Course coordinator
Associate Professor Jen Jen Chung
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
Discrete-time signals and systems, system properties (linearity, time-invariance, memory, causality, stability), sampling and reconstruction, A/D and D/A converters, DFT/FFT, z transform, stochastic processes, frequency-selective filters, effect of feedback, introduction to control.
This course covers the fundamental theory and practiceﾠof signal processing, systems analysis and control.ﾠﾠIt is about seeing a system and a signal for what it is -- information in motion.
Some of the topics touched by the class include: Linear algebra, a mathematical perspective on signals (singularity functions, complex exponentials and geometrics), system properties (linearity, time-invariance, memory, causality, stability), Fourier representations, Laplace and Z transforms, sampling & reconstruction, A/D and D/A converters, DFT/FFT, stochastic processes, filters, representations of linear, time-invariant systems (difference and differential equations, block diagrams, system functions, poles and zeros, convolution, impulse and step responses, frequency responses), the effects of feedback, and an introduction to discrete digital control. Applications are drawn broadly from engineering, including feedback and control, signal processing and computer science (e.g. computer vision, computer graphics and robotics).
Course Changes in Response to Previous Student Feedback
Course material has been reworked to deal with the three components of signals, systems and control in a cohesive manner and with an emphasis on discrete-time, digital signals and systems.
Course requirements
Assumed background
Students should have completed ELEC2004, STAT2201/STAT2202 and MATH2000/MATH2001. It will be assumed that students have an understanding of linear algebra and familiarity with programming in MATLAB. Students are expected to have some familiarity with basic C/C++ programming (Arduino) concepts which will be used in practicals but C/C++ programming is not directly assessed in the course.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ELEC2004 and (STAT2201 or STAT2202) and (MATH2000 or MATH2001)
Incompatible
You can't enrol in this course if you've already completed the following:
ELEC3600 or ELEC7312 or ELEC7601
Course contact
Course staff
Lecturer
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
This course covers the modelling, analysis, and design of linear systems with an emphasis on their discrete-time, digital implementations. The course aims are as follows:
- At the end of this course, students should acquire the skills to apply concepts of linear signals and systems theory including singularity functions, complex exponentials and geometrics, system properties (linearity, time-invariance, memory, causality, stability), Fourier representations, Laplace and Z transforms, sampling & reconstruction, A/D and D/A converters, DFT/FFT, stochastic processes, filters, representations of linear, time-invariant systems (difference and differential equations, block diagrams, system functions, poles and zeros, convolution, impulse and step responses, frequency responses),
- Students should have the ability to design and analyse linear dynamic systems using discrete-time (and continuous-time) control theory.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Demonstrate the role of the Fourier, Laplace and z-transforms, poles and zeros in the analysis of continuous-time and discrete-time signals and systems.
LO2.
Evaluate Laplace and z-transforms and their inverses and be able to use them to solve differential and difference equations
LO3.
Explain the Nyquist sampling theorem and its implications in signal and image processing systems
LO4.
Analyse the concepts of amplitude distortion, phase distortion and group delay in filters
LO5.
Design continuous-time and discrete-time filters
LO6.
Explain the computational benefits of the Laplace and Fourier transforms in system characterisation.
LO7.
Outline the architectural features necessary for digital signal processors and obtain hands-on experience with DSP programming.
LO8.
Apply and explain the basics of linear control, stability and controller design
LO9.
Analyse system stability via root loci and bode plots
LO10.
Design analogue and digital controllers and compensators
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Tutorial/ Problem Set | Problem Set 1 | 10% |
21/03/2025 4:00 pm |
| Tutorial/ Problem Set | Problem Set 2 | 30% |
2/05/2025 4:00 pm |
| Tutorial/ Problem Set | Problem Set 3 | 20% |
30/05/2025 4:00 pm |
| Examination |
Final Exam
|
40% |
End of Semester Exam Period 7/06/2025 - 21/06/2025 |
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
Problem Set 1
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 10%
- Due date
21/03/2025 4:00 pm
Task description
Exercises on Systems, Signals and Controls (in general), with an emphasis on Signals & Systems
Please submit via Blackboard. No hand-written solutions. No scanned copies of hand-written solutions.
Due by 21 March 16:00 AEST.
This task has been designed to be challenging, authentic and complex. Whilst students may use Artificial Intelligence (AI) and/or Machine Translation (MT) technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
Online submission via Blackboard
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.
This course uses a progressive assessment approach where feedback and/or detailed solutions will be released to students within 14 days.
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.
Problem Set 2
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 30%
- Due date
2/05/2025 4:00 pm
Task description
Exercises on Systems, Signals and Controls (in general), with an emphasis on Signals/Filters.
May include material (including experimental results) from Lab 1 or 2.
Please submit via Blackboard. No hand-written solutions. No scanned copies of hand-written solutions.
Due by 2 May 16:00 AEST.
This task has been designed to be challenging, authentic and complex. Whilst students may use Artificial Intelligence (AI) and/or Machine Translation (MT) technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
Online submission via Blackboard
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.
This course uses a progressive assessment approach where feedback and/or detailed solutions will be released to students within 14 days.
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.
Problem Set 3
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 20%
- Due date
30/05/2025 4:00 pm
Task description
Exercises on Systems, Signals and Controls (in general), with an emphasis on Controls
May include material (including experimental results) from Labs 1-4.
Please submit via Blackboard. No hand-written solutions. No scanned copies of hand-written solutions.
Due by 30 May 16:00 AEST.
This task has been designed to be challenging, authentic and complex. Whilst students may use Artificial Intelligence (AI) and/or Machine Translation (MT) technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
Online submission via Blackboard
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.
This course uses a progressive assessment approach where feedback and/or detailed solutions will be released to students within 14 days.
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
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 40%
- Due date
End of Semester Exam Period
7/06/2025 - 21/06/2025
- Other conditions
- Time limited.
Task description
A three-hour final examination will be held during the final examination period. This will be an on-campus, invigilated and closed-book exam.
You may bring one (1) two-sided A4 sheet of handwritten (not typed) formulae at the time of the exam. A sheet that does not meet these requirements will NOT be permitted.
Calculators must be UQ approved (and labelled if not Casio fx-82).
Hurdle requirements
A mark of greater than or equal to 40% on the final examination is required to pass the course.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 180 minutes |
| Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
| Open/closed book | Closed Book examination - specified written materials permitted |
| Materials | One A4 sheet of handwritten notes, double sided, is permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
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.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. Course grade description: A Grade of 1 will be awarded for an overall mark below 20%. |
| 2 (Fail) | 20 - 46 |
Minimal evidence of achievement of course learning outcomes. Course grade description: A Grade of 2 will be awarded for an overall mark below 47% but greater than or equal to 20%. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: A Grade of 3 will be awarded for an overall mark below 50% but greater than or equal to 47%. Alternatively, a Grade of 3 will be awarded for an overall mark greater than or equal to 50% if a mark less than or equal to 40% is achieved on the final examination. |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: A Grade of 4 will be awarded if there is both: (1) an overall mark of 50% or greater AND (2) a mark of greater than or equal to 40% on the final examination |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: A Grade of 5 will be awarded if there is both: (1) an overall mark of 65% or greater AND (2) a mark of greater than or equal to 40% on the final examination |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: A Grade of 6 will be awarded if there is both: (1) an overall mark of 75% or greater AND (2) a mark of greater than or equal to 50% on the final examination |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: A Grade of 7 will be awarded if there is both: (1) an overall mark of 85% or greater AND (2) a mark of greater than or equal to 60% on the final examination |
Additional course grading information
To pass the course and receive a grade of 4 or greater, students must achieve a mark of greater than or equal to 40% on the FINAL EXAM in addition to the requirement that the overall mark should be at least 50%. Students who do not achieve greater than or equal to 40% on the final exam will have their grade capped at a 3.
Standard algebraic rounding (e.g., 84.5 rounds to an 85, whereas 84.4 rounds to an 84) will be applied to the final mark prior to allocation of final grade.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
The problem sets have been designed to be challenging, authentic and complex. Whilst students may use Artificial Intelligence (AI) and/or Machine Translation (MT) technologies, successful completion of the problem sets in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass the problem sets, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
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.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 (24 Feb - 02 Mar) |
Lecture |
Introduction to Signals, Systems and Control Introduction to the course and to the basic mathematical concepts. |
Multiple weeks From Week 2 To Week 8 |
Lecture |
Signals & Systems The course material for the signals and systems module of the course. This includes signal representation, sampling theory, Fourier etc. transforms, filtering, and discrete systems analysis. |
Multiple weeks From Week 2 To Week 13 |
Tutorial |
Contact Sessions Contact Sessions will occur every week starting from Week 2, except for the 4 lab sessions. |
Practical |
Practical Sessions There will be four prac/lab sessions, announced via Blackboard |
|
Multiple weeks From Week 9 To Week 13 |
Lecture |
Control Course material on the control module is presented. This includes analogue and digital control design, design processes and practical digital control. |
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.
You'll also need to be aware of the following policies and procedures while completing this course:
School guidelines
Your school has additional guidelines you'll need to follow for this course: