ECE5883 Advanced signal processing
Faculty of Engineering
ECE5883 Advanced signal processing is a level 5, 6-credit-point, postgraduate unit from the Faculty of Engineering, offered in 2022 in Semester 1 at Clayton. It has no prerequisites.
- Credit points
- 6
- Offered in 2022
- Semester 1
- Clayton
- Assessment
- No exam
- 3 tasks
- Workload
- 144 hours
- per semester
This is the 2022 handbook entry. See the 2027 entry.
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Overview
The unit introduces the fundamentals of statistical signal processing with emphasis on stochastic models, estimation theory, parametric and non-parametric modelling and least squares methods.
After a review of basic probability and random processes, the use of stochastic models for real world signals is illustrated. A family of algorithms for the creation, efficient representation and effective modelling is presented.
Specifically, linear stochastic models are presented and the importance of correlation structure in deriving the parameters of such models is illustrated.
The unit also covers how parametric and non-parametric models as well as statistical techniques are used to extract information from data signals corrupted by noise. The concept of estimation from real world data is presented, as opposed to the basic analysis of signals, transfer functions and power spectra. In particular, the fundamentals of linear estimation theory and optimal filtering to design advanced signal processing algorithms are presented.
Offerings in 2022
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
Assessment
- AssignmentsThreshold hurdle40%
- Weekly lab questionThreshold hurdle10%
- Final assessmentThreshold hurdle50%
Learning outcomes
When you finish this unit, you should be able to:
- 1
Describe various models for real world signals
- 2
Analyse the performance of a range of estimation methods
- 3
Simulate a wide range of stochastic signal processing algorithms and interpret the results
- 4
Design specific algorithms for processing real world signals such as audio, financial data and biomedical data.
Workload and teaching
- Laboratories22 hours
- Lectures12 hours
- Practical activities12 hours
- Teaching approachProblem-based learning
- Teaching approachActive learning
The minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of 3-6 hours of scheduled learning activities and 6-9 hours of independent study per week. Scheduled activities may include a combination of teacher-directed learning, peer-directed learning and online engagement. Independent study may include associated readings, assessment and preparation for scheduled activities.
Learning resources
Required resources
- MATLAB for assignments and the final assessment.
- Zoom
- Recommended textbook
Where it fits
ECE5883 is part of 1 area of study in the 2022 handbook.
Contacts
- Chief Examiners
- Professor Emanuele Viterbo
- Unit Coordinators
- Professor Emanuele Viterbo
Common questions
What are the prerequisites for ECE5883?
ECE5883 has no prerequisites.
When is ECE5883 offered?
In 2022, ECE5883 runs in Semester 1 at Clayton.
How much work is ECE5883?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ECE5883 have an exam?
No. ECE5883 has 3 assessment tasks and no exam.
Which majors and minors include ECE5883?
ECE5883 is part of Electrical and computer systems engineering.