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The aim of this study was to apply signal processing techniques to a potential known as the contingent negative variation (CNV) in order to aid detection of schizophrenia, Parkinson's disease (PO) and Huntington's Disease (lID). A data recording system was constructed and used to obtain data from 20 schizophrenic patients, 16 PO patients, 21 -at-risk- of HD patients, 11 HD patients and 43 normal control subjects. The data included the CNV, electro-oculograms (required for the preprocessing of the CNV) and the subjects reaction times to an acoustic stimulus. The CNV waveforms were initially preprocessed. This reduced the effects of background electroencephalogram and ocular artefact potentials. The CNV waveforms were then processed using a method which involved the discrete Fourier transform (OFf) and discriminant analysis. This method developed from the work of Martin Nichols and Michael Coelho. It was possible to successfully identify the majority of the patients using this method. In order to reduce the complexity of patients' Identification a different method of CNV signal processing was considered. This involved obtaining the CNV features in the time domain and using them in neural networks. This method was as effective as the method which used OFf and discriminant analysis in identifying the patients. To establish whether HO could presymptomatically be detected in the at-risk of HD group, the CNV was analysed using principal component analysis (PCA) and Ward's clustering method. This resulted in identification of 7 patients who were suggested would develop HO. The subjects' reaction times were also analysed. This indicated that the reaction times of schizophrenic, PO, HO and some at-risk of HD patients were significantly different from the reaction times of their normal control subjects.

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This thesis is about query optimisation studies in a Prolog object-oriented database (P/FDM). Although many of the query optimisation techniques used in relational database systems can be adapted to solve problems in query optimisation for object-oriented databases (OODBs), there are also many problems that are unique to the OODBs, and new techniques need to be developed. Techniques developed for optimising queries in OODB systems can be categorised into three distinct approaches, namely, <i>algebraic transformation, type-based transformation</i>, and <i>method optimisation</i>. However, few OODB systems have used the combination of all three techniques in the design of their query optimisers. We have adopted an unified approach by combining them into the optimisation strategy of a query optimiser for P/FDM. The overall architecture of the P/FDM query optimiser is based on the <i>top-down</i> framework. The source language of the query optimiser is an extension of Shipman's original <i>DAPLEX</i> language. We choose <i>ZF notation</i>, based on set comprehensions in Zermelo-Frankel (ZF) set theory, to represent the intermediate forms of DAPLEX queries. The target language of optimiser is a set of P/FDM primitives which are implemented in Prolog. We have taken the approach of asking the database to reveal the definitions of methods to the optimiser as privileged code. Most OODB systems do not perform method optimisation because of the difficulties caused by defining methods in a procedural programming language. We have been greatly helped by using a functional programming approach which holds methods in a declarative form, and thus makes it much easier to combine and transform them. Heuristics are incorporated into the query optimisation strategy to avoid exhaustive search, so that the optimiser is able to produce efficient query evaluation plans within satisfactory response times.

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