Robert S. Lynch

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In this paper, a method of classification referred to as the Bayesian data reduction algorithm (BDRA) is developed. The algorithm is based on the assumption that the discrete symbol probabilities of each class are a priori uniformly Dirichlet distributed, and it employs a "greedy" approach (which is similar to a backward sequential feature search) for(More)
Modeling sea clutter by chaotic dynamics has been an exciting yet heatedly debated topic. To resolve controversies associated with this approach, we use the scale-dependent Lyapunov exponent (SDLE) to study sea clutter. The SDLE has been shown to be able to unambiguously distinguish chaos from noise. Our analyses of almost 400 sea clutter datasets measured(More)
Sea clutter, the radar backscatter from the ocean surface, has been observed to be highly non-Gaussian. K distribution is among the best distributions proposed to fit non-Gaussian sea clutter data. Using diffusive models, K distributed sea clutter can be casted as a Gaussian speckle, with a de-correlation time of 0.1 s, modulated by a Gamma distribution,(More)
The understanding of how humans process information, determine salience, and combine seemingly unrelated information is essential to automated processing of large amounts of information that is partially relevant, or of unknown relevance. Recent neurological science research in human perception, and in information science regarding context-based modeling,(More)