A comparative study of different deblurring methods using filters

This paper attempts to undertake the study of Restored Gaussian Blurred Images by using four types of techniques of deblurring image viz., Wiener filter, Regularized filter, Lucy Richardson deconvolution algorithm and Blind deconvolution algorithm with an information of the Point Spread Function (PSF) corrupted blurred image. The same is applied to the scanned image of seven months baby in the womb and they are compared with one another, so as to choose the best technique for restored or deblurring image. This paper also attempts to undertake the study of restored blurred image using Regualr Filter(RF) with no information about the Point Spread Function (PSF) by using the same four techniques after executing the guess of the PSF. The number of iterations and the weight threshold of it to choose the best guesses for restored or deblurring image of these techniques are determined. © 2011 American Institute of Physics

Improvement of traditional k-means algorithm through the regulation of distance metric parameters

This paper discusses in detail the behavior of the basic k-means algorithm with four more new algorithms with varied distance measures on gene expression data. In data mining, k-means clustering is a method which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. The traditional k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers from major shortcomings. It is sensitive to initial partitions and it is only applicable to data with spherical-shape clusters. The results of the present study show that the performances of the new algorithms are extremely well when compared to the traditional k-means and also emphasizes that through the regulation of distance metric parameters, one can achieve better clustering effects then the traditional k-means, and has an advantage in sensitivity, specificity and run time. Finally it is found that Canberra k-means performs extremely well. © 2013 IEEE

Neurocomputing motivation, models, and hybridzation

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Job Scheduling for Torus Connected Networks

In this paper we investigate the problem of how to schedule n independent jobs on an m \Theta m torus based network. We develop a model to to quantify the effect of contention for communication links on the dilation of job execution time when multiple jobs share communication links; we then design an efficient algorithm to schedule a set of n independent jobs with different torus size requirements on a given torus with an objective to minimize the total schedule length. We also develop a feasibility algorithm for preemptively scheduling a given set of jobs on a torus of given size with a given deadline. We provide analysis for both the algorithms. 1 Introduction The mesh and torus networks have been recognized as versatile interconnection networks for massively parallel computing. Mesh/torus-like low-dimensional networks have recently received a lot of attention for their better scalability to larger networks, as opposed to more complex networks such as hypercubes [BP95]. Examples of m..

Cored-Based Tree with Forwarding Regions (CBT-FR); A Protocol for Reliable Multicasting in Mobile Ad Hoc Networks

this paper we propose a new protocol for reliable multicast in a multihop mobile radio network. The protocol is reliable, i.e., it guarantees message delivery to all multicast nodes even when the topology of the network changes during multicasting. The proposed protocol uses a core-based shared tree. The multicast tree may get fragmented due to node movements. The notion of a forwarding region is introduced which is used to glue together fragments of multicast trees. The gluing process involves flooding the forwarding region of only those nodes that witness topology change due to node mobility. Delivery of multicast messages to mobile nodes is expedited through (i) pushing the message by witness nodes in their forwarding regions and (ii) pulling messages by a mobile node during (re)joining process. Hence, the protocol conserves network bandwidth by using a combination of the push#pull approach and by restricting flooding only to the essential parts of the network that are affected by topology chang