Outline

Overview

This unit aims to teach how signals are processed by computers. It describes the key concepts of digital signal processing, including details of various transforms and filter design. Students are expected to implement and test some of these ideas on a digital signal processor (DSP). Completion of the unit will facilitate progression to advanced study in the area and to work in the industrial use of DSP. The following topics are covered. Review of analog and digital signals. Analog to digital and digital to analog conversion. Some useful digital signals. Difference equations and filtering. Impulse and step response of filters. Convolution representation of filters. The Z-transform. Transfer functions and stability. Discrete time Fourier transform (DTft) and frequency response of filters. Finite impulse response (FIR) filter design: windowing method. Infinite impulse response (IIR) filter design: Butterworth filters, Chebyshev filters, Elliptic filters and impulse invariant design. Discrete Fourier Transform (Dft): windowing effects. Fast Fourier Transform (Fft): decimation in time algorithm. DSP hardware.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Familiarity with basic Algebra, Differential and Integral Calculus, continuous linear time-invariant systems and their time and frequency domain representations, Fourier transform, sampling of continuous time signals
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Craig Jin, craig.jin@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Theory exam Theory	20%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Out-of-class quiz	Weekly Lecture/Tutorial Questions Online Lecture/Tutorial Questions with Multiple Attempts	10%	Multiple weeks Due date: 30 May 2025 at 23:59	-	AI allowed
Outcomes assessed:
Practical skill	Lab Participation Lab Participation with Work Submission	9%	Multiple weeks	-	AI allowed
Outcomes assessed:
Portfolio or journal	Something Awesome Blog Create one blog entry indicating how Signal Processing relates to Real Life Experience	1%	Week 03 Due date: 23 May 2025 at 23:59	-	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Mid-Semester Practical/Written Exam Mid-Semester Practical/Written Exam	20%	Week 07	-	AI prohibited
Outcomes assessed:
Practical skill	Written Assignment Signal Processing Calculations	10%	Week 10 Due date: 25 May 2025 at 23:59	-	AI allowed
Outcomes assessed:
Experimental design	Air-SONAR Project Team Lab Project	5%	Week 12	-	AI allowed
Outcomes assessed:
Presentation	Air-SONAR Project: Video Team Lab Project	5%	Week 12 Due date: 25 May 2025 at 23:59	-	AI allowed
Outcomes assessed:
Practical test	Practical Lab Final Exam Practical Lab Final Exam in Week 13	20%	Week 13	2 hours	AI prohibited
Outcomes assessed:
= group assignment ?

Assessment summary

Conceptual Review Quizzes on the Lectures: Review questions highlighting the most significant aspects of the lecture are provided each week in an on-going manner. You will have multiple attempts at answering these questions.
Tutorials: Tutorials will include analytical problem solving sessions on the material covered in the lectures and computer aided solution / illustration. These sessions will give you the opportunity to explore the concepts in detail and are very helpful in understanding the material covered in the lecture. Please see the unit of study web page for the details of tutorial assessment scheme. It stresses the importance of your preparation work and enhances your presentation skills. There will be regular in-class tutorial quizzes and you will submit a portfolio of your tutorial work.
Labs: Laboratories are designed to introduce you to modern signal processing platforms. They will require you to develop working software. You will hopefully enjoy doing them. There will be regular in-class lab quizzes.
Team Project: An Air-SONAR project will be completed in small teams. You will first Design and Plan a SONAR system and then Implement a Real-Time system.
Written Assignments: The written assignments and the final theory exam will be similar. The written assignments provide you the opportunity to clearly describe solutions to specific DSP questions that form the core of the course.
Something Awesome Blog: This project will require you to blog on something of your own choice related to signal processing.
Exams: There will be a practical exam and a theory exam. The practical exam will taken in Week 13 and the theory exam will be taken during the formal exam period.

Detailed information for each assessment can be found 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	Demonstrates an outstanding level of understanding and application of DSP concepts, theories, and techniques. Solutions are exceptionally well-structured, with rigorous mathematical analysis, efficient implementation, and insightful interpretation of results. Code (if applicable) is optimized, well-documented, and demonstrates advanced problem-solving. Shows originality and critical thinking in approaching problems.
Distinction	75 - 84	Demonstrates a strong understanding of DSP concepts, with accurate and well-explained solutions. Mathematical and computational techniques are correctly applied, with minor errors or inefficiencies. Shows a good ability to analyze and interpret results with logical reasoning. Work is well-organized and mostly free of conceptual misunderstandings.
Credit	65 - 74	Demonstrates a competent understanding of DSP principles with generally correct solutions. Some errors in application or analysis, but key concepts are understood. Interpretation of results is present but may lack depth. Coding (if applicable) is functional but may have inefficiencies or missing comments. Requires some refinement in explanations and problem-solving approach.
Pass	50 - 64	Demonstrates a basic but sufficient understanding of DSP concepts. There are noticeable errors in calculations, coding, or analysis, but the fundamental ideas are present. Work may lack depth, clarity, or coherence. Limited interpretation of results, with minimal critical thinking. Requires improvement in technical accuracy and problem-solving skills.
Fail	0 - 49	Does not meet the learning outcomes of the unit to a satisfactory standard. Shows significant misunderstandings or gaps in DSP concepts. Work is incomplete, incorrect, or lacks proper structure. Solutions are unclear, calculations are mostly incorrect, and coding (if applicable) is non-functional or absent. Minimal effort in explaining or interpreting results.

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:

The conceptual lecture review quizzes, tutorial quizzes, lab quizzes and blog are on-going and do not incur a late penalty. There is a late penalty for the Written Assignments and Team Projects following University standard practice which is generally five-percent late penalty per day.

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 to Digital Signal Processing	Lecture (2 hr)
Week 01	Introduction to Digital Signal Processing	Tutorial (2 hr)
Week 02	Signal Models and Introduce the Air-SONAR Project	Lecture (2 hr)
	Signal Models and Introduce the Air-SONAR Project	Practical (2 hr)
	Signal Models and Introduce the Air-SONAR Project	Tutorial (2 hr)
Week 03	Convolution, Impulse Response, Frequency Response	Lecture (2 hr)
	Convolution, Impulse Response, and Frequency Response	Practical (2 hr)
	Convolution, Impulse Response, Frequency Response	Tutorial (2 hr)
Week 04	Discrete-Time Fourier Transform and Filter Design Using Package Software	Lecture (2 hr)
	Discrete-Time Fourier Transform and Filter Design Using Package Software	Practical (2 hr)
	Discrete-Time Fourier Transform and Filter Design Using Package Software	Tutorial (2 hr)
Week 05	Z-Transform and Transfer Functions	Lecture (2 hr)
	Z-Transform and Transfer Functions	Practical (2 hr)
	Z-Transform and Transfer Functions	Tutorial (2 hr)
Week 06	Characterize FIR/IIR Filters Using the Z-Transform and Introduction to Block Diagrams and Introduce Air-SONAR Project Part 2	Lecture (2 hr)
	Characterize FIR/IIR Filters Using the Z-Transform and Introduction to Block Diagrams and Introduce Air-SONAR Project Part 2	Practical (2 hr)
	Characterize FIR/IIR Filters Using the Z-Transform and Introduction to Block Diagrams and Introduce Air-SONAR Project Part 2	Tutorial (2 hr)
Week 07	Making the DTFT Discrete (DFT)	Lecture (2 hr)
	Making the DTFT Discrete (DFT)	Practical (2 hr)
	Making the DTFT Discrete (DFT)	Tutorial (2 hr)
Week 08	Properties of the DFT and Achieving Linear Convolution via Circular Convolution	Lecture (2 hr)
	Properties of the DFT and Achieving Linear Convolution via Circular Convolution	Practical (2 hr)
	Properties of the DFT and Achieving Linear Convolution via Circular Convolution	Tutorial (2 hr)
Week 09	Fast Fourier Transform	Lecture (2 hr)
	Fast Fourier Transform	Practical (2 hr)
	Fast Fourier Transform	Tutorial (2 hr)
Week 10	Spectral Analysis Using the DFT	Lecture (2 hr)
	Spectral Analysis Using the DFT	Practical (2 hr)
	Spectral Analysis Using the DFT	Tutorial (2 hr)
Week 11	Sampling and Aliasing, Shannon Sampling Theorem	Lecture (2 hr)
	Sampling and Aliasing, Shannon Sampling Theorem	Practical (2 hr)
	Sampling and Aliasing, Shannon Sampling Theorem	Tutorial (2 hr)
Week 12	Resampling and Practical ADC/DAC	Lecture (2 hr)
	Resampling and Practical ADC/DAC	Practical (2 hr)
	Resampling and Practical ADC/DAC	Tutorial (2 hr)
Week 13	Course Review	Lecture (2 hr)
	Practical Exam in Class	Practical (2 hr)
	Course Review	Tutorial (2 hr)
Weekly	Independent Study (Review Quizzes, Something Awesome Blog, Readings)	Self-directed learning (4 hr)

Attendance and class requirements

You are expected to attend lectures, tutorials and labs.

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.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

Alan Oppenheim and Robert Schafer, Discrete Time Signal Processing (third). Pearson, 2014. 978-1-292-02572.

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. Describe signals using mathematical concepts such as convolutions, transforms, spectral analyses, linear difference equations, filters, correlation and covariance
LO2. Use software, such as MATLAB and Python, to develop code to analyse and process signals including filtering, inverse filtering, spectral analyses, resampling, time-frequency analyses
LO3. Identify linear signal models for time-invariant systems such as convolutional models, sinusoidal and harmonic models, echo and reverberation models, modulation models and time-frequency models.
LO4. Design and evaluate signal processing systems.

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.

Learning outcomes were simplified and made more direct using Bloom's/Fink's taxonomy. Assessment and learning activities have been revised to improve learning outcomes and reduce workload. The course material is being adjusted to teach at the "Edges". Simplified descriptions will be provided as well as more advanced descriptions.

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

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

ELEC3305: Digital Signal Processing

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links