Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Applications of the random neural network to some inverse problems in image processing
Applications of the random neural network to some inverse problems in image processing
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Landmines are a major problem facing the world today; there aremillions of these deadly weapons still buried in various countriesaround the world. Humanitarian organizations dedicate animmeasurable amount of time, effort, and money to find and removeas many of these mines as possible. Unfortunately, landmines can bemade out of common materials which make the correct detection ofthem very difficult. This paper analyzes the effectiveness ofcombining certain statistical techniques with a neural network toimprove detection. The detection method must not only detect themajority of landmines in the ground, it must also filter out asmany of the false alarms as possible. This is the true challenge todeveloping landmine detection algorithms. Our approach combines aBack-Propagation Neural Network (BPNN) with statistical techniquesand compares the performance of mine detection against theperformance of the energy detector and the δ-technique. Ourresults show that the combination of the δ-technique and theS-statistics with a neural network improves the performance.