Algorithms for Efficient Computation of Convolution

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F13%3A00065930" target="_blank" >RIV/00216224:14330/13:00065930 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.intechopen.com/books/design-and-architectures-for-digital-signal-processing/algorithms-for-efficient-computation-of-convolution" target="_blank" >http://www.intechopen.com/books/design-and-architectures-for-digital-signal-processing/algorithms-for-efficient-computation-of-convolution</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5772/3456" target="_blank" >10.5772/3456</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Algorithms for Efficient Computation of Convolution

Popis výsledku v původním jazyce

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 fastalgorithms 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).

Název v anglickém jazyce

Algorithms for Efficient Computation of Convolution

Popis výsledku anglicky

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 fastalgorithms 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).

Druh

C - Kapitola v odborné knize

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GBP302%2F12%2FG157" target="_blank" >GBP302/12/G157: Dynamika a organizace chromosomů během buněčného cyklu a při diferenciaci v normě a patologii</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Design and Architectures for Digital Signal Processing

ISBN

9789535108740

Počet stran výsledku

30

Strana od-do

179-208

Počet stran knihy

314

Název nakladatele

InTech

Místo vydání

Rijeka (CRO)

Kód UT WoS kapitoly

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