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

Unit code	ELEC9305
Academic unit	Electrical and Information Engineering
Credit points	6
Prohibitions ?	ELEC5736
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	Specifically the following concepts are assumed knowledge for this unit: 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	No

Teaching staff

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

Type	Description	Weight	Due	Length
In-semester test	Practical Exam Programming	30%	Week 13	Two hours
Outcomes assessed:
In-semester test	Theory Exam Theory	30%	Week 13	Two hours
Outcomes assessed:
Presentation	Teamwork Portfolio	10%	Week 13	n/a
Outcomes assessed:
Assignment	Something Awesome Portfolio	10%	Week 13	n/a
Outcomes assessed:
Assignment	Tutorial Portfolio	10%	Week 13	n/a
Outcomes assessed:
Assignment	Lab Portfolio	10%	Week 13	n/a
Outcomes assessed:
= group assignment ?

Assessment summary

Tutorials: Tutorials will include analytical problem solving sessions on the material covered in the lectures and computer aided solution/illustration. The tutorial assessment scheme requires preparation work and aims to enhance your communication skills. The solutions for the tutorials and computer codes will be available from the unit of study web page after the session.
Labs: Laboratories are designed to introduce you to the DSP hardware and give you some experience in report writing. They require you to do some design, make measurements and perform demonstrations. You need to submit a brief written lab report (worth 3%) for at least one of three labs. Your best lab report mark will be counted towards the final assessment.
Project: Project will require you to design and test a simple DSP system, write a report and do the demonstration of your work. It is optional and worth a bonus mark of 10% (however, if you end up with a total mark of over 100 for the unit then it will be rounded down to 100). It is expected that you would enjoy the challenge.
Midterm exam: The midterm exam is scheduled to provide you an assessment halfway through the semester and more importantly to give you a practice run for the final exam. It will be of the same format as the final exam (but of shorter duration). Again the solutions will be available on the unit of study web page after the exam. Both the midterm exam and the final exam will be based on the lecture material and tutorials. Both exams will be closed book and closed notes. They will test your conceptual understanding of the material. Any complex formulae needed, will be provided on the question paper.
Critical self-reflection essay: The critical self-reflection essay helps you to identify your strengths and weaknesses and thus take charge of your learning.

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
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.

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.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to digital signal processing	Lecture (2 hr)
Week 02	Discrete time Fourier transform and Z-transform	Lecture and tutorial (4 hr)
Week 02	Discrete time Fourier transform and Z-transform	Computer laboratory (2 hr)
Week 03	Z-transform and Sampling	Lecture and tutorial (4 hr)
Week 03	Z-transform and Sampling	Computer laboratory (2 hr)
Week 04	Discrete Fourier transform and Convolution	Lecture and tutorial (4 hr)
Week 04	Discrete Fourier transform and Convolution	Computer laboratory (2 hr)
Week 05	Fast Fourier Transform	Lecture and tutorial (4 hr)
Week 05	Fast Fourier transform	Computer laboratory (2 hr)
Week 06	Spectral analysis using the DFT	Lecture and tutorial (4 hr)
Week 06	Spectral analysis using the DFT	Computer laboratory (2 hr)
Week 07	Resampling	Lecture and tutorial (4 hr)
Week 07	Resampling	Computer laboratory (2 hr)
Week 08	Polyphase decomposition and filter banks	Lecture and tutorial (4 hr)
Week 08	Polyphase decomposition and filter banks	Computer laboratory (2 hr)
Week 09	ADC/DAC	Lecture and tutorial (4 hr)
Week 09	ADC/DAC	Computer laboratory (2 hr)
Week 10	Transform analysis and phase response	Lecture and tutorial (4 hr)
Week 10	Transform analysis and phase response	Computer laboratory (2 hr)
Week 11	Structures for discrete-time systems and quantization	Lecture and tutorial (4 hr)
Week 11	Structures for discrete-time systems and quantization	Computer laboratory (2 hr)
Week 12	Filter Design	Lecture and tutorial (4 hr)
Week 12	Filter Design	Lecture and tutorial (2 hr)

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.

Joyce Van de Vegte, Fundamentals of Digital Signal Processing (first). Prentice Hall, 2002. 0130160776.

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. Demonstrate mastery of analytical and mathematical skills related to signal processing. These skills include convolutions, transforms, spectral analyses, linear difference equations, filters, correlation and covariance, rudimentary information theory.
LO2. Demonstrate proficiency in developing signal processing software to solve signal processing problems and tasks. These include spectral analyses, filtering, inverse filtering, resampling, signal modelling, deep learning for signals.
LO3. Plan, design and review signal processing systems
LO4. Apply diverse strategies to develop and implement innovative ideas in signal processing systems.
LO5. Present compelling oral, written, and graphic evidence to communicate signal processing practice.
LO6. Contribute as an individual to a team to deliver signal processing related projects.

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.

Modified learning outcomes, assessment, and learning activities.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

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

Media

Student links

About us

Connect

Current students

ELEC9305: Digital Signal Processing

Semester 1, 2020 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

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

Media

Student links

About us

Connect