Multicore Parallel Computing and DSP Processor for the Design of Bio-inspired Soft Computing Framework for Speech and Image Processing Applications

2015 ◽  
pp. 125-134 ◽  
Author(s):  
Dipjyoti Sarma ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Image Processing ◽  
Parallel Computing ◽  
Soft Computing ◽  
Computing Framework

Matlab and Parallel Computing

2012 ◽  
Vol 17 (4) ◽  
pp. 207-216 ◽  
Author(s):  
Magdalena Szymczyk ◽  
Piotr Szymczyk
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Parallel Computing ◽  
Distributed Computing ◽  
Control Systems ◽  
High Performance ◽  
Parallel Applications ◽  
Process Simulations ◽  
Key Features ◽  
Financial Process

Abstract The MATLAB is a technical computing language used in a variety of fields, such as control systems, image and signal processing, visualization, financial process simulations in an easy-to-use environment. MATLAB offers "toolboxes" which are specialized libraries for variety scientific domains, and a simplified interface to high-performance libraries (LAPACK, BLAS, FFTW too). Now MATLAB is enriched by the possibility of parallel computing with the Parallel Computing ToolboxTM and MATLAB Distributed Computing ServerTM. In this article we present some of the key features of MATLAB parallel applications focused on using GPU processors for image processing.

One-step models for soft computing techniques. Industrial application to image processing in quality assurance process

2015 ◽  
Vol 81 (5-8) ◽  
pp. 771-778 ◽  
Author(s):  
Pascual Noradino Montes Dorantes ◽  
Marco Aurelio Jiménez Gómez ◽  
Gerardo Maximiliano Méndez ◽  
Juan Pablo Nieto González ◽  
Jesús de la Rosa Elizondo
Keyword(s):  
Image Processing ◽  
Quality Assurance ◽  
Soft Computing ◽  
Industrial Application ◽  
Quality Assurance Process ◽  
Soft Computing Techniques ◽  
One Step ◽  
Assurance Process

Snow: A Parallel Computing Framework for the R System

2008 ◽  
Vol 37 (1) ◽  
pp. 78-90 ◽  
Author(s):  
Luke Tierney ◽  
A. J. Rossini ◽  
Na Li
Keyword(s):  
Parallel Computing ◽  
Computing Framework

A Practical Approach for Scalable Record Linkage on Hadoop

2013 ◽  
Vol 753-755 ◽  
pp. 3018-3024 ◽  
Author(s):  
Fen Gyu Yang ◽  
Ying Chen ◽  
Ye Zhang
Keyword(s):  
Parallel Computing ◽  
Record Linkage ◽  
Practical Approach ◽  
Massive Data ◽  
Traditional Methods ◽  
High Recall ◽  
Data Parallel ◽  
Computing Framework ◽  
Data Parallel Computing ◽  
Hadoop Cluster

As increasing data have been collected in many applications, we have to face with millions of data in record linkage. With respect to traditional methods, there comes out a big challenge in performance while dealing with massive data. Parallel computing framework, such as MapReduce, has become an efficient and practical way to address this problem. In this paper, we propose a practical 3-phase MapReduce approach that fulfills blocking, filtering, and linking in 3 consecutive processes on Hadoop cluster. Experiments show that our approach functions efficiently and effectively with keeping high recall in contrast to tradition method.

Prediction of rock strain using soft computing framework

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
T. Pradeep ◽  
Abidhan Bardhan ◽  
Pijush Samui
Keyword(s):  
Soft Computing ◽  
Computing Framework
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