The problem of reversible circuit synthesis has become very important with increasing emphasis on low-power design and quantum computation. Many synthesis approaches for reversible circuits have been reported over the last decade. Among these approaches, those based on the exclusive-or sum-of-products (ESOP) realization of functions have been explored by many researchers because of two important reasons: large circuits can be handled, and the mapping from ESOP cubes to reversible gate netlist is fairly straightforward. This paper proposes a simulated annealing (SA)-based approach for transforming the ESOP cubes generated from Exorcism-4 tool using some cube mapping rules, followed by a strategy to map the ESOP cubes to a netlist of reversible gates. Both positive-and negative-control Toffoli gates are used for synthesis. Synthesis results on a number of reversible logic benchmarks show that for many of the cases, it is possible to get a reduction in quantum cost against the best-known methods.

This work addresses the problem of automatic classification and labeling of 19th-and 20th-century quilts from photographs. The photographs are classified according to the quilt patterns into crazy and non -crazy categories. Based on the classification labels, humanists try to understand the distinct characteristics of an individual quilt-maker or relevant quilt-making groups in terms of their choices of pattern selection, color choices, layout, and original deviations from traditional patterns. While manual assignment of crazy and non-crazy labels can be achieved by visual inspection, there does not currently exist a clear definition of the level of crazy-ness, nor an automated method for classifying patterns as crazy and non-crazy.We approach the problem by modeling the level of crazy-ness by the distribution of clusters of color-homogeneous connected image segments of similar shapes. First, we extract signatures (a set of features) of quilt images that represent our model of crazy-ness. Next, we use a supervised classification method, such as the Support Vector Machine (SVM) with the radial basis function, to train and test the SVM model. Finally, the SVM model is optimized using N-fold cross validation and the classification accuracy is reported over a set of 39 quilt images.