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<p>This thesis presents an approach to image classification via a Multi-Layer Perceptron (MLP) Artificial Neural  Network (ANN) on the SRC-6 reconfigurable computer for use in classifying Low Probability of Intercept (LPI) radar  emitters. The rationale behind the previously unexplored use of new reconfigurable computers combined with neural  networks for this application is the potential for near real-time classification. Current potential near-peer competitors  have access to LPI technology, so development of quick classification methods is crucial for ships to determine intent  and to enable the possibility for self-defense against these types of emitters. The neural network, based on work  conducted by Professor Phillip E. Pace of the Naval Postgraduate School (NPS), generates integer-cast weights by  first using a sequential processor to conduct floating-point backpropagation to train the network on potential timefrequency  images that allows generation of weights with lower overall Root Mean Squared (RMS) errors. The  weights are then used in a parallel-processing reconfigurable computer for close to real-time classification. A second  method of direct pixel comparison using Exclusive-Or (XOR) logic is presented as an alternative image classification  method. Comparisons to similar representations in C++ are provided, for use in judging comparative error levels and  timing between parallel and sequential processing methods.
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<br>Subjects: Electrical engineering; Computer logic; Artificial intelligence</div>
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Bailey, Scott P.</div>
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