Abstract:
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Algorithms are used to process voice communications, medical images, video, sonar, radar, and other types of signals. Standard algorithms can work poorly if the noise is non-Gaussian impulsive noise. We show that some real noise can be better modeled by a heavy-tailed stable distribution and that non-linear methods based on such models can improve results in such systems, reducing the risk of wrong decisions.
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