Exploring ingenious universal gate structures is of prime importance in cost effective design of QCA (Quantum-dot Cellular Automata) based logic circuits. The 3-input Coupled Majority-Minority (CMVMIN) gate structure, that realizes majority and minority functions simultaneously in its 2-outputs, is such a QCA device that enables area saving implementation of complex logic. This work characterizes the defective behavior of CMVMIN gate based designs, under cell deposition and cell misplacement defects and measures testability of such designs. The fault tolerance capability is further analyzed to evaluate the possible two alternate structures of the 2-output CMVMIN gate.
Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method.
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