Titlebook: Neural Nets; 13th Italian Worksho Maria Marinaro,Roberto Tagliaferri Conference proceedings 2002 Springer-Verlag Berlin Heidelberg 2002 Ada

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Extended Random Neural Networksraints imposed on their parameters. In the paper two possible extensions are proposed in order to remove this difficulty. Moreover, the proposed learning algorithm is tailored to the specific architecture in order to reduce the computational cost. Two architectures are considered and illustrated by

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MLP Neural Network Implementation on a SIMD Architecture have proposed an .. able to detect and extract sign regions from real world scenes on the basis of their color and shape features. Classification is then performed on extracted candidate regions using Multi-Layer Perceptron neural networks. Although system performances are good in terms of both sig

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A New Approach to Detection of Muscle Activation by Independent Component Analysis and Wavelet Transliable in monitoring repetitive movements and better correspond with ongoing brain-wave activity than raw sEMG recordings. In this paper we propose to detect single muscle activation, when the arms reach a target, by means of ICs time-scale decomposition. Our analysis starts with acquisition of sEMG

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Detection of Facial Featuresmatching technique, a neural network classifier, and a distance measure. It proceeds localizing lips and nose using a non-linear edge detector and color information. The method is scale-independent, works on images of either frontal, rotated or slightly tilted faces, and does not require any manual

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Automatic Discrimination of Earthquakes and False Events in Seismological Recording for Volcanic Moneismic stations in the Vesuvius area in Naples, Italy. For each station we set up a specialized neural classifier, able to discriminate the two classes of events recordered by that station. Feature extraction is done using both the linear predictor coding technique and the waveform features of the s