Detailed Information on Publication Record
2013
Algorithms for Efficient Computation of Convolution
KARAS, Pavel and David SVOBODABasic information
Original name
Algorithms for Efficient Computation of Convolution
Name in Czech
Algoritmy pro efektivní výpočet konvoluce
Authors
KARAS, Pavel (203 Czech Republic, belonging to the institution) and David SVOBODA (203 Czech Republic, guarantor, belonging to the institution)
Edition
1st ed. Rijeka (CRO), Design and Architectures for Digital Signal Processing, p. 179-208, 30 pp. 2013
Publisher
InTech
Other information
Language
English
Type of outcome
Kapitola resp. kapitoly v odborné knize
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
Croatia
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
printed version "print"
References:
RIV identification code
RIV/00216224:14330/13:00065930
Organization unit
Faculty of Informatics
ISBN
978-953-51-0874-0
Keywords (in Czech)
konvoluce; algoritmy; FFT; separabilní konvoluce; rekurzivní filtry; paralelizace; dekompozice
Keywords in English
convolution; algorithms; FFT; separable convolution; recursive filters; parallelization; decomposition
Tags
Tags
International impact, Reviewed
Změněno: 28/4/2014 11:28, RNDr. Pavel Šmerk, Ph.D.
Abstract
V originále
Convolution is an important mathematical tool in both fields of signal and image processing. It is em-ployed in filtering, denoising, edge detection, correlation, compression, deconvolution, simulation, and in many other applications. Although the concept of convolution is not new, the efficient computation of convolution is still an open topic. As the amount of processed data is constantly increasing, there is considerable request for fast manipulation with huge data. Moreover, there is demand for fast algorithms which can exploit computational power of modern parallel architectures. The aim of this chapter is to review the algorithms and approaches for computation of convolution with regards to various properties such as signal and kernel size or kernel separability (when pro-cessing n-dimensional signals). Target architectures include superscalar and parallel processing units (namely CPU, DSP, and GPU), programmable architectures (e.g. FPGA), and distributed systems (such as grids). The structure of the chapter is designed to cover various applications with respect to the signal size, from small to large scales.
Links
GBP302/12/G157, research and development project |
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MUNI/A/0760/2012, interní kód MU |
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MUNI/A/0914/2009, interní kód MU |
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