Unit of study_

# ELEC3305: Digital Signal Processing

## 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 ELEC3305 Electrical and Information Engineering 6 None None None 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. Yes

### Teaching staff

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

## Assessment

Type Description Weight Due Length
Tutorial quiz Tutorial Quizzes
An in-class tutorial quiz.
5% Multiple weeks 30 minutes
Outcomes assessed:
Skills-based evaluation Lab Quiz
In-class lab quiz
5% Multiple weeks 30 minutes
Outcomes assessed:
Programming
30% Week 12 Two hours
Outcomes assessed:
Presentation Teamwork Portfolio
10% Week 13 n/a
Outcomes assessed:
Theory
30% Week 13 Two hours
Outcomes assessed:
Assignment Lab Portfolio
5% Week 13 n/a
Outcomes assessed:
Assignment Tutorial Portfolio
5% Week 13 n/a
Outcomes assessed:
Assignment Something Awesome 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. 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 tutorial quizzes and you will submit a portfolio of your tutorial work at the end of the course.
• 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 lab quizzes and you will submit a portfolio of your lab work.
• Something Awesome: This project will require you to do something of your own choice related to signal processing.
• Team Portfolio: The tutorial and lab work will be in groups or teams and you will submit a portfolio to indicate your teamwork participation.
• Exams: Exams will be conducted during in-class sessions. There will be a practical exam and a theory exam.

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.

### 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.

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 Learning outcomes
Week 01 Introduction and discrete time systems Lecture (2 hr)
Week 02 Discrete time fourier transform and Z-transform Lecture and tutorial (4 hr)
Discrete time Fourier transform and Z-transform Computer laboratory (2 hr)
Week 03 Z-transform and sampling Lecture and tutorial (4 hr)
Z-transform and Sampling Computer laboratory (2 hr)
Week 04 Discrete fourier transform and convolution Lecture and tutorial (4 hr)
Discrete Fourier transform and convolution Computer laboratory (2 hr)
Week 05 Fast fourier transform Lecture and tutorial (4 hr)
Fast Fourier transform Computer laboratory (2 hr)
Week 06 Spectral analysis Lecture and tutorial (4 hr)
Spectral Analysis Computer laboratory (2 hr)
Week 07 Resampling Lecture and tutorial (4 hr)
Resampling Computer laboratory (2 hr)
Week 08 Polyphase decomposition and filter banks Lecture and tutorial (4 hr)
Polyphase decomposition and filter banks Computer laboratory (2 hr)
Week 09 ADC/DAC Lecture and tutorial (4 hr)
Week 10 Transform analysis and phase analysis Lecture and tutorial (4 hr)
Transform analysis and phase analysis Computer laboratory (2 hr)
Week 11 Structure of discrete time systems and quantization effects Lecture and tutorial (4 hr)
Structure of discrete time systems and quantization effects Computer laboratory (2 hr)
Week 12 Filter design Lecture and tutorial (4 hr)
Filter Design Computer laboratory (2 hr)

### Attendance and class requirements

There are no other requirements for this unit.

### 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.

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.

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

• LO1. Demonstrate mastery of analytical and mathematical skills related to signal processing. These 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, signal analyses, 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.

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.