Unit of study_

# ELEC9305: 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.

### Details

Academic unit Electrical and Information Engineering ELEC9305 Digital Signal Processing Semester 1, 2022 Normal day Camperdown/Darlington, Sydney 6

### Enrolment rules

 Prohibitions ? ELEC5736 None None 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 No

### Teaching staff and contact details

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

## Assessment

Type Description Weight Due Length
Final exam (Record+) Theory Exam
Theory
30% Formal exam period 2 hours
Outcomes assessed:
Tutorial quiz Tutorial Quiz
Quiz on tutorial work.
10% Multiple weeks 30 minutes
Outcomes assessed:
Skills based evaluation Lab Quiz
Lab Quiz
10% Multiple weeks 30 minutes
Outcomes assessed:
Assignment Tutorial Assignment 1
Signal Processing Calculations
5% Week 07 Four weeks
Outcomes assessed:
Creative assessments / demonstrations Savitzy-Golay Filter Project
Team Lab Project 1
5% Week 08 Three weeks
Outcomes assessed:
Final exam (Practical) Practical Exam
Signal Processing Programming
30% Week 13 n/a
Outcomes assessed:
Creative assessments / demonstrations Real-time SONAR Project
Team Lab Project 2
5% Week 13 Four weeks
Outcomes assessed:
Assignment Tutorial Assignment 2
Signal Processing Calculations
5% Week 13 Four weeks
Outcomes assessed:
= group assignment
= Type B final exam
• 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 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.

### Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

## Weekly schedule

WK Topic Learning activity Learning outcomes
Week 01 Introduction to digital signal processing 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 using the DFT Lecture and tutorial (4 hr)
Spectral analysis using the DFT 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 response Lecture and tutorial (4 hr)
Transform analysis and phase response Computer laboratory (2 hr)
Week 11 Structures for discrete-time systems and quantization Lecture and tutorial (4 hr)
Structures for discrete-time systems and quantization Computer laboratory (2 hr)
Week 12 Filter Design Lecture and tutorial (4 hr)
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.

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 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.

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.