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Overview

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 ProcessingSignal 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 ProcessingProbability 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.

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.

Course Prerequisites, Corequisites and Restrictions

Prerequisite
Restriction

Locations and Semesters Offered

LocationSemester
City

Teaching and Learning

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.

Teaching and 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.

Learning Resources

Taught courses use a learning and collaboration tool called Canvas to provide students with learning materials including 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.

Additional Information on Learning Resources

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. 

Copyright

The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.


You may copy the course content for the purposes of private study or research, but you may not upload onto any third-party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Health and Safety

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

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 course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework 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. This requirement also applies to sources on the internet. A student's assessed work may be reviewed for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

Similarly, research students must meet the University’s expectations of good research practice. This requires:

  • Honesty - in all aspects of research work
  • Accountability - in the conduct of research
  • Professional courtesy and fairness – in working with others
  • Good stewardship – on behalf of others
  • Transparency – of research process and presentation of results
  • Clarity - communication to be understandable, explainable and accessible

For more information on the University’s expectations of academic integrity, please see the Academic Conduct section of the University policy hub.

Disclaimer

Elements of this outline may be subject to change. The latest information about taught courses is made available to enrolled students in Canvas.

Students may be asked to submit assessments digitally. The University reserves the right to conduct scheduled tests and examinations online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.


Assessment and Learning Outcomes

Course Learning Outcomes

CLO #OutcomeProgramme Capability Link
1
2
3

Assessments

Assessment TypeAssessment PercentageAssessment Classification

Additional Information on Assessment

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.

Assessment to CLO Mapping

Assessment Type123

Student Feedback, Support and Charter

Student Feedback

Feedback on taught courses is gathered from students at the end of each semester 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. In addition, class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

Additional Information on Student Feedback

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.

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.

Tuākana

Tuākana is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials, one-on-one sessions, test and exam preparation and more. Explore your options at Tuakana Learning Communities.

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.

Wellbeing

We all go through tough times during the semester, or see our friends struggling. There is lots of help out there - please see the Support Services page for information on support services in the University and the wider community.

Special Circumstances

If your ability to complete assessed work 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. This should be done as soon as possible and no later than seven days after the affected test or exam date.

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.

Student Academic Complaints and Disputes

Students with concerns about teaching including how a course is delivered, the resources provided, or supervision arrangements, have the right to express their concerns and seek resolution. The university encourages informal resolution where possible, as this is quicker and less stressful. For information on the informal and formal complaints processes, please refer to the Student Academic Complaints Statute in the Student Policies and Guidelines section of the Policy Hub.