Strategies for neural networks in ballistocardiography with a view towards hardware implementation

A thesis submitted for the degree of Doctor of Philosophy at the University of LutonThe work described in this thesis is based on the results of a clinical trial conducted by the research team at the Medical Informatics Unit of the University of Cambridge, which show that the Ballistocardiogram (BCG) has prognostic value in detecting impaired left ventricular function before it becomes clinically overt as myocardial infarction leading to sudden death. The objective of this study is to develop and demonstrate a framework for realising an on-line BCG signal classification model in a portable device that would have the potential to find pathological signs as early as possible for home health care. Two new on-line automatic BeG classification models for time domain BeG classification are proposed. Both systems are based on a two stage process: input feature extraction followed by a neural classifier. One system uses a principal component analysis neural network, and the other a discrete wavelet transform, to reduce the input dimensionality. Results of the classification, dimensionality reduction, and comparison are presented. It is indicated that the combined wavelet transform and MLP system has a more reliable performance than the combined neural networks system, in situations where the data available to determine the network parameters is limited. Moreover, the wavelet transfonn requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced. Overall, a methodology for realising an automatic BeG classification system for a portable instrument is presented. A fully paralJel neural network design for a low cost platform using field programmable gate arrays (Xilinx's XC4000 series) is explored. This addresses the potential speed requirements in the biomedical signal processing field. It also demonstrates a flexible hardware design approach so that an instrument's parameters can be updated as data expands with time. To reduce the hardware design complexity and to increase the system performance, a hybrid learning algorithm using random optimisation and the backpropagation rule is developed to achieve an efficient weight update mechanism in low weight precision learning. The simulation results show that the hybrid learning algorithm is effective in solving the network paralysis problem and the convergence is much faster than by the standard backpropagation rule. The hidden and output layer nodes have been mapped on Xilinx FPGAs with automatic placement and routing tools. The static time analysis results suggests that the proposed network implementation could generate 2.7 billion connections per second performance

Efficient implementation of video processing algorithms on FPGA

The work contained in this portfolio thesis was carried out as part of an Engineering Doctorate (Eng.D) programme from the Institute for System Level Integration. The work was sponsored by Thales Optronics, and focuses on issues surrounding the implementation of video processing algorithms on field programmable gate arrays (FPGA). A description is given of FPGA technology and the currently dominant methods of designing and verifying firmware. The problems of translating a description of behaviour into one of structure are discussed, and some of the latest methodologies for tackling this problem are introduced. A number of algorithms are then looked at, including methods of contrast enhancement, deconvolution, and image fusion. Algorithms are characterised according to the nature of their execution flow, and this is used as justification for some of the design choices that are made. An efficient method of performing large two-dimensional convolutions is also described. The portfolio also contains a discussion of an FPGA implementation of a PID control algorithm, an overview of FPGA dynamic reconfigurability, and the development of a demonstration platform for rapid deployment of video processing algorithms in FPGA hardware