Professor Dr Barrie W Jervis

Professor of  Electronic Engineering

Applied Electronics Research Group

School of Engineering

Sheffield Hallam University

City Campus

Pond Street

Sheffield

S1 1WB

England

 Tel: (44)-(0)114-225-3282

Fax:(44)-(0)114-225-3433

http://bwjervis.freeservers.com/

B.W.Jervis@shu.ac.uk

Sheffield Hallam University, the School of Engineering, and the Engineering Information Technology (EIT) and Electronics Area.

Sheffield Hallam University is one of the ten largest Universities in the UK with a budget of over £100 million. Over 23,000 students are enrolled on University Programmes supported by 3000 staff. The University offers more than 400 flexible programmes of full time and part time studies and a broad range of vocational Continuing Professional Development (CPD) training. The University is committed to high quality applied research and technology and knowledge transfer to support the region but also in a national context.

The University comprises ten academic Schools supported by a number of central administrative departments. The Schools form the academic core of the University and deliver teaching, research and other non-HEFCE (Higher Education Funding council) fully funded income. To provide a focus for research the University also operates five research Institutes which includes the Materials Research Institute and a number of Research Centres and Units The outstanding and modern learning facilities provided by the Adsetts Centre support both students and staff  in their study work and all other scholarly activities including research.

The School of Engineering is one of the largest in the UK specialising in a range of engineering disciplines  including Mechanical, Enterprise and Manufacturing, Materials, Electronic and Electrical Engineering and Control Engineering. The annual income for the School is approximately £9 million. We currently provide comprehensive and flexible programmes to approximately 1200 undergraduate students and 200 postgraduate students. The School has a total of more than 70 academic staff supported by 40 technical and 30 administrative staff. There are 7 Professors in the School. All academic staff are allocated to three Subject Areas with each Subject Area being led by a Senior Academic at Professorial level.  

The School of Engineering is based in purpose built facilities with up to date,  sophisticated engineering laboratories and facilities including powerful and industrial standard CAD/CAM systems covering both mechanical and electronic engineering. The School also operates well equipped laboratories to support all its programmes of study and research including a new range of  networking and communication laboratories, modern equipment  for materials and component testing and newly equipped automated manufacturing facilities which include the most modern and up to date PLCs. This state of the art equipment  is used to support both teaching, research and technology transfer work. The School also prides itself on the range of software available. This includes software for stress analysis,  fatigue life prediction, fluid dynamics, control, manufacturing systems simulation and electronic circuit design. The School of Engineering has a long history of successful research with a focus in Engineering Design, Manufacturing Systems and Automations and Electronics.

ELECTRONICS AND INFORMATION TECHNOLOGY

The Electronics & IT subject area comprises two subject groups, Communication and Digital Signal Processing and Electronics.  The Subject Area comprises 20 academic staff who are actively involved in high quality research and teaching.

Communications Engineering and Digital Signal Processing

This group comprises 7 academic staff members who are involved in the areas of Optical Communications, Digital Signal Processing & Neural Networks, and Computer Networks.

The group is research active and activity is focused mainly in the areas of the development and application of Digital Signal Processing and Artificial Intelligence techniques to signal analysis, electronics, sensor array signal analysis, and telecommunication engineering projects, and to Optical Communications Applications. The group is a major provider of courses at both undergraduate and postgraduate levels in Computer Network Engineering and Business Network Engineering.

Electronics and Computer Engineering

This group comprises 10 academic staff members who are involved in the areas of Sensor Technology & Physical Electronics, Microsystems Technology, computer engineering, electronics and embedded system design, VLSI design and associated computer aided design techniques.

Research takes place in the subject areas of sensor technology & physical electronics, microsystems technology including MEMs devices and image processing. The group supports the teaching of electronics, information technology, computing and computer network engineering.

Relevant experience and skills.

Development of the Integrated Probabilistic Simplified Fuzzy ARTMAP neural network which outputs the Baye's posterior probability of the class and can be trained on-line.

Development of the Supervised Forced Organisation neural network, derived from the Kohonen Self-organising network and which offers quantitative outputs.

Blind source separation using Independent Components Analysis (ICA).

Neural Networks used: For off-line purposes: multilayer perceptron (MLP) for classification and regression trained by the Bayesian  method as well as by back-propagation, Kohonen self-organising map for classification, Adaptive Resonance Theory (ART) networks for classification; For on-line purposes (rapid training, alternated classification and training (without the need for total retraining)): Simplified Fuzzy ARTMAP for classification, Integrated Probabilistic Simplified Fuzzy ARTMAP for classification including Baye's posterior probability of class, Cerebellar Model Arithmetic Computer (CMAC).

Signal processing techniques employed: Trend removal, low and high pass digital filtering, Fast Fourier transform, autoregressive modelling, autoregressive spectrum analysis, blind source separation by Independent Components Analysis, Least squares methods.

Statistical techniques employed: Discriminant analysis, Principal Components Analysis (PCA), circular statistics, Ward's clustering method (for classification),

t-tests.

Professor Dr Barrie W Jervis, BA Hons, (University of Cambridge), MA (University of Cambridge), PhD (University of Sheffield), Chartered Electrical Engineer, FIEE (Fellow of the Institution of Electrical Engineers), Member of the British Society for Clinical Neurophysiology, is Professor of Electronic Engineering at Sheffield Hallam University. His teaching expertise is in signal processing, artificial neural networks and genetic algorithms, and in electronic, communication, and microwave engineering. His current research interests include the application of both artificial intelligence techniques, particularly neural networks, and signal processing methods to electronic circuit diagnosis, biomedical signal processing, and the quantification of gas mixtures using gas sensor arrays. A current interest is in blind source separation techniques using Independent Components Analysis. Previous research and teaching included semiconductor materials and devices, microwave devices and instrumentation, and expert systems. He has published many journal and conference papers, and has supervised successful higher degree candidates (MPhil and PhD). He has been jointly awarded the IEE Premium Prize and the Eurel Prize (best European paper). He is a committee member of two international conferences, “Neural Networks and Expert Systems in Medicine and Healthcare” and “Medical Signal Processing”. Professor Jervis speaks French and German. His Web address with further professional details and list of publications is http://www.homepages.shu.ac.uk/~eitbwj/.

Main Related Publications

“Extracting single trial event related potentials”, J. Britton, B.W. Jervis and R.A. Grünewald, IEE Proceedings - Science, Measurement and Technology, Vol. 147, No. 6, p.382-388, November 2000 

“Residual ocular artefact subsequent to ocular artefact removal from the electroencephalogram”, B. W. Jervis, M. Thomlinson, C. Mair, J. M. L. Lopez and M. I. B. Garcia, IEE Proc.-Sci. Meas. Technol., vol 146, No. 6, 293-298, 1999.

“The Probabilistic Simplified Fuzzy ARTMAP (PSFAM)”, B. W. Jervis, T. Garcia,

E. P. Giahnakis, IEE Proc. Science, Measurement and Technology, vol. 146, pp. 165-169, July, 1999.

"Use of Artificial Neural Networks for Clinical Diagnosis", G Papadourakis, M Vourkas, S Micheloyannis, B W Jervis, Mathematics and Computers in Simulation, 40, 623-635, 1996.

"Signal Processing of the Contingent Negative Variation in Schizophrenia Using Multilayer Perceptrons and Predictive Statistical Diagnosis". M R Saatchi, S Oke, E M Allen, B W Jervis and N Hudson, IEE Proc., Pt A, Science, Measurement and Technology, 142, No 4, 269-276, July, 1995.

Awarded the Institution of Electrical Engineers Institute Premium 1996. (The best of all published IEE journal papers in 1995/6.)

Awarded the 1997 EUREL prize. A unanimous vote by the prize assessment panel of the Federation of National Societies of Electrical Engineers of Europe.

“Independent components analysis of single trial CNV data”, J. H. Britton and B. W. Jervis, Proceedings Fourth International Conference on Neural Networks and Expert Systems in Medicine and Healthcare, Milos, Greece, 20-22 September, 2001, pp. 208 – 211.

"Extracting Single Trial Event Related Potentials", J  Britton, B W Jervis and R A Grünewald, Int. Conference Medical Signal and Information Processing, MEDSIP 2000, University of Bristol, 4-6 September, 2000.

"Rapid clinical classification by the Probabilistic Simplified Fuzzy ARTMAP", B W Jervis, T Garcia, E P Giahnakis, Proc Third International Conference Neural Networks and Expert Systems in Medicine and Healthcare (NNESMED), Pisa, Italy, September 2-4,  1998, pp 205-216, World Scientific, 1998, ISBN 981-02-3611-5.

“Sub-classification of Parkinson’s disease patients based on neuropsychological data using the Kohonen Self-organising Map”, M. Grimsley, B. W. Jervis, H. J. Sagar, R. Woodcock, Second International Conference, “Neural Networks and Expert Systems in Medicine and Healthcare”, Plymouth, 28-30 August, 1996

“Classification of brain conditions using multilayer perceptrons trained with a recursive least squares algorithm”, B. W. Jervis, M. Sayadi, F. Fnaiech, Second International Conference, “Neural Networks and Expert Systems in Medicine and Healthcare”, Plymouth, 28-30 August, 1996