Module code: EEEM007

Module provider

Electrical and Electronic Engineering

Module Leader

PLUMBLEY MD Prof (Elec Elec En)

Number of Credits


ECT Credits



FHEQ Level 7

JACs code


Module cap (Maximum number of students)


Module Availability

Semester 2

Overall student workload

Independent Study Hours: 100

Lecture Hours: 33

Assessment pattern

Assessment type Unit of assessment Weighting
Examination EXAMINATION - 2HRS 60%

Alternative Assessment

Not applicable: students failing a unit of assessment resit the assessment in its original format.

Prerequisites / Co-requisites


Module overview

Expected prior learning:  Knowledge of the basic principles of signal processing. MSc students might have acquired this by study of module EEEM019-Mathematics of Signal Processing       (7-msp). MEng students might have acquired this by study of module EEE3008-Digital Signal Processing A (6-dpA).

 Module purpose:  Advanced signal processing, which includes adaptive filtering, signal detection, matching and recognition, is a key expertise required for designing and building high-tech. electronic systems such as robots, automatic speech recognition systems, driver warning systems, biometrics technology, etc. The module will introduce students to advanced techniques of signal processing and interpretation.


Module aims

Equip students with advanced analytical tools for solving the statistical and adaptive signal processing problems encountered in communications, telematics, and related engineering areas; and

To introduce students to statistical and adaptive techniques for the detection, filtering and matching of signals in noise;

Make students aware of the industrial relevance of these techniques.

Learning outcomes

Attributes Developed
Explain the concepts and theory of statistical and adaptive signal detection, filtering and matching   K
Demonstrate the ability to apply mathematical models of signal processing to solve problems and predict effects  C
Describe the relevance of the presented material to applications in machine perception and discuss its engineering significance   P
Design pattern-recognition systems KCP
Demonstrate technical expertise required for performance characterisation of machine perception systems KCPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Module content

Indicative content includes the following.

Lecture Component: Adaptive Digital Filtering (JI)
Hours 10 Lecture hours + 2 problem classes

[1 Introduction - Approaches to adaptive filters. State space model. Cost functions.

[2-3] Correlation matrix, autoregressive and moving - average models.

[4] Spectral analysis.

[5-6] Linear Prediction.

[7-8] Mean Square Estimation - Conditional expectation and orthogonality. Wiener filtering.

[9-10] FIR Adaptive Filters.

[11-12] Problem Classes.

Lecture Component Statistical Pattern Recognition (JK)
Hours: 20 Lecture hours + 4 problem classes

[1-2] Elements of Statistical Decision Theory - Model of pattern recognition system. Decision theoretic approach to pattern classification. Bayes decision rule for minimum loss and minimum error rate. Optimum error acceptance trade-off. Learning algorithms.

[3-4] Nearest Neighbour (NN) Technique - 1-NN, k-NN pattern classifiers. Error bounds. Editing techniques.

[5-6] Discriminant Functions: Discriminant functions and learning algorithms. Deterministic learning. The least square criterion and learning scheme. Perceptron. Multilayer Perceptron. Neural nets. Stochastic approximation.

[7] Probability Density Function Estimation - Parzen estimator, k-NN estimator.

[8] Classification Error Rate Estimation - re-substitution method, leave-one-out method, error estimation based on unclassified test samples.

[9-10] Applications – Examples of successful applications of pattern recognition systems. ( Reading week)

[11-12] Feature Selection - Concepts and criteria of feature selection. Algorithms for selecting optimal and sub-optimal sets of features. Recursive calculation of parametric separability measures.

[13-14] Feature Extraction - Probabilistic distance measures in feature extraction. Properties of the Karhunen-Loeve expansion, feature extraction techniques based on the Karhunen-Loeve expansion. Discriminant analysis.

[15-16] Cluster Analysis - Concepts of a cluster, dissemblance and resemblance measures, globally sensitive methods, global representation of clusters by pivot points and kernels, locally sensitive methods (methods for seeking valleys in probability density functions), hierarchical methods, minimum spanning tree methods, clustering algorithms.

[17-18] Contextual Classification Methods - The role of context in pattern recognition. Heuristic approaches to contextual pattern recognition. Labelling of objects arranged in networks (chains, regular and irregular lattices). Neighbourhood systems. Elements of compound decision theory.

[19-20] Classifier Fusion - Fusion System architecture. Fusion rules and their properties


Methods of Teaching / Learning

The learning and teaching strategy is designed to achieve the specified learning outcomes by teaching the module syllabus in lectures, and supporting the assimilation and understanding of the taught material via tutorial classes. The practical design and technical skills related to the subject are acquired through coursework involving an assignment on pattern recognition system design; the performance of the designed pattern recognition system is characterised via laboratory experiments.

Learning and teaching methods include:

Lectures: 10 weeks, 3 hours per week
Tutorials: 6-8 weeks, 1 hour per week
Labs: Pattern Recognition Experiment – set and marked by JK, starting in Week 6, due Week 9, Preparation via assignment
Assignment(s): Pattern recognition system design (linked to Pattern Recognition Lab) issued in Week 3, due Week 6.



Assessment Strategy

The assessment strategy for this module is designed to provide students with the opportunity to demonstrate the learning outcomes. The written examination will assess the knowledge and assimilation of the terminology, concepts and theory of statistical and adaptive signal processing, as well as the ability to analyse problems and apply mathematical models of signal processing to solve and predict effects. The Assignment will assess the ability to design pattern recognition systems. The laboratory experiment will evaluate the acquired technical skills and expertise required for performance characterisation of pattern recognition systems.


Thus, the summative assessment for this module consists of the following.

·         2-hour, closed-book written examination

·         Pattern recognition system design:  An assignment involving the design of a pattern classification system (5-10 pages), due Tuesday Week 6 (assignment deadline should be checked in the Assignment Calendar)

·         Pattern Recognition Experiment  A laboratory experiment concerned with pattern recognition system evaluation (circa 10 pages) due Tuesday Week 9 (assignment deadline should be checked in the Assignment Calendar)


Formative assessment and feedback

The main mechanism for formative assessment will be a set of five tutorial problem sheets which the students will be expected to solve prior to the timetabled tutorial classes. The tutorial classes will provide the main forum for formative feedback. In addition, formative assessment and feedback may occur:

·         During lectures, by question and answer sessions

·         During tutorials/tutorial classes

·         During supervised laboratory sessions

·         During meetings with lecturers and tutor.



Reading list


Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2017/8 academic year.