Batteries, battery charging systems, and management; Test and measurement; Sensors. View detailed information in the Handbook
Lightwave Systems · 12.5 pts	AIMS Lightwave systems are fundamentally changing the way we communicate through broadband communications, helping clinicians to perform a range of medical procedures and diagnosis supported by advanced biomedical instrumentation, and even in the way we live in our homes through sophisticated interactive televisions and security systems. This subject will explore the physical principles and issues that arise in the design of lightwave systems often found in those key industry sectors. Students will study topics from: transmission of light over wave guides; production of light by lasers; light modulation; conversion of light signals to electrical signals; optical multiplexing and demultiplexing; light amplification; dispersion and dispersion compensation; optical nonlinearities; modulation and advanced detection schemes. This material will be complemented by exposure to lightwave systems and measurement techniques in the laboratory. INDICATIVE CONTENT This subject will explore the physical principles governing the generation, modulation, amplification, guiding, transmission, multiplexing, demultiplexing and detection of light and issues that arise in the design of lightwave systems such as transmission impairments, noise. Students learn selected examples of lightwave systems and methods for design, modelling and testing of simple lightwave systems. View detailed information in the Handbook
Power System Analysis · 12.5 pts	AIMS This subject provides an insight into the fundamental elements to analyse electrical power transmission and distribution systems, with both analytical and simulation tools for analysis of operations of these systems. Problems related to power flow and use of Newton-Raphson and other algorithms such as backward-forward sweep will be discussed. Fault calculation and analysis, symmetrical components, and analytical methods for solving symmetrical (balanced) faults will be covered. Principles, concepts and problems related to power system dynamics and control, particularly for frequency and voltage regulation, will be discussed and analysed in detail. Finally, small-signal, transient, voltage and frequency stability will be introduced and exemplified. Focus will be put on real-world examples, particularly to prepare the student for the ongoing transition towards a low-carbon grid dominated by renewables and distributed energy resources. INDICATIVE CONTENT Power flow calculations, Newton-Raphson, Gauss-Seidel and backward-forward sweep methods; Fault calculations, balanced and unbalanced, symmetrical components, fundamentals of protection; Frequency regulation and frequency stability, Voltage regulation in transmission and distribution networks, including use of flexible AC transmission systems (FACTS); Voltage stability, small-signal stability, transient stability; Computer simulations. View detailed information in the Handbook
Communication Networks · 12.5 pts	AIMS This subject introduces the basic principles, analysis, and design of communication networks. It covers relevant analytical methods, the layered network architecture of the Internet, and a multitude of network protocols. Analytical tools from queueing, optimisation, and graph theories are used to develop an in-depth understanding of basic principles and the role they play in network design. Specifically, queueing and graph theories are emphasised as methodological frameworks for communication network delay and structure analysis. The concepts taught in this subject lead to a better understanding of the Internet as well as modern communication paradigms such as Software-Defined Networks, Machine-to-Machine communication, Internet of Things, and social networks. INDICATIVE CONTENT Topics covered may include: The layered network architecture with a focus on physical-layer multiple access (TDM, FDM, WDM), link layer protocols and medium access control (MAC), network layer topologies, least-cost routing algorithms and protocols, transport layer protocols and the principles and techniques of practical reliable transport; LAN protocols, Ethernet, Wi-Fi, and serial communications; The Internet's network layer including the Internet Protocol (IP) and routing protocols including an introduction to BGP and the operation of forwarding tables in routers and shortest prefix routing; The Internet's transport layer protocols UDP and TCP, including the flow and congestion control algorithms; Network security, application layer, cloud and fog computing, Machine-to-Machine communication, and Internet of Things; Queuing theory: basics, birth-death processes, M/M/x and Markovian queues, networks of queues; Basics of graph theory and social network analysis relevant to communication networks. View detailed information in the Handbook
High Speed Electronics · 12.5 pts	AIMS The aim of the subject is to provide theoretical and practical treatment of high-speed electronics. Through the subject, students will grasp the fundamental properties and models of high-speed signals and interconnects, acquire high-speed digital design skills with a focus on the modelling, analysis, design and application of high speed transistors, logic gates and modern logic families, and master the high-speed analogue design capability including the design of oscillators and filters for RF applications. The students will be exposed to the state-of-the-art technologies that are shaping the fast evolving semiconductor industry. INDICATIVE CONTENT The topics include: Fundamental properties of analogue systems; Smith charts: principles and applications; High-speed analogue circuits: voltage control oscillators, matching networks, and low noise amplifiers; Bipolar junction transistors: device, switching, and logic; CMOS: device, switching and logic; High-speed signalling consideration: power dissipation, heat, signal propagation, and termination. View detailed information in the Handbook
Advanced Control Systems · 12.5 pts	AIMS This subject provides an introduction to modern control theory with a particular focus on design of advanced control laws via state-space methods and optimal control. The role of feedback in control design will be reinforced within this context, alongside the role of optimisation techniques in control system synthesis. INDICATIVE CONTENT Topics include: State-space models - first-order vector differential/difference equations; Lyapunov stability; linearisation; discretisation; Kalman decomposition (observable, detectable, reachable and stabilisable subspaces); state-feedback and pole placement; output-feedback and observer design in both continuous-time and discrete-time. Optimal control - dynamic programming; linear quadratic regulation in both continuous-time and discrete-time. Model predictive control in discrete-time; moving-horizon with constraints. View detailed information in the Handbook
Power Electronics · 12.5 pts	AIMS The aim of this subject is to understand the fundamental concepts and basic theory involved in modelling and analysis of the power electronic components that comprise power electronic devices such as power supplies, inverters, converters and their control systems. It is expected that at the end of this subject the student has a sound understanding of the physical concepts and mathematical models behind each of the basic components and of their functionality within a system, such as a high voltage DC transmission system. Furthermore this subject seeks to combine the fields of electronics, semiconductor devices, power system operation, power system measurement and control. It is expected that through this subject the students are exposed to examples of real electrical engineering systems where the three disciplines of electronics, power systems and control come together. INDICATIVE CONTENT Topics covered in this subject include: introduction to power semiconductor switches; discussion on the role of power electronics in the operation of electric power systems; models of power semiconductor devices and circuit components, including diodes, Thyristors, IGBT, Snubber circuits. Also basic concepts of single- and three-phase diode bridge rectifiers; single- and three-phase converters and inverters; operation and design of DC-AC inverters with emphases on switch-mode inverters, i.e. single- and three-phase inverters. Finally, the acquired knowledge of power electronic devices is applied to wind and PV solar systems where the design of voltage source converters and associated control loops are used to interface the wind/solar system with the power grid. View detailed information in the Handbook
Grid Integration of Renewables · 12.5 pts	AIMS This subject develops a foundation for pursuing electrical engineering oriented research in the area of sustainable energy systems. This subject aims to introduce the concepts behind smart grids, future low-carbon energy networks, sustainable electricity systems as well as the main renewable and low-carbon generation technologies. The subject will introduce students to tools and techniques so that distributed energy resources (e.g. distributed renewable generation, storage, electric vehicles, demand response, etc.) may be integrated effectively into the power system in the context of both traditional grids and future smart grids. INDICATIVE CONTENT This subject will cover the following topics: Distributed low-carbon technologies Introduction to distribution networks Introduction to distributed low-carbon technologies (wind energy, photovoltaic systems, electric vehicles, electric heating, storage) Wind Energy: impacts and challenges Photovoltaic systems: impacts and challenges Electric vehicles: impacts and challenges Electric heat pumps and electric heating: impacts and challenges Storage: impacts and challenges Smart Distribution and Smart Transmission Networks Distributed low-carbon technologies and active network management Towards Smart Grids Smart grids - Transmission and Distribution perspectives Smart Transmission: HVDC and FACTS, dynamic line rating, post-contingency security, special protection schemes The role of future Distribution System Operators Low-carbon Electricity System Towards low-carbon networks: relationship between sustainability and smart grids Introduction to low-carbon thermal generation (nuclear, Carbon Capture and Storage, Concentrated Solar Power, biomass, etc.) Utility-scale renewable technologies: wind farms; solar farms; other large-scale renewables; utility-scale batteries System-level operational challenges and solutions for renewables integration: variability and uncertainty; low-inertia operation; low system-strength operation; minimum load issues; DER visibility; indistinct events; general stability issues; flexibility System-level planning challenges and solutions for renewables integration: system adequacy and reliability; capacity credit of renewables and storage; extreme weather events and resilience; role of transmission Sector coupling and multi-energy systems: decarbonisation of gas, heating and transport; role of hydrogen Distributed energy systems: new technical and commercial architectures for two-sided systems and markets; demand response; aggregators and virtual power plants; distributed energy markets; peer-to-peer trading; local energy communities; microgrids View detailed information in the Handbook
System Optimisation & Machine Learning · 12.5 pts	This subject introduces the basic principles, analysis methods, and applications of optimisation and machine learning to engineering systems; encompassing fundamental concepts and practical algorithms. It covers the fundamentals of continuous optimisation followed by machine learning basics for engineering applications. The concepts and methods discussed are illustrated in multiple application areas including Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks.The concepts taught in this subject will allow a better understanding of continuous optimisation and machine learning for systems engineering. INDICATIVE CONTENT Topics covered may include: Fundamentals of continuous optimisation: convex sets and functions; local vs global solutions, constrained optimisation and Lagrange multipliers; linear, quadratic, and nonlinear programming Basics of machine learning encompassing supervised and unsupervised learning: binary classification, linear and nonlinear regression, kernel methods, and clustering. Specific machine learning methods such as Support Vector Machines (SVMs), Neural Networks (NNs), k-means clustering, and reinforcement learning. Applications to Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks. View detailed information in the Handbook
Communication Design Clinic · 12.5 pts	Students work collaboratively in small groups to implement and optimize components in a modern communication system or network with the goal of supporting a targeted application. To meet this goal students will need to: determine system requirements based on the target application and additional constraints; propose and evaluate multiple solutions through theoretical analysis and detailed simulations; implement, integrate, verify, and iterate on their selected solutions. Lectures will cast content from prerequisite subjects into the context at hand and cover additional topics relevant to the task. Each student group is expected to demonstrate initiative and independence while pursuing the goal of designing and optimizing their communication system or network, with a key focus being that students learn through hands-on experience. Students will receive early exposure to advanced topics critical to modern communication systems, such as: source and channel coding, multicarrier modulation, multiantenna transmission, and network architectures and protocols. Successful completion of the project will require the student to draw upon knowledge, understanding, and skills learned in prerequisite subjects, which may include: Communication Systems – analog-to-digital conversion, signal-to-noise ratio, modulation and demodulation, bandwidth/power trade-off, error probability calculations, distortion, inter-symbol interference, pulse shaping, equalization, sequence detection, and synchronization. Signal Processing - design and implementation of digital filters (low-, high-, band-, all- pass filters); ARMA systems; up-sampling and down-sampling. Embedded System Design – system-level programming, operating systems concepts, real-time issues, and standard software tools. Additional topics required for the assigned project may also be covered, such as: ideation, prototyping, and design practices; analog RF components; software packages for modelling and implementation; and the use of test & measurement equipment. View detailed information in the Handbook
Autonomous Systems Clinic · 12.5 pts	AIMS: Students work collaboratively in small groups to engineer an autonomous system that performs a specified task. This includes carrying out steps such as: task analysis; proposing multiple solutions; feasibility analysis through prototyping and computer-aided design; detailed design, construction, and testing of the chosen solution; and demonstrating the solution in a proving ground. The lectures will cast content from the pre-requisite subjects into the context of the task at hand, as well as covering additional topics relevant to the task. Each student group is expected to demonstrate initiative and independence while pursuing the goal of designing and building their autonomous system, with a focus of the subject being that students learn through hands-on experience, implementation, and verification. INDICATIVE CONTENT: Successful completion of the project requires the student to draw upon knowledge, understanding, and skills learned in the prerequisite subjects, namely: • Embedded System Design - including topics such as: finite, extended, and hierarchical state machines; modelling cyber-physical systems; scheduling, multi-tasking, and real-time issues; interfacing to the analogue world. • Control Systems - including topics such as: modelling; linearisation; feedback interconnections; proportional, integral, derivative (PID) control; actuator constraint considerations. • Signal Processing - including topics such as: design and implementation of digital filters (low-, high-, band-, all- pass filters); ARMA systems; up-sampling and down-sampling. Additional topics, specific to the task as hand, will be covered, such as: ideation, prototyping, and design practices; image processing and computer vision tools; software introductions; safety and failure analysis. A range of materials, components, and fabrication facilities are provided, from which the students are expected to utilise a subset for designing and building their autonomous system, such as: electric motors, range sensors, camera, voltage converters, compute power, sheet wood, soldering stations, laser wood cutting, 3D printing. The task to be performed is motivated by a real-world application of autonomous systems, such as: operating in hazardous environments or performing repetitive tasks. Please view this video for further information: Autonomous Systems Clinic View detailed information in the Handbook
Semiconductor Devices · 12.5 pts	This subject serves as an introduction to semiconductor devices. It describes the fundamentals, theory, material and physical properties of semiconductor devices. The following topics will be covered. Fundamentals: Crystal properties and of the growth of bulk crystals and of epitaxial layers. Physical concepts related to atoms and electrons. These concepts may include the photoelectric effect, the Bohr model, quantum mechanics, and the periodic table. Energy bands and charge carriers in semiconductors: Bonding forces and energy bands in solids, charge carriers in semiconductors, carrier concentrations, the drift of carriers in electric and magnetic fields, and the Fermi level. Excess carriers in semiconductors: Optical absorption, luminescence, carrier lifetime and photoconductivity, and the diffusion of carriers. Junctions: Fabrication of pn junctions, equilibrium conditions, forward and reverse biased junctions in steady state, reverse bias breakdown, transient and AC conditions, metal-semiconductor junctions and heterojunctions. In the next part of the subject PN junction diodes: Junction diodes, tunnel diodes, photodiodes, and light-emitting diodes and lasers. Bipolar junction transistors (BJTs): Amplification and switching, fundamentals of BJT operation, BJT fabrication, minority carrier distributions and terminal currents, generalised biasing, switching, the frequency limitations of transistors, and heterojunction bipolar transistors. Field effect transistors (FETs): Topics may include junction FETs, the metal semiconductor FET and the metal-insulator-semiconductor FET. Additional topics (if time permits): Integrated circuits, pnpn switching devices, and microwave devices. View detailed information in the Handbook
Low-carbon Grids: Operation & Economics · 12.5 pts	This subject introduces the student to foundational aspects of economic, secure and reliable operation of low-carbon power systems and electricity markets with large shares of variable and uncertain renewable energy sources. The underlying framework is the so-called “affordability-sustainability-security” energy trilemma, which seeks to strike a delicate balance among: the desire to operate power systems at low cost (“affordability”); the desire to meet specific environmental targets (“sustainability”); and the need to “keep the lights on” (“security”). In order for the energy trilemma to be analysed in the context of a competitive market environment, the subject will provide the student with fundamentals of economics, operation of electricity markets, optimal bidding strategies of different market stakeholders, economics of transmission and distribution networks, and role of new technologies and commercial entities such as storage and aggregators. Different aspects of power system security will be analysed, from system-level requirements and constraints to provision of security services from market stakeholders. Basic concepts of optimization, including linear, quadratic, and mixed integer linear programming, will also be taught to provide the student with the tools required to understand and model current and future power system and energy market operation. View detailed information in the Handbook
Microprocessor Design Clinic · 12.5 pts	Students in this subject will be introduced to computer architectures, microprocessors, microcontrollers, operating systems, compilers and software design. The proposed course will cover a broad range of topics necessary to make students knowledgeable in the art of microprocessor design including advanced concepts such as in line and out of order execution and execution unit resource optimisation. Students in this course will learn to design execution units, arithmetic logic units, memory hierarchies and learn strategies for cache sizing. As part of this, students will become proficient in microcode and instruction set design, multi-processor and multi core theory and design, including new design methodologies such as chiplet design. Upon completion, students will be familiar with the specification and synthesis of microprocessor systems using high level generator languages such as Chisel and Scala. The course will also introduce students to compiler and linker design, enhancements to instruction sets, c-language and the theory of operating systems. View detailed information in the Handbook
Large Data Methods & Applications · 12.5 pts	This course provides an introduction to an important contemporary statistical toolset for applications including data science, machine learning, signal processing, financial engineering, biomedical engineering, communication systems and other high-dimensional statistical applications. The course will cover topics including introduction to random matrix theory models in engineering; eigenvalue distributions; finite-dimensional and large-dimensional techniques, covariance estimation, principal component analysis and spectral clustering. These topics will be supplemented by applications across a range of traditional and emerging domains involving big data sets. View detailed information in the Handbook
Directed Studies · 12.5 pts	AIMS Directed studies provide the students with broader experience in addition to the regular class based learning. The directed studies can be conducted in the forms of: Industrial internship or research placements in the department’s research groups based on availability. This is only open to students who have completed a minimum of one semester of study and who have achieved an average of H2A or above in their prior subjects; Individually arranged supervised study of current research topics with staff members associated with the Department of Electrical and Electronic Engineering. INDICATIVE CONTENT The examples of the research topics are: Cloud Computing, Content Distribution and Information Logistics; Internet Services Energy Star Rating; Energy Efficiency of Future Modulation Formats; Low-Energy Fibre Access Networks; Video Coding for Energy Efficient Telecommunications; Fundamental Limits of Electronics and Photonics; Broadband fibre wireless networks and systems; Optimal design of few-mode fibres. View detailed information in the Handbook
AI for Robotics · 12.5 pts	AIMS: This subject focuses on the software and algorithms (i.e., artificial intelligence) that enable robotic systems to move autonomously through their environment and perform tasks. The key focus of this subject is the foundations of robotic systems that use software to move autonomously through their environment. This subject focus on the software & algorithms that enable the robot to perform tasks autonomously. Hence, this subject focused on artificial intelligence (AI) software & algorithms for robotics. The first main aim of the subject is to provide a foundation of the feedback loop that is core to all AI-enabled robots, namely: sensors measure the world around the robot; AI algorithms decide what action to take; the robot enacts that action by moving its joint or wheels; and the loop repeats endlessly. The second main aim of the subject is to provide implementation experience with cutting edge AI algorithm applicable to consumer and industrial robotics, where we consider both model-based method and reinforcement-learning methods. INDICATIVE CONTENT: Topics covered are at the intersection of automatic control and artificial intelligence, including: Cyber-physical feedback system formulation, such as: black-box and grey-box modelling, stability and robustness safety requirements, hierarchical and network control architectures. Safety and convergence guarantees for model-based methods, such as: learning models from data; adaptive control schemes; stability and robustness of PID and MPC control approaches. Connections between optimal control and reinforcement learning formulations for robotics. Reinforcement learning for robotics, such as: actor-critic methods, on-policy versus off-policy learning, sample efficiency, transferring simulation-based learning to real-world robots View detailed information in the Handbook
Hardware Accelerated Computing · 12.5 pts	Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include: Review of combinational and sequential digital logic FPGA architectures and fundamentals Hardware description languages (Verilog/VHDL) and hardware design flows High-level synthesis and OpenCL The use of parallelism, locality, and precision in hardware accelerators Host-accelerator interactions and hardware-software co-design Optimisation of hardware designs with respect to throughput, latency, energy, and area Accelerator design for selected applications such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application View detailed information in the Handbook
Electrical Engineering Research Project · 25 pts	This subject is for students to undertake a substantial individual research project on an approved topic over the semester, requiring independent investigation with a chosen supervisor either from a university (research institute) or from an industry partner. Note: the student is responsible for contacting the potential supervisor for the project. This subject can also be taken by Master of Electrical Engineering outgoing exchange students for research projects carried out in an overseas university. If the project is to be carried out within the EEE Department, the student is encouraged to take ELEN90011 (Directed Studies) if possible. The emphasis of the project can be associated with either A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, often as an adjunct to existing academic research initiatives. It is expected that the project will incorporate findings associated with both well-defined professional practice and research principles. View detailed information in the Handbook
Applied Deep Learning for Engineers · 12.5 pts	This subject covers a modern deep learning approach to engineering using a project-centric pedagogy. Building upon system optimisation and machine learning fundamentals presented in ELEN90088, the subject will present advanced deep learning architectures to address long-standing engineering challenges such as system complexity, curse of dimensionality, and modelling gap. Subject will specifically focus on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how advanced deep learning frameworks can be applied to modern engineering and cyber-physical systems. INDICATIVE CONTENT Topics covered may include: Latent spaces, auto encoder architectures. Advanced deep learning architectures, auto-differentiation, physics-inspired neural networks. Sequential data analysis and predictive models such as transformers. Generative models such as GANs and GPT variants. Other advanced topics such as meta parameter optimisation, Markov Chain Monte Carlo sampling. Distributed machine learning, federated learning, graph neural networks. Cyber-physical security of modern engineering systems, including data-based anomaly and threat detection and prediction. Subject projects will focus on engineering applications in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. View detailed information in the Handbook
Modelling and Analysis for AI · 12.5 pts	This subject builds up the fundamentals for modelling dynamical systems, with a key focus on the aspects and decisions of modelling that are relevant for the application of AI and data-intensive learning methods. The discussion and evaluation of modelling methods focuses on how model fidelity influences simulation-to-real transfer; how modelling and simulation decisions influence computation time required for training and validation; and how discrete-time models introduce complexity when representing continuous-time engineering systems. Subsequently, it introduces the basic principles and engineering applications of programming and data structures in a condensed form with a project-centric pedagogy. It covers the fundamentals of databases and data structures, basic algorithms, scientific programming, and classic AI problem solving. It will focus specifically on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how programming and databases play a role in modern engineering and cyber-physical systems. INDICATIVE CONTENT Topics covered may include: Models for engineering systems in multiple disciplines, including analysis of what makes the models amenable to AI and data-intensive learning methods. Principles for simulating dynamic systems that are most relevant for the use of AI methods and to address these principles with existing software tools. Scientific programming for modelling using Python programming language and libraries such as scipy and numpy. Engineering data structures and time series data and their storage in SQL and noSQL databases. Example engineering applications will be taught via projects in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. View detailed information in the Handbook
Reinforcement Learning for Engineering · 12.5 pts	The key focus of this subject is the design and implementation of decision-making policies for enabling a dynamical system to behave autonomously and achieve a desired objective. This subject covers both model-based and model-free learning methods, with a focus on evaluating, contrasting, and combining methods. The influence of noisy sensor data on performance, and the trade-offs between exploration and exploitation during a learning phase, will also be covered. The examples used in this subject range across existing and emerging decision-making methods, and their application to consumer and industrial engineering systems. INDICATIVE CONTENT Topics covered may include: Reinforcement learning fundamentals such as principle of optimality, Bellman equation, value and policy iteration. Temporal-difference learning, Q-learning, Deep Q-learning, Actor critic methods and hybrid approaches in engineering context. Model based vs model free approaches, multi-agent RL and their engineering applications. RL methods for Cyber-physical resilience and security such as fuzzing methods. View detailed information in the Handbook

Approved Electives (Group B)

Students must complete 25 points of Year 3 elective subjects.

Accordion
Optimisation for Industry · 12.5 pts	The use of mathematical optimisation is widespread in business, where it is a key analytical tool for managing and planning business operations. It is also required in many industrial processes and is useful to government and community organizations. This subject will expose students to operations research techniques as used in industry. A heavy emphasis will be placed on the modelling process that turns an industrial problem into a mathematical formulation. The focus will then be on how to solve the resulting mathematical problem with mixed-integer programming techniques. View detailed information in the Handbook
Distributed Systems · 12.5 pts	AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. View detailed information in the Handbook
Advanced Motion Control · 12.5 pts	AIMS This subject is intended to give students an overview of the present state-of-the-art in industrial motion control and the likely future trends in control design. Students will be exposed to and have practical experience in the design and implementation of advanced controllers for various motion control problems. Advanced modelling and control topics will include system identification, modelling and compensation of friction and other disturbances, industrial servo loops, model-based and model-free controller design, and adaptive control. Applications will be drawn from industrial, medical and transport automation (eg robots, machine tools, production machines, laboratory automation, automotive and aerospace by-wire systems). INDICATIVE CONTENT Advanced modelling and control topics will include system identification, modelling and compensation of friction and other disturbances, industrial servo loops, model-based and model-free controller design, and adaptive control. Applications will be drawn from industrial, medical and transport automation (eg robots, machine tools, production machines, laboratory automation, automotive and aerospace by-wire systems). View detailed information in the Handbook
Leadership for Innovation · 12.5 pts	This subject, which is offered to students who have completed ENGR90034 Creating Innovative Engineering (CIE), will give participants core leadership skills for managing professionals engaged in innovation and other ambiguous project-based work. The subject teaches leadership at three levels (12 hours each). The first level, taught intensively before the start of the semester, will enable you to learn basic management theory that allows you to bridge from the skills and theory taught in CIE to the level needed to start mentoring a team in CIE or another subject. The second level, taught as four three-hour workshops during the semester, will focus on key thematic issues in the leadership of innovative teams. The third level, taught in twelve one-hour sessions, will focus on specific leadership skills. These include facilitation, coaching, mentoring, conflict resolution, etc. Students will apply the theory and skills to the mentoring of a student project team in CIE or another subject within the University. You will apply what you are learning, and develop skills, by mentoring an industry-sponsored project within CIE or a project within another subject. CIE mentors will also need to manage their relationship with the external sponsor of the project. View detailed information in the Handbook
Global Business Practicum · 12.5 pts	This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. View detailed information in the Handbook
Engineering Entrepreneurship · 12.5 pts	AIMS This subject is available as an elective in many of the Faculty of Engineering and IT Masters programs. It is aimed both at students who have immediate entrepreneurial intentions and at students who may be considering starting their own business at some point in their careers. The subject is designed to introduce all participants to their potential as entrepreneurs. By developing their own enterprise proposal within small groups, students will learn and demonstrate various processes by which successful new ventures move from idea to launch. INDICATIVE CONTENT Business modelling, opportunity analysis, value creation, financial management, sources of finance, creativity, innovation, entrepreneurial behaviour, successful engineering entrepreneurs. TEACHING METHOD The teaching method is based around a structured process of mini-lectures, class exercises, and active hands-on learning by doing. Intensive field research and minimum viable product development are very important to the subject. Learning is further enhanced through meetings with the lecturer and review by peers. View detailed information in the Handbook
Internship · 25 pts	AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship View detailed information in the Handbook
Nuclear Engineering · 12.5 pts	This subject provides an introduction to nuclear science and engineering. It presents the properties of atomic nuclei, radioactivity, nuclear reactions, and selected topics in thermodynamics as required for the analysis of power systems based on nuclear fission. The working principles of nuclear reactors and nuclear power plants are discussed, focusing on pressurised-water reactor systems. Indicative content: Introduction to nuclear physics Thermodynamics of nuclear power plants Nuclear power generation View detailed information in the Handbook
Radiation Protection · 12.5 pts	Nuclear technology involves the risk of exposure to ionising radiation, with potentially harmful effects on human health. This subject equips students with the necessary knowledge and skills to understand this risk and to manage it by applying established methods of radiation protection. Indicative content: Effects of ionising radiation on human health Methods of radiation detection and measurement Principles and methods of radiation protection Radiation shielding View detailed information in the Handbook
Engineering of Nuclear Systems · 12.5 pts	This subject presents nuclear reactor theory and its applications to reactor operation. It examines reactor response to control actions, feedback effects, and the intermediate and long-term effects on reactivity due to fission product poisoning and fuel burnup. Furthermore, it covers the fundamentals of thermal and hydraulic analysis of pressurised-water reactors. Indicative content: Nuclear reactor theory and engineering Reactor dynamics and control Effects of fuel burnup and the long-term evolution of the reactor core properties Heat generation and heat transfer from fuel to coolant Thermal design of nuclear reactors View detailed information in the Handbook
Nuclear Safety, Security and Safeguards · 12.5 pts	Safety, security and safeguards are critical requirements in the operation of nuclear facilities. This subject presents the safety aspects and safety assessment methods of nuclear power plants. Nuclear security and safeguards are discussed in the context of the nuclear fuel cycle. Indicative content: Nuclear fuel cycle Fundamentals of nuclear safety Safety systems and safety features of nuclear reactors Probabilistic safety assessment Nuclear security and safeguards View detailed information in the Handbook
Design Innovation and Leadership · 12.5 pts	A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects.  In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge.  Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication.  In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change?  How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information.  The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. View detailed information in the Handbook

Intelligent Networks and Communications specialisation

Core

Year 1

Students must complete 100 points of Year 1 compulsory subjects.

Accordion
Intro. to Numerical Computation in C · 12.5 pts	AIMS Many engineering disciplines make use of numerical solutions to computational problems. In this subject students will be introduced to the key elements of programming in a high level language, and will then use that skill to explore methods for solving numerical problems in a range of discipline areas. INDICATIVE CONTENT Algorithmic problem solving Fundamental data types: numbers and characters Approximation and errors in numerical computation Fundamental program structures: sequencing, selection, repetition, functions Simple data storage structures, variables, arrays, and structures Roots of equations and of linear algebraic equations Curve fitting and splines Interpolation and extrapolation Numerical differentiation and integration View detailed information in the Handbook
Foundations of Electrical Networks · 12.5 pts	INDICATIVE CONTENT Foundations of Electrical Networks develops an understanding of fundamental modelling techniques for the analysis of systems that involve electrical phenomena. This includes networks models of “flow-drop” one-port elements in steady state (DC and AC), electrical power systems, simple RC and RL transient analysis, and networks involving ideal and non-ideal operational amplifiers. It forms the foundation of many engineering subjects exploring fundamental concepts in electrical and electronic engineering. The subject will cover key electrical engineering topics in the areas of: Electrical phenomena – charge, current, electrical potential, conservation of energy and charge, the generation, storage, transport and dissipation of electrical power. Network models – networks of “flow-drop” one-port elements, Kirchoff’s laws, standard current-voltage models for one-ports (independent sources, resistors, capacitors, inductors, transducers, diodes), analysis of static networks, properties of linear time-invariant (LTI) one-ports and impedance functions, diodes, transformers, steady-state (DC and AC) analysis of LTI networks via mesh and node techniques, equivalent circuits, and transient analysis of simple circuits; Electrical power systems – overview of power generation and transmission, analysis of single-phase and balanced three-phase AC power systems. Analysis and design of networks involving ideal and non-ideal operational amplifiers. This material will be complemented by exposure to software tools for the simulation of electrical and electronic systems and the opportunity to develop basic electrical engineering laboratory skills using a prototyping breadboard, digital multimeter, function generator, DC power supply, and oscilloscope. Please view this video for further information: Foundations of Electrical Networks View detailed information in the Handbook
Digital Systems · 12.5 pts	AIMS This subject develops a fundamental understanding of concepts used in the analysis, design and building of digital systems. Such systems form the information and communication technologies (ICT) that underpin modern society. This subject provides a foundation for subsequent subjects, including ELEN30013 Electronic System Implementation, ELEN90066 Embedded System Design and ELEN90061 Communication Networks. INDICATIVE CONTENT Topics include: Digital systems - quantifying and encoding information, digital data processing, design process abstractions; Combinational logic – timing contracts, acyclic networks, switching algebra, logic synthesis; Sequential logic – cyclic networks and finite-state machines, metastability, microcode; These topics will be complemented by exposure to the hardware description language such as Verilog and the use of engineering design automation tools and configurable logic devices (e.g. FPGAs) in the laboratory. Please view this video for further information: Digital Systems View detailed information in the Handbook
Electrical Network Analysis and Design · 12.5 pts	AIMS This subject develops a fundamental understanding of linear time-invariant network models for the analysis and design of electrical and electronic systems. Such models arise in the study of systems ranging from large-scale power grids to tiny radio frequency signal amplifiers. This subject is one of four subjects that define the Electrical Systems Major in the Bachelor of Science and it is a core requirement for the Master of Engineering (Electrical). It provides a foundation for various subsequent subjects, including ELEN30013 Electronic System Implementation, ELEN90066 Embedded System Design, and ELEN30012 Signal and Systems. INDICATIVE CONTENT Topics include: Transient and frequency domain analysis of linear time-invariant (LTI) models – linearity, time-invariance, impulse response and convolution, oscillations and damping, the Laplace transform and transfer functions, frequency response and bode plots, lumped versus distributed parameter transfer functions, poles, zeros, and resonance, stability of circuits, modelling and simulation with simulation tools; Electrical network models – one-port elements, impedance functions, two-port elements, dependent sources, matrix representations of two-ports, driving point impedances and network functions, ladder and lattice networks, passive versus active networks, multi-stage modelling and design, and multi-port generalisations; Analysis and design of networks involving ideal and non-ideal operational amplifiers with emphasis on the design of active filters and broadband circuits with specific frequency characteristics; Circuits and networks for managing voltage and power requirements for common electronic circuits. These topics will be complemented by tutorials and workshops designed to develop skills in design and modelling of electronic circuits through software tools and building, testing, and verification of electronic circuits. Please view this video for further information: Electrical Network Analysis and Design View detailed information in the Handbook
Electrical Device Modelling · 12.5 pts	AIM This subject develops the theoretical and practical tools required to understand, construct, validate and apply models of standard electrical and electronic devices. In particular, students will study the theoretical and practical development of models for devices such as resistors, capacitors, inductors, transformers, motors, batteries, diodes, transistors, and transmission lines. In doing so, students will gain exposure to a variety of fundamental fields in physics, including electromagnetism, semiconductor materials and quantum electronics. This material will be complemented by exposure to experiment design and measurement techniques in the laboratory, the application of models from device manufacturers, and the use of electronic circuit simulation software. INDICATIVE CONTENT Topics include: Vector calculus for device modelling, Maxwell’s equations, physics of conductors and insulators, passive device models (including for resistors, capacitors and inductors), lumped and distributed circuit models for wired interconnections (including treatment of signal integrity and termination strategies), semiconductors and quantum electronics, static and dynamic models for p-n junctions diodes and bipolar junction transistors. View detailed information in the Handbook
Signals and Systems · 12.5 pts	AIMS The aim of this subject is twofold: firstly, to develop an understanding of the fundamental tools and concepts used in the analysis of signals and the analysis and design of linear time-invariant systems path in continuous–time and discrete-time; secondly, to develop an understanding of their application in a broad range of areas, including electrical networks, telecommunications, signal-processing and automatic control. The subject formally introduces the fundamental mathematical techniques that underpin the analysis and design of electrical networks, telecommunication systems, signal-processing systems and automatic control systems. Such systems lie at the heart of the electrical engineering technologies that underpin modern society. This subject is one of four Level 3 subjects that define the Electrical Engineering Systems Major in the Bachelor of Science. . It provides the foundation for various subsequent subjects, including ELEN90057 Communication Systems, ELEN90058 Signal Processing and ELEN90055 Control Systems. INDICATIVE CONTENT Topics include: Signals – continuously and discretely indexed signals, important signal types, frequency-domain analysis (Fourier, Laplace and Z transforms), nonlinear transformations and harmonics, sampling; Systems – viewing differential / difference equations as systems that process signals, the notions of input, output and internal signals, block diagrams (series, parallel and feedback connections), properties of input-output models (causality, delay, stability, gain, shift-invariance, linearity), transient and steady state behaviour; Linear time-invariant systems – continuous and discrete impulse response; convolution operation, transfer functions and frequency response, time-domain interpretation of stable and unstable poles and zeros, state-space models (construction from high-order ODEs, canonical forms, state transformations and stability), and the discretisation of models for systems of continuously indexed signals. This material is complemented by exposure to the use of MATLAB for computation and simulation and examples from diverse areas including electrical engineering, biology, population dynamics and economics. View detailed information in the Handbook
Electronic System Implementation · 12.5 pts	AIMSThis subject provides students with hands-on electronic skills to gain basic competencies in design and implementation of simple circuits. Students will design with a range of standard electrical and electronic devices, basic circuit construction methods and electrical measurement techniques to test and verify the function of electronic systems. This subject is one of four subjects that define the Electrical Systems Major in the Bachelor of Science and it is a core requirement for the Master of Engineering (Electrical) and the Master of Engineering (Electrical with Business). This includes hands-on experience with: • Operation and selection of electrical and electronic devices used in various electronic circuits; • Common electronic circuit realisations to meet the most commonly required signal processing and conditioning applications; • Programmable digital circuits and microprocessor programming; • Circuit design and simulation tools; • Printed circuit board layout, circuit assembly, and soldering techniques; • Test and Measurement equipment and methods; • Managing design issues and requirements. Students will complete electronic circuit implementation projects in small groups and be required to prepare technical documentation and present project outcomes. INDICATIVE CONTENT • Devices such as resistors, capacitors, inductors, switches, transducers, motors, diodes, transistors, op-amps, voltage regulators, comparators, oscillators, timers, A/D and D/A converters, microprocessors and controllers; • Circuit functions and techniques such as buffering, referencing, signal conditioning, filtering, bridges, detection, waveform generation, and pulse-width modulation; • Microprocessor programming, the role of assembly and high-level languages, assemblers, compilers and debuggers; • PCB layout, circuit assembly, and soldering techniques; • Test and Measurement methods and working with common equipment such as multimeters and oscilloscopes. View detailed information in the Handbook
Engineering Mathematics · 12.5 pts	This subject introduces important mathematical methods required in engineering such as manipulating vector differential operators, computing multiple integrals and using integral theorems. A range of ordinary and partial differential equations are solved by a variety of methods and their solution behaviour is interpreted. The subject also introduces series including the concepts of convergence and divergence. Topics include: Vector calculus, including Gauss’ and Stokes’ Theorems; systems of homogeneous ordinary differential equations, including phase plane and linearisation for nonlinear systems; Laplace transforms; series, including Taylor series and power series; Fourier series and Fourier integrals; second order partial differential equations and separation of variables. View detailed information in the Handbook

Year 2

Students must complete 100 points of Year 2 compulsory subjects.

Accordion
Probability and Random Models · 12.5 pts	AIMS This subject provides an introduction to probability theory, random variables, random vectors, decision tests, and stochastic processes. Uncertainty is inevitable in real engineering systems, and the laws of probability offer a powerful way to evaluate uncertainty, to predict and to make decisions according to well-defined, quantitative principles. The material covered is important in fields such as communications, data networks, signal processing and electronics. This subject is a core requirement in the Master of Engineering (Electrical, Mechanical and Mechatronics). INDICATIVE CONTENT Topics include: Foundations – combinatorial analysis, axioms of probability, independence, conditional probability, Bayes’ rule; Random variables (rv’s)– definition; cumulative distribution, probability mass and probability density functions; expectation and variance; functions of an rv; important distributions and their properties and uses; Multiple random variables – joint cumulative distribution, probability mass and probability density functions; independent rv’s; correlation and covariance; conditional distributions and expectation; functions of several rv’s; jointly Gaussian rv’s; random vectors; Sums, inequalities and limit theorems – sums of rv’s, moment generating function; Markov and Chebychev inequalities; weak and strong laws of large numbers; the Central Limit Theorem; Decision testing - maximum likelihood, maximum a posterior, minimum cost and Neyman-Pearson rules; basic minimum mean-square error estimation; Stochastic processes – mean and autocorrelation functions, strict and wide-sense stationarity; ergodicity; important processes and their properties and uses; Introduction to Markov chains. This material is complemented by exposure to examples from electrical engineering and software tools (e.g. MATLAB) for computation and simulations. View detailed information in the Handbook
Control Systems · 12.5 pts	AIMS This subject provides an introduction to automatic control systems, with an emphasis on classical techniques for the analysis and design of feedback interconnections. The main challenge in automatic control is to achieve desired performance in the presence of uncertainty about the system dynamics and the operating environment. Feedback control is one way to deal with modelling uncertainty in the design of engineering systems. This subject is a core requirement in the Master of Engineering (Electrical, Electrical with Business, Mechanical, Mechanical with Business and Mechatronics). INDICATIVE CONTENT Topics include: * Modelling for control, linearization, relationships between time and frequency domain models of linear time-invariant dynamical systems, and the structure, stability, performance, and robustness of feedback interconnections; * Frequency-domain analysis and design, Nyquist and Bode plots, gain and phase margins, loop-shaping with proportional, integral, lead, and lag compensators, loop delays, and fundamental limitations in design; and * Actuator constraints and anti-windup compensation. This material is complemented by the use of software tools (e.g. MATLAB/Simulink) for computation and simulation, and exposure to control system hardware in the laboratory. View detailed information in the Handbook
Electronic Circuit Design · 12.5 pts	AIMS This subject provides an in-depth coverage of transistor (MOSFET and BJT) devices and their use in common circuits. In particular, students will study topics including: transistor operating modes and switching; principles of CMOS circuits; transistor biasing; current-source/emitter-amplifiers; low-frequency response; followers; class B amplifiers; current limiting; current sources and mirrors; differential pairs; feedback in amplifiers and stability; operational amplifiers; operational amplifier circuits; and voltage regulation. This material will be complemented by exposure to circuit simulation software tools and the opportunity to further develop circuit construction/test skills in the laboratory. INDICATIVE CONTENT Design-focused field-effect and bipolar elementary transistor models, and design of elementary amplifier stages and biasing circuits. Static and dynamic behaviour of amplifier circuits including frequency response, feedback and stability, slew-rate and clipping. Operational amplifiers and opamp based circuits; voltage regulators, references and voltage converters. Verification of electronic circuits using simulation and constructing them in the laboratory. Please view this video for further information: Electronic Circuit Design View detailed information in the Handbook
Communication Systems · 12.5 pts	AIMS This subject provides an introduction to the analysis and design of telecommunication signals and systems, in the presence of uncertainty. The emphasis is on understanding the basic concepts that underpin the physical layer of modern communication systems. INDICATIVE CONTENT Topics to be covered include: Introduction to communication systems including historical developments and comparisons between analogue and digital communications. Review of assumed knowledge from linear algebra, signals and systems and probability and random processes. The sampling theorem, analog-to-digital conversion, complex baseband representation of passband signals, filtering of random processes, power spectral density, bandwidth of random signals, additive white Gaussian noise (AWGN), signal-to-noise ratio. Communication over baseband AWGN channels including modulation techniques (pulse amplitude modulation, orthogonal modulation), signal space representation, optimal detectors, matched filters, error probability calculations and bandwidth / power trade-off. Communication over passband AWGN channels including modulation techniques (phase shift keying, quadrature amplitude modulation and frequency shift keying), optimal coherent detectors, noncoherent detectors and error probability calculations. Communication over linear time-invariant channels including concepts of distortion, inter-symbol interference, pulse shaping, Nyquist’s criterion, equalization, sequence detection and the Viterbi algorithm. Synchronization including carrier, symbol and frame synchronization. View detailed information in the Handbook
Signal Processing · 12.5 pts	AIMS This subject provides an introduction to the fundamental theory of time domain and frequency domain representation of discrete time signals and linear time invariant dynamical systems, and how this theory is used to analyse and design digital signal processing systems and algorithms. Topics include: Applications of signal processing techniques; Sampling of analog signals, anti-aliasing filters; Frequency-domain analysis of signals and systems, Discrete Time Fourier Transform, Discrete Fourier Transform, Fast Fourier Transform; Digital filters, low-pass, high-pass, band-pass, stop-band and all pass filters. Phase and group delay, FIR and IIR filters; Design of digital FIR and IIR filters; Multi-rate signal processing, with a focus on up-sampling, down-sampling, and sampling rate conversion; Simple non-parametric methods for spectral estimation. This fundamental material will be complemented by exposure to MATLAB tools for signal analysis and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory. INDICATIVE CONTENT Sampling of continuous time signals, Design of anti-aliasing filters, Time and frequency representation of discrete time signals and discrete time linear time invariant systems, Discrete Time Fourier Transform and z-transform and their properties, Low order lowpass, highpass, bandpass, bandstop filters, All-pass filter, Design of IIR filters using the bilinear transformation, Design of FIR filters with linear phase using windowing techniques and the Parks McClelland method, Discrete Time Fourier transform and its properties, Fast Fourier Transform, The use of the DFT in implementation of linear filtering algorithms, Up-sampling and down-sampling, multistage and computationally efficient implementations of up-samplers and down-samplers, Energy and power spectra for deterministic signals. View detailed information in the Handbook
Embedded System Design · 12.5 pts	AIMS This subject provides a practical introduction to the basics of modelling, analysis, and design of microprocessor-based embedded systems. Students will learn how to integrate computation with physical processes to meet a desired specification within the context of a design project. The project work will expose students to the various stages in an engineering project (design, implementation, testing and documentation) and a range of embedded system concepts. INDICATIVE CONTENT Topics covered may include: digital computer and microprocessor architectures, modelling of dynamic behaviours, control, models of computation, operating systems concepts, multi-tasking, resource management and real-time behaviours, interfacing with the physical world, analysis and verification, safety, reliability, and security and privacy. This material will be complemented by exposure to standard software tools including compilers and debuggers, finite state machine design and analysis software, and simulation tools. The subject will include a level of industry engagement, to provide broader examples of engineering projects, through guest lectures. View detailed information in the Handbook
Introduction to Power Engineering · 12.5 pts	AIMS To develop a solid foundation for the study of systems that involve the generation, transport, and conversion of electric power. INDICATIVE CONTENT Physical principles of electromagnetism, magnetic circuits, energy storage, loss mechanisms, electromechanical energy conversion. Modelling of transmission lines, transformers, motors and generators (synchronous and asynchronous), and other loads. Circuit theory for power system analysis, three phase-phase circuits, power flow and maximum power transfer, per-unit system. Please view this video for further information: Introduction to Power Engineering View detailed information in the Handbook
Interdisciplinary Design for Engineers · 12.5 pts	In this subject, students will actively engage in an interdisciplinary, collaborative and project-based learning environment, offering insights into the professional nature of engineering work. Through a real-world project, students will gain hands-on design experience addressing a complex challenge. The project will require students to integrate discipline knowledge and apply professional skills like teamwork and communication. Students will experience the entire engineering design process, covering problem definition, ideation, concept development, analysis, prototyping, testing and iteration. The project provides practical experience, equipping students with tools and methods to address complex challenges. Students are expected to integrate diverse perspectives, considering factors like stakeholders, sustainability (including environmental and social issues), safety, feasibility, and technical and ethical considerations. View detailed information in the Handbook

Capstone

Year 3

Students must complete 25 points of Year 3 compulsory capstone project subjects.

Accordion
Engineering Capstone Project Part 1 · 12.5 pts	The subject involves undertaking a substantial group project (typically in groups of three students) requiring an independent investigation on an approved topic in advanced engineering design and / or research. Each project is carried out under the supervision of a member of academic staff and where appropriate an industry partner. The emphasis of the project can be associated with either: A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the engineering science disciplines, often as an adjunct to existing academic research initiatives. It is expected that the Capstone Project will incorporate findings associated with both well-defined professional practice and research principles and will provide students with the opportunity to integrate technical knowledge and generic skills gained in earlier years. The project component of this subject is supplemented by a lecture course dealing with project management tools and practices. Please note: Students enrolled in the suite of Master of Engineering programs must be within the final 112.5 points of their degree to enrol. Students enrolled in the Master of Industrial Engineering must be within the final 100 points of their degree to enrol. Students are to take Engineering Capstone Project Part 1 and then subsequently continue with Engineering Capstone Project Part 2 in the following semester. Upon successful completion of this project, students will receive 25 points credit. View detailed information in the Handbook
Engineering Capstone Project Part 2 · 12.5 pts	Please refer to ENGR90037 Engineering Capstone Project Part 1 for this information. View detailed information in the Handbook

Accordion

Engineering Capstone Project Part 1 · 12.5 pts

The emphasis of the project can be associated with either:

A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or
A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the engineering science disciplines, often as an adjunct to existing academic research initiatives.

The project component of this subject is supplemented by a lecture course dealing with project management tools and practices.

Please note:

Students enrolled in the suite of Master of Engineering programs must be within the final 112.5 points of their degree to enrol.

Students enrolled in the Master of Industrial Engineering must be within the final 100 points of their degree to enrol.

View detailed information in the Handbook

Engineering Capstone Project Part 2 · 12.5 pts

Please refer to ENGR90037 Engineering Capstone Project Part 1 for this information.

View detailed information in the Handbook

Specialisation

Year 3

Students must complete 50 credit points of Year 3 core specialisation subjects.

Accordion
Advanced Communication Systems · 12.5 pts	AIMS The aim of this subject is to develop a thorough understanding of the main concepts, techniques and performance criteria used in the analysis and design of digital communication systems and wireless networks. Such systems and networks lie at the heart of the information and communication technologies (ICT) that underpin modern society and are very much part of the Internet of Things which involves machine to machine communication. INDICATIVE CONTENT This subject provides an in-depth treatment of the main concepts and techniques used in the analysis and design of digital communication systems and wireless networks. Topics include: Source coding; entropy, Shannon source coding bound, data compression techniques; Channel modelling, modulation over time-varying fading channels, time and frequency diversity, energy and spectral efficiency, multiple carrier modulation including orthogonal frequency division multiplexing (OFDM) modulation, phase noise characteristics and its impact on single-carrier and multicarrier systems, spatial multiplexing for multiple access protocols, multiple antenna technologies (MIMO systems), cellular networks; Channel coding for error control: mutual information, channel capacity, Shannon channel coding bound, channel coding concepts, block codes; convolutional / trellis codes; introduction to LDPC codes, turbo codes, polar codes. Examples include short, medium and long range communication systems such as bluetooth, cellular and satellite communication systems. View detailed information in the Handbook
Communication Networks · 12.5 pts	AIMS This subject introduces the basic principles, analysis, and design of communication networks. It covers relevant analytical methods, the layered network architecture of the Internet, and a multitude of network protocols. Analytical tools from queueing, optimisation, and graph theories are used to develop an in-depth understanding of basic principles and the role they play in network design. Specifically, queueing and graph theories are emphasised as methodological frameworks for communication network delay and structure analysis. The concepts taught in this subject lead to a better understanding of the Internet as well as modern communication paradigms such as Software-Defined Networks, Machine-to-Machine communication, Internet of Things, and social networks. INDICATIVE CONTENT Topics covered may include: The layered network architecture with a focus on physical-layer multiple access (TDM, FDM, WDM), link layer protocols and medium access control (MAC), network layer topologies, least-cost routing algorithms and protocols, transport layer protocols and the principles and techniques of practical reliable transport; LAN protocols, Ethernet, Wi-Fi, and serial communications; The Internet's network layer including the Internet Protocol (IP) and routing protocols including an introduction to BGP and the operation of forwarding tables in routers and shortest prefix routing; The Internet's transport layer protocols UDP and TCP, including the flow and congestion control algorithms; Network security, application layer, cloud and fog computing, Machine-to-Machine communication, and Internet of Things; Queuing theory: basics, birth-death processes, M/M/x and Markovian queues, networks of queues; Basics of graph theory and social network analysis relevant to communication networks. View detailed information in the Handbook
System Optimisation & Machine Learning · 12.5 pts	This subject introduces the basic principles, analysis methods, and applications of optimisation and machine learning to engineering systems; encompassing fundamental concepts and practical algorithms. It covers the fundamentals of continuous optimisation followed by machine learning basics for engineering applications. The concepts and methods discussed are illustrated in multiple application areas including Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks.The concepts taught in this subject will allow a better understanding of continuous optimisation and machine learning for systems engineering. INDICATIVE CONTENT Topics covered may include: Fundamentals of continuous optimisation: convex sets and functions; local vs global solutions, constrained optimisation and Lagrange multipliers; linear, quadratic, and nonlinear programming Basics of machine learning encompassing supervised and unsupervised learning: binary classification, linear and nonlinear regression, kernel methods, and clustering. Specific machine learning methods such as Support Vector Machines (SVMs), Neural Networks (NNs), k-means clustering, and reinforcement learning. Applications to Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks. View detailed information in the Handbook
Communication Design Clinic · 12.5 pts	Students work collaboratively in small groups to implement and optimize components in a modern communication system or network with the goal of supporting a targeted application. To meet this goal students will need to: determine system requirements based on the target application and additional constraints; propose and evaluate multiple solutions through theoretical analysis and detailed simulations; implement, integrate, verify, and iterate on their selected solutions. Lectures will cast content from prerequisite subjects into the context at hand and cover additional topics relevant to the task. Each student group is expected to demonstrate initiative and independence while pursuing the goal of designing and optimizing their communication system or network, with a key focus being that students learn through hands-on experience. Students will receive early exposure to advanced topics critical to modern communication systems, such as: source and channel coding, multicarrier modulation, multiantenna transmission, and network architectures and protocols. Successful completion of the project will require the student to draw upon knowledge, understanding, and skills learned in prerequisite subjects, which may include: Communication Systems – analog-to-digital conversion, signal-to-noise ratio, modulation and demodulation, bandwidth/power trade-off, error probability calculations, distortion, inter-symbol interference, pulse shaping, equalization, sequence detection, and synchronization. Signal Processing - design and implementation of digital filters (low-, high-, band-, all- pass filters); ARMA systems; up-sampling and down-sampling. Embedded System Design – system-level programming, operating systems concepts, real-time issues, and standard software tools. Additional topics required for the assigned project may also be covered, such as: ideation, prototyping, and design practices; analog RF components; software packages for modelling and implementation; and the use of test & measurement equipment. View detailed information in the Handbook

Electives

Electrical Engineering Electives (Group A)

Students must complete 25 points of Year 3 elective subjects.

Accordion
Introduction to Optimisation · 12.5 pts	AIMS This subject provides a rigorous introduction to numerical nonlinear optimization, as used across all of science and particularly in engineering design. There is an emphasis on both the theory and application of optimization techniques, with a focus on solving unconstrained and constrained nonlinear programmes. This subject is intended for graduate and research higher-degree students in engineering. INDICATIVE CONTENT Topics include: Algorithms for unconstrained optimization Algorithms for constrained optimization Convex sets and functions Convex optimization problems Duality theory Computational complexity Approximation algorithms and penalty methods. View detailed information in the Handbook
Advanced Communication Systems · 12.5 pts	AIMS The aim of this subject is to develop a thorough understanding of the main concepts, techniques and performance criteria used in the analysis and design of digital communication systems and wireless networks. Such systems and networks lie at the heart of the information and communication technologies (ICT) that underpin modern society and are very much part of the Internet of Things which involves machine to machine communication. INDICATIVE CONTENT This subject provides an in-depth treatment of the main concepts and techniques used in the analysis and design of digital communication systems and wireless networks. Topics include: Source coding; entropy, Shannon source coding bound, data compression techniques; Channel modelling, modulation over time-varying fading channels, time and frequency diversity, energy and spectral efficiency, multiple carrier modulation including orthogonal frequency division multiplexing (OFDM) modulation, phase noise characteristics and its impact on single-carrier and multicarrier systems, spatial multiplexing for multiple access protocols, multiple antenna technologies (MIMO systems), cellular networks; Channel coding for error control: mutual information, channel capacity, Shannon channel coding bound, channel coding concepts, block codes; convolutional / trellis codes; introduction to LDPC codes, turbo codes, polar codes. Examples include short, medium and long range communication systems such as bluetooth, cellular and satellite communication systems. View detailed information in the Handbook
Advanced Signal Processing · 12.5 pts	AIMS This subject provides an in-depth introduction to statistical signal processing. INDICATIVE CONTENT Students will study a selection of the following topics: Applications of statistical signal processing; A review of stochastic signals and systems fundamentals – random processes, white noise, stationarity, auto- and cross-correlation functions, spectral- and cross-spectral densities, properties of linear time-invariant systems excited by white noise; Parameter estimation - least squares and its properties, recursive least squares and least mean squares, optimisation-based methods, maximum likelihood methods; Kalman, Wiener and Markov filtering; Power spectrum estimation. This material will be complemented with the use of software tools (e.g. MATLAB) for computation and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory. View detailed information in the Handbook
Electronic System Design · 12.5 pts	AIMS This subject will explore the design of various electrical and electronic systems and provide students with a range of common and practical design techniques and circuits in the context of a guided laboratory based project. INDICATIVE CONTENT Subject may cover specific concepts surrounding the design and implementation of: Design process; Design for manufacture and assembly; Advanced PCB design; Oscillators; Phase-locked loops and frequency synthesis; Base-band signalling schemes and clock recovery; Mixers and logarithmic amplification; Automatic gain control; Filters; Synchronous detection; High-speed analog-digital conversion; High-frequency amplification; Low noise amplifiers; Power supply design; Batteries, battery charging systems, and management; Test and measurement; Sensors. View detailed information in the Handbook
Lightwave Systems · 12.5 pts	AIMS Lightwave systems are fundamentally changing the way we communicate through broadband communications, helping clinicians to perform a range of medical procedures and diagnosis supported by advanced biomedical instrumentation, and even in the way we live in our homes through sophisticated interactive televisions and security systems. This subject will explore the physical principles and issues that arise in the design of lightwave systems often found in those key industry sectors. Students will study topics from: transmission of light over wave guides; production of light by lasers; light modulation; conversion of light signals to electrical signals; optical multiplexing and demultiplexing; light amplification; dispersion and dispersion compensation; optical nonlinearities; modulation and advanced detection schemes. This material will be complemented by exposure to lightwave systems and measurement techniques in the laboratory. INDICATIVE CONTENT This subject will explore the physical principles governing the generation, modulation, amplification, guiding, transmission, multiplexing, demultiplexing and detection of light and issues that arise in the design of lightwave systems such as transmission impairments, noise. Students learn selected examples of lightwave systems and methods for design, modelling and testing of simple lightwave systems. View detailed information in the Handbook
Power System Analysis · 12.5 pts	AIMS This subject provides an insight into the fundamental elements to analyse electrical power transmission and distribution systems, with both analytical and simulation tools for analysis of operations of these systems. Problems related to power flow and use of Newton-Raphson and other algorithms such as backward-forward sweep will be discussed. Fault calculation and analysis, symmetrical components, and analytical methods for solving symmetrical (balanced) faults will be covered. Principles, concepts and problems related to power system dynamics and control, particularly for frequency and voltage regulation, will be discussed and analysed in detail. Finally, small-signal, transient, voltage and frequency stability will be introduced and exemplified. Focus will be put on real-world examples, particularly to prepare the student for the ongoing transition towards a low-carbon grid dominated by renewables and distributed energy resources. INDICATIVE CONTENT Power flow calculations, Newton-Raphson, Gauss-Seidel and backward-forward sweep methods; Fault calculations, balanced and unbalanced, symmetrical components, fundamentals of protection; Frequency regulation and frequency stability, Voltage regulation in transmission and distribution networks, including use of flexible AC transmission systems (FACTS); Voltage stability, small-signal stability, transient stability; Computer simulations. View detailed information in the Handbook
Communication Networks · 12.5 pts	AIMS This subject introduces the basic principles, analysis, and design of communication networks. It covers relevant analytical methods, the layered network architecture of the Internet, and a multitude of network protocols. Analytical tools from queueing, optimisation, and graph theories are used to develop an in-depth understanding of basic principles and the role they play in network design. Specifically, queueing and graph theories are emphasised as methodological frameworks for communication network delay and structure analysis. The concepts taught in this subject lead to a better understanding of the Internet as well as modern communication paradigms such as Software-Defined Networks, Machine-to-Machine communication, Internet of Things, and social networks. INDICATIVE CONTENT Topics covered may include: The layered network architecture with a focus on physical-layer multiple access (TDM, FDM, WDM), link layer protocols and medium access control (MAC), network layer topologies, least-cost routing algorithms and protocols, transport layer protocols and the principles and techniques of practical reliable transport; LAN protocols, Ethernet, Wi-Fi, and serial communications; The Internet's network layer including the Internet Protocol (IP) and routing protocols including an introduction to BGP and the operation of forwarding tables in routers and shortest prefix routing; The Internet's transport layer protocols UDP and TCP, including the flow and congestion control algorithms; Network security, application layer, cloud and fog computing, Machine-to-Machine communication, and Internet of Things; Queuing theory: basics, birth-death processes, M/M/x and Markovian queues, networks of queues; Basics of graph theory and social network analysis relevant to communication networks. View detailed information in the Handbook
High Speed Electronics · 12.5 pts	AIMS The aim of the subject is to provide theoretical and practical treatment of high-speed electronics. Through the subject, students will grasp the fundamental properties and models of high-speed signals and interconnects, acquire high-speed digital design skills with a focus on the modelling, analysis, design and application of high speed transistors, logic gates and modern logic families, and master the high-speed analogue design capability including the design of oscillators and filters for RF applications. The students will be exposed to the state-of-the-art technologies that are shaping the fast evolving semiconductor industry. INDICATIVE CONTENT The topics include: Fundamental properties of analogue systems; Smith charts: principles and applications; High-speed analogue circuits: voltage control oscillators, matching networks, and low noise amplifiers; Bipolar junction transistors: device, switching, and logic; CMOS: device, switching and logic; High-speed signalling consideration: power dissipation, heat, signal propagation, and termination. View detailed information in the Handbook
Advanced Control Systems · 12.5 pts	AIMS This subject provides an introduction to modern control theory with a particular focus on design of advanced control laws via state-space methods and optimal control. The role of feedback in control design will be reinforced within this context, alongside the role of optimisation techniques in control system synthesis. INDICATIVE CONTENT Topics include: State-space models - first-order vector differential/difference equations; Lyapunov stability; linearisation; discretisation; Kalman decomposition (observable, detectable, reachable and stabilisable subspaces); state-feedback and pole placement; output-feedback and observer design in both continuous-time and discrete-time. Optimal control - dynamic programming; linear quadratic regulation in both continuous-time and discrete-time. Model predictive control in discrete-time; moving-horizon with constraints. View detailed information in the Handbook
Power Electronics · 12.5 pts	AIMS The aim of this subject is to understand the fundamental concepts and basic theory involved in modelling and analysis of the power electronic components that comprise power electronic devices such as power supplies, inverters, converters and their control systems. It is expected that at the end of this subject the student has a sound understanding of the physical concepts and mathematical models behind each of the basic components and of their functionality within a system, such as a high voltage DC transmission system. Furthermore this subject seeks to combine the fields of electronics, semiconductor devices, power system operation, power system measurement and control. It is expected that through this subject the students are exposed to examples of real electrical engineering systems where the three disciplines of electronics, power systems and control come together. INDICATIVE CONTENT Topics covered in this subject include: introduction to power semiconductor switches; discussion on the role of power electronics in the operation of electric power systems; models of power semiconductor devices and circuit components, including diodes, Thyristors, IGBT, Snubber circuits. Also basic concepts of single- and three-phase diode bridge rectifiers; single- and three-phase converters and inverters; operation and design of DC-AC inverters with emphases on switch-mode inverters, i.e. single- and three-phase inverters. Finally, the acquired knowledge of power electronic devices is applied to wind and PV solar systems where the design of voltage source converters and associated control loops are used to interface the wind/solar system with the power grid. View detailed information in the Handbook
Grid Integration of Renewables · 12.5 pts	AIMS This subject develops a foundation for pursuing electrical engineering oriented research in the area of sustainable energy systems. This subject aims to introduce the concepts behind smart grids, future low-carbon energy networks, sustainable electricity systems as well as the main renewable and low-carbon generation technologies. The subject will introduce students to tools and techniques so that distributed energy resources (e.g. distributed renewable generation, storage, electric vehicles, demand response, etc.) may be integrated effectively into the power system in the context of both traditional grids and future smart grids. INDICATIVE CONTENT This subject will cover the following topics: Distributed low-carbon technologies Introduction to distribution networks Introduction to distributed low-carbon technologies (wind energy, photovoltaic systems, electric vehicles, electric heating, storage) Wind Energy: impacts and challenges Photovoltaic systems: impacts and challenges Electric vehicles: impacts and challenges Electric heat pumps and electric heating: impacts and challenges Storage: impacts and challenges Smart Distribution and Smart Transmission Networks Distributed low-carbon technologies and active network management Towards Smart Grids Smart grids - Transmission and Distribution perspectives Smart Transmission: HVDC and FACTS, dynamic line rating, post-contingency security, special protection schemes The role of future Distribution System Operators Low-carbon Electricity System Towards low-carbon networks: relationship between sustainability and smart grids Introduction to low-carbon thermal generation (nuclear, Carbon Capture and Storage, Concentrated Solar Power, biomass, etc.) Utility-scale renewable technologies: wind farms; solar farms; other large-scale renewables; utility-scale batteries System-level operational challenges and solutions for renewables integration: variability and uncertainty; low-inertia operation; low system-strength operation; minimum load issues; DER visibility; indistinct events; general stability issues; flexibility System-level planning challenges and solutions for renewables integration: system adequacy and reliability; capacity credit of renewables and storage; extreme weather events and resilience; role of transmission Sector coupling and multi-energy systems: decarbonisation of gas, heating and transport; role of hydrogen Distributed energy systems: new technical and commercial architectures for two-sided systems and markets; demand response; aggregators and virtual power plants; distributed energy markets; peer-to-peer trading; local energy communities; microgrids View detailed information in the Handbook
System Optimisation & Machine Learning · 12.5 pts	This subject introduces the basic principles, analysis methods, and applications of optimisation and machine learning to engineering systems; encompassing fundamental concepts and practical algorithms. It covers the fundamentals of continuous optimisation followed by machine learning basics for engineering applications. The concepts and methods discussed are illustrated in multiple application areas including Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks.The concepts taught in this subject will allow a better understanding of continuous optimisation and machine learning for systems engineering. INDICATIVE CONTENT Topics covered may include: Fundamentals of continuous optimisation: convex sets and functions; local vs global solutions, constrained optimisation and Lagrange multipliers; linear, quadratic, and nonlinear programming Basics of machine learning encompassing supervised and unsupervised learning: binary classification, linear and nonlinear regression, kernel methods, and clustering. Specific machine learning methods such as Support Vector Machines (SVMs), Neural Networks (NNs), k-means clustering, and reinforcement learning. Applications to Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks. View detailed information in the Handbook
Communication Design Clinic · 12.5 pts	Students work collaboratively in small groups to implement and optimize components in a modern communication system or network with the goal of supporting a targeted application. To meet this goal students will need to: determine system requirements based on the target application and additional constraints; propose and evaluate multiple solutions through theoretical analysis and detailed simulations; implement, integrate, verify, and iterate on their selected solutions. Lectures will cast content from prerequisite subjects into the context at hand and cover additional topics relevant to the task. Each student group is expected to demonstrate initiative and independence while pursuing the goal of designing and optimizing their communication system or network, with a key focus being that students learn through hands-on experience. Students will receive early exposure to advanced topics critical to modern communication systems, such as: source and channel coding, multicarrier modulation, multiantenna transmission, and network architectures and protocols. Successful completion of the project will require the student to draw upon knowledge, understanding, and skills learned in prerequisite subjects, which may include: Communication Systems – analog-to-digital conversion, signal-to-noise ratio, modulation and demodulation, bandwidth/power trade-off, error probability calculations, distortion, inter-symbol interference, pulse shaping, equalization, sequence detection, and synchronization. Signal Processing - design and implementation of digital filters (low-, high-, band-, all- pass filters); ARMA systems; up-sampling and down-sampling. Embedded System Design – system-level programming, operating systems concepts, real-time issues, and standard software tools. Additional topics required for the assigned project may also be covered, such as: ideation, prototyping, and design practices; analog RF components; software packages for modelling and implementation; and the use of test & measurement equipment. View detailed information in the Handbook
Autonomous Systems Clinic · 12.5 pts	AIMS: Students work collaboratively in small groups to engineer an autonomous system that performs a specified task. This includes carrying out steps such as: task analysis; proposing multiple solutions; feasibility analysis through prototyping and computer-aided design; detailed design, construction, and testing of the chosen solution; and demonstrating the solution in a proving ground. The lectures will cast content from the pre-requisite subjects into the context of the task at hand, as well as covering additional topics relevant to the task. Each student group is expected to demonstrate initiative and independence while pursuing the goal of designing and building their autonomous system, with a focus of the subject being that students learn through hands-on experience, implementation, and verification. INDICATIVE CONTENT: Successful completion of the project requires the student to draw upon knowledge, understanding, and skills learned in the prerequisite subjects, namely: • Embedded System Design - including topics such as: finite, extended, and hierarchical state machines; modelling cyber-physical systems; scheduling, multi-tasking, and real-time issues; interfacing to the analogue world. • Control Systems - including topics such as: modelling; linearisation; feedback interconnections; proportional, integral, derivative (PID) control; actuator constraint considerations. • Signal Processing - including topics such as: design and implementation of digital filters (low-, high-, band-, all- pass filters); ARMA systems; up-sampling and down-sampling. Additional topics, specific to the task as hand, will be covered, such as: ideation, prototyping, and design practices; image processing and computer vision tools; software introductions; safety and failure analysis. A range of materials, components, and fabrication facilities are provided, from which the students are expected to utilise a subset for designing and building their autonomous system, such as: electric motors, range sensors, camera, voltage converters, compute power, sheet wood, soldering stations, laser wood cutting, 3D printing. The task to be performed is motivated by a real-world application of autonomous systems, such as: operating in hazardous environments or performing repetitive tasks. Please view this video for further information: Autonomous Systems Clinic View detailed information in the Handbook
Semiconductor Devices · 12.5 pts	This subject serves as an introduction to semiconductor devices. It describes the fundamentals, theory, material and physical properties of semiconductor devices. The following topics will be covered. Fundamentals: Crystal properties and of the growth of bulk crystals and of epitaxial layers. Physical concepts related to atoms and electrons. These concepts may include the photoelectric effect, the Bohr model, quantum mechanics, and the periodic table. Energy bands and charge carriers in semiconductors: Bonding forces and energy bands in solids, charge carriers in semiconductors, carrier concentrations, the drift of carriers in electric and magnetic fields, and the Fermi level. Excess carriers in semiconductors: Optical absorption, luminescence, carrier lifetime and photoconductivity, and the diffusion of carriers. Junctions: Fabrication of pn junctions, equilibrium conditions, forward and reverse biased junctions in steady state, reverse bias breakdown, transient and AC conditions, metal-semiconductor junctions and heterojunctions. In the next part of the subject PN junction diodes: Junction diodes, tunnel diodes, photodiodes, and light-emitting diodes and lasers. Bipolar junction transistors (BJTs): Amplification and switching, fundamentals of BJT operation, BJT fabrication, minority carrier distributions and terminal currents, generalised biasing, switching, the frequency limitations of transistors, and heterojunction bipolar transistors. Field effect transistors (FETs): Topics may include junction FETs, the metal semiconductor FET and the metal-insulator-semiconductor FET. Additional topics (if time permits): Integrated circuits, pnpn switching devices, and microwave devices. View detailed information in the Handbook
Low-carbon Grids: Operation & Economics · 12.5 pts	This subject introduces the student to foundational aspects of economic, secure and reliable operation of low-carbon power systems and electricity markets with large shares of variable and uncertain renewable energy sources. The underlying framework is the so-called “affordability-sustainability-security” energy trilemma, which seeks to strike a delicate balance among: the desire to operate power systems at low cost (“affordability”); the desire to meet specific environmental targets (“sustainability”); and the need to “keep the lights on” (“security”). In order for the energy trilemma to be analysed in the context of a competitive market environment, the subject will provide the student with fundamentals of economics, operation of electricity markets, optimal bidding strategies of different market stakeholders, economics of transmission and distribution networks, and role of new technologies and commercial entities such as storage and aggregators. Different aspects of power system security will be analysed, from system-level requirements and constraints to provision of security services from market stakeholders. Basic concepts of optimization, including linear, quadratic, and mixed integer linear programming, will also be taught to provide the student with the tools required to understand and model current and future power system and energy market operation. View detailed information in the Handbook
Microprocessor Design Clinic · 12.5 pts	Students in this subject will be introduced to computer architectures, microprocessors, microcontrollers, operating systems, compilers and software design. The proposed course will cover a broad range of topics necessary to make students knowledgeable in the art of microprocessor design including advanced concepts such as in line and out of order execution and execution unit resource optimisation. Students in this course will learn to design execution units, arithmetic logic units, memory hierarchies and learn strategies for cache sizing. As part of this, students will become proficient in microcode and instruction set design, multi-processor and multi core theory and design, including new design methodologies such as chiplet design. Upon completion, students will be familiar with the specification and synthesis of microprocessor systems using high level generator languages such as Chisel and Scala. The course will also introduce students to compiler and linker design, enhancements to instruction sets, c-language and the theory of operating systems. View detailed information in the Handbook
Large Data Methods & Applications · 12.5 pts	This course provides an introduction to an important contemporary statistical toolset for applications including data science, machine learning, signal processing, financial engineering, biomedical engineering, communication systems and other high-dimensional statistical applications. The course will cover topics including introduction to random matrix theory models in engineering; eigenvalue distributions; finite-dimensional and large-dimensional techniques, covariance estimation, principal component analysis and spectral clustering. These topics will be supplemented by applications across a range of traditional and emerging domains involving big data sets. View detailed information in the Handbook
Directed Studies · 12.5 pts	AIMS Directed studies provide the students with broader experience in addition to the regular class based learning. The directed studies can be conducted in the forms of: Industrial internship or research placements in the department’s research groups based on availability. This is only open to students who have completed a minimum of one semester of study and who have achieved an average of H2A or above in their prior subjects; Individually arranged supervised study of current research topics with staff members associated with the Department of Electrical and Electronic Engineering. INDICATIVE CONTENT The examples of the research topics are: Cloud Computing, Content Distribution and Information Logistics; Internet Services Energy Star Rating; Energy Efficiency of Future Modulation Formats; Low-Energy Fibre Access Networks; Video Coding for Energy Efficient Telecommunications; Fundamental Limits of Electronics and Photonics; Broadband fibre wireless networks and systems; Optimal design of few-mode fibres. View detailed information in the Handbook
AI for Robotics · 12.5 pts	AIMS: This subject focuses on the software and algorithms (i.e., artificial intelligence) that enable robotic systems to move autonomously through their environment and perform tasks. The key focus of this subject is the foundations of robotic systems that use software to move autonomously through their environment. This subject focus on the software & algorithms that enable the robot to perform tasks autonomously. Hence, this subject focused on artificial intelligence (AI) software & algorithms for robotics. The first main aim of the subject is to provide a foundation of the feedback loop that is core to all AI-enabled robots, namely: sensors measure the world around the robot; AI algorithms decide what action to take; the robot enacts that action by moving its joint or wheels; and the loop repeats endlessly. The second main aim of the subject is to provide implementation experience with cutting edge AI algorithm applicable to consumer and industrial robotics, where we consider both model-based method and reinforcement-learning methods. INDICATIVE CONTENT: Topics covered are at the intersection of automatic control and artificial intelligence, including: Cyber-physical feedback system formulation, such as: black-box and grey-box modelling, stability and robustness safety requirements, hierarchical and network control architectures. Safety and convergence guarantees for model-based methods, such as: learning models from data; adaptive control schemes; stability and robustness of PID and MPC control approaches. Connections between optimal control and reinforcement learning formulations for robotics. Reinforcement learning for robotics, such as: actor-critic methods, on-policy versus off-policy learning, sample efficiency, transferring simulation-based learning to real-world robots View detailed information in the Handbook
Hardware Accelerated Computing · 12.5 pts	Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include: Review of combinational and sequential digital logic FPGA architectures and fundamentals Hardware description languages (Verilog/VHDL) and hardware design flows High-level synthesis and OpenCL The use of parallelism, locality, and precision in hardware accelerators Host-accelerator interactions and hardware-software co-design Optimisation of hardware designs with respect to throughput, latency, energy, and area Accelerator design for selected applications such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application View detailed information in the Handbook
Electrical Engineering Research Project · 25 pts	This subject is for students to undertake a substantial individual research project on an approved topic over the semester, requiring independent investigation with a chosen supervisor either from a university (research institute) or from an industry partner. Note: the student is responsible for contacting the potential supervisor for the project. This subject can also be taken by Master of Electrical Engineering outgoing exchange students for research projects carried out in an overseas university. If the project is to be carried out within the EEE Department, the student is encouraged to take ELEN90011 (Directed Studies) if possible. The emphasis of the project can be associated with either A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, often as an adjunct to existing academic research initiatives. It is expected that the project will incorporate findings associated with both well-defined professional practice and research principles. View detailed information in the Handbook
Applied Deep Learning for Engineers · 12.5 pts	This subject covers a modern deep learning approach to engineering using a project-centric pedagogy. Building upon system optimisation and machine learning fundamentals presented in ELEN90088, the subject will present advanced deep learning architectures to address long-standing engineering challenges such as system complexity, curse of dimensionality, and modelling gap. Subject will specifically focus on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how advanced deep learning frameworks can be applied to modern engineering and cyber-physical systems. INDICATIVE CONTENT Topics covered may include: Latent spaces, auto encoder architectures. Advanced deep learning architectures, auto-differentiation, physics-inspired neural networks. Sequential data analysis and predictive models such as transformers. Generative models such as GANs and GPT variants. Other advanced topics such as meta parameter optimisation, Markov Chain Monte Carlo sampling. Distributed machine learning, federated learning, graph neural networks. Cyber-physical security of modern engineering systems, including data-based anomaly and threat detection and prediction. Subject projects will focus on engineering applications in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. View detailed information in the Handbook
Modelling and Analysis for AI · 12.5 pts	This subject builds up the fundamentals for modelling dynamical systems, with a key focus on the aspects and decisions of modelling that are relevant for the application of AI and data-intensive learning methods. The discussion and evaluation of modelling methods focuses on how model fidelity influences simulation-to-real transfer; how modelling and simulation decisions influence computation time required for training and validation; and how discrete-time models introduce complexity when representing continuous-time engineering systems. Subsequently, it introduces the basic principles and engineering applications of programming and data structures in a condensed form with a project-centric pedagogy. It covers the fundamentals of databases and data structures, basic algorithms, scientific programming, and classic AI problem solving. It will focus specifically on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how programming and databases play a role in modern engineering and cyber-physical systems. INDICATIVE CONTENT Topics covered may include: Models for engineering systems in multiple disciplines, including analysis of what makes the models amenable to AI and data-intensive learning methods. Principles for simulating dynamic systems that are most relevant for the use of AI methods and to address these principles with existing software tools. Scientific programming for modelling using Python programming language and libraries such as scipy and numpy. Engineering data structures and time series data and their storage in SQL and noSQL databases. Example engineering applications will be taught via projects in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. View detailed information in the Handbook
Reinforcement Learning for Engineering · 12.5 pts	The key focus of this subject is the design and implementation of decision-making policies for enabling a dynamical system to behave autonomously and achieve a desired objective. This subject covers both model-based and model-free learning methods, with a focus on evaluating, contrasting, and combining methods. The influence of noisy sensor data on performance, and the trade-offs between exploration and exploitation during a learning phase, will also be covered. The examples used in this subject range across existing and emerging decision-making methods, and their application to consumer and industrial engineering systems. INDICATIVE CONTENT Topics covered may include: Reinforcement learning fundamentals such as principle of optimality, Bellman equation, value and policy iteration. Temporal-difference learning, Q-learning, Deep Q-learning, Actor critic methods and hybrid approaches in engineering context. Model based vs model free approaches, multi-agent RL and their engineering applications. RL methods for Cyber-physical resilience and security such as fuzzing methods. View detailed information in the Handbook

Approved Electives (Group B)

Students must complete 25 points of Year 3 elective subjects.

Accordion
Optimisation for Industry · 12.5 pts	The use of mathematical optimisation is widespread in business, where it is a key analytical tool for managing and planning business operations. It is also required in many industrial processes and is useful to government and community organizations. This subject will expose students to operations research techniques as used in industry. A heavy emphasis will be placed on the modelling process that turns an industrial problem into a mathematical formulation. The focus will then be on how to solve the resulting mathematical problem with mixed-integer programming techniques. View detailed information in the Handbook
Distributed Systems · 12.5 pts	AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. View detailed information in the Handbook
Advanced Motion Control · 12.5 pts	AIMS This subject is intended to give students an overview of the present state-of-the-art in industrial motion control and the likely future trends in control design. Students will be exposed to and have practical experience in the design and implementation of advanced controllers for various motion control problems. Advanced modelling and control topics will include system identification, modelling and compensation of friction and other disturbances, industrial servo loops, model-based and model-free controller design, and adaptive control. Applications will be drawn from industrial, medical and transport automation (eg robots, machine tools, production machines, laboratory automation, automotive and aerospace by-wire systems). INDICATIVE CONTENT Advanced modelling and control topics will include system identification, modelling and compensation of friction and other disturbances, industrial servo loops, model-based and model-free controller design, and adaptive control. Applications will be drawn from industrial, medical and transport automation (eg robots, machine tools, production machines, laboratory automation, automotive and aerospace by-wire systems). View detailed information in the Handbook
Leadership for Innovation · 12.5 pts	This subject, which is offered to students who have completed ENGR90034 Creating Innovative Engineering (CIE), will give participants core leadership skills for managing professionals engaged in innovation and other ambiguous project-based work. The subject teaches leadership at three levels (12 hours each). The first level, taught intensively before the start of the semester, will enable you to learn basic management theory that allows you to bridge from the skills and theory taught in CIE to the level needed to start mentoring a team in CIE or another subject. The second level, taught as four three-hour workshops during the semester, will focus on key thematic issues in the leadership of innovative teams. The third level, taught in twelve one-hour sessions, will focus on specific leadership skills. These include facilitation, coaching, mentoring, conflict resolution, etc. Students will apply the theory and skills to the mentoring of a student project team in CIE or another subject within the University. You will apply what you are learning, and develop skills, by mentoring an industry-sponsored project within CIE or a project within another subject. CIE mentors will also need to manage their relationship with the external sponsor of the project. View detailed information in the Handbook
Global Business Practicum · 12.5 pts	This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. View detailed information in the Handbook
Engineering Entrepreneurship · 12.5 pts	AIMS This subject is available as an elective in many of the Faculty of Engineering and IT Masters programs. It is aimed both at students who have immediate entrepreneurial intentions and at students who may be considering starting their own business at some point in their careers. The subject is designed to introduce all participants to their potential as entrepreneurs. By developing their own enterprise proposal within small groups, students will learn and demonstrate various processes by which successful new ventures move from idea to launch. INDICATIVE CONTENT Business modelling, opportunity analysis, value creation, financial management, sources of finance, creativity, innovation, entrepreneurial behaviour, successful engineering entrepreneurs. TEACHING METHOD The teaching method is based around a structured process of mini-lectures, class exercises, and active hands-on learning by doing. Intensive field research and minimum viable product development are very important to the subject. Learning is further enhanced through meetings with the lecturer and review by peers. View detailed information in the Handbook
Internship · 25 pts	AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship View detailed information in the Handbook
Nuclear Engineering · 12.5 pts	This subject provides an introduction to nuclear science and engineering. It presents the properties of atomic nuclei, radioactivity, nuclear reactions, and selected topics in thermodynamics as required for the analysis of power systems based on nuclear fission. The working principles of nuclear reactors and nuclear power plants are discussed, focusing on pressurised-water reactor systems. Indicative content: Introduction to nuclear physics Thermodynamics of nuclear power plants Nuclear power generation View detailed information in the Handbook
Radiation Protection · 12.5 pts	Nuclear technology involves the risk of exposure to ionising radiation, with potentially harmful effects on human health. This subject equips students with the necessary knowledge and skills to understand this risk and to manage it by applying established methods of radiation protection. Indicative content: Effects of ionising radiation on human health Methods of radiation detection and measurement Principles and methods of radiation protection Radiation shielding View detailed information in the Handbook
Engineering of Nuclear Systems · 12.5 pts	This subject presents nuclear reactor theory and its applications to reactor operation. It examines reactor response to control actions, feedback effects, and the intermediate and long-term effects on reactivity due to fission product poisoning and fuel burnup. Furthermore, it covers the fundamentals of thermal and hydraulic analysis of pressurised-water reactors. Indicative content: Nuclear reactor theory and engineering Reactor dynamics and control Effects of fuel burnup and the long-term evolution of the reactor core properties Heat generation and heat transfer from fuel to coolant Thermal design of nuclear reactors View detailed information in the Handbook
Nuclear Safety, Security and Safeguards · 12.5 pts	Safety, security and safeguards are critical requirements in the operation of nuclear facilities. This subject presents the safety aspects and safety assessment methods of nuclear power plants. Nuclear security and safeguards are discussed in the context of the nuclear fuel cycle. Indicative content: Nuclear fuel cycle Fundamentals of nuclear safety Safety systems and safety features of nuclear reactors Probabilistic safety assessment Nuclear security and safeguards View detailed information in the Handbook
Design Innovation and Leadership · 12.5 pts	A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects.  In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge.  Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication.  In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change?  How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information.  The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. View detailed information in the Handbook

Low Carbon Power Systems specialisation

Core

Year 1

Students must complete 100 points of Year 1 compulsory subjects.

Accordion
Intro. to Numerical Computation in C · 12.5 pts	AIMS Many engineering disciplines make use of numerical solutions to computational problems. In this subject students will be introduced to the key elements of programming in a high level language, and will then use that skill to explore methods for solving numerical problems in a range of discipline areas. INDICATIVE CONTENT Algorithmic problem solving Fundamental data types: numbers and characters Approximation and errors in numerical computation Fundamental program structures: sequencing, selection, repetition, functions Simple data storage structures, variables, arrays, and structures Roots of equations and of linear algebraic equations Curve fitting and splines Interpolation and extrapolation Numerical differentiation and integration View detailed information in the Handbook
Foundations of Electrical Networks · 12.5 pts	INDICATIVE CONTENT Foundations of Electrical Networks develops an understanding of fundamental modelling techniques for the analysis of systems that involve electrical phenomena. This includes networks models of “flow-drop” one-port elements in steady state (DC and AC), electrical power systems, simple RC and RL transient analysis, and networks involving ideal and non-ideal operational amplifiers. It forms the foundation of many engineering subjects exploring fundamental concepts in electrical and electronic engineering. The subject will cover key electrical engineering topics in the areas of: Electrical phenomena – charge, current, electrical potential, conservation of energy and charge, the generation, storage, transport and dissipation of electrical power. Network models – networks of “flow-drop” one-port elements, Kirchoff’s laws, standard current-voltage models for one-ports (independent sources, resistors, capacitors, inductors, transducers, diodes), analysis of static networks, properties of linear time-invariant (LTI) one-ports and impedance functions, diodes, transformers, steady-state (DC and AC) analysis of LTI networks via mesh and node techniques, equivalent circuits, and transient analysis of simple circuits; Electrical power systems – overview of power generation and transmission, analysis of single-phase and balanced three-phase AC power systems. Analysis and design of networks involving ideal and non-ideal operational amplifiers. This material will be complemented by exposure to software tools for the simulation of electrical and electronic systems and the opportunity to develop basic electrical engineering laboratory skills using a prototyping breadboard, digital multimeter, function generator, DC power supply, and oscilloscope. Please view this video for further information: Foundations of Electrical Networks View detailed information in the Handbook
Digital Systems · 12.5 pts	AIMS This subject develops a fundamental understanding of concepts used in the analysis, design and building of digital systems. Such systems form the information and communication technologies (ICT) that underpin modern society. This subject provides a foundation for subsequent subjects, including ELEN30013 Electronic System Implementation, ELEN90066 Embedded System Design and ELEN90061 Communication Networks. INDICATIVE CONTENT Topics include: Digital systems - quantifying and encoding information, digital data processing, design process abstractions; Combinational logic – timing contracts, acyclic networks, switching algebra, logic synthesis; Sequential logic – cyclic networks and finite-state machines, metastability, microcode; These topics will be complemented by exposure to the hardware description language such as Verilog and the use of engineering design automation tools and configurable logic devices (e.g. FPGAs) in the laboratory. Please view this video for further information: Digital Systems View detailed information in the Handbook
Electrical Network Analysis and Design · 12.5 pts	AIMS This subject develops a fundamental understanding of linear time-invariant network models for the analysis and design of electrical and electronic systems. Such models arise in the study of systems ranging from large-scale power grids to tiny radio frequency signal amplifiers. This subject is one of four subjects that define the Electrical Systems Major in the Bachelor of Science and it is a core requirement for the Master of Engineering (Electrical). It provides a foundation for various subsequent subjects, including ELEN30013 Electronic System Implementation, ELEN90066 Embedded System Design, and ELEN30012 Signal and Systems. INDICATIVE CONTENT Topics include: Transient and frequency domain analysis of linear time-invariant (LTI) models – linearity, time-invariance, impulse response and convolution, oscillations and damping, the Laplace transform and transfer functions, frequency response and bode plots, lumped versus distributed parameter transfer functions, poles, zeros, and resonance, stability of circuits, modelling and simulation with simulation tools; Electrical network models – one-port elements, impedance functions, two-port elements, dependent sources, matrix representations of two-ports, driving point impedances and network functions, ladder and lattice networks, passive versus active networks, multi-stage modelling and design, and multi-port generalisations; Analysis and design of networks involving ideal and non-ideal operational amplifiers with emphasis on the design of active filters and broadband circuits with specific frequency characteristics; Circuits and networks for managing voltage and power requirements for common electronic circuits. These topics will be complemented by tutorials and workshops designed to develop skills in design and modelling of electronic circuits through software tools and building, testing, and verification of electronic circuits. Please view this video for further information: Electrical Network Analysis and Design View detailed information in the Handbook
Electrical Device Modelling · 12.5 pts	AIM This subject develops the theoretical and practical tools required to understand, construct, validate and apply models of standard electrical and electronic devices. In particular, students will study the theoretical and practical development of models for devices such as resistors, capacitors, inductors, transformers, motors, batteries, diodes, transistors, and transmission lines. In doing so, students will gain exposure to a variety of fundamental fields in physics, including electromagnetism, semiconductor materials and quantum electronics. This material will be complemented by exposure to experiment design and measurement techniques in the laboratory, the application of models from device manufacturers, and the use of electronic circuit simulation software. INDICATIVE CONTENT Topics include: Vector calculus for device modelling, Maxwell’s equations, physics of conductors and insulators, passive device models (including for resistors, capacitors and inductors), lumped and distributed circuit models for wired interconnections (including treatment of signal integrity and termination strategies), semiconductors and quantum electronics, static and dynamic models for p-n junctions diodes and bipolar junction transistors. View detailed information in the Handbook
Signals and Systems · 12.5 pts	AIMS The aim of this subject is twofold: firstly, to develop an understanding of the fundamental tools and concepts used in the analysis of signals and the analysis and design of linear time-invariant systems path in continuous–time and discrete-time; secondly, to develop an understanding of their application in a broad range of areas, including electrical networks, telecommunications, signal-processing and automatic control. The subject formally introduces the fundamental mathematical techniques that underpin the analysis and design of electrical networks, telecommunication systems, signal-processing systems and automatic control systems. Such systems lie at the heart of the electrical engineering technologies that underpin modern society. This subject is one of four Level 3 subjects that define the Electrical Engineering Systems Major in the Bachelor of Science. . It provides the foundation for various subsequent subjects, including ELEN90057 Communication Systems, ELEN90058 Signal Processing and ELEN90055 Control Systems. INDICATIVE CONTENT Topics include: Signals – continuously and discretely indexed signals, important signal types, frequency-domain analysis (Fourier, Laplace and Z transforms), nonlinear transformations and harmonics, sampling; Systems – viewing differential / difference equations as systems that process signals, the notions of input, output and internal signals, block diagrams (series, parallel and feedback connections), properties of input-output models (causality, delay, stability, gain, shift-invariance, linearity), transient and steady state behaviour; Linear time-invariant systems – continuous and discrete impulse response; convolution operation, transfer functions and frequency response, time-domain interpretation of stable and unstable poles and zeros, state-space models (construction from high-order ODEs, canonical forms, state transformations and stability), and the discretisation of models for systems of continuously indexed signals. This material is complemented by exposure to the use of MATLAB for computation and simulation and examples from diverse areas including electrical engineering, biology, population dynamics and economics. View detailed information in the Handbook
Electronic System Implementation · 12.5 pts	AIMSThis subject provides students with hands-on electronic skills to gain basic competencies in design and implementation of simple circuits. Students will design with a range of standard electrical and electronic devices, basic circuit construction methods and electrical measurement techniques to test and verify the function of electronic systems. This subject is one of four subjects that define the Electrical Systems Major in the Bachelor of Science and it is a core requirement for the Master of Engineering (Electrical) and the Master of Engineering (Electrical with Business). This includes hands-on experience with: • Operation and selection of electrical and electronic devices used in various electronic circuits; • Common electronic circuit realisations to meet the most commonly required signal processing and conditioning applications; • Programmable digital circuits and microprocessor programming; • Circuit design and simulation tools; • Printed circuit board layout, circuit assembly, and soldering techniques; • Test and Measurement equipment and methods; • Managing design issues and requirements. Students will complete electronic circuit implementation projects in small groups and be required to prepare technical documentation and present project outcomes. INDICATIVE CONTENT • Devices such as resistors, capacitors, inductors, switches, transducers, motors, diodes, transistors, op-amps, voltage regulators, comparators, oscillators, timers, A/D and D/A converters, microprocessors and controllers; • Circuit functions and techniques such as buffering, referencing, signal conditioning, filtering, bridges, detection, waveform generation, and pulse-width modulation; • Microprocessor programming, the role of assembly and high-level languages, assemblers, compilers and debuggers; • PCB layout, circuit assembly, and soldering techniques; • Test and Measurement methods and working with common equipment such as multimeters and oscilloscopes. View detailed information in the Handbook
Engineering Mathematics · 12.5 pts	This subject introduces important mathematical methods required in engineering such as manipulating vector differential operators, computing multiple integrals and using integral theorems. A range of ordinary and partial differential equations are solved by a variety of methods and their solution behaviour is interpreted. The subject also introduces series including the concepts of convergence and divergence. Topics include: Vector calculus, including Gauss’ and Stokes’ Theorems; systems of homogeneous ordinary differential equations, including phase plane and linearisation for nonlinear systems; Laplace transforms; series, including Taylor series and power series; Fourier series and Fourier integrals; second order partial differential equations and separation of variables. View detailed information in the Handbook

Year 2

Students must complete 100 points of Year 2 compulsory subjects.

Accordion
Probability and Random Models · 12.5 pts	AIMS This subject provides an introduction to probability theory, random variables, random vectors, decision tests, and stochastic processes. Uncertainty is inevitable in real engineering systems, and the laws of probability offer a powerful way to evaluate uncertainty, to predict and to make decisions according to well-defined, quantitative principles. The material covered is important in fields such as communications, data networks, signal processing and electronics. This subject is a core requirement in the Master of Engineering (Electrical, Mechanical and Mechatronics). INDICATIVE CONTENT Topics include: Foundations – combinatorial analysis, axioms of probability, independence, conditional probability, Bayes’ rule; Random variables (rv’s)– definition; cumulative distribution, probability mass and probability density functions; expectation and variance; functions of an rv; important distributions and their properties and uses; Multiple random variables – joint cumulative distribution, probability mass and probability density functions; independent rv’s; correlation and covariance; conditional distributions and expectation; functions of several rv’s; jointly Gaussian rv’s; random vectors; Sums, inequalities and limit theorems – sums of rv’s, moment generating function; Markov and Chebychev inequalities; weak and strong laws of large numbers; the Central Limit Theorem; Decision testing - maximum likelihood, maximum a posterior, minimum cost and Neyman-Pearson rules; basic minimum mean-square error estimation; Stochastic processes – mean and autocorrelation functions, strict and wide-sense stationarity; ergodicity; important processes and their properties and uses; Introduction to Markov chains. This material is complemented by exposure to examples from electrical engineering and software tools (e.g. MATLAB) for computation and simulations. View detailed information in the Handbook
Control Systems · 12.5 pts	AIMS This subject provides an introduction to automatic control systems, with an emphasis on classical techniques for the analysis and design of feedback interconnections. The main challenge in automatic control is to achieve desired performance in the presence of uncertainty about the system dynamics and the operating environment. Feedback control is one way to deal with modelling uncertainty in the design of engineering systems. This subject is a core requirement in the Master of Engineering (Electrical, Electrical with Business, Mechanical, Mechanical with Business and Mechatronics). INDICATIVE CONTENT Topics include: * Modelling for control, linearization, relationships between time and frequency domain models of linear time-invariant dynamical systems, and the structure, stability, performance, and robustness of feedback interconnections; * Frequency-domain analysis and design, Nyquist and Bode plots, gain and phase margins, loop-shaping with proportional, integral, lead, and lag compensators, loop delays, and fundamental limitations in design; and * Actuator constraints and anti-windup compensation. This material is complemented by the use of software tools (e.g. MATLAB/Simulink) for computation and simulation, and exposure to control system hardware in the laboratory. View detailed information in the Handbook
Electronic Circuit Design · 12.5 pts	AIMS This subject provides an in-depth coverage of transistor (MOSFET and BJT) devices and their use in common circuits. In particular, students will study topics including: transistor operating modes and switching; principles of CMOS circuits; transistor biasing; current-source/emitter-amplifiers; low-frequency response; followers; class B amplifiers; current limiting; current sources and mirrors; differential pairs; feedback in amplifiers and stability; operational amplifiers; operational amplifier circuits; and voltage regulation. This material will be complemented by exposure to circuit simulation software tools and the opportunity to further develop circuit construction/test skills in the laboratory. INDICATIVE CONTENT Design-focused field-effect and bipolar elementary transistor models, and design of elementary amplifier stages and biasing circuits. Static and dynamic behaviour of amplifier circuits including frequency response, feedback and stability, slew-rate and clipping. Operational amplifiers and opamp based circuits; voltage regulators, references and voltage converters. Verification of electronic circuits using simulation and constructing them in the laboratory. Please view this video for further information: Electronic Circuit Design View detailed information in the Handbook
Communication Systems · 12.5 pts	AIMS This subject provides an introduction to the analysis and design of telecommunication signals and systems, in the presence of uncertainty. The emphasis is on understanding the basic concepts that underpin the physical layer of modern communication systems. INDICATIVE CONTENT Topics to be covered include: Introduction to communication systems including historical developments and comparisons between analogue and digital communications. Review of assumed knowledge from linear algebra, signals and systems and probability and random processes. The sampling theorem, analog-to-digital conversion, complex baseband representation of passband signals, filtering of random processes, power spectral density, bandwidth of random signals, additive white Gaussian noise (AWGN), signal-to-noise ratio. Communication over baseband AWGN channels including modulation techniques (pulse amplitude modulation, orthogonal modulation), signal space representation, optimal detectors, matched filters, error probability calculations and bandwidth / power trade-off. Communication over passband AWGN channels including modulation techniques (phase shift keying, quadrature amplitude modulation and frequency shift keying), optimal coherent detectors, noncoherent detectors and error probability calculations. Communication over linear time-invariant channels including concepts of distortion, inter-symbol interference, pulse shaping, Nyquist’s criterion, equalization, sequence detection and the Viterbi algorithm. Synchronization including carrier, symbol and frame synchronization. View detailed information in the Handbook
Signal Processing · 12.5 pts	AIMS This subject provides an introduction to the fundamental theory of time domain and frequency domain representation of discrete time signals and linear time invariant dynamical systems, and how this theory is used to analyse and design digital signal processing systems and algorithms. Topics include: Applications of signal processing techniques; Sampling of analog signals, anti-aliasing filters; Frequency-domain analysis of signals and systems, Discrete Time Fourier Transform, Discrete Fourier Transform, Fast Fourier Transform; Digital filters, low-pass, high-pass, band-pass, stop-band and all pass filters. Phase and group delay, FIR and IIR filters; Design of digital FIR and IIR filters; Multi-rate signal processing, with a focus on up-sampling, down-sampling, and sampling rate conversion; Simple non-parametric methods for spectral estimation. This fundamental material will be complemented by exposure to MATLAB tools for signal analysis and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory. INDICATIVE CONTENT Sampling of continuous time signals, Design of anti-aliasing filters, Time and frequency representation of discrete time signals and discrete time linear time invariant systems, Discrete Time Fourier Transform and z-transform and their properties, Low order lowpass, highpass, bandpass, bandstop filters, All-pass filter, Design of IIR filters using the bilinear transformation, Design of FIR filters with linear phase using windowing techniques and the Parks McClelland method, Discrete Time Fourier transform and its properties, Fast Fourier Transform, The use of the DFT in implementation of linear filtering algorithms, Up-sampling and down-sampling, multistage and computationally efficient implementations of up-samplers and down-samplers, Energy and power spectra for deterministic signals. View detailed information in the Handbook
Embedded System Design · 12.5 pts	AIMS This subject provides a practical introduction to the basics of modelling, analysis, and design of microprocessor-based embedded systems. Students will learn how to integrate computation with physical processes to meet a desired specification within the context of a design project. The project work will expose students to the various stages in an engineering project (design, implementation, testing and documentation) and a range of embedded system concepts. INDICATIVE CONTENT Topics covered may include: digital computer and microprocessor architectures, modelling of dynamic behaviours, control, models of computation, operating systems concepts, multi-tasking, resource management and real-time behaviours, interfacing with the physical world, analysis and verification, safety, reliability, and security and privacy. This material will be complemented by exposure to standard software tools including compilers and debuggers, finite state machine design and analysis software, and simulation tools. The subject will include a level of industry engagement, to provide broader examples of engineering projects, through guest lectures. View detailed information in the Handbook
Introduction to Power Engineering · 12.5 pts	AIMS To develop a solid foundation for the study of systems that involve the generation, transport, and conversion of electric power. INDICATIVE CONTENT Physical principles of electromagnetism, magnetic circuits, energy storage, loss mechanisms, electromechanical energy conversion. Modelling of transmission lines, transformers, motors and generators (synchronous and asynchronous), and other loads. Circuit theory for power system analysis, three phase-phase circuits, power flow and maximum power transfer, per-unit system. Please view this video for further information: Introduction to Power Engineering View detailed information in the Handbook
Interdisciplinary Design for Engineers · 12.5 pts	In this subject, students will actively engage in an interdisciplinary, collaborative and project-based learning environment, offering insights into the professional nature of engineering work. Through a real-world project, students will gain hands-on design experience addressing a complex challenge. The project will require students to integrate discipline knowledge and apply professional skills like teamwork and communication. Students will experience the entire engineering design process, covering problem definition, ideation, concept development, analysis, prototyping, testing and iteration. The project provides practical experience, equipping students with tools and methods to address complex challenges. Students are expected to integrate diverse perspectives, considering factors like stakeholders, sustainability (including environmental and social issues), safety, feasibility, and technical and ethical considerations. View detailed information in the Handbook

Capstone

Year 3

Students must complete 25 points of Year 3 compulsory capstone project subjects.

Accordion
Engineering Capstone Project Part 1 · 12.5 pts	The subject involves undertaking a substantial group project (typically in groups of three students) requiring an independent investigation on an approved topic in advanced engineering design and / or research. Each project is carried out under the supervision of a member of academic staff and where appropriate an industry partner. The emphasis of the project can be associated with either: A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the engineering science disciplines, often as an adjunct to existing academic research initiatives. It is expected that the Capstone Project will incorporate findings associated with both well-defined professional practice and research principles and will provide students with the opportunity to integrate technical knowledge and generic skills gained in earlier years. The project component of this subject is supplemented by a lecture course dealing with project management tools and practices. Please note: Students enrolled in the suite of Master of Engineering programs must be within the final 112.5 points of their degree to enrol. Students enrolled in the Master of Industrial Engineering must be within the final 100 points of their degree to enrol. Students are to take Engineering Capstone Project Part 1 and then subsequently continue with Engineering Capstone Project Part 2 in the following semester. Upon successful completion of this project, students will receive 25 points credit. View detailed information in the Handbook
Engineering Capstone Project Part 2 · 12.5 pts	Please refer to ENGR90037 Engineering Capstone Project Part 1 for this information. View detailed information in the Handbook

Accordion

Engineering Capstone Project Part 1 · 12.5 pts

The emphasis of the project can be associated with either:

A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or
A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the engineering science disciplines, often as an adjunct to existing academic research initiatives.

The project component of this subject is supplemented by a lecture course dealing with project management tools and practices.

Please note:

Students enrolled in the suite of Master of Engineering programs must be within the final 112.5 points of their degree to enrol.

Students enrolled in the Master of Industrial Engineering must be within the final 100 points of their degree to enrol.

View detailed information in the Handbook

Engineering Capstone Project Part 2 · 12.5 pts

Please refer to ENGR90037 Engineering Capstone Project Part 1 for this information.

View detailed information in the Handbook

Specialisation

Year 3

Students must complete 50 credit points of Year 3 core specialisation subjects.

Accordion
Power System Analysis · 12.5 pts	AIMS This subject provides an insight into the fundamental elements to analyse electrical power transmission and distribution systems, with both analytical and simulation tools for analysis of operations of these systems. Problems related to power flow and use of Newton-Raphson and other algorithms such as backward-forward sweep will be discussed. Fault calculation and analysis, symmetrical components, and analytical methods for solving symmetrical (balanced) faults will be covered. Principles, concepts and problems related to power system dynamics and control, particularly for frequency and voltage regulation, will be discussed and analysed in detail. Finally, small-signal, transient, voltage and frequency stability will be introduced and exemplified. Focus will be put on real-world examples, particularly to prepare the student for the ongoing transition towards a low-carbon grid dominated by renewables and distributed energy resources. INDICATIVE CONTENT Power flow calculations, Newton-Raphson, Gauss-Seidel and backward-forward sweep methods; Fault calculations, balanced and unbalanced, symmetrical components, fundamentals of protection; Frequency regulation and frequency stability, Voltage regulation in transmission and distribution networks, including use of flexible AC transmission systems (FACTS); Voltage stability, small-signal stability, transient stability; Computer simulations. View detailed information in the Handbook
Power Electronics · 12.5 pts	AIMS The aim of this subject is to understand the fundamental concepts and basic theory involved in modelling and analysis of the power electronic components that comprise power electronic devices such as power supplies, inverters, converters and their control systems. It is expected that at the end of this subject the student has a sound understanding of the physical concepts and mathematical models behind each of the basic components and of their functionality within a system, such as a high voltage DC transmission system. Furthermore this subject seeks to combine the fields of electronics, semiconductor devices, power system operation, power system measurement and control. It is expected that through this subject the students are exposed to examples of real electrical engineering systems where the three disciplines of electronics, power systems and control come together. INDICATIVE CONTENT Topics covered in this subject include: introduction to power semiconductor switches; discussion on the role of power electronics in the operation of electric power systems; models of power semiconductor devices and circuit components, including diodes, Thyristors, IGBT, Snubber circuits. Also basic concepts of single- and three-phase diode bridge rectifiers; single- and three-phase converters and inverters; operation and design of DC-AC inverters with emphases on switch-mode inverters, i.e. single- and three-phase inverters. Finally, the acquired knowledge of power electronic devices is applied to wind and PV solar systems where the design of voltage source converters and associated control loops are used to interface the wind/solar system with the power grid. View detailed information in the Handbook
Grid Integration of Renewables · 12.5 pts	AIMS This subject develops a foundation for pursuing electrical engineering oriented research in the area of sustainable energy systems. This subject aims to introduce the concepts behind smart grids, future low-carbon energy networks, sustainable electricity systems as well as the main renewable and low-carbon generation technologies. The subject will introduce students to tools and techniques so that distributed energy resources (e.g. distributed renewable generation, storage, electric vehicles, demand response, etc.) may be integrated effectively into the power system in the context of both traditional grids and future smart grids. INDICATIVE CONTENT This subject will cover the following topics: Distributed low-carbon technologies Introduction to distribution networks Introduction to distributed low-carbon technologies (wind energy, photovoltaic systems, electric vehicles, electric heating, storage) Wind Energy: impacts and challenges Photovoltaic systems: impacts and challenges Electric vehicles: impacts and challenges Electric heat pumps and electric heating: impacts and challenges Storage: impacts and challenges Smart Distribution and Smart Transmission Networks Distributed low-carbon technologies and active network management Towards Smart Grids Smart grids - Transmission and Distribution perspectives Smart Transmission: HVDC and FACTS, dynamic line rating, post-contingency security, special protection schemes The role of future Distribution System Operators Low-carbon Electricity System Towards low-carbon networks: relationship between sustainability and smart grids Introduction to low-carbon thermal generation (nuclear, Carbon Capture and Storage, Concentrated Solar Power, biomass, etc.) Utility-scale renewable technologies: wind farms; solar farms; other large-scale renewables; utility-scale batteries System-level operational challenges and solutions for renewables integration: variability and uncertainty; low-inertia operation; low system-strength operation; minimum load issues; DER visibility; indistinct events; general stability issues; flexibility System-level planning challenges and solutions for renewables integration: system adequacy and reliability; capacity credit of renewables and storage; extreme weather events and resilience; role of transmission Sector coupling and multi-energy systems: decarbonisation of gas, heating and transport; role of hydrogen Distributed energy systems: new technical and commercial architectures for two-sided systems and markets; demand response; aggregators and virtual power plants; distributed energy markets; peer-to-peer trading; local energy communities; microgrids View detailed information in the Handbook
Low-carbon Grids: Operation & Economics · 12.5 pts	This subject introduces the student to foundational aspects of economic, secure and reliable operation of low-carbon power systems and electricity markets with large shares of variable and uncertain renewable energy sources. The underlying framework is the so-called “affordability-sustainability-security” energy trilemma, which seeks to strike a delicate balance among: the desire to operate power systems at low cost (“affordability”); the desire to meet specific environmental targets (“sustainability”); and the need to “keep the lights on” (“security”). In order for the energy trilemma to be analysed in the context of a competitive market environment, the subject will provide the student with fundamentals of economics, operation of electricity markets, optimal bidding strategies of different market stakeholders, economics of transmission and distribution networks, and role of new technologies and commercial entities such as storage and aggregators. Different aspects of power system security will be analysed, from system-level requirements and constraints to provision of security services from market stakeholders. Basic concepts of optimization, including linear, quadratic, and mixed integer linear programming, will also be taught to provide the student with the tools required to understand and model current and future power system and energy market operation. View detailed information in the Handbook

Electives

Electrical Engineering Electives (Group A)

Students must complete 25 points of Year 3 elective subjects.

Accordion
Introduction to Optimisation · 12.5 pts	AIMS This subject provides a rigorous introduction to numerical nonlinear optimization, as used across all of science and particularly in engineering design. There is an emphasis on both the theory and application of optimization techniques, with a focus on solving unconstrained and constrained nonlinear programmes. This subject is intended for graduate and research higher-degree students in engineering. INDICATIVE CONTENT Topics include: Algorithms for unconstrained optimization Algorithms for constrained optimization Convex sets and functions Convex optimization problems Duality theory Computational complexity Approximation algorithms and penalty methods. View detailed information in the Handbook
Advanced Communication Systems · 12.5 pts	AIMS The aim of this subject is to develop a thorough understanding of the main concepts, techniques and performance criteria used in the analysis and design of digital communication systems and wireless networks. Such systems and networks lie at the heart of the information and communication technologies (ICT) that underpin modern society and are very much part of the Internet of Things which involves machine to machine communication. INDICATIVE CONTENT This subject provides an in-depth treatment of the main concepts and techniques used in the analysis and design of digital communication systems and wireless networks. Topics include: Source coding; entropy, Shannon source coding bound, data compression techniques; Channel modelling, modulation over time-varying fading channels, time and frequency diversity, energy and spectral efficiency, multiple carrier modulation including orthogonal frequency division multiplexing (OFDM) modulation, phase noise characteristics and its impact on single-carrier and multicarrier systems, spatial multiplexing for multiple access protocols, multiple antenna technologies (MIMO systems), cellular networks; Channel coding for error control: mutual information, channel capacity, Shannon channel coding bound, channel coding concepts, block codes; convolutional / trellis codes; introduction to LDPC codes, turbo codes, polar codes. Examples include short, medium and long range communication systems such as bluetooth, cellular and satellite communication systems. View detailed information in the Handbook
Advanced Signal Processing · 12.5 pts	AIMS This subject provides an in-depth introduction to statistical signal processing. INDICATIVE CONTENT Students will study a selection of the following topics: Applications of statistical signal processing; A review of stochastic signals and systems fundamentals – random processes, white noise, stationarity, auto- and cross-correlation functions, spectral- and cross-spectral densities, properties of linear time-invariant systems excited by white noise; Parameter estimation - least squares and its properties, recursive least squares and least mean squares, optimisation-based methods, maximum likelihood methods; Kalman, Wiener and Markov filtering; Power spectrum estimation. This material will be complemented with the use of software tools (e.g. MATLAB) for computation and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory. View detailed information in the Handbook
Electronic System Design · 12.5 pts	AIMS This subject will explore the design of various electrical and electronic systems and provide students with a range of common and practical design techniques and circuits in the context of a guided laboratory based project. INDICATIVE CONTENT Subject may cover specific concepts surrounding the design and implementation of: Design process; Design for manufacture and assembly; Advanced PCB design; Oscillators; Phase-locked loops and frequency synthesis; Base-band signalling schemes and clock recovery; Mixers and logarithmic amplification; Automatic gain control; Filters; Synchronous detection; High-speed analog-digital conversion; High-frequency amplification; Low noise amplifiers; Power supply design; Batteries, battery charging systems, and management; Test and measurement; Sensors. View detailed information in the Handbook
Lightwave Systems · 12.5 pts	AIMS Lightwave systems are fundamentally changing the way we communicate through broadband communications, helping clinicians to perform a range of medical procedures and diagnosis supported by advanced biomedical instrumentation, and even in the way we live in our homes through sophisticated interactive televisions and security systems. This subject will explore the physical principles and issues that arise in the design of lightwave systems often found in those key industry sectors. Students will study topics from: transmission of light over wave guides; production of light by lasers; light modulation; conversion of light signals to electrical signals; optical multiplexing and demultiplexing; light amplification; dispersion and dispersion compensation; optical nonlinearities; modulation and advanced detection schemes. This material will be complemented by exposure to lightwave systems and measurement techniques in the laboratory. INDICATIVE CONTENT This subject will explore the physical principles governing the generation, modulation, amplification, guiding, transmission, multiplexing, demultiplexing and detection of light and issues that arise in the design of lightwave systems such as transmission impairments, noise. Students learn selected examples of lightwave systems and methods for design, modelling and testing of simple lightwave systems. View detailed information in the Handbook
Power System Analysis · 12.5 pts	AIMS This subject provides an insight into the fundamental elements to analyse electrical power transmission and distribution systems, with both analytical and simulation tools for analysis of operations of these systems. Problems related to power flow and use of Newton-Raphson and other algorithms such as backward-forward sweep will be discussed. Fault calculation and analysis, symmetrical components, and analytical methods for solving symmetrical (balanced) faults will be covered. Principles, concepts and problems related to power system dynamics and control, particularly for frequency and voltage regulation, will be discussed and analysed in detail. Finally, small-signal, transient, voltage and frequency stability will be introduced and exemplified. Focus will be put on real-world examples, particularly to prepare the student for the ongoing transition towards a low-carbon grid dominated by renewables and distributed energy resources. INDICATIVE CONTENT Power flow calculations, Newton-Raphson, Gauss-Seidel and backward-forward sweep methods; Fault calculations, balanced and unbalanced, symmetrical components, fundamentals of protection; Frequency regulation and frequency stability, Voltage regulation in transmission and distribution networks, including use of flexible AC transmission systems (FACTS); Voltage stability, small-signal stability, transient stability; Computer simulations. View detailed information in the Handbook
Communication Networks · 12.5 pts	AIMS This subject introduces the basic principles, analysis, and design of communication networks. It covers relevant analytical methods, the layered network architecture of the Internet, and a multitude of network protocols. Analytical tools from queueing, optimisation, and graph theories are used to develop an in-depth understanding of basic principles and the role they play in network design. Specifically, queueing and graph theories are emphasised as methodological frameworks for communication network delay and structure analysis. The concepts taught in this subject lead to a better understanding of the Internet as well as modern communication paradigms such as Software-Defined Networks, Machine-to-Machine communication, Internet of Things, and social networks. INDICATIVE CONTENT Topics covered may include: The layered network architecture with a focus on physical-layer multiple access (TDM, FDM, WDM), link layer protocols and medium access control (MAC), network layer topologies, least-cost routing algorithms and protocols, transport layer protocols and the principles and techniques of practical reliable transport; LAN protocols, Ethernet, Wi-Fi, and serial communications; The Internet's network layer including the Internet Protocol (IP) and routing protocols including an introduction to BGP and the operation of forwarding tables in routers and shortest prefix routing; The Internet's transport layer protocols UDP and TCP, including the flow and congestion control algorithms; Network security, application layer, cloud and fog computing, Machine-to-Machine communication, and Internet of Things; Queuing theory: basics, birth-death processes, M/M/x and Markovian queues, networks of queues; Basics of graph theory and social network analysis relevant to communication networks. View detailed information in the Handbook
High Speed Electronics · 12.5 pts	AIMS The aim of the subject is to provide theoretical and practical treatment of high-speed electronics. Through the subject, students will grasp the fundamental properties and models of high-speed signals and interconnects, acquire high-speed digital design skills with a focus on the modelling, analysis, design and application of high speed transistors, logic gates and modern logic families, and master the high-speed analogue design capability including the design of oscillators and filters for RF applications. The students will be exposed to the state-of-the-art technologies that are shaping the fast evolving semiconductor industry. INDICATIVE CONTENT The topics include: Fundamental properties of analogue systems; Smith charts: principles and applications; High-speed analogue circuits: voltage control oscillators, matching networks, and low noise amplifiers; Bipolar junction transistors: device, switching, and logic; CMOS: device, switching and logic; High-speed signalling consideration: power dissipation, heat, signal propagation, and termination. View detailed information in the Handbook
Advanced Control Systems · 12.5 pts	AIMS This subject provides an introduction to modern control theory with a particular focus on design of advanced control laws via state-space methods and optimal control. The role of feedback in control design will be reinforced within this context, alongside the role of optimisation techniques in control system synthesis. INDICATIVE CONTENT Topics include: State-space models - first-order vector differential/difference equations; Lyapunov stability; linearisation; discretisation; Kalman decomposition (observable, detectable, reachable and stabilisable subspaces); state-feedback and pole placement; output-feedback and observer design in both continuous-time and discrete-time. Optimal control - dynamic programming; linear quadratic regulation in both continuous-time and discrete-time. Model predictive control in discrete-time; moving-horizon with constraints. View detailed information in the Handbook
Power Electronics · 12.5 pts	AIMS The aim of this subject is to understand the fundamental concepts and basic theory involved in modelling and analysis of the power electronic components that comprise power electronic devices such as power supplies, inverters, converters and their control systems. It is expected that at the end of this subject the student has a sound understanding of the physical concepts and mathematical models behind each of the basic components and of their functionality within a system, such as a high voltage DC transmission system. Furthermore this subject seeks to combine the fields of electronics, semiconductor devices, power system operation, power system measurement and control. It is expected that through this subject the students are exposed to examples of real electrical engineering systems where the three disciplines of electronics, power systems and control come together. INDICATIVE CONTENT Topics covered in this subject include: introduction to power semiconductor switches; discussion on the role of power electronics in the operation of electric power systems; models of power semiconductor devices and circuit components, including diodes, Thyristors, IGBT, Snubber circuits. Also basic concepts of single- and three-phase diode bridge rectifiers; single- and three-phase converters and inverters; operation and design of DC-AC inverters with emphases on switch-mode inverters, i.e. single- and three-phase inverters. Finally, the acquired knowledge of power electronic devices is applied to wind and PV solar systems where the design of voltage source converters and associated control loops are used to interface the wind/solar system with the power grid. View detailed information in the Handbook
Grid Integration of Renewables · 12.5 pts	AIMS This subject develops a foundation for pursuing electrical engineering oriented research in the area of sustainable energy systems. This subject aims to introduce the concepts behind smart grids, future low-carbon energy networks, sustainable electricity systems as well as the main renewable and low-carbon generation technologies. The subject will introduce students to tools and techniques so that distributed energy resources (e.g. distributed renewable generation, storage, electric vehicles, demand response, etc.) may be integrated effectively into the power system in the context of both traditional grids and future smart grids. INDICATIVE CONTENT This subject will cover the following topics: Distributed low-carbon technologies Introduction to distribution networks Introduction to distributed low-carbon technologies (wind energy, photovoltaic systems, electric vehicles, electric heating, storage) Wind Energy: impacts and challenges Photovoltaic systems: impacts and challenges Electric vehicles: impacts and challenges Electric heat pumps and electric heating: impacts and challenges Storage: impacts and challenges Smart Distribution and Smart Transmission Networks Distributed low-carbon technologies and active network management Towards Smart Grids Smart grids - Transmission and Distribution perspectives Smart Transmission: HVDC and FACTS, dynamic line rating, post-contingency security, special protection schemes The role of future Distribution System Operators Low-carbon Electricity System Towards low-carbon networks: relationship between sustainability and smart grids Introduction to low-carbon thermal generation (nuclear, Carbon Capture and Storage, Concentrated Solar Power, biomass, etc.) Utility-scale renewable technologies: wind farms; solar farms; other large-scale renewables; utility-scale batteries System-level operational challenges and solutions for renewables integration: variability and uncertainty; low-inertia operation; low system-strength operation; minimum load issues; DER visibility; indistinct events; general stability issues; flexibility System-level planning challenges and solutions for renewables integration: system adequacy and reliability; capacity credit of renewables and storage; extreme weather events and resilience; role of transmission Sector coupling and multi-energy systems: decarbonisation of gas, heating and transport; role of hydrogen Distributed energy systems: new technical and commercial architectures for two-sided systems and markets; demand response; aggregators and virtual power plants; distributed energy markets; peer-to-peer trading; local energy communities; microgrids View detailed information in the Handbook
System Optimisation & Machine Learning · 12.5 pts	This subject introduces the basic principles, analysis methods, and applications of optimisation and machine learning to engineering systems; encompassing fundamental concepts and practical algorithms. It covers the fundamentals of continuous optimisation followed by machine learning basics for engineering applications. The concepts and methods discussed are illustrated in multiple application areas including Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks.The concepts taught in this subject will allow a better understanding of continuous optimisation and machine learning for systems engineering. INDICATIVE CONTENT Topics covered may include: Fundamentals of continuous optimisation: convex sets and functions; local vs global solutions, constrained optimisation and Lagrange multipliers; linear, quadratic, and nonlinear programming Basics of machine learning encompassing supervised and unsupervised learning: binary classification, linear and nonlinear regression, kernel methods, and clustering. Specific machine learning methods such as Support Vector Machines (SVMs), Neural Networks (NNs), k-means clustering, and reinforcement learning. Applications to Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks. View detailed information in the Handbook
Communication Design Clinic · 12.5 pts	Students work collaboratively in small groups to implement and optimize components in a modern communication system or network with the goal of supporting a targeted application. To meet this goal students will need to: determine system requirements based on the target application and additional constraints; propose and evaluate multiple solutions through theoretical analysis and detailed simulations; implement, integrate, verify, and iterate on their selected solutions. Lectures will cast content from prerequisite subjects into the context at hand and cover additional topics relevant to the task. Each student group is expected to demonstrate initiative and independence while pursuing the goal of designing and optimizing their communication system or network, with a key focus being that students learn through hands-on experience. Students will receive early exposure to advanced topics critical to modern communication systems, such as: source and channel coding, multicarrier modulation, multiantenna transmission, and network architectures and protocols. Successful completion of the project will require the student to draw upon knowledge, understanding, and skills learned in prerequisite subjects, which may include: Communication Systems – analog-to-digital conversion, signal-to-noise ratio, modulation and demodulation, bandwidth/power trade-off, error probability calculations, distortion, inter-symbol interference, pulse shaping, equalization, sequence detection, and synchronization. Signal Processing - design and implementation of digital filters (low-, high-, band-, all- pass filters); ARMA systems; up-sampling and down-sampling. Embedded System Design – system-level programming, operating systems concepts, real-time issues, and standard software tools. Additional topics required for the assigned project may also be covered, such as: ideation, prototyping, and design practices; analog RF components; software packages for modelling and implementation; and the use of test & measurement equipment. View detailed information in the Handbook
Autonomous Systems Clinic · 12.5 pts	AIMS: Students work collaboratively in small groups to engineer an autonomous system that performs a specified task. This includes carrying out steps such as: task analysis; proposing multiple solutions; feasibility analysis through prototyping and computer-aided design; detailed design, construction, and testing of the chosen solution; and demonstrating the solution in a proving ground. The lectures will cast content from the pre-requisite subjects into the context of the task at hand, as well as covering additional topics relevant to the task. Each student group is expected to demonstrate initiative and independence while pursuing the goal of designing and building their autonomous system, with a focus of the subject being that students learn through hands-on experience, implementation, and verification. INDICATIVE CONTENT: Successful completion of the project requires the student to draw upon knowledge, understanding, and skills learned in the prerequisite subjects, namely: • Embedded System Design - including topics such as: finite, extended, and hierarchical state machines; modelling cyber-physical systems; scheduling, multi-tasking, and real-time issues; interfacing to the analogue world. • Control Systems - including topics such as: modelling; linearisation; feedback interconnections; proportional, integral, derivative (PID) control; actuator constraint considerations. • Signal Processing - including topics such as: design and implementation of digital filters (low-, high-, band-, all- pass filters); ARMA systems; up-sampling and down-sampling. Additional topics, specific to the task as hand, will be covered, such as: ideation, prototyping, and design practices; image processing and computer vision tools; software introductions; safety and failure analysis. A range of materials, components, and fabrication facilities are provided, from which the students are expected to utilise a subset for designing and building their autonomous system, such as: electric motors, range sensors, camera, voltage converters, compute power, sheet wood, soldering stations, laser wood cutting, 3D printing. The task to be performed is motivated by a real-world application of autonomous systems, such as: operating in hazardous environments or performing repetitive tasks. Please view this video for further information: Autonomous Systems Clinic View detailed information in the Handbook
Semiconductor Devices · 12.5 pts	This subject serves as an introduction to semiconductor devices. It describes the fundamentals, theory, material and physical properties of semiconductor devices. The following topics will be covered. Fundamentals: Crystal properties and of the growth of bulk crystals and of epitaxial layers. Physical concepts related to atoms and electrons. These concepts may include the photoelectric effect, the Bohr model, quantum mechanics, and the periodic table. Energy bands and charge carriers in semiconductors: Bonding forces and energy bands in solids, charge carriers in semiconductors, carrier concentrations, the drift of carriers in electric and magnetic fields, and the Fermi level. Excess carriers in semiconductors: Optical absorption, luminescence, carrier lifetime and photoconductivity, and the diffusion of carriers. Junctions: Fabrication of pn junctions, equilibrium conditions, forward and reverse biased junctions in steady state, reverse bias breakdown, transient and AC conditions, metal-semiconductor junctions and heterojunctions. In the next part of the subject PN junction diodes: Junction diodes, tunnel diodes, photodiodes, and light-emitting diodes and lasers. Bipolar junction transistors (BJTs): Amplification and switching, fundamentals of BJT operation, BJT fabrication, minority carrier distributions and terminal currents, generalised biasing, switching, the frequency limitations of transistors, and heterojunction bipolar transistors. Field effect transistors (FETs): Topics may include junction FETs, the metal semiconductor FET and the metal-insulator-semiconductor FET. Additional topics (if time permits): Integrated circuits, pnpn switching devices, and microwave devices. View detailed information in the Handbook
Low-carbon Grids: Operation & Economics · 12.5 pts	This subject introduces the student to foundational aspects of economic, secure and reliable operation of low-carbon power systems and electricity markets with large shares of variable and uncertain renewable energy sources. The underlying framework is the so-called “affordability-sustainability-security” energy trilemma, which seeks to strike a delicate balance among: the desire to operate power systems at low cost (“affordability”); the desire to meet specific environmental targets (“sustainability”); and the need to “keep the lights on” (“security”). In order for the energy trilemma to be analysed in the context of a competitive market environment, the subject will provide the student with fundamentals of economics, operation of electricity markets, optimal bidding strategies of different market stakeholders, economics of transmission and distribution networks, and role of new technologies and commercial entities such as storage and aggregators. Different aspects of power system security will be analysed, from system-level requirements and constraints to provision of security services from market stakeholders. Basic concepts of optimization, including linear, quadratic, and mixed integer linear programming, will also be taught to provide the student with the tools required to understand and model current and future power system and energy market operation. View detailed information in the Handbook
Microprocessor Design Clinic · 12.5 pts	Students in this subject will be introduced to computer architectures, microprocessors, microcontrollers, operating systems, compilers and software design. The proposed course will cover a broad range of topics necessary to make students knowledgeable in the art of microprocessor design including advanced concepts such as in line and out of order execution and execution unit resource optimisation. Students in this course will learn to design execution units, arithmetic logic units, memory hierarchies and learn strategies for cache sizing. As part of this, students will become proficient in microcode and instruction set design, multi-processor and multi core theory and design, including new design methodologies such as chiplet design. Upon completion, students will be familiar with the specification and synthesis of microprocessor systems using high level generator languages such as Chisel and Scala. The course will also introduce students to compiler and linker design, enhancements to instruction sets, c-language and the theory of operating systems. View detailed information in the Handbook
Large Data Methods & Applications · 12.5 pts	This course provides an introduction to an important contemporary statistical toolset for applications including data science, machine learning, signal processing, financial engineering, biomedical engineering, communication systems and other high-dimensional statistical applications. The course will cover topics including introduction to random matrix theory models in engineering; eigenvalue distributions; finite-dimensional and large-dimensional techniques, covariance estimation, principal component analysis and spectral clustering. These topics will be supplemented by applications across a range of traditional and emerging domains involving big data sets. View detailed information in the Handbook
Directed Studies · 12.5 pts	AIMS Directed studies provide the students with broader experience in addition to the regular class based learning. The directed studies can be conducted in the forms of: Industrial internship or research placements in the department’s research groups based on availability. This is only open to students who have completed a minimum of one semester of study and who have achieved an average of H2A or above in their prior subjects; Individually arranged supervised study of current research topics with staff members associated with the Department of Electrical and Electronic Engineering. INDICATIVE CONTENT The examples of the research topics are: Cloud Computing, Content Distribution and Information Logistics; Internet Services Energy Star Rating; Energy Efficiency of Future Modulation Formats; Low-Energy Fibre Access Networks; Video Coding for Energy Efficient Telecommunications; Fundamental Limits of Electronics and Photonics; Broadband fibre wireless networks and systems; Optimal design of few-mode fibres. View detailed information in the Handbook
AI for Robotics · 12.5 pts	AIMS: This subject focuses on the software and algorithms (i.e., artificial intelligence) that enable robotic systems to move autonomously through their environment and perform tasks. The key focus of this subject is the foundations of robotic systems that use software to move autonomously through their environment. This subject focus on the software & algorithms that enable the robot to perform tasks autonomously. Hence, this subject focused on artificial intelligence (AI) software & algorithms for robotics. The first main aim of the subject is to provide a foundation of the feedback loop that is core to all AI-enabled robots, namely: sensors measure the world around the robot; AI algorithms decide what action to take; the robot enacts that action by moving its joint or wheels; and the loop repeats endlessly. The second main aim of the subject is to provide implementation experience with cutting edge AI algorithm applicable to consumer and industrial robotics, where we consider both model-based method and reinforcement-learning methods. INDICATIVE CONTENT: Topics covered are at the intersection of automatic control and artificial intelligence, including: Cyber-physical feedback system formulation, such as: black-box and grey-box modelling, stability and robustness safety requirements, hierarchical and network control architectures. Safety and convergence guarantees for model-based methods, such as: learning models from data; adaptive control schemes; stability and robustness of PID and MPC control approaches. Connections between optimal control and reinforcement learning formulations for robotics. Reinforcement learning for robotics, such as: actor-critic methods, on-policy versus off-policy learning, sample efficiency, transferring simulation-based learning to real-world robots View detailed information in the Handbook
Hardware Accelerated Computing · 12.5 pts	Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include: Review of combinational and sequential digital logic FPGA architectures and fundamentals Hardware description languages (Verilog/VHDL) and hardware design flows High-level synthesis and OpenCL The use of parallelism, locality, and precision in hardware accelerators Host-accelerator interactions and hardware-software co-design Optimisation of hardware designs with respect to throughput, latency, energy, and area Accelerator design for selected applications such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application View detailed information in the Handbook
Electrical Engineering Research Project · 25 pts	This subject is for students to undertake a substantial individual research project on an approved topic over the semester, requiring independent investigation with a chosen supervisor either from a university (research institute) or from an industry partner. Note: the student is responsible for contacting the potential supervisor for the project. This subject can also be taken by Master of Electrical Engineering outgoing exchange students for research projects carried out in an overseas university. If the project is to be carried out within the EEE Department, the student is encouraged to take ELEN90011 (Directed Studies) if possible. The emphasis of the project can be associated with either A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, often as an adjunct to existing academic research initiatives. It is expected that the project will incorporate findings associated with both well-defined professional practice and research principles. View detailed information in the Handbook
Applied Deep Learning for Engineers · 12.5 pts	This subject covers a modern deep learning approach to engineering using a project-centric pedagogy. Building upon system optimisation and machine learning fundamentals presented in ELEN90088, the subject will present advanced deep learning architectures to address long-standing engineering challenges such as system complexity, curse of dimensionality, and modelling gap. Subject will specifically focus on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how advanced deep learning frameworks can be applied to modern engineering and cyber-physical systems. INDICATIVE CONTENT Topics covered may include: Latent spaces, auto encoder architectures. Advanced deep learning architectures, auto-differentiation, physics-inspired neural networks. Sequential data analysis and predictive models such as transformers. Generative models such as GANs and GPT variants. Other advanced topics such as meta parameter optimisation, Markov Chain Monte Carlo sampling. Distributed machine learning, federated learning, graph neural networks. Cyber-physical security of modern engineering systems, including data-based anomaly and threat detection and prediction. Subject projects will focus on engineering applications in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. View detailed information in the Handbook
Modelling and Analysis for AI · 12.5 pts	This subject builds up the fundamentals for modelling dynamical systems, with a key focus on the aspects and decisions of modelling that are relevant for the application of AI and data-intensive learning methods. The discussion and evaluation of modelling methods focuses on how model fidelity influences simulation-to-real transfer; how modelling and simulation decisions influence computation time required for training and validation; and how discrete-time models introduce complexity when representing continuous-time engineering systems. Subsequently, it introduces the basic principles and engineering applications of programming and data structures in a condensed form with a project-centric pedagogy. It covers the fundamentals of databases and data structures, basic algorithms, scientific programming, and classic AI problem solving. It will focus specifically on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how programming and databases play a role in modern engineering and cyber-physical systems. INDICATIVE CONTENT Topics covered may include: Models for engineering systems in multiple disciplines, including analysis of what makes the models amenable to AI and data-intensive learning methods. Principles for simulating dynamic systems that are most relevant for the use of AI methods and to address these principles with existing software tools. Scientific programming for modelling using Python programming language and libraries such as scipy and numpy. Engineering data structures and time series data and their storage in SQL and noSQL databases. Example engineering applications will be taught via projects in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. View detailed information in the Handbook
Reinforcement Learning for Engineering · 12.5 pts	The key focus of this subject is the design and implementation of decision-making policies for enabling a dynamical system to behave autonomously and achieve a desired objective. This subject covers both model-based and model-free learning methods, with a focus on evaluating, contrasting, and combining methods. The influence of noisy sensor data on performance, and the trade-offs between exploration and exploitation during a learning phase, will also be covered. The examples used in this subject range across existing and emerging decision-making methods, and their application to consumer and industrial engineering systems. INDICATIVE CONTENT Topics covered may include: Reinforcement learning fundamentals such as principle of optimality, Bellman equation, value and policy iteration. Temporal-difference learning, Q-learning, Deep Q-learning, Actor critic methods and hybrid approaches in engineering context. Model based vs model free approaches, multi-agent RL and their engineering applications. RL methods for Cyber-physical resilience and security such as fuzzing methods. View detailed information in the Handbook

Approved Electives (Group B)

Students must complete 25 points of Year 3 elective subjects.

Accordion
Optimisation for Industry · 12.5 pts	The use of mathematical optimisation is widespread in business, where it is a key analytical tool for managing and planning business operations. It is also required in many industrial processes and is useful to government and community organizations. This subject will expose students to operations research techniques as used in industry. A heavy emphasis will be placed on the modelling process that turns an industrial problem into a mathematical formulation. The focus will then be on how to solve the resulting mathematical problem with mixed-integer programming techniques. View detailed information in the Handbook
Distributed Systems · 12.5 pts	AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. View detailed information in the Handbook
Advanced Motion Control · 12.5 pts	AIMS This subject is intended to give students an overview of the present state-of-the-art in industrial motion control and the likely future trends in control design. Students will be exposed to and have practical experience in the design and implementation of advanced controllers for various motion control problems. Advanced modelling and control topics will include system identification, modelling and compensation of friction and other disturbances, industrial servo loops, model-based and model-free controller design, and adaptive control. Applications will be drawn from industrial, medical and transport automation (eg robots, machine tools, production machines, laboratory automation, automotive and aerospace by-wire systems). INDICATIVE CONTENT Advanced modelling and control topics will include system identification, modelling and compensation of friction and other disturbances, industrial servo loops, model-based and model-free controller design, and adaptive control. Applications will be drawn from industrial, medical and transport automation (eg robots, machine tools, production machines, laboratory automation, automotive and aerospace by-wire systems). View detailed information in the Handbook
Leadership for Innovation · 12.5 pts	This subject, which is offered to students who have completed ENGR90034 Creating Innovative Engineering (CIE), will give participants core leadership skills for managing professionals engaged in innovation and other ambiguous project-based work. The subject teaches leadership at three levels (12 hours each). The first level, taught intensively before the start of the semester, will enable you to learn basic management theory that allows you to bridge from the skills and theory taught in CIE to the level needed to start mentoring a team in CIE or another subject. The second level, taught as four three-hour workshops during the semester, will focus on key thematic issues in the leadership of innovative teams. The third level, taught in twelve one-hour sessions, will focus on specific leadership skills. These include facilitation, coaching, mentoring, conflict resolution, etc. Students will apply the theory and skills to the mentoring of a student project team in CIE or another subject within the University. You will apply what you are learning, and develop skills, by mentoring an industry-sponsored project within CIE or a project within another subject. CIE mentors will also need to manage their relationship with the external sponsor of the project. View detailed information in the Handbook
Global Business Practicum · 12.5 pts	This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. View detailed information in the Handbook
Engineering Entrepreneurship · 12.5 pts	AIMS This subject is available as an elective in many of the Faculty of Engineering and IT Masters programs. It is aimed both at students who have immediate entrepreneurial intentions and at students who may be considering starting their own business at some point in their careers. The subject is designed to introduce all participants to their potential as entrepreneurs. By developing their own enterprise proposal within small groups, students will learn and demonstrate various processes by which successful new ventures move from idea to launch. INDICATIVE CONTENT Business modelling, opportunity analysis, value creation, financial management, sources of finance, creativity, innovation, entrepreneurial behaviour, successful engineering entrepreneurs. TEACHING METHOD The teaching method is based around a structured process of mini-lectures, class exercises, and active hands-on learning by doing. Intensive field research and minimum viable product development are very important to the subject. Learning is further enhanced through meetings with the lecturer and review by peers. View detailed information in the Handbook
Internship · 25 pts	AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship View detailed information in the Handbook
Nuclear Engineering · 12.5 pts	This subject provides an introduction to nuclear science and engineering. It presents the properties of atomic nuclei, radioactivity, nuclear reactions, and selected topics in thermodynamics as required for the analysis of power systems based on nuclear fission. The working principles of nuclear reactors and nuclear power plants are discussed, focusing on pressurised-water reactor systems. Indicative content: Introduction to nuclear physics Thermodynamics of nuclear power plants Nuclear power generation View detailed information in the Handbook
Radiation Protection · 12.5 pts	Nuclear technology involves the risk of exposure to ionising radiation, with potentially harmful effects on human health. This subject equips students with the necessary knowledge and skills to understand this risk and to manage it by applying established methods of radiation protection. Indicative content: Effects of ionising radiation on human health Methods of radiation detection and measurement Principles and methods of radiation protection Radiation shielding View detailed information in the Handbook
Engineering of Nuclear Systems · 12.5 pts	This subject presents nuclear reactor theory and its applications to reactor operation. It examines reactor response to control actions, feedback effects, and the intermediate and long-term effects on reactivity due to fission product poisoning and fuel burnup. Furthermore, it covers the fundamentals of thermal and hydraulic analysis of pressurised-water reactors. Indicative content: Nuclear reactor theory and engineering Reactor dynamics and control Effects of fuel burnup and the long-term evolution of the reactor core properties Heat generation and heat transfer from fuel to coolant Thermal design of nuclear reactors View detailed information in the Handbook
Nuclear Safety, Security and Safeguards · 12.5 pts	Safety, security and safeguards are critical requirements in the operation of nuclear facilities. This subject presents the safety aspects and safety assessment methods of nuclear power plants. Nuclear security and safeguards are discussed in the context of the nuclear fuel cycle. Indicative content: Nuclear fuel cycle Fundamentals of nuclear safety Safety systems and safety features of nuclear reactors Probabilistic safety assessment Nuclear security and safeguards View detailed information in the Handbook
Design Innovation and Leadership · 12.5 pts	A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects.  In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge.  Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication.  In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change?  How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information.  The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. View detailed information in the Handbook

No specialisation

Core

Year 1

Students must complete 100 points of Year 1 compulsory subjects.

Accordion
Intro. to Numerical Computation in C · 12.5 pts	AIMS Many engineering disciplines make use of numerical solutions to computational problems. In this subject students will be introduced to the key elements of programming in a high level language, and will then use that skill to explore methods for solving numerical problems in a range of discipline areas. INDICATIVE CONTENT Algorithmic problem solving Fundamental data types: numbers and characters Approximation and errors in numerical computation Fundamental program structures: sequencing, selection, repetition, functions Simple data storage structures, variables, arrays, and structures Roots of equations and of linear algebraic equations Curve fitting and splines Interpolation and extrapolation Numerical differentiation and integration View detailed information in the Handbook
Foundations of Electrical Networks · 12.5 pts	INDICATIVE CONTENT Foundations of Electrical Networks develops an understanding of fundamental modelling techniques for the analysis of systems that involve electrical phenomena. This includes networks models of “flow-drop” one-port elements in steady state (DC and AC), electrical power systems, simple RC and RL transient analysis, and networks involving ideal and non-ideal operational amplifiers. It forms the foundation of many engineering subjects exploring fundamental concepts in electrical and electronic engineering. The subject will cover key electrical engineering topics in the areas of: Electrical phenomena – charge, current, electrical potential, conservation of energy and charge, the generation, storage, transport and dissipation of electrical power. Network models – networks of “flow-drop” one-port elements, Kirchoff’s laws, standard current-voltage models for one-ports (independent sources, resistors, capacitors, inductors, transducers, diodes), analysis of static networks, properties of linear time-invariant (LTI) one-ports and impedance functions, diodes, transformers, steady-state (DC and AC) analysis of LTI networks via mesh and node techniques, equivalent circuits, and transient analysis of simple circuits; Electrical power systems – overview of power generation and transmission, analysis of single-phase and balanced three-phase AC power systems. Analysis and design of networks involving ideal and non-ideal operational amplifiers. This material will be complemented by exposure to software tools for the simulation of electrical and electronic systems and the opportunity to develop basic electrical engineering laboratory skills using a prototyping breadboard, digital multimeter, function generator, DC power supply, and oscilloscope. Please view this video for further information: Foundations of Electrical Networks View detailed information in the Handbook
Digital Systems · 12.5 pts	AIMS This subject develops a fundamental understanding of concepts used in the analysis, design and building of digital systems. Such systems form the information and communication technologies (ICT) that underpin modern society. This subject provides a foundation for subsequent subjects, including ELEN30013 Electronic System Implementation, ELEN90066 Embedded System Design and ELEN90061 Communication Networks. INDICATIVE CONTENT Topics include: Digital systems - quantifying and encoding information, digital data processing, design process abstractions; Combinational logic – timing contracts, acyclic networks, switching algebra, logic synthesis; Sequential logic – cyclic networks and finite-state machines, metastability, microcode; These topics will be complemented by exposure to the hardware description language such as Verilog and the use of engineering design automation tools and configurable logic devices (e.g. FPGAs) in the laboratory. Please view this video for further information: Digital Systems View detailed information in the Handbook
Electrical Network Analysis and Design · 12.5 pts	AIMS This subject develops a fundamental understanding of linear time-invariant network models for the analysis and design of electrical and electronic systems. Such models arise in the study of systems ranging from large-scale power grids to tiny radio frequency signal amplifiers. This subject is one of four subjects that define the Electrical Systems Major in the Bachelor of Science and it is a core requirement for the Master of Engineering (Electrical). It provides a foundation for various subsequent subjects, including ELEN30013 Electronic System Implementation, ELEN90066 Embedded System Design, and ELEN30012 Signal and Systems. INDICATIVE CONTENT Topics include: Transient and frequency domain analysis of linear time-invariant (LTI) models – linearity, time-invariance, impulse response and convolution, oscillations and damping, the Laplace transform and transfer functions, frequency response and bode plots, lumped versus distributed parameter transfer functions, poles, zeros, and resonance, stability of circuits, modelling and simulation with simulation tools; Electrical network models – one-port elements, impedance functions, two-port elements, dependent sources, matrix representations of two-ports, driving point impedances and network functions, ladder and lattice networks, passive versus active networks, multi-stage modelling and design, and multi-port generalisations; Analysis and design of networks involving ideal and non-ideal operational amplifiers with emphasis on the design of active filters and broadband circuits with specific frequency characteristics; Circuits and networks for managing voltage and power requirements for common electronic circuits. These topics will be complemented by tutorials and workshops designed to develop skills in design and modelling of electronic circuits through software tools and building, testing, and verification of electronic circuits. Please view this video for further information: Electrical Network Analysis and Design View detailed information in the Handbook
Electrical Device Modelling · 12.5 pts	AIM This subject develops the theoretical and practical tools required to understand, construct, validate and apply models of standard electrical and electronic devices. In particular, students will study the theoretical and practical development of models for devices such as resistors, capacitors, inductors, transformers, motors, batteries, diodes, transistors, and transmission lines. In doing so, students will gain exposure to a variety of fundamental fields in physics, including electromagnetism, semiconductor materials and quantum electronics. This material will be complemented by exposure to experiment design and measurement techniques in the laboratory, the application of models from device manufacturers, and the use of electronic circuit simulation software. INDICATIVE CONTENT Topics include: Vector calculus for device modelling, Maxwell’s equations, physics of conductors and insulators, passive device models (including for resistors, capacitors and inductors), lumped and distributed circuit models for wired interconnections (including treatment of signal integrity and termination strategies), semiconductors and quantum electronics, static and dynamic models for p-n junctions diodes and bipolar junction transistors. View detailed information in the Handbook
Signals and Systems · 12.5 pts	AIMS The aim of this subject is twofold: firstly, to develop an understanding of the fundamental tools and concepts used in the analysis of signals and the analysis and design of linear time-invariant systems path in continuous–time and discrete-time; secondly, to develop an understanding of their application in a broad range of areas, including electrical networks, telecommunications, signal-processing and automatic control. The subject formally introduces the fundamental mathematical techniques that underpin the analysis and design of electrical networks, telecommunication systems, signal-processing systems and automatic control systems. Such systems lie at the heart of the electrical engineering technologies that underpin modern society. This subject is one of four Level 3 subjects that define the Electrical Engineering Systems Major in the Bachelor of Science. . It provides the foundation for various subsequent subjects, including ELEN90057 Communication Systems, ELEN90058 Signal Processing and ELEN90055 Control Systems. INDICATIVE CONTENT Topics include: Signals – continuously and discretely indexed signals, important signal types, frequency-domain analysis (Fourier, Laplace and Z transforms), nonlinear transformations and harmonics, sampling; Systems – viewing differential / difference equations as systems that process signals, the notions of input, output and internal signals, block diagrams (series, parallel and feedback connections), properties of input-output models (causality, delay, stability, gain, shift-invariance, linearity), transient and steady state behaviour; Linear time-invariant systems – continuous and discrete impulse response; convolution operation, transfer functions and frequency response, time-domain interpretation of stable and unstable poles and zeros, state-space models (construction from high-order ODEs, canonical forms, state transformations and stability), and the discretisation of models for systems of continuously indexed signals. This material is complemented by exposure to the use of MATLAB for computation and simulation and examples from diverse areas including electrical engineering, biology, population dynamics and economics. View detailed information in the Handbook
Electronic System Implementation · 12.5 pts	AIMSThis subject provides students with hands-on electronic skills to gain basic competencies in design and implementation of simple circuits. Students will design with a range of standard electrical and electronic devices, basic circuit construction methods and electrical measurement techniques to test and verify the function of electronic systems. This subject is one of four subjects that define the Electrical Systems Major in the Bachelor of Science and it is a core requirement for the Master of Engineering (Electrical) and the Master of Engineering (Electrical with Business). This includes hands-on experience with: • Operation and selection of electrical and electronic devices used in various electronic circuits; • Common electronic circuit realisations to meet the most commonly required signal processing and conditioning applications; • Programmable digital circuits and microprocessor programming; • Circuit design and simulation tools; • Printed circuit board layout, circuit assembly, and soldering techniques; • Test and Measurement equipment and methods; • Managing design issues and requirements. Students will complete electronic circuit implementation projects in small groups and be required to prepare technical documentation and present project outcomes. INDICATIVE CONTENT • Devices such as resistors, capacitors, inductors, switches, transducers, motors, diodes, transistors, op-amps, voltage regulators, comparators, oscillators, timers, A/D and D/A converters, microprocessors and controllers; • Circuit functions and techniques such as buffering, referencing, signal conditioning, filtering, bridges, detection, waveform generation, and pulse-width modulation; • Microprocessor programming, the role of assembly and high-level languages, assemblers, compilers and debuggers; • PCB layout, circuit assembly, and soldering techniques; • Test and Measurement methods and working with common equipment such as multimeters and oscilloscopes. View detailed information in the Handbook
Engineering Mathematics · 12.5 pts	This subject introduces important mathematical methods required in engineering such as manipulating vector differential operators, computing multiple integrals and using integral theorems. A range of ordinary and partial differential equations are solved by a variety of methods and their solution behaviour is interpreted. The subject also introduces series including the concepts of convergence and divergence. Topics include: Vector calculus, including Gauss’ and Stokes’ Theorems; systems of homogeneous ordinary differential equations, including phase plane and linearisation for nonlinear systems; Laplace transforms; series, including Taylor series and power series; Fourier series and Fourier integrals; second order partial differential equations and separation of variables. View detailed information in the Handbook

Year 2

Students must complete 100 points of Year 2 compulsory subjects.

Accordion
Probability and Random Models · 12.5 pts	AIMS This subject provides an introduction to probability theory, random variables, random vectors, decision tests, and stochastic processes. Uncertainty is inevitable in real engineering systems, and the laws of probability offer a powerful way to evaluate uncertainty, to predict and to make decisions according to well-defined, quantitative principles. The material covered is important in fields such as communications, data networks, signal processing and electronics. This subject is a core requirement in the Master of Engineering (Electrical, Mechanical and Mechatronics). INDICATIVE CONTENT Topics include: Foundations – combinatorial analysis, axioms of probability, independence, conditional probability, Bayes’ rule; Random variables (rv’s)– definition; cumulative distribution, probability mass and probability density functions; expectation and variance; functions of an rv; important distributions and their properties and uses; Multiple random variables – joint cumulative distribution, probability mass and probability density functions; independent rv’s; correlation and covariance; conditional distributions and expectation; functions of several rv’s; jointly Gaussian rv’s; random vectors; Sums, inequalities and limit theorems – sums of rv’s, moment generating function; Markov and Chebychev inequalities; weak and strong laws of large numbers; the Central Limit Theorem; Decision testing - maximum likelihood, maximum a posterior, minimum cost and Neyman-Pearson rules; basic minimum mean-square error estimation; Stochastic processes – mean and autocorrelation functions, strict and wide-sense stationarity; ergodicity; important processes and their properties and uses; Introduction to Markov chains. This material is complemented by exposure to examples from electrical engineering and software tools (e.g. MATLAB) for computation and simulations. View detailed information in the Handbook
Control Systems · 12.5 pts	AIMS This subject provides an introduction to automatic control systems, with an emphasis on classical techniques for the analysis and design of feedback interconnections. The main challenge in automatic control is to achieve desired performance in the presence of uncertainty about the system dynamics and the operating environment. Feedback control is one way to deal with modelling uncertainty in the design of engineering systems. This subject is a core requirement in the Master of Engineering (Electrical, Electrical with Business, Mechanical, Mechanical with Business and Mechatronics). INDICATIVE CONTENT Topics include: * Modelling for control, linearization, relationships between time and frequency domain models of linear time-invariant dynamical systems, and the structure, stability, performance, and robustness of feedback interconnections; * Frequency-domain analysis and design, Nyquist and Bode plots, gain and phase margins, loop-shaping with proportional, integral, lead, and lag compensators, loop delays, and fundamental limitations in design; and * Actuator constraints and anti-windup compensation. This material is complemented by the use of software tools (e.g. MATLAB/Simulink) for computation and simulation, and exposure to control system hardware in the laboratory. View detailed information in the Handbook
Electronic Circuit Design · 12.5 pts	AIMS This subject provides an in-depth coverage of transistor (MOSFET and BJT) devices and their use in common circuits. In particular, students will study topics including: transistor operating modes and switching; principles of CMOS circuits; transistor biasing; current-source/emitter-amplifiers; low-frequency response; followers; class B amplifiers; current limiting; current sources and mirrors; differential pairs; feedback in amplifiers and stability; operational amplifiers; operational amplifier circuits; and voltage regulation. This material will be complemented by exposure to circuit simulation software tools and the opportunity to further develop circuit construction/test skills in the laboratory. INDICATIVE CONTENT Design-focused field-effect and bipolar elementary transistor models, and design of elementary amplifier stages and biasing circuits. Static and dynamic behaviour of amplifier circuits including frequency response, feedback and stability, slew-rate and clipping. Operational amplifiers and opamp based circuits; voltage regulators, references and voltage converters. Verification of electronic circuits using simulation and constructing them in the laboratory. Please view this video for further information: Electronic Circuit Design View detailed information in the Handbook
Communication Systems · 12.5 pts	AIMS This subject provides an introduction to the analysis and design of telecommunication signals and systems, in the presence of uncertainty. The emphasis is on understanding the basic concepts that underpin the physical layer of modern communication systems. INDICATIVE CONTENT Topics to be covered include: Introduction to communication systems including historical developments and comparisons between analogue and digital communications. Review of assumed knowledge from linear algebra, signals and systems and probability and random processes. The sampling theorem, analog-to-digital conversion, complex baseband representation of passband signals, filtering of random processes, power spectral density, bandwidth of random signals, additive white Gaussian noise (AWGN), signal-to-noise ratio. Communication over baseband AWGN channels including modulation techniques (pulse amplitude modulation, orthogonal modulation), signal space representation, optimal detectors, matched filters, error probability calculations and bandwidth / power trade-off. Communication over passband AWGN channels including modulation techniques (phase shift keying, quadrature amplitude modulation and frequency shift keying), optimal coherent detectors, noncoherent detectors and error probability calculations. Communication over linear time-invariant channels including concepts of distortion, inter-symbol interference, pulse shaping, Nyquist’s criterion, equalization, sequence detection and the Viterbi algorithm. Synchronization including carrier, symbol and frame synchronization. View detailed information in the Handbook
Signal Processing · 12.5 pts	AIMS This subject provides an introduction to the fundamental theory of time domain and frequency domain representation of discrete time signals and linear time invariant dynamical systems, and how this theory is used to analyse and design digital signal processing systems and algorithms. Topics include: Applications of signal processing techniques; Sampling of analog signals, anti-aliasing filters; Frequency-domain analysis of signals and systems, Discrete Time Fourier Transform, Discrete Fourier Transform, Fast Fourier Transform; Digital filters, low-pass, high-pass, band-pass, stop-band and all pass filters. Phase and group delay, FIR and IIR filters; Design of digital FIR and IIR filters; Multi-rate signal processing, with a focus on up-sampling, down-sampling, and sampling rate conversion; Simple non-parametric methods for spectral estimation. This fundamental material will be complemented by exposure to MATLAB tools for signal analysis and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory. INDICATIVE CONTENT Sampling of continuous time signals, Design of anti-aliasing filters, Time and frequency representation of discrete time signals and discrete time linear time invariant systems, Discrete Time Fourier Transform and z-transform and their properties, Low order lowpass, highpass, bandpass, bandstop filters, All-pass filter, Design of IIR filters using the bilinear transformation, Design of FIR filters with linear phase using windowing techniques and the Parks McClelland method, Discrete Time Fourier transform and its properties, Fast Fourier Transform, The use of the DFT in implementation of linear filtering algorithms, Up-sampling and down-sampling, multistage and computationally efficient implementations of up-samplers and down-samplers, Energy and power spectra for deterministic signals. View detailed information in the Handbook
Embedded System Design · 12.5 pts	AIMS This subject provides a practical introduction to the basics of modelling, analysis, and design of microprocessor-based embedded systems. Students will learn how to integrate computation with physical processes to meet a desired specification within the context of a design project. The project work will expose students to the various stages in an engineering project (design, implementation, testing and documentation) and a range of embedded system concepts. INDICATIVE CONTENT Topics covered may include: digital computer and microprocessor architectures, modelling of dynamic behaviours, control, models of computation, operating systems concepts, multi-tasking, resource management and real-time behaviours, interfacing with the physical world, analysis and verification, safety, reliability, and security and privacy. This material will be complemented by exposure to standard software tools including compilers and debuggers, finite state machine design and analysis software, and simulation tools. The subject will include a level of industry engagement, to provide broader examples of engineering projects, through guest lectures. View detailed information in the Handbook
Introduction to Power Engineering · 12.5 pts	AIMS To develop a solid foundation for the study of systems that involve the generation, transport, and conversion of electric power. INDICATIVE CONTENT Physical principles of electromagnetism, magnetic circuits, energy storage, loss mechanisms, electromechanical energy conversion. Modelling of transmission lines, transformers, motors and generators (synchronous and asynchronous), and other loads. Circuit theory for power system analysis, three phase-phase circuits, power flow and maximum power transfer, per-unit system. Please view this video for further information: Introduction to Power Engineering View detailed information in the Handbook
Interdisciplinary Design for Engineers · 12.5 pts	In this subject, students will actively engage in an interdisciplinary, collaborative and project-based learning environment, offering insights into the professional nature of engineering work. Through a real-world project, students will gain hands-on design experience addressing a complex challenge. The project will require students to integrate discipline knowledge and apply professional skills like teamwork and communication. Students will experience the entire engineering design process, covering problem definition, ideation, concept development, analysis, prototyping, testing and iteration. The project provides practical experience, equipping students with tools and methods to address complex challenges. Students are expected to integrate diverse perspectives, considering factors like stakeholders, sustainability (including environmental and social issues), safety, feasibility, and technical and ethical considerations. View detailed information in the Handbook

Capstone

Year 3

Students must complete 25 points of Year 3 compulsory capstone project subjects.

Accordion

Engineering Capstone Project Part 1 · 12.5 pts

The emphasis of the project can be associated with either:

A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or
A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the engineering science disciplines, often as an adjunct to existing academic research initiatives.

The project component of this subject is supplemented by a lecture course dealing with project management tools and practices.

Please note:

Students enrolled in the suite of Master of Engineering programs must be within the final 112.5 points of their degree to enrol.

Students enrolled in the Master of Industrial Engineering must be within the final 100 points of their degree to enrol.

View detailed information in the Handbook

Engineering Capstone Project Part 2 · 12.5 pts

Please refer to ENGR90037 Engineering Capstone Project Part 1 for this information.

View detailed information in the Handbook

Electives

Electrical Engineering Electives (Group A)

Students must complete a minumum of 50 points of Group A elective subjects.

Accordion
Lightwave Systems · 12.5 pts	AIMS Lightwave systems are fundamentally changing the way we communicate through broadband communications, helping clinicians to perform a range of medical procedures and diagnosis supported by advanced biomedical instrumentation, and even in the way we live in our homes through sophisticated interactive televisions and security systems. This subject will explore the physical principles and issues that arise in the design of lightwave systems often found in those key industry sectors. Students will study topics from: transmission of light over wave guides; production of light by lasers; light modulation; conversion of light signals to electrical signals; optical multiplexing and demultiplexing; light amplification; dispersion and dispersion compensation; optical nonlinearities; modulation and advanced detection schemes. This material will be complemented by exposure to lightwave systems and measurement techniques in the laboratory. INDICATIVE CONTENT This subject will explore the physical principles governing the generation, modulation, amplification, guiding, transmission, multiplexing, demultiplexing and detection of light and issues that arise in the design of lightwave systems such as transmission impairments, noise. Students learn selected examples of lightwave systems and methods for design, modelling and testing of simple lightwave systems. View detailed information in the Handbook
Power System Analysis · 12.5 pts	AIMS This subject provides an insight into the fundamental elements to analyse electrical power transmission and distribution systems, with both analytical and simulation tools for analysis of operations of these systems. Problems related to power flow and use of Newton-Raphson and other algorithms such as backward-forward sweep will be discussed. Fault calculation and analysis, symmetrical components, and analytical methods for solving symmetrical (balanced) faults will be covered. Principles, concepts and problems related to power system dynamics and control, particularly for frequency and voltage regulation, will be discussed and analysed in detail. Finally, small-signal, transient, voltage and frequency stability will be introduced and exemplified. Focus will be put on real-world examples, particularly to prepare the student for the ongoing transition towards a low-carbon grid dominated by renewables and distributed energy resources. INDICATIVE CONTENT Power flow calculations, Newton-Raphson, Gauss-Seidel and backward-forward sweep methods; Fault calculations, balanced and unbalanced, symmetrical components, fundamentals of protection; Frequency regulation and frequency stability, Voltage regulation in transmission and distribution networks, including use of flexible AC transmission systems (FACTS); Voltage stability, small-signal stability, transient stability; Computer simulations. View detailed information in the Handbook
Communication Networks · 12.5 pts	AIMS This subject introduces the basic principles, analysis, and design of communication networks. It covers relevant analytical methods, the layered network architecture of the Internet, and a multitude of network protocols. Analytical tools from queueing, optimisation, and graph theories are used to develop an in-depth understanding of basic principles and the role they play in network design. Specifically, queueing and graph theories are emphasised as methodological frameworks for communication network delay and structure analysis. The concepts taught in this subject lead to a better understanding of the Internet as well as modern communication paradigms such as Software-Defined Networks, Machine-to-Machine communication, Internet of Things, and social networks. INDICATIVE CONTENT Topics covered may include: The layered network architecture with a focus on physical-layer multiple access (TDM, FDM, WDM), link layer protocols and medium access control (MAC), network layer topologies, least-cost routing algorithms and protocols, transport layer protocols and the principles and techniques of practical reliable transport; LAN protocols, Ethernet, Wi-Fi, and serial communications; The Internet's network layer including the Internet Protocol (IP) and routing protocols including an introduction to BGP and the operation of forwarding tables in routers and shortest prefix routing; The Internet's transport layer protocols UDP and TCP, including the flow and congestion control algorithms; Network security, application layer, cloud and fog computing, Machine-to-Machine communication, and Internet of Things; Queuing theory: basics, birth-death processes, M/M/x and Markovian queues, networks of queues; Basics of graph theory and social network analysis relevant to communication networks. View detailed information in the Handbook
High Speed Electronics · 12.5 pts	AIMS The aim of the subject is to provide theoretical and practical treatment of high-speed electronics. Through the subject, students will grasp the fundamental properties and models of high-speed signals and interconnects, acquire high-speed digital design skills with a focus on the modelling, analysis, design and application of high speed transistors, logic gates and modern logic families, and master the high-speed analogue design capability including the design of oscillators and filters for RF applications. The students will be exposed to the state-of-the-art technologies that are shaping the fast evolving semiconductor industry. INDICATIVE CONTENT The topics include: Fundamental properties of analogue systems; Smith charts: principles and applications; High-speed analogue circuits: voltage control oscillators, matching networks, and low noise amplifiers; Bipolar junction transistors: device, switching, and logic; CMOS: device, switching and logic; High-speed signalling consideration: power dissipation, heat, signal propagation, and termination. View detailed information in the Handbook
Advanced Control Systems · 12.5 pts	AIMS This subject provides an introduction to modern control theory with a particular focus on design of advanced control laws via state-space methods and optimal control. The role of feedback in control design will be reinforced within this context, alongside the role of optimisation techniques in control system synthesis. INDICATIVE CONTENT Topics include: State-space models - first-order vector differential/difference equations; Lyapunov stability; linearisation; discretisation; Kalman decomposition (observable, detectable, reachable and stabilisable subspaces); state-feedback and pole placement; output-feedback and observer design in both continuous-time and discrete-time. Optimal control - dynamic programming; linear quadratic regulation in both continuous-time and discrete-time. Model predictive control in discrete-time; moving-horizon with constraints. View detailed information in the Handbook
Power Electronics · 12.5 pts	AIMS The aim of this subject is to understand the fundamental concepts and basic theory involved in modelling and analysis of the power electronic components that comprise power electronic devices such as power supplies, inverters, converters and their control systems. It is expected that at the end of this subject the student has a sound understanding of the physical concepts and mathematical models behind each of the basic components and of their functionality within a system, such as a high voltage DC transmission system. Furthermore this subject seeks to combine the fields of electronics, semiconductor devices, power system operation, power system measurement and control. It is expected that through this subject the students are exposed to examples of real electrical engineering systems where the three disciplines of electronics, power systems and control come together. INDICATIVE CONTENT Topics covered in this subject include: introduction to power semiconductor switches; discussion on the role of power electronics in the operation of electric power systems; models of power semiconductor devices and circuit components, including diodes, Thyristors, IGBT, Snubber circuits. Also basic concepts of single- and three-phase diode bridge rectifiers; single- and three-phase converters and inverters; operation and design of DC-AC inverters with emphases on switch-mode inverters, i.e. single- and three-phase inverters. Finally, the acquired knowledge of power electronic devices is applied to wind and PV solar systems where the design of voltage source converters and associated control loops are used to interface the wind/solar system with the power grid. View detailed information in the Handbook
Grid Integration of Renewables · 12.5 pts	AIMS This subject develops a foundation for pursuing electrical engineering oriented research in the area of sustainable energy systems. This subject aims to introduce the concepts behind smart grids, future low-carbon energy networks, sustainable electricity systems as well as the main renewable and low-carbon generation technologies. The subject will introduce students to tools and techniques so that distributed energy resources (e.g. distributed renewable generation, storage, electric vehicles, demand response, etc.) may be integrated effectively into the power system in the context of both traditional grids and future smart grids. INDICATIVE CONTENT This subject will cover the following topics: Distributed low-carbon technologies Introduction to distribution networks Introduction to distributed low-carbon technologies (wind energy, photovoltaic systems, electric vehicles, electric heating, storage) Wind Energy: impacts and challenges Photovoltaic systems: impacts and challenges Electric vehicles: impacts and challenges Electric heat pumps and electric heating: impacts and challenges Storage: impacts and challenges Smart Distribution and Smart Transmission Networks Distributed low-carbon technologies and active network management Towards Smart Grids Smart grids - Transmission and Distribution perspectives Smart Transmission: HVDC and FACTS, dynamic line rating, post-contingency security, special protection schemes The role of future Distribution System Operators Low-carbon Electricity System Towards low-carbon networks: relationship between sustainability and smart grids Introduction to low-carbon thermal generation (nuclear, Carbon Capture and Storage, Concentrated Solar Power, biomass, etc.) Utility-scale renewable technologies: wind farms; solar farms; other large-scale renewables; utility-scale batteries System-level operational challenges and solutions for renewables integration: variability and uncertainty; low-inertia operation; low system-strength operation; minimum load issues; DER visibility; indistinct events; general stability issues; flexibility System-level planning challenges and solutions for renewables integration: system adequacy and reliability; capacity credit of renewables and storage; extreme weather events and resilience; role of transmission Sector coupling and multi-energy systems: decarbonisation of gas, heating and transport; role of hydrogen Distributed energy systems: new technical and commercial architectures for two-sided systems and markets; demand response; aggregators and virtual power plants; distributed energy markets; peer-to-peer trading; local energy communities; microgrids View detailed information in the Handbook
System Optimisation & Machine Learning · 12.5 pts	This subject introduces the basic principles, analysis methods, and applications of optimisation and machine learning to engineering systems; encompassing fundamental concepts and practical algorithms. It covers the fundamentals of continuous optimisation followed by machine learning basics for engineering applications. The concepts and methods discussed are illustrated in multiple application areas including Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks.The concepts taught in this subject will allow a better understanding of continuous optimisation and machine learning for systems engineering. INDICATIVE CONTENT Topics covered may include: Fundamentals of continuous optimisation: convex sets and functions; local vs global solutions, constrained optimisation and Lagrange multipliers; linear, quadratic, and nonlinear programming Basics of machine learning encompassing supervised and unsupervised learning: binary classification, linear and nonlinear regression, kernel methods, and clustering. Specific machine learning methods such as Support Vector Machines (SVMs), Neural Networks (NNs), k-means clustering, and reinforcement learning. Applications to Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks. View detailed information in the Handbook
Communication Design Clinic · 12.5 pts	Students work collaboratively in small groups to implement and optimize components in a modern communication system or network with the goal of supporting a targeted application. To meet this goal students will need to: determine system requirements based on the target application and additional constraints; propose and evaluate multiple solutions through theoretical analysis and detailed simulations; implement, integrate, verify, and iterate on their selected solutions. Lectures will cast content from prerequisite subjects into the context at hand and cover additional topics relevant to the task. Each student group is expected to demonstrate initiative and independence while pursuing the goal of designing and optimizing their communication system or network, with a key focus being that students learn through hands-on experience. Students will receive early exposure to advanced topics critical to modern communication systems, such as: source and channel coding, multicarrier modulation, multiantenna transmission, and network architectures and protocols. Successful completion of the project will require the student to draw upon knowledge, understanding, and skills learned in prerequisite subjects, which may include: Communication Systems – analog-to-digital conversion, signal-to-noise ratio, modulation and demodulation, bandwidth/power trade-off, error probability calculations, distortion, inter-symbol interference, pulse shaping, equalization, sequence detection, and synchronization. Signal Processing - design and implementation of digital filters (low-, high-, band-, all- pass filters); ARMA systems; up-sampling and down-sampling. Embedded System Design – system-level programming, operating systems concepts, real-time issues, and standard software tools. Additional topics required for the assigned project may also be covered, such as: ideation, prototyping, and design practices; analog RF components; software packages for modelling and implementation; and the use of test & measurement equipment. View detailed information in the Handbook
Autonomous Systems Clinic · 12.5 pts	AIMS: Students work collaboratively in small groups to engineer an autonomous system that performs a specified task. This includes carrying out steps such as: task analysis; proposing multiple solutions; feasibility analysis through prototyping and computer-aided design; detailed design, construction, and testing of the chosen solution; and demonstrating the solution in a proving ground. The lectures will cast content from the pre-requisite subjects into the context of the task at hand, as well as covering additional topics relevant to the task. Each student group is expected to demonstrate initiative and independence while pursuing the goal of designing and building their autonomous system, with a focus of the subject being that students learn through hands-on experience, implementation, and verification. INDICATIVE CONTENT: Successful completion of the project requires the student to draw upon knowledge, understanding, and skills learned in the prerequisite subjects, namely: • Embedded System Design - including topics such as: finite, extended, and hierarchical state machines; modelling cyber-physical systems; scheduling, multi-tasking, and real-time issues; interfacing to the analogue world. • Control Systems - including topics such as: modelling; linearisation; feedback interconnections; proportional, integral, derivative (PID) control; actuator constraint considerations. • Signal Processing - including topics such as: design and implementation of digital filters (low-, high-, band-, all- pass filters); ARMA systems; up-sampling and down-sampling. Additional topics, specific to the task as hand, will be covered, such as: ideation, prototyping, and design practices; image processing and computer vision tools; software introductions; safety and failure analysis. A range of materials, components, and fabrication facilities are provided, from which the students are expected to utilise a subset for designing and building their autonomous system, such as: electric motors, range sensors, camera, voltage converters, compute power, sheet wood, soldering stations, laser wood cutting, 3D printing. The task to be performed is motivated by a real-world application of autonomous systems, such as: operating in hazardous environments or performing repetitive tasks. Please view this video for further information: Autonomous Systems Clinic View detailed information in the Handbook
Semiconductor Devices · 12.5 pts	This subject serves as an introduction to semiconductor devices. It describes the fundamentals, theory, material and physical properties of semiconductor devices. The following topics will be covered. Fundamentals: Crystal properties and of the growth of bulk crystals and of epitaxial layers. Physical concepts related to atoms and electrons. These concepts may include the photoelectric effect, the Bohr model, quantum mechanics, and the periodic table. Energy bands and charge carriers in semiconductors: Bonding forces and energy bands in solids, charge carriers in semiconductors, carrier concentrations, the drift of carriers in electric and magnetic fields, and the Fermi level. Excess carriers in semiconductors: Optical absorption, luminescence, carrier lifetime and photoconductivity, and the diffusion of carriers. Junctions: Fabrication of pn junctions, equilibrium conditions, forward and reverse biased junctions in steady state, reverse bias breakdown, transient and AC conditions, metal-semiconductor junctions and heterojunctions. In the next part of the subject PN junction diodes: Junction diodes, tunnel diodes, photodiodes, and light-emitting diodes and lasers. Bipolar junction transistors (BJTs): Amplification and switching, fundamentals of BJT operation, BJT fabrication, minority carrier distributions and terminal currents, generalised biasing, switching, the frequency limitations of transistors, and heterojunction bipolar transistors. Field effect transistors (FETs): Topics may include junction FETs, the metal semiconductor FET and the metal-insulator-semiconductor FET. Additional topics (if time permits): Integrated circuits, pnpn switching devices, and microwave devices. View detailed information in the Handbook
Low-carbon Grids: Operation & Economics · 12.5 pts	This subject introduces the student to foundational aspects of economic, secure and reliable operation of low-carbon power systems and electricity markets with large shares of variable and uncertain renewable energy sources. The underlying framework is the so-called “affordability-sustainability-security” energy trilemma, which seeks to strike a delicate balance among: the desire to operate power systems at low cost (“affordability”); the desire to meet specific environmental targets (“sustainability”); and the need to “keep the lights on” (“security”). In order for the energy trilemma to be analysed in the context of a competitive market environment, the subject will provide the student with fundamentals of economics, operation of electricity markets, optimal bidding strategies of different market stakeholders, economics of transmission and distribution networks, and role of new technologies and commercial entities such as storage and aggregators. Different aspects of power system security will be analysed, from system-level requirements and constraints to provision of security services from market stakeholders. Basic concepts of optimization, including linear, quadratic, and mixed integer linear programming, will also be taught to provide the student with the tools required to understand and model current and future power system and energy market operation. View detailed information in the Handbook
Microprocessor Design Clinic · 12.5 pts	Students in this subject will be introduced to computer architectures, microprocessors, microcontrollers, operating systems, compilers and software design. The proposed course will cover a broad range of topics necessary to make students knowledgeable in the art of microprocessor design including advanced concepts such as in line and out of order execution and execution unit resource optimisation. Students in this course will learn to design execution units, arithmetic logic units, memory hierarchies and learn strategies for cache sizing. As part of this, students will become proficient in microcode and instruction set design, multi-processor and multi core theory and design, including new design methodologies such as chiplet design. Upon completion, students will be familiar with the specification and synthesis of microprocessor systems using high level generator languages such as Chisel and Scala. The course will also introduce students to compiler and linker design, enhancements to instruction sets, c-language and the theory of operating systems. View detailed information in the Handbook
Large Data Methods & Applications · 12.5 pts	This course provides an introduction to an important contemporary statistical toolset for applications including data science, machine learning, signal processing, financial engineering, biomedical engineering, communication systems and other high-dimensional statistical applications. The course will cover topics including introduction to random matrix theory models in engineering; eigenvalue distributions; finite-dimensional and large-dimensional techniques, covariance estimation, principal component analysis and spectral clustering. These topics will be supplemented by applications across a range of traditional and emerging domains involving big data sets. View detailed information in the Handbook
Directed Studies · 12.5 pts	AIMS Directed studies provide the students with broader experience in addition to the regular class based learning. The directed studies can be conducted in the forms of: Industrial internship or research placements in the department’s research groups based on availability. This is only open to students who have completed a minimum of one semester of study and who have achieved an average of H2A or above in their prior subjects; Individually arranged supervised study of current research topics with staff members associated with the Department of Electrical and Electronic Engineering. INDICATIVE CONTENT The examples of the research topics are: Cloud Computing, Content Distribution and Information Logistics; Internet Services Energy Star Rating; Energy Efficiency of Future Modulation Formats; Low-Energy Fibre Access Networks; Video Coding for Energy Efficient Telecommunications; Fundamental Limits of Electronics and Photonics; Broadband fibre wireless networks and systems; Optimal design of few-mode fibres. View detailed information in the Handbook
AI for Robotics · 12.5 pts	AIMS: This subject focuses on the software and algorithms (i.e., artificial intelligence) that enable robotic systems to move autonomously through their environment and perform tasks. The key focus of this subject is the foundations of robotic systems that use software to move autonomously through their environment. This subject focus on the software & algorithms that enable the robot to perform tasks autonomously. Hence, this subject focused on artificial intelligence (AI) software & algorithms for robotics. The first main aim of the subject is to provide a foundation of the feedback loop that is core to all AI-enabled robots, namely: sensors measure the world around the robot; AI algorithms decide what action to take; the robot enacts that action by moving its joint or wheels; and the loop repeats endlessly. The second main aim of the subject is to provide implementation experience with cutting edge AI algorithm applicable to consumer and industrial robotics, where we consider both model-based method and reinforcement-learning methods. INDICATIVE CONTENT: Topics covered are at the intersection of automatic control and artificial intelligence, including: Cyber-physical feedback system formulation, such as: black-box and grey-box modelling, stability and robustness safety requirements, hierarchical and network control architectures. Safety and convergence guarantees for model-based methods, such as: learning models from data; adaptive control schemes; stability and robustness of PID and MPC control approaches. Connections between optimal control and reinforcement learning formulations for robotics. Reinforcement learning for robotics, such as: actor-critic methods, on-policy versus off-policy learning, sample efficiency, transferring simulation-based learning to real-world robots View detailed information in the Handbook
Hardware Accelerated Computing · 12.5 pts	Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include: Review of combinational and sequential digital logic FPGA architectures and fundamentals Hardware description languages (Verilog/VHDL) and hardware design flows High-level synthesis and OpenCL The use of parallelism, locality, and precision in hardware accelerators Host-accelerator interactions and hardware-software co-design Optimisation of hardware designs with respect to throughput, latency, energy, and area Accelerator design for selected applications such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application View detailed information in the Handbook
Electrical Engineering Research Project · 25 pts	This subject is for students to undertake a substantial individual research project on an approved topic over the semester, requiring independent investigation with a chosen supervisor either from a university (research institute) or from an industry partner. Note: the student is responsible for contacting the potential supervisor for the project. This subject can also be taken by Master of Electrical Engineering outgoing exchange students for research projects carried out in an overseas university. If the project is to be carried out within the EEE Department, the student is encouraged to take ELEN90011 (Directed Studies) if possible. The emphasis of the project can be associated with either A well-defined project description, often based on a task required by an external, industrial client. Students will be tutored in the synthesis of practical solutions to complex technical problems within a structured working environment, as if they were professional engineering practitioners; or A project description that will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, often as an adjunct to existing academic research initiatives. It is expected that the project will incorporate findings associated with both well-defined professional practice and research principles. View detailed information in the Handbook
Applied Deep Learning for Engineers · 12.5 pts	This subject covers a modern deep learning approach to engineering using a project-centric pedagogy. Building upon system optimisation and machine learning fundamentals presented in ELEN90088, the subject will present advanced deep learning architectures to address long-standing engineering challenges such as system complexity, curse of dimensionality, and modelling gap. Subject will specifically focus on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how advanced deep learning frameworks can be applied to modern engineering and cyber-physical systems. INDICATIVE CONTENT Topics covered may include: Latent spaces, auto encoder architectures. Advanced deep learning architectures, auto-differentiation, physics-inspired neural networks. Sequential data analysis and predictive models such as transformers. Generative models such as GANs and GPT variants. Other advanced topics such as meta parameter optimisation, Markov Chain Monte Carlo sampling. Distributed machine learning, federated learning, graph neural networks. Cyber-physical security of modern engineering systems, including data-based anomaly and threat detection and prediction. Subject projects will focus on engineering applications in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. View detailed information in the Handbook
Modelling and Analysis for AI · 12.5 pts	This subject builds up the fundamentals for modelling dynamical systems, with a key focus on the aspects and decisions of modelling that are relevant for the application of AI and data-intensive learning methods. The discussion and evaluation of modelling methods focuses on how model fidelity influences simulation-to-real transfer; how modelling and simulation decisions influence computation time required for training and validation; and how discrete-time models introduce complexity when representing continuous-time engineering systems. Subsequently, it introduces the basic principles and engineering applications of programming and data structures in a condensed form with a project-centric pedagogy. It covers the fundamentals of databases and data structures, basic algorithms, scientific programming, and classic AI problem solving. It will focus specifically on engineering problems from multiple application areas including Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. The concepts taught in this subject will lead to a better understanding of how programming and databases play a role in modern engineering and cyber-physical systems. INDICATIVE CONTENT Topics covered may include: Models for engineering systems in multiple disciplines, including analysis of what makes the models amenable to AI and data-intensive learning methods. Principles for simulating dynamic systems that are most relevant for the use of AI methods and to address these principles with existing software tools. Scientific programming for modelling using Python programming language and libraries such as scipy and numpy. Engineering data structures and time series data and their storage in SQL and noSQL databases. Example engineering applications will be taught via projects in areas such as Internet of Things (IoT), smart grid and power systems, robotics, cyber-security, and communication networks. View detailed information in the Handbook
Reinforcement Learning for Engineering · 12.5 pts	The key focus of this subject is the design and implementation of decision-making policies for enabling a dynamical system to behave autonomously and achieve a desired objective. This subject covers both model-based and model-free learning methods, with a focus on evaluating, contrasting, and combining methods. The influence of noisy sensor data on performance, and the trade-offs between exploration and exploitation during a learning phase, will also be covered. The examples used in this subject range across existing and emerging decision-making methods, and their application to consumer and industrial engineering systems. INDICATIVE CONTENT Topics covered may include: Reinforcement learning fundamentals such as principle of optimality, Bellman equation, value and policy iteration. Temporal-difference learning, Q-learning, Deep Q-learning, Actor critic methods and hybrid approaches in engineering context. Model based vs model free approaches, multi-agent RL and their engineering applications. RL methods for Cyber-physical resilience and security such as fuzzing methods. View detailed information in the Handbook
Optimisation for Industry · 12.5 pts	The use of mathematical optimisation is widespread in business, where it is a key analytical tool for managing and planning business operations. It is also required in many industrial processes and is useful to government and community organizations. This subject will expose students to operations research techniques as used in industry. A heavy emphasis will be placed on the modelling process that turns an industrial problem into a mathematical formulation. The focus will then be on how to solve the resulting mathematical problem with mixed-integer programming techniques. View detailed information in the Handbook
Distributed Systems · 12.5 pts	AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. View detailed information in the Handbook
Advanced Motion Control · 12.5 pts	AIMS This subject is intended to give students an overview of the present state-of-the-art in industrial motion control and the likely future trends in control design. Students will be exposed to and have practical experience in the design and implementation of advanced controllers for various motion control problems. Advanced modelling and control topics will include system identification, modelling and compensation of friction and other disturbances, industrial servo loops, model-based and model-free controller design, and adaptive control. Applications will be drawn from industrial, medical and transport automation (eg robots, machine tools, production machines, laboratory automation, automotive and aerospace by-wire systems). INDICATIVE CONTENT Advanced modelling and control topics will include system identification, modelling and compensation of friction and other disturbances, industrial servo loops, model-based and model-free controller design, and adaptive control. Applications will be drawn from industrial, medical and transport automation (eg robots, machine tools, production machines, laboratory automation, automotive and aerospace by-wire systems). View detailed information in the Handbook
Leadership for Innovation · 12.5 pts	This subject, which is offered to students who have completed ENGR90034 Creating Innovative Engineering (CIE), will give participants core leadership skills for managing professionals engaged in innovation and other ambiguous project-based work. The subject teaches leadership at three levels (12 hours each). The first level, taught intensively before the start of the semester, will enable you to learn basic management theory that allows you to bridge from the skills and theory taught in CIE to the level needed to start mentoring a team in CIE or another subject. The second level, taught as four three-hour workshops during the semester, will focus on key thematic issues in the leadership of innovative teams. The third level, taught in twelve one-hour sessions, will focus on specific leadership skills. These include facilitation, coaching, mentoring, conflict resolution, etc. Students will apply the theory and skills to the mentoring of a student project team in CIE or another subject within the University. You will apply what you are learning, and develop skills, by mentoring an industry-sponsored project within CIE or a project within another subject. CIE mentors will also need to manage their relationship with the external sponsor of the project. View detailed information in the Handbook

Approved Electives (Group B)

Students must complete a maximum of 25 points of Group B elective subjects.

Accordion
Global Business Practicum · 12.5 pts	This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. View detailed information in the Handbook
Engineering Entrepreneurship · 12.5 pts	AIMS This subject is available as an elective in many of the Faculty of Engineering and IT Masters programs. It is aimed both at students who have immediate entrepreneurial intentions and at students who may be considering starting their own business at some point in their careers. The subject is designed to introduce all participants to their potential as entrepreneurs. By developing their own enterprise proposal within small groups, students will learn and demonstrate various processes by which successful new ventures move from idea to launch. INDICATIVE CONTENT Business modelling, opportunity analysis, value creation, financial management, sources of finance, creativity, innovation, entrepreneurial behaviour, successful engineering entrepreneurs. TEACHING METHOD The teaching method is based around a structured process of mini-lectures, class exercises, and active hands-on learning by doing. Intensive field research and minimum viable product development are very important to the subject. Learning is further enhanced through meetings with the lecturer and review by peers. View detailed information in the Handbook
Internship · 25 pts	AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship View detailed information in the Handbook
Nuclear Engineering · 12.5 pts	This subject provides an introduction to nuclear science and engineering. It presents the properties of atomic nuclei, radioactivity, nuclear reactions, and selected topics in thermodynamics as required for the analysis of power systems based on nuclear fission. The working principles of nuclear reactors and nuclear power plants are discussed, focusing on pressurised-water reactor systems. Indicative content: Introduction to nuclear physics Thermodynamics of nuclear power plants Nuclear power generation View detailed information in the Handbook
Radiation Protection · 12.5 pts	Nuclear technology involves the risk of exposure to ionising radiation, with potentially harmful effects on human health. This subject equips students with the necessary knowledge and skills to understand this risk and to manage it by applying established methods of radiation protection. Indicative content: Effects of ionising radiation on human health Methods of radiation detection and measurement Principles and methods of radiation protection Radiation shielding View detailed information in the Handbook
Engineering of Nuclear Systems · 12.5 pts	This subject presents nuclear reactor theory and its applications to reactor operation. It examines reactor response to control actions, feedback effects, and the intermediate and long-term effects on reactivity due to fission product poisoning and fuel burnup. Furthermore, it covers the fundamentals of thermal and hydraulic analysis of pressurised-water reactors. Indicative content: Nuclear reactor theory and engineering Reactor dynamics and control Effects of fuel burnup and the long-term evolution of the reactor core properties Heat generation and heat transfer from fuel to coolant Thermal design of nuclear reactors View detailed information in the Handbook
Nuclear Safety, Security and Safeguards · 12.5 pts	Safety, security and safeguards are critical requirements in the operation of nuclear facilities. This subject presents the safety aspects and safety assessment methods of nuclear power plants. Nuclear security and safeguards are discussed in the context of the nuclear fuel cycle. Indicative content: Nuclear fuel cycle Fundamentals of nuclear safety Safety systems and safety features of nuclear reactors Probabilistic safety assessment Nuclear security and safeguards View detailed information in the Handbook
Design Innovation and Leadership · 12.5 pts	A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects.  In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge.  Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication.  In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change?  How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information.  The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. View detailed information in the Handbook

Master of Electrical Engineering

Course structure

Overview

Course structure

First year

Second and third year

Choose your specialisation

Artificial Intelligence

Autonomous Systems

Business

Electronics and Embedded Systems

Low-Carbon Power Systems

Industry, design and research subjects

Internship subject

Creating Innovative Engineering subject

Engineering Capstone subject

Handbook entries

Sample course plan

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Explore this course

Artificial Intelligence specialisation

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Electrical Engineering Electives (Group A)

AIMS

INDICATIVE CONTENT

Approved Electives (Group B)

Autonomous Systems specialisation

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INDICATIVE CONTENT

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Business specialisation

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Electrical Engineering Electives (Group A)

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Electronics and Embedded Systems specialisation

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Electrical Engineering Electives (Group A)

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Approved Electives (Group B)

Intelligent Networks and Communications specialisation

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Electrical Engineering Electives (Group A)