Optimizing the detection of nonstationary signals by using recurrence analysis
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRN |
Texto Completo: | https://repositorio.ufrn.br/handle/123456789/30824 |
Resumo: | Recurrence analysis and its quantifiers are strongly dependent on the evaluation of the vicinity threshold parameter, i.e., the threshold to regard two points close enough in phase space to be considered as just one. We develop a new way to optimize the evaluation of the vicinity threshold in order to assure a higher level of sensitivity to recurrence quantifiers to allow the detection of even small changes in the dynamics. It is used to promote recurrence analysis as a tool to detect nonstationary behavior of time signals or space profiles. We show that the ability to detect small changes provides information about the present status of the physical process responsible to generate the signal and offers mechanisms to predict future states. Here, a higher sensitive recurrence analysis is proposed as a precursor, a tool to predict near future states of a particular system, based on just (experimentally) obtained signals of some available variables of the system. Comparisons with traditional methods of recurrence analysis show that the optimization method developed here is more sensitive to small variations occurring in a signal. The method is applied to numerically generated time series as well as experimental data from physiology |
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Prado, Thiago de LimaLima, Gustavo Zampier dos SantosLobão-Soares, BrunoNascimento, George Carlos doCorso, GilbertoAraújo, John FonteneleKurths, JürgenLopes, Sérgio Roberto2020-12-04T19:42:22Z2020-12-04T19:42:22Z2018-08-24PRADO, Thiago de Lima; LIMA, Gustavo Zampier dos Santos; LOBÃO-SOARES, Bruno; NASCIMENTO, George C. do; CORSO, Gilberto; FONTENELE-ARAUJO, John; KURTHS, Jürgen; LOPES, Sergio Roberto. Optimizing the detection of nonstationary signals by using recurrence analysis. Chaos: An Interdisciplinary Journal of Nonlinear Science, [S.L.], v. 28, n. 8, p. 085703-085703, ago. 2018. Disponível em: https://aip.scitation.org/doi/10.1063/1.5022154. Acesso em: 20 nov. 2020. http://dx.doi.org/10.1063/1.5022154.1054-15001089-7682https://repositorio.ufrn.br/handle/123456789/3082410.1063/1.5022154American Institute of PhysicsCoupled oscillatorsMeasuring instrumentsMusculoskeletal systemData visualizationLorenz systemFourier analysisData acquisitionMammalsDynamical systemsNeuroanatomyOptimizing the detection of nonstationary signals by using recurrence analysisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleRecurrence analysis and its quantifiers are strongly dependent on the evaluation of the vicinity threshold parameter, i.e., the threshold to regard two points close enough in phase space to be considered as just one. We develop a new way to optimize the evaluation of the vicinity threshold in order to assure a higher level of sensitivity to recurrence quantifiers to allow the detection of even small changes in the dynamics. It is used to promote recurrence analysis as a tool to detect nonstationary behavior of time signals or space profiles. We show that the ability to detect small changes provides information about the present status of the physical process responsible to generate the signal and offers mechanisms to predict future states. Here, a higher sensitive recurrence analysis is proposed as a precursor, a tool to predict near future states of a particular system, based on just (experimentally) obtained signals of some available variables of the system. Comparisons with traditional methods of recurrence analysis show that the optimization method developed here is more sensitive to small variations occurring in a signal. The method is applied to numerically generated time series as well as experimental data from physiologyengreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNinfo:eu-repo/semantics/openAccessORIGINALOptimizingTheDetection_Lima_2018.pdfOptimizingTheDetection_Lima_2018.pdfArtigoapplication/pdf3997736https://repositorio.ufrn.br/bitstream/123456789/30824/1/OptimizingTheDetection_Lima_2018.pdf2087d46d9dc78d2afe51b894cde292a3MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81484https://repositorio.ufrn.br/bitstream/123456789/30824/2/license.txte9597aa2854d128fd968be5edc8a28d9MD52TEXTOptimizingTheDetection_LIMA_2018.pdf.txtOptimizingTheDetection_LIMA_2018.pdf.txtExtracted texttext/plain64776https://repositorio.ufrn.br/bitstream/123456789/30824/3/OptimizingTheDetection_LIMA_2018.pdf.txt0a79c70fc5752b371d4100d55411f3e2MD53THUMBNAILOptimizingTheDetection_LIMA_2018.pdf.jpgOptimizingTheDetection_LIMA_2018.pdf.jpgGenerated Thumbnailimage/jpeg2021https://repositorio.ufrn.br/bitstream/123456789/30824/4/OptimizingTheDetection_LIMA_2018.pdf.jpgb8c6b2d938081d357933cabe58f9dd40MD54123456789/308242021-11-10 15:40:49.505oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2021-11-10T18:40:49Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false |
dc.title.pt_BR.fl_str_mv |
Optimizing the detection of nonstationary signals by using recurrence analysis |
title |
Optimizing the detection of nonstationary signals by using recurrence analysis |
spellingShingle |
Optimizing the detection of nonstationary signals by using recurrence analysis Prado, Thiago de Lima Coupled oscillators Measuring instruments Musculoskeletal system Data visualization Lorenz system Fourier analysis Data acquisition Mammals Dynamical systems Neuroanatomy |
title_short |
Optimizing the detection of nonstationary signals by using recurrence analysis |
title_full |
Optimizing the detection of nonstationary signals by using recurrence analysis |
title_fullStr |
Optimizing the detection of nonstationary signals by using recurrence analysis |
title_full_unstemmed |
Optimizing the detection of nonstationary signals by using recurrence analysis |
title_sort |
Optimizing the detection of nonstationary signals by using recurrence analysis |
author |
Prado, Thiago de Lima |
author_facet |
Prado, Thiago de Lima Lima, Gustavo Zampier dos Santos Lobão-Soares, Bruno Nascimento, George Carlos do Corso, Gilberto Araújo, John Fontenele Kurths, Jürgen Lopes, Sérgio Roberto |
author_role |
author |
author2 |
Lima, Gustavo Zampier dos Santos Lobão-Soares, Bruno Nascimento, George Carlos do Corso, Gilberto Araújo, John Fontenele Kurths, Jürgen Lopes, Sérgio Roberto |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Prado, Thiago de Lima Lima, Gustavo Zampier dos Santos Lobão-Soares, Bruno Nascimento, George Carlos do Corso, Gilberto Araújo, John Fontenele Kurths, Jürgen Lopes, Sérgio Roberto |
dc.subject.por.fl_str_mv |
Coupled oscillators Measuring instruments Musculoskeletal system Data visualization Lorenz system Fourier analysis Data acquisition Mammals Dynamical systems Neuroanatomy |
topic |
Coupled oscillators Measuring instruments Musculoskeletal system Data visualization Lorenz system Fourier analysis Data acquisition Mammals Dynamical systems Neuroanatomy |
description |
Recurrence analysis and its quantifiers are strongly dependent on the evaluation of the vicinity threshold parameter, i.e., the threshold to regard two points close enough in phase space to be considered as just one. We develop a new way to optimize the evaluation of the vicinity threshold in order to assure a higher level of sensitivity to recurrence quantifiers to allow the detection of even small changes in the dynamics. It is used to promote recurrence analysis as a tool to detect nonstationary behavior of time signals or space profiles. We show that the ability to detect small changes provides information about the present status of the physical process responsible to generate the signal and offers mechanisms to predict future states. Here, a higher sensitive recurrence analysis is proposed as a precursor, a tool to predict near future states of a particular system, based on just (experimentally) obtained signals of some available variables of the system. Comparisons with traditional methods of recurrence analysis show that the optimization method developed here is more sensitive to small variations occurring in a signal. The method is applied to numerically generated time series as well as experimental data from physiology |
publishDate |
2018 |
dc.date.issued.fl_str_mv |
2018-08-24 |
dc.date.accessioned.fl_str_mv |
2020-12-04T19:42:22Z |
dc.date.available.fl_str_mv |
2020-12-04T19:42:22Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
PRADO, Thiago de Lima; LIMA, Gustavo Zampier dos Santos; LOBÃO-SOARES, Bruno; NASCIMENTO, George C. do; CORSO, Gilberto; FONTENELE-ARAUJO, John; KURTHS, Jürgen; LOPES, Sergio Roberto. Optimizing the detection of nonstationary signals by using recurrence analysis. Chaos: An Interdisciplinary Journal of Nonlinear Science, [S.L.], v. 28, n. 8, p. 085703-085703, ago. 2018. Disponível em: https://aip.scitation.org/doi/10.1063/1.5022154. Acesso em: 20 nov. 2020. http://dx.doi.org/10.1063/1.5022154. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufrn.br/handle/123456789/30824 |
dc.identifier.issn.none.fl_str_mv |
1054-1500 1089-7682 |
dc.identifier.doi.none.fl_str_mv |
10.1063/1.5022154 |
identifier_str_mv |
PRADO, Thiago de Lima; LIMA, Gustavo Zampier dos Santos; LOBÃO-SOARES, Bruno; NASCIMENTO, George C. do; CORSO, Gilberto; FONTENELE-ARAUJO, John; KURTHS, Jürgen; LOPES, Sergio Roberto. Optimizing the detection of nonstationary signals by using recurrence analysis. Chaos: An Interdisciplinary Journal of Nonlinear Science, [S.L.], v. 28, n. 8, p. 085703-085703, ago. 2018. Disponível em: https://aip.scitation.org/doi/10.1063/1.5022154. Acesso em: 20 nov. 2020. http://dx.doi.org/10.1063/1.5022154. 1054-1500 1089-7682 10.1063/1.5022154 |
url |
https://repositorio.ufrn.br/handle/123456789/30824 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
American Institute of Physics |
publisher.none.fl_str_mv |
American Institute of Physics |
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reponame:Repositório Institucional da UFRN instname:Universidade Federal do Rio Grande do Norte (UFRN) instacron:UFRN |
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Universidade Federal do Rio Grande do Norte (UFRN) |
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UFRN |
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UFRN |
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