Module Catalogues, Xi'an Jiaotong-Liverpool University   
Module Code: EEE411
Module Title: Advanced Signal Processing
Module Level: Level 4
Module Credits: 5.00
Academic Year: 2019/20
Semester: SEM1
Originating Department: Electrical and Electronic Engineering
Pre-requisites: N/A
▪ 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.

Week 1 I. Introduction to statistical signal processing, review of probability, statistics, and random processes

Weeks 2-4 II. Signal detection and classification

Hypothesis testing, detection of signals in noise, matched filters, detection in the presence of unknowns

Weeks 5-8 III. Signal estimation theory

Estimation of signal parameters, mean-squared errors, maximum-likelihood estimation, Bayesian, maximum a posteriori estimation, least squares estimation, Wiener and Kalman Filtering

Weeks 9-10 IV. Adaptive filtering

Iterative minimization and gradient descent, LMS algorithm

Weeks 11-13 V. Spectrum estimation

Auto-correlation, cross-correlation, power spectrum, moving average (MA), autoregressive (AR), autoregressive moving average (ARMA), various non-parametetric approaches
Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 30       20    100  150 


Sequence Method % of Final Mark
1 Assignment 15.00
2 Practical Report 15.00
3 Final Exam 70.00

Module Catalogue generated from SITS CUT-OFF: 8/20/2019 6:18:05 PM