ECE6883 Advanced signal processing
Faculty of Engineering
ECE6883 Advanced signal processing is a level 6, 0-credit-point, postgraduate unit from the Faculty of Engineering, offered in 2027 in Semester 1 at Clayton. It has no prerequisites.
- Credit points
- 0
- Offered in 2027
- Semester 1
- Clayton
- Assessment
- Exam 50%
- and 3 other tasks
- Workload
- 144 hours
- per semester
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Requisites
Before ECE6883
Prohibitions
You can't enrol if you have passed any of these.
After ECE6883
No unit lists ECE6883 as a prerequisite in the 2027 handbook.
Enrolment rules
Equivalent units
The same content under another code. Only one of them counts.
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 2027
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
Assessment
- AssignmentExercise20%
- Lab reports & quizzesExercise15%
- Workshop quizzesQuiz / Test15%
- Final assessmentExamination50%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
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
- Practical activities22 hours
- Workshops24 hours
- Teaching approachActive learning
- Teaching approachEnquiry-based learning
- Teaching approachProblem-based learning
- Teaching approachPeer assisted 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.
Participation in applied sessions, participation in discussions forum, active involvement in laboratories.
Active participation in laboratories.
Active participation in assignments.
Active participation in laboratories and group discussions.
Learning resources
Technology resources
- MATLAB for assignments and the final assessment. You are required to bring your laptop to all lab sessions with MATLAB installed.
- Zoom
- Recommended textbook
Contacts
- Unit Coordinators
- Professor Emanuele Viterbo
- Chief Examiners
- Professor Emanuele Viterbo
Common questions
What are the prerequisites for ECE6883?
ECE6883 has no prerequisites, but enrolment rules apply.
When is ECE6883 offered?
In 2027, ECE6883 runs in Semester 1 at Clayton.
How much work is ECE6883?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ECE6883 have an exam?
Yes. The exam is worth 50% of the final mark, alongside 3 other tasks.