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The use of general descriptive names, trade names, trademarks, etc. in this publication, even if the former are not especially identified, is not to be taken as a sign that such names, as understood by the Trade Marks and Merchandise Marks Act, may accordingly be used freely by anyone. 98765 4 3 2 1

India, in the Computer Science and Engineering Department. He obtained a bachelor's degree, a master's of engineering (CSE), and a PhD (CSE) from SGBAU Amravati University, Maharashtra, India. He also holds a master's degree and PhD in Business Administration. His primary research interests are in artificial intelligence, big data, analytics, embedded systems, and e-commerce. He has supervised eighteen master's degree and twenty-four bachelor's degree students. He has published forty-seven papers in refereed journals and published six books with international publishers. He has also organized various workshops, sessions, conferences, and trainings. He has two patents filed and published in his name in India. He is a member of the Board of Studies (Computer Science and Engineering

The use of the star graph as a viable interconnection scheme for parallel computers has been examined by a number of authors in recent times. An attractive feature of this class of graphs is that it has sublogarithmic diameter and has a great deal of symmetry akin to the binary hypercube. In this paper we describe a new class of algorithms for embedding (a) Hamiltonian cycle (b) the set of all even cycles and (c) a variety of two- and multi-dimensional grids in a star graph. In addition, we also derive an algorithm for the ranking and the unranking problem with respect to the Hamiltonian cycle.

This paper introduces a new class of interconnection scheme based on the Cayley graph of the alternating group. It is shown that this class of graphs are edge symmetric and 2-transitive. We then describe an algorithm for (a) packet routing based on the shortest path analysis, (b) finding a Hamiltonian cycle, (c) ranking and unranking along the chosen Hamiltonian cycle, (d) unit expansion and dilation three embedding of a class of two-dimensional grids, (e) unit dilation embedding of a variety of cycles, and (f) algorithm for broadcasting messages. The paper concludes with a short analysis of contention resulting from a typical communication scheme. Although this class of graphs does not possess many of the symmetry properties of the binary hypercube, with respect to the one source broadcasting, these graphs perform better than does a hypercube, and with respect to the contention problem, these graphs perform better than do the star graphs and are close to the hypercube. 0 1993 by John Wiley & Sons, Inc.

An automated procedure for classifying rainfall systems (meso-α scale and larger) was developed using an operational analysis of hourly precipitation estimates from radar and rain gauge data. The development process followed two main phases: a training phase and a testing phase. First, 48 hand-selected cases were used to create a training dataset, from which a set of attributes related to morphological aspects of rainfall systems were extracted. A hierarchy of classes for rainfall systems, in which the systems are separated into general convective (heavy rain) and nonconvective (light rain) classes, was envisioned. At the next level of classification hierarchy, convective events are divided into linear and cellular subclasses, and nonconvective events belong to the stratiform subclass. Essential attributes of precipitating systems, related to the rainfall intensity and degree of linear organization, were determined during the training phase. The attributes related to the rainfall intensity were chosen to be the parameters of the gamma probability distribution fit to observed rainfall amount frequency distributions using the generalized method of moments. Attributes related to the degree of spatial continuity of each rainfall system were acquired from correlogram analysis. Rainfall systems were categorized using hierarchical cluster analysis experiments with various combinations of these attributes. The combination of attributes that resulted in the best match between cluster analysis results and an expert classification were used as the basis for an automated classification procedure. The development process shifted into the testing phase, where automated procedures for identifying and classifying rainfall systems were used to analyze every rainfall system in the contiguous 48 states during 2002. To allow for a feasible validation, a testing dataset was extracted from the 2002 data. The testing dataset consisted of 100 randomly selected rainfall systems larger than 40 000 km2 as identified by an automated identification system. This subset was shown to be representative of the full 2002 dataset. Finally, the automated classification procedure classified the testing dataset into stratiform, linear, and cellular classes with 85% accuracy, as compared to an expert classification.

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