Outline

Overview

This unit aims to teach some of the basic properties of many engineering signals and systems and the necessary mathematical tools that aid in this process. The particular emphasis is on the time and frequency domain modeling of linear time invariant systems. The concepts learnt in this unit will be heavily used in many units of study (in later years) in the areas of communication, control, power systems and signal processing. A basic knowledge of differentiation and integration, differential equations, and linear algebra is assumed.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	(MATH1021 and MATH1002 and MATH1023) or (MATH1061 and MATH1062). Basic knowledge of differentiation and integration, differential equations, and linear algebra.
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Thomas Chaffey, thomas.chaffey@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final exam Final Exam	50%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
In-person written or creative task	Bonus extension quizzes Two bonus quizzes. Can be completed if marks in tutorial quizzes are HD to earn up to an extra 5%.	0%	Multiple weeks	30 min	AI prohibited
Outcomes assessed:
Out-of-class quiz	Tutorial prework Handwritten solution to prework question must be submitted each tutorial. A genuine attempt is worth 0.5%, up to a maximum of 4% (that is, 8 out of 10 tutorials). Week 3 tutorial prework is the Early Feedback Task.	4%	Multiple weeks	-	AI allowed
Outcomes assessed:
In-person written or creative task	Quiz 1 Secure canvas quiz held in tutorial. May be reattempted the following two tutorials, best mark of three is the final mark awarded. A mark of 70% is required to pass the course.	10%	Week 04	30 min	AI prohibited
Outcomes assessed:
In-person written or creative task	Quiz 2 Secure canvas quiz held in tutorial. May be reattempted the following two tutorials, best mark of three is the final mark awarded. A mark of 70% is required to pass the course.	15%	Week 07	30 min	AI prohibited
Outcomes assessed:
Practical skill	Lab 1 milestones Oral assessment of lab 1 milestones and prework during lab.	3%	Week 07	-	AI allowed
Outcomes assessed:
Practical skill	Lab 2 milestones Oral assessment of lab 2 milestones and prework during lab.	3%	Week 09	-	AI allowed
Outcomes assessed:
In-person written or creative task	Quiz 3 Secure canvas quiz held in tutorial. May be reattempted the following two tutorials, best mark of three is the final mark awarded. A mark of 70% is required to pass the course.	15%	Week 11	30 min	AI prohibited
Outcomes assessed:
= hurdle task ? = group assignment ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Final exam (50%) – Complete a two-hour secure written examination during the formal exam period. The exam assesses your understanding of the full range of unit learning outcomes, including signal analysis, linear time-invariant systems, Fourier and Laplace transforms, filter design, modulation, stability, and system identification.
Quiz 1 (10%) – Complete a 30-minute quiz during your scheduled tutorial using Canvas with a locked-down browser. The quiz assesses foundational concepts from the first part of the unit. You may reattempt the quiz in either of the following two tutorial weeks, with your highest mark counting. This is a hurdle assessment: you must achieve at least 70% to meet the required standard.
Quiz 2 (15%) – Complete a 30-minute quiz during your scheduled tutorial using Canvas with a locked-down browser. The quiz covers material including LTI systems, convolution, Fourier series, Fourier transforms and related topics. You may reattempt the quiz in either of the following two tutorial weeks, with your highest mark counting. This is a hurdle assessment: you must achieve at least 70% to meet the required standard.
Quiz 3 (15%) – Complete a 30-minute quiz during your scheduled tutorial using Canvas with a locked-down browser. The quiz covers later unit topics, including frequency response, stability, Laplace transforms and filter design. You may reattempt the quiz in either of the following two tutorial weeks, with your highest mark counting. This is a hurdle assessment: you must achieve at least 70% to meet the required standard.
Bonus extension quizzes (up to 5 bonus marks) – Students who achieve High Distinction-level performance in the tutorial quizzes may attempt two optional extension quizzes using Canvas with a locked-down browser to earn up to five bonus marks. These quizzes are designed to challenge your understanding of advanced unit concepts.
Lab 1 milestones (3%) – Work in a group to complete the first laboratory activity and demonstrate your progress during the lab. You will be assessed orally on your preparation, pre-work, and achievement of the required laboratory milestones.
Lab 2 milestones (3%) – Work in a group to complete the second laboratory activity and demonstrate your progress during the lab. You will be assessed orally on your preparation, pre-work, and successful completion of the laboratory milestones.
Tutorial prework (4%) – Submit a handwritten solution demonstrating a genuine attempt at the weekly tutorial prework question before each tutorial. Marks are awarded for completion rather than correctness, 0.5% per submission up to a maximum of 4% across the semester. The Week 3 prework also serves as the Early Feedback Task.

Note: Must meet the required standard in the hurdle assessment(s) in order to pass the unit.

Detailed information for each assessment (e.g. assessment rubrics) and submission instructions are published on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

All assessments will be conducted in class and no late submissions will be accepted.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction and background: signals, systems, differential equations and Euler's formula	Lecture (2 hr)
Week 01	Revision of differential equations and complex numbers	Self-directed learning (6 hr)
Week 02	Signals as vectors, systems as maps	Lecture (2 hr)
	Complex numbers, differential equations, signals	Tutorial (2 hr)
	Signals as vectors, systems as maps	Self-directed learning (6 hr)
Week 03	Linear time-invariant (LTI) systems, impulse response and convolution	Lecture (2 hr)
	Linear time-invariant (LTI) systems, impulse response and convolution	Tutorial (2 hr)
	Linear time-invariant (LTI) systems, impulse response and convolution	Self-directed learning (6 hr)
Week 04	Periodic signals and the Fourier series	Lecture (2 hr)
	Periodic signals and the Fourier series	Tutorial (2 hr)
	Periodic signals and the Fourier series	Self-directed learning (6 hr)
Week 05	Aperiodic signals and the Fourier transform	Lecture (2 hr)
	Aperiodic signals and the Fourier transform	Tutorial (2 hr)
	Aperiodic signals and the Fourier transform	Self-directed learning (6 hr)
Week 06	Frequency response	Lecture (2 hr)
	System characterisation	Practical (2 hr)
	Frequency response	Self-directed learning (6 hr)
Week 07	System response and stability	Lecture (2 hr)
	System response and stability	Tutorial (2 hr)
	System response and stability	Self-directed learning (6 hr)
Week 08	The Laplace transform	Lecture (2 hr)
	The Fourier transform	Practical (2 hr)
	The Laplace transform	Self-directed learning (6 hr)
Week 09	System analysis with the Laplace transform	Lecture (2 hr)
	System analysis with the Laplace transform	Tutorial (2 hr)
	System analysis with the Laplace transform	Self-directed learning (6 hr)
Week 10	Filter design	Lecture (2 hr)
	System analysis and filter design	Tutorial (2 hr)
	Filter design	Self-directed learning (6 hr)
Week 11	Signal modulation	Lecture (2 hr)
	Signal modulation	Tutorial (2 hr)
	Signal modulation	Self-directed learning (6 hr)
Week 12	System identification	Lecture (2 hr)
	System identification	Tutorial (2 hr)
	System identification	Self-directed learning (6 hr)
Week 13	Advanced topics	Lecture (2 hr)
	Advanced topics	Tutorial (2 hr)
	Advanced topics	Self-directed learning (6 hr)

Attendance and class requirements

Quizzes may only be attempted during tutorials and are a hurdle task: a mark of 70% is required in each quiz to pass the course. Lab attendance is mandatory.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Analyse the behaviour of a linear, time invariant system using differential equation, frequency domain and convolution representations.
LO2. Calculate basic properties of continuous time signals such as energy and power.
LO3. Decompose a periodic signal into its harmonic components using the Fourier series.
LO4. Derive basic properties of the Fourier and Laplace transforms.
LO5. Compute the spectrum of a signal using the Fourier transform.
LO6. Design linear filters using frequency response.
LO7. Analyse the stability of an LTI system using the Laplace transform.
LO8. Analyse amplitude modulated signals in the time and frequency domains.
LO9. Construct models of LTI systems from time and frequency domain measurements.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This unit is being taught by a new teaching team this semester, with a new course outline. Please let us know what you think as the course progresses!

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

ELEC2302: Signals and Systems

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links