Aims and Fit of Module

▪	To develop higher level signal processing techniques and apply them to some problems. 

▪	To develop FIR adaptive filters and demonstrate their applications. 

Learning outcomes

Knowledge and Understanding

On successful completion of this module the student should have: 

▪	appreciation of the concepts of time and frequency domain descriptions of signals. 

▪	appreciation of 'fixed' filter choices for noise reduction for different types of noise and signals. 

▪	appreciation of correlations, linear prediction and matched filtering. 

▪	appreciation of optimum filters and Wiener filters in particular, and their applications. 

▪	appreciation of FIR adaptive filters and their applications. 

▪	some knowledge of Kalman filters. 

Intellectual Abilities 

On successful completion of this module the student should be: 

▪	knowledgeable about some signal processing techniques and their applications. 

▪	knowledgeable about adaptive filters and their applications. 


Practical Skills 

On successful completion of this module the student should: 

▪	have the ability to apply an appropriate signal processing technique to a given problem. 

▪	be able to apply the appropriate adaptive filter. 

Method of teaching and learning

This module will be delivered through a combination of formal lectures, tutorials and supervised laboratory sessions. 

Coursework is not normally anonymously marked as staff wish to provide meaningful feedback.