ECE5883 · Advanced signal processing

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.

Areas of study: E6011 Master of Professional Engineering - Specialisation: Electrical engineering E6014 Master of Engineering - Specialisation: Electrical engineering E6014 Master of Engineering - Specialisation: Power systems engineering E6017 Master of Advanced Engineering - Specialisation: Telecommunications engineering

Offerings

Assessment

The Handbook lists an examination for this unit.

Continuous assessment: 50% Final assessment: 50%

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

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

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.

Student experience, not official rules. Nothing here changes what the Handbook says.