Analogue neuromorphic systems.

This thesis addresses a new area of science and technology, that of neuromorphic systems, namely the problems and prospects of analogue neuromorphic systems. The subject is subdivided into three chapters. Chapter 1 is an introduction. It formulates the oncoming problem of the creation of highly computationally costly systems of nonlinear information processing (such as artificial neural networks and artificial intelligence systems). It shows that an analogue technology could make a vital contribution to the creation such systems. The basic principles of creation of analogue neuromorphic systems are formulated. The importance will be emphasised of the principle of orthogonality for future highly efficient complex information processing systems. Chapter 2 reviews the basics of neural and neuromorphic systems and informs on the present situation in this field of research, including both experimental and theoretical knowledge gained up-to-date. The chapter provides the necessary background for correct interpretation of the results reported in Chapter 3 and for a realistic decision on the direction for future work. Chapter 3 describes my own experimental and computational results within the framework of the subject, obtained at De Montfort University. These include: the building of (i) Analogue Polynomial Approximator/lnterpolatoriExtrapolator, (ii) Synthesiser of orthogonal functions, (iii) analogue real-time video filter (performing the homomorphic filtration), (iv) Adaptive polynomial compensator of geometrical distortions of CRT- monitors, (v) analogue parallel-learning neural network (backpropagation algorithm). Thus, this thesis makes a dual contribution to the chosen field: it summarises the present knowledge on the possibility of utilising analogue technology in up-to-date and future computational systems, and it reports new results within the framework of the subject. The main conclusion is that due to its promising power characteristics, small sizes and high tolerance to degradation, the analogue neuromorphic systems will playa more and more important role in future computational systems (in particular in systems of artificial intelligence)

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

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and non-epileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763)

Quarterly research review no. 11, 1 october - 31 december 1964

Plasma, quantum electronics, system theory and electron tube studie

A novel approach to handwritten character recognition

A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field. First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition. A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition. In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules

A novel approach to handwritten character recognition

A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field. First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition. A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition. In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules

Systematics, morphology, phylogeny and historical biogeography of the Mayfly family Prosopistomatidae (Ephemeroptera: Insecta) of the world

The diversity, classification and historical biogeography of the mayfly family Prosopistomatidae are explored. First, the higher classification of the Ephemeroptera is reviewed, focussing on the phylogenetic placement of the Prosopistomatidae relative to other mayfly families. All relevant literature from 1762 to 2010 is synthesized. Baetiscidae are established as the probable sister lineage of Prosopistomatidae, the two constituting the superfamily Baetiscoidea. Next, qualitative morphological variation within the Prosopistomatidae is reviewed and revised, emphasizing nymphs because imaginal specimens are few. The labium and associated structures and the hypopharynx of nymphs, and the highly-derived wing venation of the imaginal stages, are re-interpreted. The structure of the male tarsal claws changes considerably between subimago and imago, which, together with deeply scalloped ridges on male imaginal forelegs and unusual pits on the female thorax, are interpreted as providing an unusual mating mechanism. These structures provide morphological characters for species definition and phylogenetic analyses. Two approaches to species delimitation are explored. First, morphometric variation is analysed using Principal Component Analysis, revealing groupings that can be interpreted as species, although there is some overlap between them. Discriminant Function Analysis shows that head width and carapace shape have the most value in identifying nymphs of different species. The carapace of Prosopistoma nymphs is shown to grow allometrically and gradually, in contrast with that of Baetisca, indicating a difference in early ontogeny. Second, an Artificial Neural Network algorithm applied to nymphal morphological characters accurately identified species. This computer-driven artificial intelligence method has power to provide future easy-to-use electronic identification aids. Phylogenetic analysis of nymphal morphology using the parsimony method shows two clades of Prosopistomatidae, one sharing characters with the type species, Prosopistoma variegatum and the other predominating in Africa, although also occurring in Asia; these clades are named the “P. variegatum” and “African” clades, respectively. Parsimony analysis of adult morphology supports these two clades, but supertree analysis obscures the relationships, nesting the “P. variegatum” lineage within the other clade. Preliminary molecular phylogenetic analysis of the 16S rRNA, (mitochondrial) 18S rRNA and Histone-3 genes using Bayesian Inference methods does not support the two clades shown by morphology. Instead, there is a strong relationship between the European species and one African species, with the single Asian representative being most distantly related. These results are limited by lack of fresh material, patchy taxon sampling, and problems with finding suitable primers. A molecular clock program, BEAST, calibrated using fossils, suggests divergence times for the oldest crown-group Prosopistoma clade, represented by the Asian P. wouterae, of about 131 Ma, with the youngest species, the African P. crassi, of 1.21 Ma. Stem-group relationships are analysed using parsimony analysis, focussing on wing characters of the Baetiscoidea, other extant mayfly lineages, and extinct stem-group lineages. This suggests that the Baetiscoidea diverged from main-line Ephemeroptera earlier than any other extant mayfly lineage. This approach expands upon ideas hinted at by earlier scientists. Finally, historical biogeographical analysis of the distribution of known Baetiscoidea s.s. stem-group fossils implies a once Pangean distribution of the lineage. Changing palaeo-climate, catastrophic extinction events and plate tectonic movements in relation to the distribution of crown-group species are reviewed. Other approaches to historical biogeography that build on both morphological and molecular phylogenies are used to interpret disperalist and vacarianist arguments. Distribution patterns of eight unrelated freshwater organisms which share a similar distribution pattern are compared, assuming that shared patterns indicate similar historic biogeographic processes. The distribution of recent Prosopistoma species is seen to be the product of evolution resulting from both vicariance and dispersal. In conclusion, this thesis encompasses a variety of disciplines. It successfully recognises new characters and distinguishes previously unknown species. It uses new approaches to delimiting species and known methods to determine phylogeny from several angles. The analysis of stem-group relationships offers an insight into possible early lineage splitting within Ephemeroptera. Interpretation of historical biogeography allows for both a Gondwanan origin of Prosopistomatidae, with rafting of species on the Deccan plate to Asia, and for subsequent dispersal from Asia down to Australia and across to Europe, and possibly back to Africa