Aplikace ICA ve zpracování signálů

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F08%3A00020891" target="_blank" >RIV/60461373:22340/08:00020891 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

ICA Application in Signal Processing

Popis výsledku v původním jazyce

Independent Component Analysis (ICA) is a computational method for separation of multivariate signal X into a linear sum of statistically independent components.There are various forms of ICA methods definition, presented paper deals with noise-free ICAmodel in the form X=A.S. Measured signals X originate from the linear combination of hidden independent components S. Values of mixing matrix A are unknown. The goal of the task is in the estimation of S from the knowledge of X only by means of indepencymaximization of estimated S signals. Kurtosis or negentrophy can be used as proper criteria and various algorithms were proposed with their use. Some assumption concerning of the input vector X and signal preprocessing are required as well. ICA methodsfind application in signal separation or feature extraction solving these problems in image, audio or other data processing. Presented paper is focused in MRI data processing with emphasis to signal noise rejection.

Název v anglickém jazyce

ICA Application in Signal Processing

Popis výsledku anglicky

Independent Component Analysis (ICA) is a computational method for separation of multivariate signal X into a linear sum of statistically independent components.There are various forms of ICA methods definition, presented paper deals with noise-free ICAmodel in the form X=A.S. Measured signals X originate from the linear combination of hidden independent components S. Values of mixing matrix A are unknown. The goal of the task is in the estimation of S from the knowledge of X only by means of indepencymaximization of estimated S signals. Kurtosis or negentrophy can be used as proper criteria and various algorithms were proposed with their use. Some assumption concerning of the input vector X and signal preprocessing are required as well. ICA methodsfind application in signal separation or feature extraction solving these problems in image, audio or other data processing. Presented paper is focused in MRI data processing with emphasis to signal noise rejection.

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2008

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings the 8th International Scientific - Technological Conference Process Control 2008

ISBN

978-80-7395-077-4

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

—

Název nakladatele

Univerzita Pardubice

Místo vydání

Pardubice

Místo konání akce

Kouty nad Desnou

Datum konání akce

9. 6. 2008

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—