PARALLEL COMPUTATION OF A CLASS OF NON-LINEAR HYBRID FILTERS AND THEIR IMPLEMENTATION

Author(s):  
Amelia Fong
2016 ◽  
Vol 72 (4) ◽  
pp. 1055-1065 ◽  
Author(s):  
Amir Malvandi ◽  
Amirmahdi Ghasemi ◽  
Rasoul Nikbakhti ◽  
Amirreza Ghasemi ◽  
Faraz Hedayati

2019 ◽  
Vol 8 (2) ◽  
pp. 4753-4756

Digital data has been accelerating day by day with a bulk of dimensions. Analysis of such an immense quantity of data popularly termed as big data, which requires tremendous data analysis scalable techniques. Clustering is an appropriate tool for data analysis to observe hidden similar groups inside the data. Clustering distinct datasets involve both Linear Separable and Non-Linear Separable clustering algorithms by defining and measuring their inter-point similarities as well as non-linear similarity measures. Problem Statement: Yet there are many productive clustering algorithms to cluster linearly; they do not maintain quality clusters.Kernel-based algorithms make use of non-linear similarity measures to define similarity while forming clusters specifically with arbitrary shapes and frequencies. Existing System:Current Kernel-based clustering algorithms have few restraints concerning complexity, memory, and performance. Time and Memory will increase equally when the size of the dataset increase. It is challenging to elect kernel similarity function for different datasets. We have classical random sampling and low-rank matrix approximation linear clustering algorithms with high cluster quality and low memory essentials. Proposed work: in our research, we have introduced a parallel computation performing Kernel-based clustering algorithm using Particle Swarm Optimization approach. This methodology can cluster large datasets having maximum dimensional values accurately and overcomes the issues of high dimensional datasets.


2003 ◽  
Author(s):  
Eduardo Divo ◽  
Alain J. Kassab ◽  
Franklin Rodriguez

In this paper, we develop a domain decomposition, or the artificial sub-sectioning technique, along with a region-by-region iteration algorithm particularly tailored for parallel computation to address storage and memory issues arising from large-scale boundary element models. A coarse surface grid solution coupled with an efficient physically-based procedure provides an effective initial guess for a fine surface grid model. The process converges very efficiently offering substantial savings in memory. We discuss the implementation of the iterative domain decomposition approach for parallel computation on a modest Windows XP Pentium P4 PC-cluster running under MPI. Results from 3-D BEM heat conduction models including models of upwards of 85,000 nodes demonstrate that the BEM can practically be used to solve large-scale linear- and non-linear heat conduction problems using this algorithm.


1967 ◽  
Vol 28 ◽  
pp. 105-176
Author(s):  
Robert F. Christy

(Ed. note: The custom in these Symposia has been to have a summary-introductory presentation which lasts about 1 to 1.5 hours, during which discussion from the floor is minor and usually directed at technical clarification. The remainder of the session is then devoted to discussion of the whole subject, oriented around the summary-introduction. The preceding session, I-A, at Nice, followed this pattern. Christy suggested that we might experiment in his presentation with a much more informal approach, allowing considerable discussion of the points raised in the summary-introduction during its presentation, with perhaps the entire morning spent in this way, reserving the afternoon session for discussion only. At Varenna, in the Fourth Symposium, several of the summaryintroductory papers presented from the astronomical viewpoint had been so full of concepts unfamiliar to a number of the aerodynamicists-physicists present, that a major part of the following discussion session had been devoted to simply clarifying concepts and then repeating a considerable amount of what had been summarized. So, always looking for alternatives which help to increase the understanding between the different disciplines by introducing clarification of concept as expeditiously as possible, we tried Christy's suggestion. Thus you will find the pattern of the following different from that in session I-A. I am much indebted to Christy for extensive collaboration in editing the resulting combined presentation and discussion. As always, however, I have taken upon myself the responsibility for the final editing, and so all shortcomings are on my head.)


Optimization ◽  
1975 ◽  
Vol 6 (4) ◽  
pp. 549-559
Author(s):  
L. Gerencsér

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