ADVANCED SIGNAL PROCESSING - 2020/1
Module code: EEEM007
In light of the Covid-19 pandemic, and in a departure from previous academic years and previously published information, the University has had to change the delivery (and in some cases the content) of its programmes, together with certain University services and facilities for the academic year 2020/21.
These changes include the implementation of a hybrid teaching approach during 2020/21. Detailed information on all changes is available at: https://www.surrey.ac.uk/coronavirus/course-changes. This webpage sets out information relating to general University changes, and will also direct you to consider additional specific information relating to your chosen programme.
Prior to registering online, you must read this general information and all relevant additional programme specific information. By completing online registration, you acknowledge that you have read such content, and accept all such changes.
Expected prior learning: Knowledge of the basic principles of matrices and vectors; probability and statistics; and digital signal processing (Fourier Transforms, LTI Systems, Z-Transforms). MEng students might have acquired this by study of modules EEE2035 Engineering Mathematics III and EEE3008 Fundamentals of Digital Signal Processing. MSc students might have acquired this by study of a module in engineering mathematics, and module EEE3008 Fundamentals of Digital Signal Processing or similar.
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.
Electrical and Electronic Engineering
PLUMBLEY Mark (Elec Elec En)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: H690
Module cap (Maximum number of students): N/A
Prerequisites / Co-requisites
Indicative content includes the following:
Lecture Component Statistical Pattern Recognition
Hours: 22 Lecture/Tutorial hours
Review of linear algebra; review of probability theory.
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. Discriminant Functions.
Supervised learning. Learning algorithms. Classification Error Rate Estimation.
Nearest Neighbour (NN) Technique - 1-NN, k-NN pattern classifiers. Error bounds. Editing techniques. Probability Density Function Estimation - Parzen estimator, k-NN estimator.
Unsupervised Learning and Cluster Analysis - Concepts of a cluster, k-means algorithm, hierarchical clustering.
Feature Selection - Concepts and criteria of feature selection. Algorithms for selecting optimal and sub-optimal sets of features. Recursive calculation of parametric separability measures.
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.
Classifier Fusion - Fusion System architecture. Fusion rules and their properties
Lecture Component: Adaptive Digital Filtering
Hours: 11 Lecture/Tutorial hours
Introduction – Review of Fourier Transform, Z-Transform, LTI system analysis.
Approaches to adaptive filters. State space model. Cost functions.
Correlation matrix, autoregressive and moving - average models.
Mean Square Estimation - Conditional expectation and orthogonality. Wiener filtering.
FIR Adaptive Filters.
|Assessment type||Unit of assessment||Weighting|
|Coursework||LABORATORY & ASSIGNMENT||25|
|Examination||EXAMINATION - 2HRS||75|
Not applicable: students failing a unit of assessment resit the assessment in its original format.
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 assignment and experiment: An assignment and experiment involving the design and evaluation of pattern classification systems;
Formative assessment and feedback
The main mechanism for formative assessment will be a set of 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.
- 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.
|1||Explain the concepts and theory of statistical and adaptive signal detection, filtering and matching||K|
|2||Demonstrate the ability to apply mathematical models of signal processing to solve problems and predict effects||C|
|3||Describe the relevance of the presented material to applications in machine perception and discuss its engineering significance||P|
|4||Design pattern-recognition systems||KCP|
|5||Demonstrate technical expertise required for performance characterisation of machine perception systems||KCPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Overall student workload
Independent Study Hours: 109
Lecture Hours: 33
Laboratory Hours: 8
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, 2-3 hours per week
Tutorials: 6-8 weeks, 1 hour per week
Revision classes: 2-3 hours
Assignment and Lab: Pattern recognition system design assignment and pattern recognition experiment
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: EEEM007
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 2020/1 academic year.