Digital Signal Processing (ELEC4620)

Course overview

Advanced digital filtering: polyphase, multirate, all-pass, lattice and IIR filters. Signal conditioning, analogue filter types, sigma delta converters. Fast algorithms; Cooley-Tukey FFT, mixed radix formulations, Good-Thomas algorithm. Autoregressive, moving average signals. DSP applications and programming.

This course is an introduction to the exciting and rapidly advancing field of digital signal processing. These daysﾠDigital Signal Processing has largely replaced analog electronics for virtually all applications.ﾠDSP technology levers off computer hardware that becomes cheaper and faster every year, so a DSP solution will invariably replace analog solutions over time.ﾠ The course will be delivered in a practical manner and students will be asked to code algorithms in Matlab to demonstrate deep and practical understanding of the course material.ﾠDSP research strengths at UQ will be highlighted and applications of DSP in commodity devices such as CD and DVD players will be used as course examples.

Course Changes in Response to Previous Student Feedback

Course was rated highly by students last year so no significant changes were necessary. The exam will be invigilated and identity verified as it will be a traditional paper exam on-campus. Some modules on deep learning will be added due to the impact of this technology on modern DSP.

Course requirements

Assumed background

Some basic knowledge of Fourier Transforms, Signals, and Systems is assumed. MATLAB will be used for DSP assignments.

Prerequisites

You'll need to complete the following courses before enrolling in this one:

ELEC3004

Recommended prerequisites

We recommend completing the following courses before enrolling in this one:

CSSE3010

Incompatible

You can't enrol in this course if you've already completed the following:

ELEC3600 or ELEC4600 or ELEC7462 or ELEC7601 or ELEC760

Course contact

Course staff

Timetable

The timetable for this course is available on the UQ Public Timetable.

Aims and outcomes

Learning outcomes

After successfully completing this course you should be able to:

Assessment

Assessment summary

A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.

Assessment details

Assignment 1

Mode

Written

Category

Tutorial/ Problem Set

Weight

13%

Due date

16/08/2024 4:00 pm

Learning outcomes

L01, L05

Task description

Submission guidelines

Electronic submission via Blackboard using Gradescope

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.

Marked assignments with feedback and/or detailed solutions will be released to students within 14-21 days where the earlier time frame applies if no extensions.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

Assignment 2

Mode

Written

Category

Tutorial/ Problem Set

Weight

13%

Due date

13/09/2024 4:00 pm

Learning outcomes

L01, L02

Task description

Submission guidelines

Electronic submission via Blackboard using Gradescope

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.

Marked assignments with feedback and/or detailed solutions will be released to students within 14-21 days where the earlier time frame applies if no extensions.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

Assignment 3

Mode

Written

Category

Tutorial/ Problem Set

Weight

14%

Due date

25/10/2024 4:00 pm

Learning outcomes

L01, L03, L04

Task description

Submission guidelines

Electronic submission via Blackboard using Gradescope

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.

Marked assignments with feedback and/or detailed solutions will be released to students within 14-21 days where the earlier time frame applies if no extensions.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

Final Exam

• Hurdle

Mode

Written

Category

Examination

Weight

60%

Due date

End of Semester Exam Period

2/11/2024 - 16/11/2024

Learning outcomes

L03, L04

Hurdle requirements

Exam details

Submission guidelines

Deferral or extension

You may be able to defer this exam.

Course grading

Full criteria for each grade is available in the Assessment Procedure.

Additional course grading information

Partial marks may be awarded to incomplete answers to problem solving questions.

Each passing grade subsumes and goes beyond the grades lower than it. At the discretion of the course coordinator, final grades may be scaled upwards but not decreased.

Students must attain at least 40% in the final exam to be awarded a grade of 4 or more in the course. If less than 40% is obtained, grade is capped at 3.

Grades are calculated based on 100% mark that is comprised of : Assignment 1 (13%) + Assignment 2 (13%) + Assignment 3 (14%) + Final Exam (60%)

Supplementary assessment

Supplementary assessment is available for this course.

Additional assessment information

Use of AI Tools

This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.

Having Troubles?

If you are having difficulties with any aspect of the course material you should seek help. Speak to the course teaching staff.

If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help, for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things).

Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.

Learning resources

You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.

Library resources

Find the required and recommended resources for this course on the UQ Library website.

Learning activities

The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.

Additional learning activity information

Lectures

There are two hours of lectures each week. These will be delivered as live Echo260 lectures which will be also recorded and released via Blackboard:

Topics include:

Week 1: Linear Processing, DSP Operations, Sinusoids and Linear Systems, Complex Numbers and Phasors, Hetrodying, Fourier Series and Fourier Transforms

Week 2: Sines and Cosines, Hilbert Transformer, Even and Odd Functions, Fourier Symmetries, Implementation Issues, Circular Data, DFT Symmetries, Fourier Transform of Pulse, Fourier Transform of Pulse Train

Week 3: Sampling, Aliasing, Data Windowing, The Need for Windows, Vectors as Polynomials, Z Transform, Poles and Zeros, Convergence, Polynominal Approximations, Residues, FIR, IIR

Week 4: Non-Recursive Filters, Filter Design, Gibbs Effect, Windows, Sidelobes, Optimal Windows

Week 5: Windows Summary, Design Methods, Examples, Kaiser Window, Frequency Sampling Method

Week 6: Parks-McClellan Method, Minimax Design, Optimal Filters, Chebyshev Polynomials, Alternation Theorem, The Method, The Algorithm

Week 7: Types of Filter, Differentiator, Hilbert Transformer, Multi-Band, Quantization Effects

Week 8: Multirate Filtering, Resampling Filters, Polyphase Filters, Noble Identities, Downsampling, Upsampling, Digital Receiver Options, Polyphase Filter Partitions, Design Equations, Spectral Replication

Week 9: Single Rate Filtering with Polyphase Filters, White Noise Generator

Week 10: The DFT, DFT Tips and Tricks, FFT Tree, Radix 4 Cooley-Tukey Algorithm, Twiddle Factors, Radix-2 FFT, Scaling for Finite Arithmetic

Week 11: Recursive or IIR Filters, IIR Filter Structures, Linear Phase Filters, Sensitivity, Canonical Forms, Pole Sensitivity, First Order Sections

Week 12: All Pass Filter Structures, Microripple Filters, Digital Wave Filters

Week 13: Signal Conditioning, Sampling, Anti-Alias Filtering, Quantizing, Dual-Slope Converters, Flash Converters, Sigma-Delta Converters

 Concepts presented in Lectures will be supported by many MATLAB Demonstrations.

These lectures will be recorded.

 Tutorials/Pracs

Starting in Week 2, the following tutorial/laboratory sessions will be offered every week:

• Two hours of supervised tutorials/pracs in the computer laboratory to reinforce understanding of the course material. Active student participation is expected and students may come to any session.
• These sessions will not be recorded. So attend or you will miss out!
• Live Zoom tutorials will provide online support to the entire class. The sessions comprise help and advice on assignments for the whole class as well as one-on-one tutoring in breakout sessions.
• These sessions will be recorded.

Policies and procedures

University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:

• Student Code of Conduct Policy
• Student Integrity and Misconduct Policy and Procedure
• Assessment Procedure
• Examinations Procedure
• Reasonable Adjustments - Students Policy and Procedure

Learn more about UQ policies on my.UQ and the Policy and Procedure Library.

School guidelines

Your school has additional guidelines you'll need to follow for this course:

• EECS School Guidelines for Students