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Sonar target recognition. Neural nets using backpropagation have been used to identify different
types of targets using the frequency signature (with a Fast Fourier transform) of the reflected signal.
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Car navigation. Pomerleau developed a neural network that is able to navigate a car based on
images obtained from a camera mounted on the car’s roof, and a range finder that coded distances in
grayscale. The 30×32 pixel image and the 8×32 range finder image were fed into a hidden layer of
size 29 feeding an output layer of 45 neurons. The output neurons were arranged in a straight line
with each side representing a turn to a particular direction (right or left), while the center neurons
represented “drive straight ahead.” After 1200 road images were trained on the network, the neural
network driver was able to negotiate a part of the Carnegie−Mellon campus at a speed of about 3
miles per hour, limited only by the speed of the real−time calculations done on a trained network in
the Sun−3 computer in the car.
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