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Unit outline_

ELEC3305: Digital Signal Processing

Semester 1, 2020 [Normal day] - Camperdown/Darlington, Sydney

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.

Unit details and rules

Academic unit School of Electrical and Computer Engineering
Credit points 6
Prerequisites
? 
ELEC2302
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

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.

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Craig Jin, craig.jin@sydney.edu.au
Type Description Weight Due Length
In-semester test Practical Exam
Programming
30% Week 13 Two hours
Outcomes assessed: LO2 LO4 LO3
In-semester test Theory exam
Theory
30% Week 13 Two hours
Outcomes assessed: LO1 LO5 LO4 LO3
Presentation group assignment Teamwork Portfolio
10% Week 13 n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment Lab Portfolio
10% Week 13 n/a
Outcomes assessed: LO2 LO3 LO4 LO5 LO6
Assignment Tutorial Portfolio
10% Week 13 n/a
Outcomes assessed: LO1 LO3 LO4 LO5 LO6
Assignment Something Awesome Portfolio
10% Week 13 n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
group assignment = group assignment ?

Assessment summary

  • 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. 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 enjoy doing them. 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.

For more information see guide to grades.

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.

Academic integrity

The Current Student website provides information on academic integrity 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 integrity breaches seriously.

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

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introduction and discrete time systems Lecture (2 hr) LO1
Week 02 Discrete time fourier transform and Z-transform Lecture and tutorial (4 hr) LO1 LO3 LO4 LO5 LO6
Discrete time Fourier transform and Z-transform Computer laboratory (2 hr) LO2 LO3 LO4 LO5 LO6
Week 03 Z-transform and sampling Lecture and tutorial (4 hr) LO1 LO3 LO4 LO5 LO6
Z-transform and Sampling Computer laboratory (2 hr) LO2 LO3 LO4 LO5 LO6
Week 04 Discrete fourier transform and convolution Lecture and tutorial (4 hr) LO1 LO3 LO4 LO5 LO6
Discrete Fourier transform and convolution Computer laboratory (2 hr) LO2 LO3 LO4 LO5 LO6
Week 05 Fast fourier transform Lecture and tutorial (4 hr) LO1 LO3 LO4 LO5 LO6
Fast Fourier transform Computer laboratory (2 hr) LO2 LO3 LO4 LO5 LO6
Week 06 Spectral analysis Lecture and tutorial (4 hr) LO1 LO3 LO4 LO5 LO6
Spectral Analysis Computer laboratory (2 hr) LO2 LO3 LO4 LO5 LO6
Week 07 Resampling Lecture and tutorial (4 hr) LO1 LO3 LO4 LO5 LO6
Resampling Computer laboratory (2 hr) LO2 LO3 LO4 LO5 LO6
Week 08 Polyphase decomposition and filter banks Lecture and tutorial (4 hr) LO1 LO3 LO4 LO5 LO6
Polyphase decomposition and filter banks Computer laboratory (2 hr) LO2 LO3 LO4 LO5 LO6
Week 09 ADC/DAC Lecture and tutorial (4 hr) LO1 LO3 LO4 LO5 LO6
ADC/DAC Computer laboratory (2 hr) LO2 LO3 LO4 LO5 LO6
Week 10 Transform analysis and phase analysis Lecture and tutorial (4 hr) LO1 LO3 LO4 LO5 LO6
Transform analysis and phase analysis Computer laboratory (2 hr) LO2 LO3 LO4 LO5 LO6
Week 11 Structure of discrete time systems and quantization effects Lecture and tutorial (4 hr) LO1 LO3 LO4 LO5 LO6
Structure of discrete time systems and quantization effects Computer laboratory (2 hr) LO2 LO3 LO4 LO5 LO6
Week 12 Filter design Lecture and tutorial (4 hr) LO1 LO3 LO4 LO5 LO6
Filter Design Computer laboratory (2 hr) LO2 LO3 LO4 LO5 LO6

Attendance and class requirements

There are no other requirements for this unit.

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.

Required readings

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 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 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, signal analyses, 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.

Graduate qualities

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.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

Modified learning outcomes, assessment, and learning activities.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.