Handbook

MSc ADVANCED TECHNOLOGIES IN ELECTRONICS

HANDBOOK

2004/2005

CONTENTS

Introduction Pg 3

Structure of the course Pg 4

Programme of full-time and part-time study – 2004/2005 Pg 5

Course structure diagram Pg 7

Marking and assessment of postgraduate degrees Pg 7

Description of compulsory modules Pg 9

Description of optional modules Pg 15

Fees Pg 27

Entry requirements and contact details Pg 29

Other frequently asked questions Pg 30

Last updated by External Affairs 10 August 2004

INTRODUCTION

Electronic Engineering is an extremely broad and rapidly developing subject. There are a very large number of exciting new advanced topics. Even recent students of Electrical or Electronic Engineering may have missed an in-depth study of many of the innovative subjects covered by this course. Those who have studied other Engineering or Science subjects are unlikely to have studied them in any detail. There are also a large number of practising engineers who have already spent some time in industry and whose knowledge is now patchy or out of date in some areas, simply because the underlying technology is changing so quickly. The group of subjects that form this MSc are well known to be in high demand in modern, high-technology led industrial sectors, ranging from High Street producers through to the Aerospace industry.

AIMS AND OBJECTIVES

The prime goal of all Engineering programmes within CEMS Faculty at Bristol UWE is to produce effective practitioners. This course is no exception.

It aims to provide an educational framework by which graduates of electrical or electronic engineering, (or other appropriate sciences), and/or those with a vocational qualification coupled with considerable industrial experience can develop, deepen or update their skills and knowledge in industrially relevant areas of advanced electronic engineering technology. There is a strong underlying view that these technologies must be developed and applied in a systems environment.

The technical areas include computer and mobile communications, embedded computing, digital signal processing, machine intelligence such as neural network and fuzzy control systems, design using VLSI technology, modern power systems, and advanced control.

The purpose of this handbook is to provide some general information about the course and to address some frequently asked questions. It includes:

An outline of the structure of the award and what is taught when; Descriptions of the modules; Some notes on marking and assessment; Information on fees.


STRUCTURE OF THE COURSE

Students will qualify for the awards MSc, PG Diploma and PG Certificate by accumulating credits on completion of modules, as follows:

The MSc Advanced Technologies in Electronics requires 180 credits, including 60 credits for the dissertation;

The PG Diploma Advanced Technologies in Electronics requires 120 credits, all from the taught part of the course i.e. no dissertation is completed;

The PG Certificate Advanced Technologies in Electronics requires 60 credits, again all from the taught part of the course.

Taught modules may be worth up to 30 credits and may be considered as either compulsory (i.e. the module that must be taken), or optional. Most modules are taught over a period of 12 weeks, i.e., one semester, but some cover both semesters. Most modules are worth 15-credits, and normally involve 2-3 hours class contact time per week. A fuller description of the credit rating of modules is given in the section ‘Marking and Assessment on Postgraduate Degrees’.

The different study patterns are summarised below:

NORMAL FULL-TIME STUDY PATTERN

SEMESTER 1September to February

SEMESTER 2February to June

SUMMERJune to August/November

Compulsory modules worth 60 credits

Compulsory and optional modules worth 60 credits

Dissertation worth 60 credits

NORMAL PART-TIME STUDY PATTERN

SEMESTER 1September to

February

SEMESTER 2February to June

SUMMERJune to

August/NovemberYEAR 1 Compulsory

modules worth 30 credits

Optional modules worth 30 credits

Start dissertation project

YEAR 2 Compulsory modules worth 30 credits

Compulsory and optional modules worth 30 credits

Complete and submit dissertation


PROGRAMME OF STUDY 2004/2005

NB Please see the chart on the following page, which shows the information set out below in a diagrammatical format.

SEMESTER 1

FULL-TIME STUDY - In the first Semester, full-time students will take four compulsory 15-credit modules.

Compulsory modules: Intelligent and Adaptive Systems (UFEE7K-15-M) Communication Networks & Protocols (UFEE7H-15-M) Embedded Real Time Systems (UFSEHT-15-M) DSP for Real Time Control Systems (UFEEKM-15-M)

This gives a total of 60 credits for the first Semester.

PART-TIME STUDY - In the first Semester part-time students will take two of the compulsory modules listed above. Please note that part-time students will take the remaining two compulsory modules (shown above) during the first semester of the second year.

SEMESTER 2

FULL-TIME STUDY -In the second Semester, full-time students will take the remaining 15-credit compulsory module and three from a list of optional 15-credit modules:

Compulsory modules: Research Methods (UFQEEV-15-M)

Optional modules (3 modules drawn from the following list): Systems on Silicon (UFEE7P-15-M) Learning Classifier Systems (UFCE3N-15-M) Modern Power Systems (UFEE7M-15-M) Behavioural Systems Design (UFEE7G-15-M) Advanced Control and Dynamics (UFEE7F-15-M) Mobile Communications (UFEE7L-15-M) Neural Networks and Fuzzy Systems (UFEE7N-15-M) Object Oriented Design and Programming (UFCE3T-15-M) Actuators and Control Technologies (UFPEE5-15-M)This gives a total of 60 credits for the second Semester.

PART-TIME STUDY -In both second semesters of the two years of study, part-time students will take the same 60 credits-worth of compulsory and optional modules offered to full-time students in their second semester of study.

COURSE STRUCTURE DIAGRAM


Semester 1

Intelligent & Adaptive Systems

UFEE7K-15-M

Embedded Real Time Systems

UFSEHT-15-M

Communication Networks and Protocols

UFEE7H-15-M

DSP for Real Time Control Systems

UFEEKM-15-M

Semester 2

Research Methods

UFQEEV-15-M

Option 1(One module from the list below)



Dissertation

Masters Dissertation (Project)UFPED4-60-M

Core modules Option modules Note: Part-time RouteYear 1 = Left hand section of diagramYear 2 = Right hand section of diagram

Option Modules

Option 1 taken from:UFEE7P-15-M Systems on SiliconUFCE3N-15-M Learning Classifier SystemsUFEE7M-15-M Modern Power Systems

Options 2 and 3 from:UFEE7G-15-M Behavioural System DesignUFEE7F-15-M Advanced Control and DynamicsUFEE7L-15-M Mobile CommunicationsUFEE7N-15-M Neural Networks and Fuzzy SystemsUFCE3T-15-M Object Oriented Design and ProgrammingUFPEE5-15-M Actuators and Control Technologies


RESOURCING OF OPTIONS

When options are offered with a course, the Faculty cannot guarantee that all will run in any particular academic year and the Dean (or his nominee) will determine, in good time, which options will be running. The decision will be dependent on such factors as the number of students wishing to take an option, whether appropriate physical or staff resources are available, and whether the non-running of an option would prevent a student from completing his or her programme of study through the lack of any alternative.

MARKING AND ASSESSMENT OF POSTGRADUATE DEGREES

All students will receive a copy of the University’s student handbook, in which can be found UWE’s Modular Assessment Regulations (MAR). These are applied across the Institution and are formidably complex, since they cover all degree courses. What follows is an explanation, in slightly simpler language, of what all post-graduate students will need to know.

What does my postgraduate degree consist of?

To gain an MSc all students need to pass modules which give a total of 180 credits. Most CEMS postgraduate courses are made up of 8 taught modules, worth 15 credits each, and a dissertation or project, worth 60 credits. There are a few taught modules that are worth 10 or 20 credits.

How do I tell how many credits a module is worth?

The module code indicates how many credits a module is worth. For example:

UFEE7K-15-M (Intelligent and Adaptive Systems) is worth 15 credits: all modules with ‘-15-‘ in the code are worth 15 credits.

UFPED4-60-M (Dissertation) is worth 60 credits: all modules with ‘-60-‘ in the code are worth 60 credits.

What else do I need to know about modules?

Level M modules are only available to postgraduate students and typical postgraduate students take most or all of their modules at Level M. To be eligible for an MSc award, students must gain a minimum of 180 credits (including the dissertation) at Level M.

How are the modules assessed?

To pass a module, students must successfully complete all components of assessment, i.e. examination and, where relevant, coursework. Level M modules are marked in percentages. The ‘pass’ boundary for each component of assessment in a module is 40%, but the overall weighted average for the module must be at least 50% i.e. better performance in one component can offset poorer performance in another. Weighting of the components varies from one module to another.


What happens if I fail a module?

Students are permitted to retake any failed components. For example, should both the coursework and examination contribute to the assessment of a module and the student fail the coursework but pass the examination, the student will then be “referred” in coursework, i.e. set another piece of work. Those referred in examinations are required to take a resit. The form of referrals for each module is defined in the module specification.

If a student fails the referral, he/she may have another attempt at the module in the following academic year, but must retake all components of assessment, even those which were passed the first time around.

What if I want to bail out early?

Students can be awarded a Postgraduate Diploma (PGDip) with 120 credits. In other words, if a student passes all the taught modules of the course they are automatically eligible for a PGDip. A student can be awarded a Postgraduate Certificate (PG Cert) with 60 credits.

Modular schemes are intended to facilitate the award of degrees, diplomas and certificates to meet the needs of students in a flexible way. Students are able to take the credits they have gained and later top them up for a higher level of award at either this or another university with a similar modular scheme.

NB: All of the above is based on the University’s regulations (MAR). Any Faculty-specific or course-specific regulations add to these, they do not overrule them.


COMPULSORY MODULES

Compulsory modules must be attempted by anyone studying MSc Advanced Technologies in Electronics and must all be successfully passed. There are five compulsory modules to be completed, as follows:

INTELLIGENT & ADAPTIVE SYSTEMS (UFEE7K-15-M))

LEARNING OUTCOMES:A. Knowledge and understanding A thorough understanding of the critical features of intelligent and adaptive systems,

basic and compound architectures An understanding of the appropriate terminology and working definitionsB. Subject specific skills The student will be able to critically compare the performance characteristics of the

advanced new techniques covered in this module with traditional approaches to selected problems in signal processing, classification and control.

The student will have been exposed to examples of these advanced new techniques being transferred from the research sector to industry, based upon experiences in this and other Universities.

C. Cognitive skills Apply the principles covered in this module elsewhere Study independently where necessary for the understanding of new advancements in the

fieldD. Transferable skills Communication skills Self management skills IT skills Decision making and problem formulation Progression to independent learning

SYLLABUS OUTLINE: Introduction: Review of the links with other disciplines, e.g. classical AI, psychology,

robotics, ethology, neuroscience and classical control. Scope and limitations of this module, especially with respect to classical control and AI.

Learning and adaptive systems: Working definitions of intelligence, adaptive systems and learning. Adaptation through learning versus design.

Basic Architectures: Neural networks. Fuzzy systems. Evolutionary computation. Supervised, unsupervised and reinforcement learning.

Compound Architectures: Classifier Systems. Behaviour-based systems. Agent-based systems. Multi-agent systems.

Example applications: Review of work carried out in this Faculty, and at other establishments, in order to demonstrate the major strengths and weaknesses of the techniques. For example; intelligent multiple agents for fault diagnosis in electrical power distribution systems, fuzzy control of an automated underground transportation system, co-operative behaviour in multi-agent mobile robotics, neurocontrol of an industrial robot manipulator, fuzzy classifier systems for telecommunications network routing, evolutionary computation as an aid to engineering design, human face and handwriting recognition using neural networks.


TEACHING AND LEARNING METHODS:Lectures will introduce the fundamental concepts. Tutorial case study sessions will be used for two purposes. They will be used to expose students to demonstrations of the basic architectures in action. They will also be used to discuss real implementations of these new techniques, each designed to illustrate the essential details of a particular concept or technique, and especially its strengths and weaknesses in both technical and business contexts. At all times specific examples will be used to "ground" the theory. INDICATIVE SOURCES:Internet sources: There is a very wide range of up-to-date information on these subjects, at the appropriate introductory level for this module, available on the internet. Students will be provided with a Compact Disk that guides their access to approved and publicly available sites (such as the Evonet “Flying Circus” site for evolutionary computation). There are also some older books that still represent excellent introductions to this subject area.Fuzzy-Neural Control: Principles, Algorithms and Applications: Nie & Linkens, Prentice Hall, ISBN 0133379167, 1995The Handbook of Intelligent Control: White & Sofge, Van Nostrand-Reinhold, 1992.Neural Computing - an Introduction: R Beal & T Jackson, Adam Hilger, 1990.C++ Neural Network and Fizzy Logic (2nd Edition): Rao and Rao, MIS, ISBN 15585515526, 1995.Neurofuzzy Adaptive Modelling and Control: Brown & Harris, Prentice Hall, ISBN 0131344536, 1994.Design tool user manuals - eg MATLAB 6.2 Fuzzy, Neural Network, and Simulink Toolboxes.Neural Networks for Control: Miller, Sutton & Werbos, MIT Press, 1991.The Handbook of Brain Theory and Neural Networks: Ed M A Arbib, MIT Press, 1995.

COMMUNICATION NETWORKS AND PROTOCOLS (UFEE7H-15-M))

LEARNING OUTCOMES:A. Knowledge and Understanding Architectures, reference models and standards for communication networks. Communication network components (hardware and software) and their operation. Protocols and algorithms used for error control, medium access control, routing,

congestion control, transport and application services in communication networks.B. Subject Specific Skills Model simple protocols analytically and investigate protocol performance. Design a communication protocol using finite state models and implement this protocol in

a high-level language. Make use of network simulation tools to evaluate network and protocol performance.C. Cognitive Skills Critically evaluate developments and new applications of communication systems. To actively participate in research and development of future communication systems.D. Key (Transferable) Skills Awareness of professional literature. Communication skills.

SYLLABUS OUTLINE: Layered architectures for communication systems. Reference models and standards. The data link layer: framing, error detection and correction, error control protocols.

Performance analysis. Protocol specification and verification. Local area networks: MAC protocols and relative performance. Ethernet variants.

Wireless LANs. Bridged LANs.

The network layer: packet and circuit switching. Routing algorithms. Congestion Control Last updated by External Affairs 10 August 2004

algorithms. The Internet protocol. The transport layer: transport protocols. TCP and UDP. Performance issues. The application layer: application protocols. Network Security.

TEACHING AND LEARNING METHODS:Lectures will cover principles, backed up by directed reading from books, journals and the WWW. Laboratory/tutorial sessions will consolidate principles presented in lectures and give practical experience of protocol design, implementation and evaluation.

INDICATIVE SOURCES:Computer Networks. Andrew Tanenbaum. Fourth edition. Prentice-Hall International, 2002.Multi-media Communications – Applications, Networks, Protocols and Standards. Fred Halsall. Addison-Wesley, 2001.Data and Computer Communications. William Stallings. Seventh edition. MacMillan, 2003.Journals, such as: IEE Proceedings - Communications, IEEE Transactions on Communications,IEEE Transactions on Selected Areas in Communications.

RESEARCH METHODS (UFQEEV-15-M)

LEARNING OUTCOMES:Throughout this module emphasis will be placed on research in the discipline area and preparation for the dissertation processKnowledge and understandingAt the end of the module the student should be able to: Compare and contrast the epistemological assumptions underpinning positivistic and

phenomenological research paradigms Summarise the range of research methodologies and methods associated with each

paradigm Explain the nature of the research process Discuss the ethical considerations for research Demonstrate the need to evaluate the quality of the research process and outcomes,

and apply appropriate tools and techniques to do so.Subject-specific skillsAt the end of the module the student should be able to produce a research proposal which includes : Research aims/objectives/questions to be explored Proposal of research methodology to be applied and justification for methods Consideration of the analysis and outcomes derived from the intended research process. Exploration of the ethical considerations for the research process and outcomes Discussion of the process of evaluation for the intended researchCognitive (intellectual skills)At the end of the module the student should be able to: Critically analyse theoretical perspectives relevant to the research process Formulate and solve research questions Appraise the utility of quantitative and qualitative research methods and their capacity to

address the research question(s) formulated Evaluate the chosen methodology, tools and techniques and the process of research

reflexively.Key (transferable skills)At the end of the module the student should be able to use the following skills: Communication skills Awareness of literature IT skills


SYLLABUS OUTLINE: Scientific and ethnographic models for research The Research Process; theory and practical implications Selection and Identification of the Research Topic, Definition of Research Objectives,

Formulation of Research Questions and Hypotheses Review of Relevant Literature and Existing Research: Literature Searches; Effective

Use of the Internet and library materials, and organisation of material. Deciding the Research Strategy and Design The Research Proposal and Plan Ethical considerations for researchers Issues of reliability, validity and generalisation for researchers Features of Qualitative and Quantitative Data Collection of Primary Data: Experimental Design, Survey Methods, Sampling Design

and Procedure. Use of secondary data in the research process Collection and Analysis of Qualitative Data; Interviewing and Observation Methods. Communicating your Results Effectively: Dissertation Structure and Presentation

TEACHING AND LEARNING METHODS:The module content will be taught in lectures common to all students on the CEMS Postgraduate Modular programme. Tutorials will enable discussion/critique and relevance of the topics covered in the lectures. Some focus on research methodologies of particular relevance to the individual awards may be appropriate with input from appropriate award staff. The module will be assessed through the preparation of a comprehensive dissertation proposal.

INDICATIVE SOURCES:Bell, J, (1987),Doing Your Research Project: a Guide for First Time Researchers in Education and Social Science, OU Press, BuckinghamBlaxter, L., Hughes C., Tight M.,(1998) How to Research, , OU Press, BuckinghamBoland, R. and Hirschheim, R.,(1987), Critical Issues in Information Systems Research, Wiley, Chichester.Cornford, T. and Smithson, S.,(1996), Project Research in Information Systems, Macmillan, LondonCresswell, J. W.,(1994), Research Design: Qualitative and Quantitative Approaches, Sage., LondonDenscombe, M., (1998),The good Research Guide,OU Press, BuckinghamDenzin, N.K., and Lincoln, Y. S., (eds.), (1994), Handbook of Qualitative Research, Sage, LondonEasterby-Smith, M., Thorpe, R., & Lowe, A.,(1991) Management Research an Introduction, Sage, London.Fairbairn, G., and Winch, C.,(1991) Reading Writing and Reasoning, OU Press, Buckingham.Galliers, R.,(1992) Information Systems Research : Issues, Methods and Practical Guidelines, Blackwell Scientific, Oxford. Howard, K., and Sharp, J.,(1983) The Management of a Student Research Project, Gower, Aldershot.Jankowicz, D.,(1991), Business Research Methods, 1991, Chapman and Hall, London.Maykut, P., and Morehouse, R.,(1994) Beginning Qualitative Research – A Philosophical and Practical Guide, The Falmer Press Miles, M. B., and Huberman A. M.,(1994), Qualitative Data Analysis - An Expanded Sourcebook, 2nd ed., Sage Publications, Thousand OaksRudestam, K and Newton, R(2001),Surviving Your Dissertation: a comprehensive guide to content and process, Sage, London Orna, E., and Stevens, G., (1995),Managing Information For Research, OU Press, Buckingham.


EMBEDDED REAL TIME SYSTEMS (UFSEHT-15-M)

LEARNING OUTCOMES:A. Knowledge and understanding A thorough understanding of the critical features of multitasking real-time systems and

their development environments. An understanding of the importance of design techniques aimed at improving reliability and

fault tolerance of computer based real-time systems operating in safety-critical applications. A grasp of the range of tools available for designing, representing and modelling real time

systems.B. Subject specific skills Apply selection criteria to real-time software problems in choosing appropriate programming

environments and hardware platforms which efficiently solve them, including an appreciation of cost/performance implications.

Utilise an interrupt structure in order to design an event driven embedded system using a mixture of high and low level programming language.

C. Cognitive skills Critically evaluate the significance of embedded real-time system and their place in

computer technology. Understand the latest developments in mobile and embedded technology. Study independently where necessary for the understanding of new advancements in the

field.D. Transferable skills Communication skills. Self management skills. IT skills. Decision making and problem formulation. Progression to independent learning.

SYLLABUS OUTLINE: Introduction: Brief review of basic computer architecture, including interrupt generation and

handling. Review of approaches to embedded systems design, including microcontrollers, rack based systems, PC-based solutions. The borderlines of viability between standalone interrupt-driven systems and operating system supported solutions. For larger problems, the trade-off between general purpose languages plus a real-time operating system and real-time languages.

System supported real-time systems: Concurrent programming models. The virtual "communicating sequential processes" machine. Multi-process management. Message-based vs. shared memory-based models for interprocess communication. Reliability, fault tolerance, operating in safety-critical environments, and support for error recovery. Low-level programming. A detailed study of one multitasking real-time operating system (e.g. MINOS, OS-9).

TEACHING AND LEARNING METHODS:There will be lectures and laboratory sessions in approximately equal ratio. Lectures will introduce the general theoretical concepts. Laboratory sessions will expose students to the design tools and programming environments required to complete the “hands-on” elements of the module. There will be a laboratory “mini-project” based around implementation of a basic multi-tasking real-time operating system on a popular micro-controller, to performs a multi-threaded signal-processing or control problem.

INDICATIVE SOURCES:Principal TextsWolfe, W Computers as Components, Real Time Systems and Their Programming Languages: Burns & Wellings, 1992Various extracts from language/operating systems manuals and textsPapers and manuals from the following sites:Last updated by External Affairs 10 August 2004

URLs www.gnu.org www.redhat.com/newlib www.cms.org.uk

DSP FOR REAL TIME CONTROL SYSTEMS (UFEEKM-15-M)

LEARNING OUTCOMES:A Knowledge and understandingOn completion of this module, a student will typically have knowledge and understanding of: The architecture of a DSP processor Processors’ peripherals involved in real time system control applications The DSP design flow How to combine hardware and software to achieve optimal control implementationsB Subject specific skillsOn completion of this module, a student will typically be able to: Use a front-end DSP integrated development environment Programme, debug and implement DSP algorithms in hardware Use a predefined digital control library to shorten the design cycleC Show cognitive skills with respect toOn completion of this module, a student will typically be able to: Apply this knowledge to implement fully functional digital control systems Solve other practical engineering control problems using a DSP processorD Demonstrate key transferable skills inOn completion of this module, a student will typically be able to use these skills in an appropriate context Communication skills Self management skills. IT skills Decision making and problem formulation Progression to independent learning Working with others

SYLLABUS OUTLINE: Theory: DSP design flow, Texas Instrument F2812 processor architecture, F2812

peripherals (mainly the Analog-to-Digital converter and the event manager), and an integrated hardware/software design methodology.

Practical: A series of lab-based exercises using Code Composer Studio (CCS) integrated development environment and the eZdsp™ F2812 development kit. The programming languages adopted are Assembly and C/C++.

TEACHING AND LEARNING METHODS:The contact time consists of a weekly series of one-hour lectures followed by two hours laboratory based work. Both a study guide and a laboratory exercise book will be provided.

INDICATIVE SOURCES:TMS320C28x DSP CPU and Instruction Set Reference GuideCode Composer Studio Getting Started GuideLapsley P, Bier J, Shoham A, Lee E A, DSP Processor Fundamentals: Architectures and Features, Wiley PublicationsEmbree P, Irvine C A, C Algorithms for Real Time DSP, Prentice Hall Publications


http://www.cms.org.uk/

http://www.redahat.com/newlib

http://www.gnu.org/

OPTIONAL MODULESThe remainder of the taught part of the course is made up of option modules, allowing students to focus on subjects or areas that are relevant to their interests or career prospects.

LEARNING CLASSIFIER SYSTEMS (UFCE3N-15-M)

LEARNING OUTCOMES:A Knowledge and UnderstandingOn completion of this module, a student will typically have the knowledge and understanding to: Identify aspects of the use of production systems to represent inductive processes Identify the theory and practice of reinforcement learning Identify the theory and practice of evolutionary computation Identify the theory and practice of learning classifier systems Identify the issues associated with the application of learning classifier systemsB Subject Specific SkillsOn completion of this module, a student will typically to: Use publicly available learning classifier system software Incorporate one or more techniques/features on a range of problems autonomously Apply a suitable form of learning classifier system to a given problem autonomouslyC Cognitive (Intellectual) SkillsOn completion of this module a student will typically be able to: Critically evaluate the performance of the approach in a given situation and affect

suitable changes in its behaviour Apply their knowledge and understanding to develop new techniques in the approach to

machine learningD Key Transferable SkillsOn completion of this module a student will typically be able to use these skills in appropriate contexts: Self-management skills IT skills Decision making and problem-formulation Progression to independent learning

SYLLABUS OUTLINEIntroduction:Historical, biological and psychological inspirations of learning classifier systems. Description of common features. Comparison with other machine learning and AI architectures.Treatment of each of the various aspects of learning classifier systems, including:Evolutionary computation; Reinforcement learning; Mechanisms specifically designed for the combination of both approaches; Early implementations (eg GOFER); ZCS; XCS; Knowledge representation (eg. real numbers); Internal state, memory mechanisms, and Markov/Non-Markov problem domains; Genetic linkage; Parameter self-adaptation; Look-ahead and latent learning; ACS; Applications (eg. robot control and data mining).

TEACHING AND LEARNING METHODSLectures will introduce the fundamental knowledge. Tutorials and practical laboratory sessions will be used to illustrate aspects of the topics covered in lectures, thereby grounding the concepts. Students will be expected to write code to use and examine simple classifier systems such as ZCS, gaining practical experience of the challenges faced in their use. Independent reading will extend the students’ knowledge and deepen their


understanding. A coursework assignment will be used to gain experience of the strengths and weaknesses of the learning classifier system approach to machine learning.

INDICATIVE SOURCESHolland, JH, Holyoak, K, Nisbett, R & Thagard, P (1986). Induction: Processes of Inference, Learning and Discovery. MIT Press.Sutton, R & Barto, AG (1998). Reinforcement Learning. MIT Press.Baeck, T (1995). Evolutionary algorithms in Theory and Practice. Oxford.Lanzi, P-L, Stolzmann, W & Wilson, SW (2000) (eds). Learning Classifier Systems. Springer. Machine Learning Journal. Kluwer.Evolutionary Computation Journal. MIT Press.

OBJECT-ORIENTED DESIGN AND PROGRAMMING (UFCE3T-15-M)

Learning outcomes:A. Knowledge and Understanding Explain the concept of object, how objects differ from other software methodologies (e.g.

structured systems analysis), and the typical characteristics of object-oriented software systems;

Describe the issues associated with object collaboration and behaviour, and how object-oriented software architectures address such issues;

Identify the concerns associated with the analysis, design and implementation of object-oriented software;

Recognise and understand the merits and demerits of using the Java programming language to implement object-oriented software.

B. Subject Specific Skills Apply object-oriented analysis and design techniques to resolve the problems of

implementing software solutions using the Unified Modelling Language (UML) notation as a modelling vehicle;

Visualise the representation of object-oriented analysis and design models using a CASE tool;

Apply Java-programming skills to effectively design, implement and test object-oriented software solutions;

Effectively construct Java software solutions using a variety of tools (e.g. editors, compilers, debuggers, etc.) and/or Integrated Development Environments (IDE).

C. Cognitive (Intellectual) Skills Formulate, analyse, visualise, synthesize and communicate object-oriented designs to

resolve application software problems; Synthesize and communicate Java source code implementations that are demonstrably

traceable to object-oriented designs; Critically appraise the usefulness of object-oriented lifecycles across a variety of

application domains.D. Key (Transferable) Skills Communication skills Self-management skills IT skills in context Problem formulating and decision making Teamwork

SYLLABUS OUTLINE:Concepts of Object-OrientationEncapsulation and information hiding (visibility control). The representation of problem space concepts through abstraction and classification. Collaboration between objects, class relationships, lifecycle models (analysis, design, implementation). The challenges and rewards of achieving reuse through object-orientation.Object-Oriented ModellingLast updated by External Affairs 10 August 2004

UML Models e.g. use case, class, interaction, state, activity models. Techniques for use case identification, class identification (Class / Responsibility / Collaboration ), and refining object interaction scenarios. Refactoring class models to reflect inheritance and polymorphism.Java Programming LanguageJava class construct syntax; sequence, selection, and iteration construct syntax. Basic console input / output and the use of Java standard libraries. Code layout and comments. Class methods and attributes, access modifiers, static and non-static members. The difference between objects and references, arrays. Inheritance including abstract classes, interfaces, and polymorphism. Simple event-driven GUI programming using Java standard libraries including applets, containers, and listeners. Memory management including threads and exceptions.

TEACHING AND LEARNING METHODS:Students will learn through a combination of Interactive, discursive lectures with accompanying booklet of slide-ware; Tutorials to explore object-oriented analysis and design techniques and Java

programming exercises together with CASE tool and Java programming tool use; Self-directed study to reinforce and reflect upon concepts and techniques presented in

lectures and tutorials; Coursework involving a non-trivial case study. The case study – a fictitious cinema

booking system – is presented early in the module and provides a coherent, non-trivial yet bounded exercise for students to explore technique by technique as the module progresses. Students gain an increasing understanding of a single realistic problem domain as their knowledge of object-orientation advances, and realise their understanding through the submission of a coursework report incorporating their analysis, design and Java implementation.

INDICATIVE SOURCES:“UML Distilled”, 2nd Edition, by Martin Fowler.“Using UML”, by Perdita Stevens and Rob Pooley“Java Software Solutions”, 3rd Edition, by John Lewis and William Loftus.“Java by Dissection”, by Ira Pohl and Charlie McDowell

SYSTEMS ON SILICON (UFEE7P-15-M)

LEARNING OUTCOMES:Knowledge and understanding Demonstrate in-depth knowledge of SoS specific: combinational and sequential

structures, universal, modular and reconfigurable logic blocks, hierarchical and structured design methodologies, principles of testable and fault tolerant digital systems;

Demonstrate fundamental understanding of the use of sophisticated Computer Aided Design tools to implement digital systems.

Subject specific skills Demonstrate the application of SoS principles in the design of testability enhanced

complex digital systems; Demonstrate independent product design and design simulation/verification ability

through his/her own SoS designed system, using one of industry’s most advanced Computer Aided Design tools.

Cognitive skills Critically examine and understand the theories of designing complex digital

systems; Analyse, evaluate and undertake their design and implementation using VLSI

technology.Transferable skills Problem formulation and decision making.


Self management skills. IT skills in context.

SYLLABUS OUTLINE: Custom and Semicustom Design Methodology - Gate arrays. Cell-based systems. Full

custom design. Simple and Complex MOS Combinational Logic Design - Combinational logic design using the

Canonical form and the Shannon expansion. Static Logic and Register Design - MOS implementation of clocked single cell and master-

slave flip-flops. MOS memory Design - Static and dynamic RAMs, ROMs and memory support circuits. Programmable Logic Devices - Multiplexer and PLA as universal logic elements. System Timing of VLSI Circuits - Delay and speed properties of VLSI systems.

Isochronous zones and self timing. MOS Finite-State Machines - Structured design of synchronous systems. Algorithmic State Machines - Structured ASM controller and the data processor

design. Linked ASMs. Structured Design Methodology - Control and data structures. Multifunction modules. Iterative and Symmetric Systems - PLA and contact network implementation. Cellular and Systolic Arrays - Two dimensional and rectangular combinational and

sequential structures. Concurrent System Design - Petri nets and their application in parallel controllers. Linear Sequential Circuits - Binary filters and their application. The Linear Feedback Shift Register - Direct and indirect sequence generators. Test Generation for Combinational Logic - Faults and fault models in digital ICs. Test

generation methods. Structured Design for Testability - Ad-hoc methods. The scan path principle. Signature Analysis and Built-In Test - Signature generation. Multiple input signature

registers. Bilbo. Fault Tolerant Digital Design - Redundancy techniques. Failure tolerant logic design.

TEACHING AND LEARNING METHODS: Fundamental principles and theories of each key element of the syllabus will be

introduced and discussed via weekly lectures. Students will however be required to work independently in order to cover the topic in

greater depth and to deepen their understanding of all theories, principles and issues concerned

and apply the knowledge thus gained in the design and solution of industrially related problems.

They will implement, simulate and verify the correctness of their designs in a CAD laboratory using state-of- the-art computer-aided tools.

Their learning process will be supported by a set of comprehensive course manuals and large number of worked design examples.

INDICATIVE SOURCES:Principle source:Systems-on-Silicon vol.1 & vol.2: Gabriel Dragffy, University of the West of England, 2002Support material:Introduction to VLSI Circuits and Systems: J. P. Uyemura, John Wiley, 2002Modern VLSI Design: Wayne Wolf, Prentice Hall, 1998Basic VLSI Design: D A Pucknell, Prentice Hall, 1994Principles of VLSI Design: N H E Weste, Addison Wesley, 1992Design of Logic Systems: D Lewin, Chapman & Hall 1992

MODERN POWER SYSTEMS (UFEE7M-15-M))

LEARNING OUTCOMES: Last updated by External Affairs 10 August 2004

Knowledge and understanding A sound knowledge of the principles of modern Power Systems. Implementation knowledge of ANN, Fuzzy Set, Expert System, uP, and Fibre Optics in

Power Systems.Subject specific skills The student will be able to calculate the power flow distribution in modern networks with

regard to demand and generation. The student will be able to assess the constraints on the system by the demand or in the

event of a fault. The student should be able to distinguish the merits of FACTS over conventional

approaches and also the benefits of ANN, Fuzzy Set, Expert System, uP, and Fibre Optics implementation in complex networks.

Cognitive skills Apply the basic principles covered in this module to real systems.Transferable skills Communication skills Self management skills Decision making and problem formulation Progression to independent learning and research

SYLLABUS OUTLINE: Vector Representation of Power Flow:Reactive Power Injection, Static Var Generation, Var Compensator under microprocessor control.System Stability:Transient and Dynamic Stability, Load Angle Oscillation, The Equal Area Criterion.Load Flow Studies: Bus Classification, a PQ Bus, a PV Bus and a Swing Bus.Short Circuit Analysis: Fault level for both Symmetrical & Un-Symmetrical, Low & High Impedance Faults, Short Circuit Limitations, Real Time Fault Location Based on ANN and Fuzzy.Protection Schemes: Distance Protection, Differential Protection, Back Up Protection, Over Current Protection and associated electronics.

TEACHING AND LEARNING METHODS:The basic principles of modern power systems will be taught through examples/ seminars/demonstrations in which applications will be reviewed and students will be able to gain some experience in using power systems modelling packages.

INDICATIVE SOURCES:Electric Power Systems: B W Weedy & B J Cory, Wiley 1998Power System Analysis & Design: Glover & Sarma, PWS 1994Facts, Parts 1-4: P Moore & P Ashmole, IEE Power Engineering Journal 1995-8Optical Fibre Sensing As A Base for the Intelligent Monitoring of Power Systems: G Jones et al, 5th IEE Conf in TDS, No. 459, pp 181-186.

ADVANCED CONTROL & DYNAMICS (UFEE7F-15-M)

LEARNING OUTCOMES: In general on completion of this module a successful student will be enable to engage in the analysis and design of advanced engineering dynamic control systems and to gain the


research potential for developing advanced and novel control methods. This will be enforced with following aspects.

Knowledge and Understanding The theoretical basis of a range of advanced topics in control engineering and dynamics

at an intellectual level significantly in advanced of that normally encountered in Bachelor of Engineering courses.

Common terminology and techniques (reading and understanding questions, block diagrams, and graphs).

Simulation of dynamics engineering systems (designing program and validating results with examples).

Subject specific skill Critically evaluate subsequent advances in the subject. Use CAD packages which are commonly employed to support both advanced conventional

design and the development of new methods of control engineering both in industry and academia.

Apply and critically discuss the subject of control engineering and dynamics. Techniques in analysis and design of control systems (providing qualitative results using

state space and digital methodologies, drawing relevant plots and diagrams). Program design using Matlab and Maple (writing programs to demonstrate theoretical

results)

Cognitive skillsDemonstrate creative engineering skills which will enable the student to develop and adapt advanced theoretical ideas and apply them to practical engineering problems and to make informed judgements on the most appropriate solution.

Transferable skills Communication skills Self management skills IT skills Decision making and problem solving Progression to independent learning

SYLLABUS OUTLINE: Control mathematics, such as matrix algebra, Laplace transform, z-transformer,

differential equations, and difference equations, for control system modelling, analysis, and design.

Use of computational packages, such as Matlab and Maple, to analyse and design control systems.

Advanced control concepts such state-space representations, solution of state equations, controllability and observability; state-feedback, (pole placement) control design.

Modelling and analysis of multivariable control systems, to convert from the transfer function model to state space representation, and vice versa. Evaluation of dynamic plant performance in aspect of controllability and observability.

Modelling and analysis of multivariable control systems, to convert from the transfer function model to state space representation, and vice versa. Evaluation of dynamic plant performance in aspect of controllability and observability.

Design of multivariable state-feedback controllers, decoupling control systems, state observers.

Digital control system analysis and design and practical application

TEACHING AND LEARNING METHODS:The learning environment will be based on a combination of formal lectures and tutorial sessions in which the material presented during each lecture will be considered in greater depth Last updated by External Affairs 10 August 2004

and detail. In general, lecture and tutorial notes will be given to students at least one week in advance of the lecture. Tutorial work will require students to use simulation packages such as MATLAB and Maple, so tutorial sessions will be mainly based in the control simulation laboratory. During each tutorial session each student will be required to undertake a design task and to develop and test the design using the simulation package. An assignment will be set which will require the student to undertake the design of a more substantial system, during his/her own time, to draw on material covered thus far and to select and justify the most appropriate design solution.

INDICATIVE SOURCES:Franklin, G.E, Powell, J.D., and Workman, M.L., Digital Control of Dynamics Systems(2nd edit), Addison Wesley, 1990.Kuo, B, Digital control systems (2nd edit), Saunders, 1992.Ogata, K., Modern Control Engineering (2nd or updated edit), Prentice-Hall, 1990 or to-date.

NEURAL NETWORKS AND FUZZY SYSTEMS (UFEE7N-15-M) LEARNING OUTCOMES:A. Knowledge and understanding An in-depth knowledge of the fundamental theories and understand the latest research

ideas for those neural networks, and associated learning algorithms, which may be applied to signal processing, pattern recognition, data mining, control/monitoring and optimisation of industrial and commercial systems.

An in-depth knowledge of the fundamental theories and understand the latest research ideas for fuzzy logic that can be applied to signal processing, pattern recognition, data mining, control/monitoring and optimisation of industrial and commercial systems.

B. Subject specific skills Utilise an advanced design tool for neural network and fuzzy systems development in

order to critically evaluate implementations of the theories. Appraise the technical advantages and disadvantages of applying these leading edge

technologies in place of established classical techniques.C. Cognitive skills Apply the principles covered in this module elsewhere Study independently where necessary for the understanding of new advancements in the

fieldD. Transferable skills Communication skills Self management skills IT skills Decision making and problem formulation Progression to independent learning

SYLLABUS OUTLINE:1. Introduction: Scope and limitations of this module, especially with respect to classical

control and AI. Overview of supervised and “unsupervised” learning methods. Neurofuzzy methods versus classical methods. On-line versus off-line learning. Global versus local learning.

2. Neural Networks: Single-Layer Binary Perceptron and the Delta learning rule. Multi-Layer Real valued Perceptron (MLP) and the Error Backpropagation learning algorithm. Self-Organizing neural network architectures. Radial Basis Function (RBF) Neural Network. Cerebellum Model Articulation Controller (CMAC) neural network.

3. Fuzzy Systems: Comparison with “hard-logic” rule-based systems. Membership functions, fuzzification, the rule-base, defuzzification. Mamdani fuzzy systems. Tagaki-Sugeno fuzzy systems. Hand-crafted systems vs. use of learning algorithms. Equivalence classes between Neural Network and Fuzzy systems.

4. Review: Strengths and weaknesses of fuzzy-systems compared with neural network techniques. Relative strengths and weaknesses of these architectures, compared with


classical techniques, in signal processing, pattern recognition/classification, optimisation, data-mining and control applications for the commercial and industrial domains.

TEACHING AND LEARNING METHODS:Lectures will introduce the fundamental theoretical concepts. Tutorials will be used for "worked example" sessions and detailed case studies, each designed to illustrate the essential details of a particular concept or technique. Laboratory sessions will be used to allow students to carry out design projects in using neural networks and fuzzy logic in examples of the applications outlined below, in order to assess relative strengths and weaknesses compared with classical approaches and to assess the design tools themselves.

Example applications: Review of work carried out in this Faculty, and at other establishments. Fuzzy- versus neuro- versus classical-control of an inverted pendulum, on-line learning neurocontrol of an industrial robot manipulator, neural network based recognition of human faces and of written characters, neural networks for financial forecasting. INDICATIVE SOURCES:Internet sources: There is a very wide range of up-to-date information on these subjects, at the appropriate introductory level for this module, available on the internet. Students will be provided with a Compact Disk that guides their access to approved and publicly available sites (such as the Evonet “Flying Cirucs” site for evolutionary computation). There are also some older books that still represent excellent introductions to this subject area.Fuzzy-Neural Control: Principles, Algorithms and Applications: Nie & Linkens, Prentice Hall, ISBN 0133379167, 1995The Handbook of Intelligent Control: White & Sofge, Van Nostrand-Reinhold, 1992.Neural Computing - an Introduction: R Beal & T Jackson, Adam Hilger, 1990.C++ Neural Network and Fizzy Logic (2nd Edition): Rao and Rao, MIS, ISBN 15585515526, 1995.Neurofuzzy Adaptive Modelling and Control: Brown & Harris, Prentice Hall, ISBN 0131344536, 1994.Design tool user manuals - eg MATLAB 6.2 Fuzzy, Neural Network, and Simulink Toolboxes.Neural Networks for Control: Miller, Sutton & Werbos, MIT Press, 1991.The Handbook of Brain Theory and Neural Networks: Ed M A Arbib, MIT Press, 1995.


BEHAVIOURAL SYSTEM DESIGN (UFEE7G-15-M)

LEARNING OUTCOMES:A. Knowledge and understanding Demonstrate in-depth knowledge of the VHDL language and language specific

constructs, in the behavioural, dataflow and structural design domain. Understand the operation and principles of designing structured and hierarchical digital

systems and test benches; Demonstrate fundamental understanding of the use of sophisticated Computer Aided

Design tools to implement digital hardware systems.B. Subject specific skills Demonstrate the application of VHDL language principles through the design of complex

digital systems and prove their integrity at every level of the hierarchical abstraction; Demonstrate independent product design and design simulation/verification ability

through his/her own VHDL designed system, using “top-down” design approaches and one of industry’s most advanced Computer Aided Design tools that guarantees a product that is “right-first-time”;

C. Cognitive skills Critically examine and understand the theories and methodologies of designing complex

digital systems; Analyse, evaluate and undertake their design and implementation using VHDL design

methodology.D. Transferable skills Problem formulation and decision making. Self management skills. IT Skills in context.

SYLLABUS OUTLINE:Basic Building Blocks

Entity, architecture, signals, identifiers. VHDL Mentor Tools

Modelsim & Qicksim II design environment. VHDL debug and simulation. Structural VHDL

Component declaration, instantiation and configuration. Testing VHDL Descriptions

Test bench and VHDL test bench styles.VHDL Types

Types, arrays, records, file types, file conversion and IEEE Standard types.Generics, Default Values and Open Ports.

Generics, Default Values and Unconnected Ports.Packages, Libraries and Design Units

Package use and visibility. Predefined packages.Dataflow Description of VHDL

Signals, variables, attributes, expressions and operators. Modelling state machinesBehavioural Description of VHDLConcurrent execution: procedures, functions. Sequential execution statements.Concurrent Evaluation

Delta Delays. Inter-process communication. VHDL Timing Model and Textio

Transport and internal delay models. Resolution Functions

Resolution and Null drivers.


TEACHING AND LEARNING METHODS: Fundamental principles and theories of each key element of the syllabus will be

introduced and discussed via weekly lectures and associated hands-on problem solving. Students will however be required to work independently in order to cover the topic in

greater depth and to deepen their understanding of all theories, principles and issues concerned,

and apply the knowledge thus gained in the design and solution of industrially related problems.

They will implement, simulate and verify the correctness of their designs in a CAD laboratory using state-of- the-art computer-aided tools.

Their learning process will be supported by a comprehensive set of course manuals and large number of design examples.

INDICATIVE SOURCES:Principle source: Behavioural System Design: Gabriel Dragffy, University of the West of England, 2002Support material: VHDL: Analysis and Modelling of Digital Systems: Navabi (McGraw-Hill, 2000), VHDL Starter’s Guide: S. Yalamanchili (Prentice Hall, 1998), VHDL for Designers: Sjoholm (Prentice Hall, 1997), Introduction to VHDL: Hunter (Chapman and Hall, 1996)

MOBILE COMMUNICATIONS (UFEE7L-15-M)

LEARNING OUTCOMES:A Knowledge and Understanding Design principles, operations, management, network hierarchies/organisation and

planning of 2G and 3G cellular mobile communications systemsB. Subject Specific Skills Apply design principles for developing 2G and 3G mobile systems Suggest appropriate system components and cell coverage areas in particular

circumstancesC. Cognitive Skills Evaluate system performance Participate confidently in new developments of mobile systemsD. Key Transferable Skills Self management skills. Awareness of professional literature. Communication skills. Problem formulation and decision making.

CONTENTS:Information Theory: Information contents of signals, Transmission of information, and Hartley and Shannon’s Law and its applications.Cellular Principles: The cellular concept, Typical cell operation, System capacity, Frequency re-use distance, Determination of cell radius, Sectoring, Properties of the radio channel, Space wave propagation, Short-term fading (fast fading)Mobile Communication Systems: First generation analogue systems Total Access Communication System (TACS), TACS radio parameters, TACS mobile network control, Call management in TACS, Alternative analogue systems Second Generation Communications Systems Global System Mobile Communication (GSM), GSM radio interface, Mapping of logical channels in GSM, GSM modulation, coding and error protection, Handoff in GSM, GSM handoff measurements, Features of the GSM system, Operation of the GSM system, Security in GSM, Others Cordless Communications systems. 3G Mobile Communications Systems Universal Mobile Telecommunications System (UMTS), Comparison with GSM and others second generation systems, CDMA principle, WCDMA air interface-physical layer, Modulation techniques and spread spectrum, UMTS networks and network management.TEACHING AND LEARNING APPROACHES:Last updated by External Affairs 10 August 2004

A combination of lectures, guest lectures, tutorial, laboratory work/demonstration will be used to present and reinforce the subject matter. Students will be expected to learn independently by carrying out a mini-research project, reading and directed study outside taught classes.

INDICATIVE SOURCES:Telecommunication Engineering (4th edition): Dunlop and Smith Chapman & Hall 1994Mobile Communications, Jochen Schiller, Pearson Education Limited, 2000Cellular Radio: Principles and Design (2nd ed.) R.C.V. Macario, Macmillan Press, 1997Audio, Video and Data Telecommunications: Peterson, McGraw Hill 1992

ACTUATORS AND CONTROL TECHNIQUES (UFPEE5-15-M)

LEARNING OUTCOMES:A. Knowledge and understanding Principles of operation of conventional; electric, pneumatic and hydraulic actuators Principles of operation of mechanical and novel actuators Characteristics of different types of actuators Modelling and simulation of actuators and their potential application areasB. Subject specific Skills Select an appropriate actuator for a given application Determine the important characteristics of the specific actuator/application interface

Determine the parameter values for the chosen actuator Produce mathematical and or computer models for a family of actuators Use Matlab and Simulink as an investigation tool for modelling of actuators For a number of sample cases identify the needs, select the appropriate actuator and

perform validation testsC. Cognitive Skills Examine current systems and comment on suitability of the chosen actuator and any

improvements that can be implemented Use the modelling skills acquired in this module for investigation of large multi actuator

systemsD. Transferable Skills Progression to independent learning Identification of appropriate elements in electromechanical systems Presentation of technical information that facilitates understanding of systems under

discussion

SYLLABUS OUTLINE:The syllabus may include but not be limited to the followings:ELECTRIC ATUATORS including: AC, DC and stepper motors Commutation and electronic switching Brushed and brushless motors Pulse wave modulation and drives system PNEUMATIC and HYRAULICS ACTUATORS including: Cylinders; single and double acting Rotary actuators Pressure and Flow regulators Solenoid valves, proportional valves and circuits Proportional control valves NOVEL ACTUTORS MODELLING AND CONTROL: Introduction to matlab and application of simulink Motor selection, response and modelling

TEACHING AND LEARNING METHODS:Last updated by External Affairs 10 August 2004

A combination of formal lectures, presentations and laboratory sessions will be used as the teaching approach. It is expected that the student will carry out independent study outside the formal sessions.

INDICATIVE SOURCES:Electric motors and drives Hughes, A.Publisher: Butterworth-Heinemann; ; 2nd edition (December 1993), ISBN: 0750617411 Hydraulics and pneumatics a technician's and engineer's guide Parr Andrew 2nd ed.1998Pneumatics theory and applications Muller Rolf 1998


FEES(2004/2005)

Fees payable for MSc Advanced Technologies in Electronics for the academic year 2004/2005 are as follows.

Full-time home and EU students:

£3,150 – includes all modules plus dissertation.

Part-time home and EU students:

£3,150 – includes all modules plus dissertation, for part-time students studying over a two-year period. Fees may be spread across both years of the award.

Full- and part-time international (non-EU) students:

£7,166 – includes all modules plus dissertation.International students are required to pay 1/3rd of this sum at registration.

WAYS TO PAY – HOME AND EU STUDENTS

1. You may pay your contribution to your fees in full, or in part, at, or before, registration by any of the following methods:

Credit card Debit card Cheque, made payable to “UWE, Bristol” Banker’s draft in sterling Cash: but only if you are unable to pay by another method. Please note we

strongly urge you not to pay by cash or to carry large sums of money around. If you pay by cash you must obtain a receipt and keep it. Cash must not be sent through the post.

Please note that Euro-cheques cannot be accepted.

2. Paying by instalments

If you wish to pay in full or in part by instalments and your contribution to the fee is £100 or more, you may pay your fees by direct debit, by:

3 consecutive monthly instalments, due on the 1st day of each month, or 6 consecutive monthly instalments, due on the 1st day of each month

In order to pay by direct debit (DD): you must have a UK bank account capable of processing DDs (usually a

current account, not a deposit account) the tuition fee must be £100 or more


WAYS TO PAY – INTERNATIONAL (NON- EU) STUDENTS

International students are required to pay at least 1/3rd of their fee at or before registration by any of the methods listed in section (1) above. The balance of the fee can be paid in instalments by direct debit (DD) from a UK bank account as explained in section (2) above.

PAYMENT FOR INDIVIDUAL MODULES

Part-time students may pay for modules individually as they are attended, as follows (these rates also apply where students need to retake individual modules):

15-credit module £395 30-credit module £789 Dissertation (60 credits) £1578

Whilst this option allows students to accumulate credits at their own pace, please be aware that the total cost of your MSc will rise to over £4000. However, should you commit to take a minimum of 60 credits per year, a discount of 33% will apply which will bring the cost in line with those who follow the standard full or part-time routes.


ENTRY REQUIREMENTS

The MSc Advanced Technologies in Electronics is designed for graduates with a first or second class Honours degree (or the equivalent) in any branch of engineering, computer science, maths, or the physical sciences. However, emphasis will be given to those who have studied electrical or electronic engineering at undergraduate level. Candidates not satisfying these requirements will be considered for either the MSc, the PG Diploma or the PG Certificate, depending on their qualifications and experience. All students will be required to demonstrate a proficiency in the English language.

If you would like to receive an application form or you have questions which you feel have not been fully answered in this handbook, please contact CEMS Admissions Office.

CEMS Admissions Tel: +44 (0) 870 901 0767Fax: +44 (0) 117 32 83680E-mail: [email protected]

Alternatively, a downloadable application form can be found on the UWE website at:http://info.uwe.ac.uk/courses/viewcourse.asp?URN=9623

Please also visit our website at www.uwe.ac.uk/cems.


http://www.uwe.ac.uk/cems

http://info.uwe.ac.uk/courses/viewcourse.asp?URN=9623

OTHER FREQUENTLY ASKED QUESTIONS

How much additional time and independent study will this course require?

A 15-credit module typically involves 2-3 hours per week over a 12-week period in structured activities, although this may vary. You should reckon on devoting approximately a further 9 hours per week to each module. This means that a full-time student may need to spend up to 50 hours per week on his/her studies. In practice, of course, students spread this load over the holiday period, and there may be times of particularly intensive activity, when deadlines need to be met.

How much time and effort will I be expected to put into the dissertation?

You should view your dissertation as a part-time activity over the duration of the course. Writing the dissertation is demanding, not so much because of its length, which is about 15000 words, but because you are expected to identify a research question that is important and interesting to you, and then think analytically and creatively about this question. This will involve extensive, critical reading of relevant literature.

I am in the final year of my undergraduate degree. Why should I do a postgraduate degree and what better career prospects can this give me?

By doing a Masters degree, you would be gaining skills and knowledge valued by employers - making yourself much more attractive to employers - and much more likely to be invited to interview. It is true that there has been a recent downturn in the engineering sector, but this is perhaps an argument for taking a higher degree and postponing your entry into the job market for a year, when the economic conditions might possibly be more favourable.

Where can I find help financing my post-graduate degree?

A Career Development Loan of between £300 and £8000 can be applied for through Barclays Bank, Co-operative Bank and the Royal Bank of Scotland. This can be used to pay for course fees, living costs and other related expenses, such as childcare, textbooks, etc. Further information can be obtained from UWE’s Students’ Financial Advice and Welfare Office on +44 (0)117 32 82852 or by e-mail [email protected], your local Job Centre, or by telephoning +44 (0) 800 585505. You can also visit www.lifelonglearning.co.uk/cdl/ for further information.

Disabled Students Allowances are available for postgraduate students, provided no other funding support applies. Please contact the Disability Resource Centre for details on +44 (0) 117 344 2564, or by e-mail [email protected].

Finally, many postgraduate students are encouraged by their employer to work for a degree by research or a taught programme, and are normally willing to pay some or all of the students’ tuition fees.


mailto:[email protected]

http://www.lifelonglearning.co.uk/cdl/

mailto:[email protected]

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