Postgraduate Courses in the
School of Computing Sciences
MSc Cybernetics
Introduction
Who is The Masters Degree For?
The course is suitable for graduates from a Computing, Engineering or Mathematics discipline. With recent advances in computing, modern artificial intelligence techniques and sensor technology, the potential for creating advanced systems and devices is greater than ever. The environment in which we live and work is beginning to be populated with 'intelligent' devices, ranging from consumer devices such as microwave cookers through to the automated porters that are beginning to appear in hospitals. As our expectation of the capabilities of even the most simple devices increases so does the demand for personnel with the appropriate skills to design and develop them. The employment prospects of successful students are thus continually expanding as industry tries to satisfy ever increasing customer expectations.
The course is tailored to providing graduates with the necessary skills to enable them to make an immediate contribution in industry where they will be suited for employment in the following areas:
- Industrial design and development teams working on advanced intelligent interactive systems. These systems may range from consumer devices, embedded microprocessor systems through to large scale computer control systems used in the process industries. The key feature of the training the course provides is that it equips graduates with the necessary skills to work in high technology multi-disciplinary teams.
- Software development teams working on advanced interactive systems.
- PhD training. This course could form the basis for students wishing to study for a PhD in cybernetics, but, the primary aim of the course is to prepare graduates for employment in industry.
Course Structure
The full-time course lasts for 53 weeks and comprises of taught and project components. The taught component comprises eight modules. The project component is a substantial piece of independent work which draws on and demonstrates understanding of the material within the taught component.
The taught modules take place over two 15 week semesters. A full module comprises of 30 hours class time, made up of lectures, tutorials and laboratory periods. Additional private study may include the use of student-centered learning material, library and Internet resources. Students must study all elements of the course (there are no optional elements)
Course Structure
The most common part-time route takes two years. Academic study at the University occupies one day per week during each academic year(i.e. Semesters 1 and 2 each year). In addition, the project will normally be carried out at the students workplace during the second year. Part-time students study the same modules alongside the full-time students, but do only half the modules one year, and half the next.
The normal order in which the modules are studied is indicated in the table below
| Part time year 1 | Part time year 2 | |
| Semester 1 |
||
| Fundamentals of Cybernetics | X | |
| Robotics | X | |
| Artificial Intelligence | X | |
| Digital Signal Processing | X | |
| Semester 2 |
||
| Control Theory | X | |
| Image Processing | X | |
| Knowledge Based and Adaptive Systems | X | |
| Research Methods | X | |
| Semester 3 |
||
| Project | X | X |
Course Syllabus
- Fundamentals of Cybernetics
Aim: To provide an awareness of the fundamental techniques and methods used in the design and integration of systems.
Syllabus: Introduction to cybernetics, definitions of cybernetics, history of cybernetics. Hardware requirements, microprocessor architectures, interfacing considerations. Software requirements, language facilities, design methodologies, performance modelling, safety critical aspects.
- Robotics
Aim : To introduce the principle components of mobile robot systems with particular emphasis on sensors and actuators.
Syllabus: Overview of mobile robot architectures, implementation aspects and experiences. Sensor systems, infra-red, ultrasonic, proximity, CCD, vision. Motor systems. Designing task specific robots .
- Artificial Intelligence
Aim: To provide an awareness and appreciation of Artificial Intelligence and the problem-solving paradigms developed therein.
Syllabus: Introduction to AI, definitions of AI, history of AI, classical AI - weak methods, classical AI - knowledge representation and inference, AI paradigms - symbolic and subsymbolic. Neural networks, feedforward networks, associative memories, competitive learning. Evolutionary Computing, genetic algorithms, genetic programming, classifier systems, other variants. Criticisms of AI, Dreyfus and Winograd, Brook's approach.
- Digital Signal Processing
Aim: To provide an awareness and appreciation of fundamental Digital Signal Processing techniques. This module provides an essential basis for the image processing module.
Syllabus: Mathematical and Computational Background - Fourier transform, analytic signals, sampling and aliasing, discrete Fourier transform, digital filters. Processing techniques - Weiner filter, matched filter, Bayesian estimation, bandlimited functions, FIR filters, median filter, dynamic programming methods.
- Control Theory
Aim: To provide an awareness and appreciation of modern control techniques and their implementation on digital computers.
Syllabus: Concept of stability, Time response analysis, Frequency response analysis, Sampled data control systems, state variable analysis and design, nonlinear systems, optimal control.
- Image Processing
Aim: To provide a unified approach to processing and analysing digital images.
Syllabus: Mathematical and computational background - principles of image processing, image formation, digitisation techniques, mathematical models, 2D discrete Fourier transform. Image restoration and reconstruction - inverse filter, maximum entropy method, bandlimited images. Image enhancement - transform techniques, highpass filtering, high emphasis filtering, neighbourhood average filter, frequency vs real space techniques. Segmentation - thresholding techniques, first order edge detection, Roberts gradient, second order edge detection, Hough transform, fractal images.
- Knowledge Based and Adaptive Systems
Aim: To explore the application of artificial intelligence techniques to the development of systems which are able to adapt to their environment.
Syllabus: Principles of adaptive systems - why adaptive?, levels of adaptivity, basic architectures - symbolic, sub-symbolic and hybrid, scale issues - micro, macro and global. Knowledge based systems - knowledge acquisition of KBAS, case study - architecture and implementation. Behaviour-based adaptive systems - sub symbolic systems and applications.
- Research Methods
Aim: To provide an appreciation of the fundamental skills required to undertake a significant research project.
Syllabus: Context for research - nature of research, planning for research, organisational culture and attitude, cost benefit analysis. Research practice - information retrieval skills, oral presentation, academic and managerial report writing. Quantitative methods - statistics, parametric approaches, describing and exploring data, basic concepts of probability sampling distributions and hypothesis testing, correlation and regression, factorial analysis of variance. Quantitative methods - subjective assessment, psychophysical methods, attitude scale construction. Methods of data collection and analysis - surveys: sampling and questionnaire design, interviewing, observation.
Further Information
Assessment
Assessment of the taught modules consists of coursework and examinations. The project assessment may vary according to the individual nature of each project but typically includes, a project report, system demonstration and or viva/presentation of the project. The final award of MSc may be at 'Pass' or 'Distinction' level for exceptional candidates. Students who do not meet the full requirements of the MSc award or who leave the course without completing all assessed components may (subject to the course regulations) be awarded a Post-Graduate Diploma.
Course Document
The course document contains full module descriptions for the 1997-1998
sessions.
For contact details, see foot of page
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