A VLSI chip is fabricated by integrating several rectangular circuit blocks on a 2D silicon floor. The circuit blocks are assumed to be placed isothetically and the netlist attached to each block is given. For wire routing, the terminals belonging to the same net are to be electrically interconnected using conducting paths. A staircase channel is an empty polygonal region on the silicon floor bounded by two monotonically increasing (or decreasing) staircase paths from one corner of the floor to its diagonally opposite corner. The staircase paths are defined by the boundaries of the blocks. In this paper, the problem of determining a monotone staircase channel on the floorplan is considered such that the number of distinct nets whose terminals lie on both sides of the channel, is minimized. Two polynomial-time algorithms are presented based on the network flow paradigm. First, the simple two-terminal net model is considered, i.e., each net is assumed to connect exactly two blocks, for which an O(n×k) time algorithm is proposed, where n and k are respectively the number of blocks and nets on the floor. Next, the algorithm is extended to the more realistic case of multi-terminal net problem. The time complexity of the latter algorithm is O((n+k)×T), where T is the total number of terminals attached to all nets in the floorplan. Solutions to these problems are useful in modeling the repeater block placement that arises in interconnect-driven floorplanning for deep-submicron VLSI physical design. It is also an important problem in context to the classical global routing, where channels are used as routing space on silicon.
Recommender Systems (RS) are widely used for providing automatic personalized suggestions for information, products and services. Collaborative Filtering (CF) is one of the most popular recommendation techniques. However, with the rapid growth of the Web in terms of users and items, majority of the RS using CF technique suffer from problems like data sparsity and scalability. In this paper, we present a Recommender System based on data clustering techniques to deal with the scalability problem associated with the recommendation task. We use different voting systems as algorithms to combine opinions from multiple users for recommending items of interest to the new user. The proposed work use DBSCAN clustering algorithm for clustering the users, and then implement voting algorithms to recommend items to the user depending on the cluster into which it belongs. The idea is to partition the users of the RS using clustering algorithm and apply the Recommendation Algorithm separately to each partition. Our system recommends item to a user in a specific cluster only using the rating statistics of the other users of that cluster. This helps us to reduce the running time of the algorithm as we avoid computations over the entire data. Our objective is to improve the running time as well as maintain an acceptable recommendation quality. We have tested the algorithm on the Netflix prize dataset.
A study has been done to find out the prevalence of different kinds of parasites in Cirrhinus mrigala (Hamilton 1822) during 2010-2011. It has been found that the temperature variation affects some parasitic infestation over the fish species. It was found that ciliophoran and crustacean parasites are more prevalent from November to February whereas the myxozoan and monogenean parasites are more prevalent from January to April. Considering temperature variation throughout the year it has been inferred that most of the parasitic infections were found between November and April when the temperature range varies from 19 to 26°C. So from the study it can be concluded that lower temperature elicits the parasitic growth in fishes while the higher temperature retards the growth.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.