ELECTENG 733 : Digital Signal Processing

Engineering

2025 Semester One (1253) (15 POINTS)

Course Prescription

Analysis and manipulation of discrete-time signals and systems. Spectral representations and analysis using the z-transform, discrete Fourier transform and fast Fourier transform. Introduction to stochastic processes. Hardware systems for processing digital signals.

Course Overview

Digital signal processing is the enabling technology for the generation, transformation, extraction, and interpretation of digital information. This course is designed to provide insights into these processes from both theoretical and practical perspectives. It aims to foster a thorough understanding of the underlying mathematical and statistical modelling techniques for processing and learning from signals. Selected applications from relevant fields such as speech, image, audio, wireless communication, and control systems are introduced to give context and guide further studies and research directions. The topics covered are packaged into two integrated modules:

Module 1 Discrete-Time Signal Processing
Signal and system representations: sampling and quantisation, complex exponentials, linear time-invariant systems, discrete-time Fourier transform, z-transform, fast Fourier transform.
Digital filter design: FIR filter, IIR filter, windowing, bilinear transform, phase and group delay, filter stability.

Module 2 Random Signal Processing
Probability concepts: probability measures, probability density function (PDF), cumulative distribution function (CDF), random variables, expected values, functions of random variables, correlation and covariance.
Stochastic processes: ensembles, stationarity, ergodicity, power spectral density.

Course Requirements

Prerequisite: ELECTENG 303 or 331 or ENGSCI 311 or 313 Restriction: ELECTENG 413

Capabilities Developed in this Course

Capability 3: Knowledge and Practice
Capability 4: Critical Thinking

Learning Outcomes

By the end of this course, students will be able to:
  1. Analyse, interpret, and evaluate digital signals and systems through appropriate mathematical representation theory and statistical modelling methods. (Capability 3.1 and 4.1)
  2. Compare and contrast the performance, functional suitability, and properties of various digital processes and relevant implementations. (Capability 3.2)
  3. Develop, design, and justify digital implementations for investigating practical problems in applications involving digital signal processing techniques. (Capability 4.1)

Assessments

Assessment Type Percentage Classification
Tutorials 6% Individual Coursework
Assignments 24% Individual Coursework
Tests 30% Individual Test
Final Exam 40% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3
Tutorials
Assignments
Tests
Final Exam

  • A passing mark is 50% or higher, according to the University policy.
  • Students must sit the exam to pass the course. Otherwise, a DNS (did not sit) result will be returned.
  • No late submission is allowed unless late submission penalties are specified on Canvas.

Teaching & Learning Methods

Signal processing is best learnt by doing many examples, both on paper and on the computer. The teaching will be primarily instructional during lectures, with integrated lectorials. Lecture notes and learning materials will be provided via Canvas with details to be discussed and completed in class. Weekly tutorials are in-person and dialogical. Tutorials are problem-based, collaborative in nature, and will be guided by instructors.

Laboratories and assignments will bring in the implementation of many digital signal processing algorithms on the computer. MATLAB will be used.

Workload Expectations

This course is a standard 15-point course, and students are expected to spend 10 hours per week involved in each 15-point course they are enrolled in.

For this course, you can expect 3 hours of lectures, a 1-hour tutorial, 2 hours of reading, thinking about the content, and solving prescribed problems, and 4 hours of work on a mixture of assignments and/or laboratories and/or test preparation. For the 12 teaching weeks, this totals 120 hours and leaves 30 hours across the entire semester for independent or supplementary study, including during breaks.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities including labs and tutorials to receive credit for components of the course.
Lectures will be available as recordings. Other learning activities including tutorials and labs will not be available as recordings.
The course will not include live online events.
Attendance on campus is required for the tests and exam.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

This course has no prescribed textbook. All learning materials will be made available digitally on Canvas, this includes lecture notes, resources for tutorials and laboratories, self-study materials, and additional recommended readings. 

Health & Safety

Students must ensure they are familiar with their Health and Safety responsibilities, as described in the University's Health and Safety policy.

Student Feedback

At the end of every semester students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions.

Your feedback helps teachers to improve the course and its delivery for future students.

Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

The students in 2024 felt that ELECTENG 733 was split into two unrelated topics. This was not the case, in 2025 stronger links  will be made between content of the two topics, via a new lab will draw on content from both parts of the course. Additionally in 2024 students wanted more context for digital filter design, this will be covered in 2025.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework, tests and examinations as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. A student's assessed work may be reviewed against electronic source material using computerised detection mechanisms. Upon reasonable request, students may be required to provide an electronic version of their work for computerised review.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website http://disability.auckland.ac.nz

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.

Disclaimer

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 03/12/2024 12:42 p.m.