Noise-Assisted EMD Methods in Action

Autores
Colominas, Marcelo Alejandro; Schlotthauer, Gaston; Torres, Maria Eugenia; Flandrin, Patrick
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work we explore the capabilities of two noise-assisted EMD methods: Ensemble EMD (EEMD) and the recently proposed Complete Ensemble EMD with Adaptive Noise (CEEMDAN), to recover a pure tone embedded in dierent kinds of noise, both stationary and nonstationary. Experiments are carried out for assessing their performances with respect to the level of the added noise and the number of realizations used for averaging. The obtained results partly support empirical recommendations reported in the literature while evidencing new distinctive features. While EEMD presents quite dierent behaviors for dierent situations, CEEMDAN evidences some robustness with an almost unaected performance for the studied cases.
Fil: Colominas, Marcelo Alejandro. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Torres, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina
Fil: Flandrin, Patrick. Centre National de la Recherche Scientifique; Francia
Materia
Empirical Mode Decomposition
Ensemble Empirical Mode Decomposition
Complete Ensemble Empirical Mode Decomposition with Adaptive Noise
Adaptive Signal Processing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/197442
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Noise-Assisted EMD Methods in ActionColominas, Marcelo AlejandroSchlotthauer, GastonTorres, Maria EugeniaFlandrin, PatrickEmpirical Mode DecompositionEnsemble Empirical Mode DecompositionComplete Ensemble Empirical Mode Decomposition with Adaptive NoiseAdaptive Signal Processinghttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2In this work we explore the capabilities of two noise-assisted EMD methods: Ensemble EMD (EEMD) and the recently proposed Complete Ensemble EMD with Adaptive Noise (CEEMDAN), to recover a pure tone embedded in dierent kinds of noise, both stationary and nonstationary. Experiments are carried out for assessing their performances with respect to the level of the added noise and the number of realizations used for averaging. The obtained results partly support empirical recommendations reported in the literature while evidencing new distinctive features. While EEMD presents quite dierent behaviors for dierent situations, CEEMDAN evidences some robustness with an almost unaected performance for the studied cases.Fil: Colominas, Marcelo Alejandro. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Torres, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; ArgentinaFil: Flandrin, Patrick. Centre National de la Recherche Scientifique; FranciaWorld Scientific2012-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/197442Colominas, Marcelo Alejandro; Schlotthauer, Gaston; Torres, Maria Eugenia; Flandrin, Patrick; Noise-Assisted EMD Methods in Action; World Scientific; Advances in Adaptive Data Analysis; 4; 4; 10-2012; 1-111793-5369CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.worldscientific.com/doi/abs/10.1142/S1793536912500252info:eu-repo/semantics/altIdentifier/doi/10.1142/S1793536912500252info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:48:12Zoai:ri.conicet.gov.ar:11336/197442instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:48:12.595CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Noise-Assisted EMD Methods in Action
title Noise-Assisted EMD Methods in Action
spellingShingle Noise-Assisted EMD Methods in Action
Colominas, Marcelo Alejandro
Empirical Mode Decomposition
Ensemble Empirical Mode Decomposition
Complete Ensemble Empirical Mode Decomposition with Adaptive Noise
Adaptive Signal Processing
title_short Noise-Assisted EMD Methods in Action
title_full Noise-Assisted EMD Methods in Action
title_fullStr Noise-Assisted EMD Methods in Action
title_full_unstemmed Noise-Assisted EMD Methods in Action
title_sort Noise-Assisted EMD Methods in Action
dc.creator.none.fl_str_mv Colominas, Marcelo Alejandro
Schlotthauer, Gaston
Torres, Maria Eugenia
Flandrin, Patrick
author Colominas, Marcelo Alejandro
author_facet Colominas, Marcelo Alejandro
Schlotthauer, Gaston
Torres, Maria Eugenia
Flandrin, Patrick
author_role author
author2 Schlotthauer, Gaston
Torres, Maria Eugenia
Flandrin, Patrick
author2_role author
author
author
dc.subject.none.fl_str_mv Empirical Mode Decomposition
Ensemble Empirical Mode Decomposition
Complete Ensemble Empirical Mode Decomposition with Adaptive Noise
Adaptive Signal Processing
topic Empirical Mode Decomposition
Ensemble Empirical Mode Decomposition
Complete Ensemble Empirical Mode Decomposition with Adaptive Noise
Adaptive Signal Processing
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this work we explore the capabilities of two noise-assisted EMD methods: Ensemble EMD (EEMD) and the recently proposed Complete Ensemble EMD with Adaptive Noise (CEEMDAN), to recover a pure tone embedded in dierent kinds of noise, both stationary and nonstationary. Experiments are carried out for assessing their performances with respect to the level of the added noise and the number of realizations used for averaging. The obtained results partly support empirical recommendations reported in the literature while evidencing new distinctive features. While EEMD presents quite dierent behaviors for dierent situations, CEEMDAN evidences some robustness with an almost unaected performance for the studied cases.
Fil: Colominas, Marcelo Alejandro. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Torres, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina
Fil: Flandrin, Patrick. Centre National de la Recherche Scientifique; Francia
description In this work we explore the capabilities of two noise-assisted EMD methods: Ensemble EMD (EEMD) and the recently proposed Complete Ensemble EMD with Adaptive Noise (CEEMDAN), to recover a pure tone embedded in dierent kinds of noise, both stationary and nonstationary. Experiments are carried out for assessing their performances with respect to the level of the added noise and the number of realizations used for averaging. The obtained results partly support empirical recommendations reported in the literature while evidencing new distinctive features. While EEMD presents quite dierent behaviors for dierent situations, CEEMDAN evidences some robustness with an almost unaected performance for the studied cases.
publishDate 2012
dc.date.none.fl_str_mv 2012-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/197442
Colominas, Marcelo Alejandro; Schlotthauer, Gaston; Torres, Maria Eugenia; Flandrin, Patrick; Noise-Assisted EMD Methods in Action; World Scientific; Advances in Adaptive Data Analysis; 4; 4; 10-2012; 1-11
1793-5369
CONICET Digital
CONICET
url http://hdl.handle.net/11336/197442
identifier_str_mv Colominas, Marcelo Alejandro; Schlotthauer, Gaston; Torres, Maria Eugenia; Flandrin, Patrick; Noise-Assisted EMD Methods in Action; World Scientific; Advances in Adaptive Data Analysis; 4; 4; 10-2012; 1-11
1793-5369
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.worldscientific.com/doi/abs/10.1142/S1793536912500252
info:eu-repo/semantics/altIdentifier/doi/10.1142/S1793536912500252
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv World Scientific
publisher.none.fl_str_mv World Scientific
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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