Advanced Signal Processing (ELEN90052)
Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)
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About this subject
Contact information
Semester 1
Contact 1: Dr Jingge Zhu
Email: jingge.zhu@unimelb.edu.au
Contact 2: Professor Erik Weyer
Email: ewey@unimelb.edu.au
Overview
| Availability | Semester 1 - Dual-Delivery |
|---|---|
| Fees | Look up fees |
AIMS
This subject provides an in-depth introduction to statistical signal processing.
INDICATIVE CONTENT
Students will study a selection of the following topics:
- Applications of statistical signal processing;
- A review of stochastic signals and systems fundamentals – random processes, white noise, stationarity, auto- and cross-correlation functions, spectral- and cross-spectral densities, properties of linear time-invariant systems excited by white noise;
- Parameter estimation - least squares and its properties, recursive least squares and least mean squares, optimisation-based methods, maximum likelihood methods;
- Kalman, Wiener and Markov filtering;
- Power spectrum estimation.
This material will be complemented with the use of software tools (e.g. MATLAB) for computation and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory.
Intended learning outcomes
On completing this subject the student should be able to:
- Apply fundamental mathematical tools, in particular stochastic techniques, in the analysis and design of signal processing systems
- Recognise estimation problems and design, implement and analyse algorithms for solving them
- Use software packages such as MATLAB for the analysis and design of signal processing systems
- Implement signal processing systems with DSP based development platforms
Generic skills
On completing this subject, students will have developed the following skills:
- Ability to apply knowledge of basic science and engineering fundamentals;
- In-depth technical competence in at least one engineering discipline;
- Ability to undertake problem identification, formulation and solution;
- Ability to utilise a systems approach to design and operational performance;
- Capacity for independent critical thought, rational inquiry and self-directed learning;
- Ability to communicate effectively, with the engineering team and with the community at large.
Last updated: 8 November 2024
Eligibility and requirements
Prerequisites
All of
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| ELEN90058 | Signal Processing |
Semester 2 (Dual-Delivery - Parkville)
Semester 1 (Dual-Delivery - Parkville)
|
12.5 |
| ELEN90054 | Probability and Random Models |
Semester 1 (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - Parkville)
|
12.5 |
(prior to 2011, ELEN30008 Signal Processing 1 and ELEN30002 Stochastic Signals and Systems)
Corequisites
None
Non-allowed subjects
ELEN40004 Signal Processing 2
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 8 November 2024
Assessment
| Description | Timing | Percentage |
|---|---|---|
One written examination
| End of semester | 60% |
A mid-semester test
| Mid semester | 10% |
Two laboratory projects, each worth 15%.
| 30% |
Additional details
Intended Learning Outcomes (ILOs) 1 and 2 are assessed in the final written examination, the mid-semester test, and submitted reports for two projects.
ILOs 3 and 4 are assessed as part of submitted project work and workshops.
Last updated: 8 November 2024
Dates & times
- Semester 1
Principal coordinator Jingge Zhu Coordinator Erik Weyer Mode of delivery Dual-Delivery (Parkville) Contact hours 36 hours of lectures (3 x one hour lectures per week) and up to 24 hours of workshops Total time commitment 200 hours Teaching period 28 February 2022 to 29 May 2022 Last self-enrol date 11 March 2022 Census date 31 March 2022 Last date to withdraw without fail 6 May 2022 Assessment period ends 24 June 2022 Semester 1 contact information
Contact 1: Dr Jingge Zhu
Email: jingge.zhu@unimelb.edu.au
Contact 2: Professor Erik Weyer
Email: ewey@unimelb.edu.au
Time commitment details
200 hours
Last updated: 8 November 2024
Further information
- Texts
- Subject notes
Credit may not be obtained for both ELEN40004 (431-461) Signal Processing 2 and ELEN90052 Advanced Signal Processing.
LEARNING AND TEACHING METHODS
The subject is delivered through lectures and workshop classes that combine both tutorial and hands-on laboratory activities.
INDICATIVE KEY LEARNING RESOURCES
Students are provided with lecture slides, tutorial questions and solutions, project specifications, and reference text lists.
CAREERS / INDUSTRY LINKS
Exposure to industry standard DSP design tools through laboratory activities.
- Related Handbook entries
This subject contributes to the following:
Type Name Specialisation (formal) Electrical Specialisation (formal) Mechatronics Specialisation (formal) Electrical with Business - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
Additional information for this subject
Subject coordinator approval required
- Available to Study Abroad and/or Study Exchange Students
Last updated: 8 November 2024