Binay K. Bhattacharya

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We consider clustering problems under two different optimization criteria. One is to minimize the maximum intracluster distance (diameter), and the other is to maximize the minimum intercluster distance. In particular, we present an algorithm which partitions a set <italic>S</italic> of <italic>n</italic> points in the plane into two subsets so that their(More)
Intrusion detection, area coverage and border surveillance are important applications of wireless sensor networks today. They can be (and are being) used to monitor large unprotected areas so as to detect intruders as they cross a border or as they penetrate a protected area. We consider the problem of how to optimally move mobile sensors to the fence(More)
Software development remains mentally challenging despite the continual advancement of training, techniques, and tools. Because completely automating software development is currently impossible, it makes sense to seriously consider how tools can improve the mental activities of developers apart from automating them away. Such mental assistance can be(More)
Efficient algorithms for solving the center problems in weighted cactus networks are presented. In particular, we have proposed the following algorithms for the weighted cactus networks of size n: an O(n log n) time algorithm to solve the 1center problem, an O(n log 3n) time algorithm to solve the weighted continuous 2-center problem. We have also provided(More)
In this paper, we provide efficient algorithms for solving the weighted center problems in a cactus graph. In particular, an O(n log n) time algorithm is proposed that finds the weighted 1-center in a cactus graph, where n is the number of vertices in the graph. For the weighted 2-center problem, an O(n log n) time algorithm is devised for its continuous(More)
Non-parametric decision rules, such as the nearest neighbor (NN) rule, are attractive because no a priori knowledge is required concerning the underlying distributions of the data. Two traditional criticisms directed at the NN-rule concern the large amounts of storage and computation involved due to the apparent necessity to store all the sample (training)(More)