In carbon nanotube (CNT) polymer nanocomposites (PNC), the formation of conductive CNT networks results in
electrical conductance and piezoresistive behavior. The latter occurs as applied strain affects the electric properties of the
nanotubes. Modeling of piezoresistive behavior is investigated in two discrete scales. At the nanoscale, where for the
prediction of the CNT piezoresistive behavior the Tight-Binding approximation is employed together with the Miller-
Good approximation. At the microscale where percolation is studied using both two- and three- dimensional models and
as well as the differences in resultant predictions. Numerical results at both scales are presented.
We present a fuzzy classifier for detecting microcalcification sin digitized mammograms. The classifier post-processes the output form a wavelets-based multiscale correlation filter. Each local peak in the correlation filter output is represented by a set of five features describing the shape, size and definition of the peak. These features are used in linguistic rules by a fuzzy system that is trained to distinguish between microcalcification sand normal mammogram texture. In borderline cases where microcalcifications are buried in dense tissue or appear only faintly, simply drawing a straight threshold across the feature vector values will likely not produce the correct classification. the fuzzy system allows the effective 'threshold' to be drawn across ranges of features values depending upon how they interact with one another. Compared to wavelet processing alone, the fuzzy detection system produces a significant increase in true positive fraction when tested on a public domain mammogram database.
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