Complexity-entropy causality plane as a complexity measure for two-dimensional patterns

Autores
Ribeiro, Haroldo V.; Zunino, Luciano José; Lenzi, Ervin K.; Santoro, Perseu A.; Mendes, Renio S.
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Complexity measures are essential to understand complex systems and there are numerous definitions to analyze one-dimensional data. However, extensions of these approaches to two or higher-dimensional data, such as images, are much less common. Here, we reduce this gap by applying the ideas of the permutation entropy combined with a relative entropic index. We build up a numerical procedure that can be easily implemented to evaluate the complexity of two or higher-dimensional patterns. We work out this method in different scenarios where numerical experiments and empirical data were taken into account. Specifically, we have applied the method to i) fractal landscapes generated numerically where we compare our measures with the Hurst exponent; ii) liquid crystal textures where nematic-isotropic-nematic phase transitions were properly identified; iii) 12 characteristic textures of liquid crystals wher the different values show that the method can distinguish different phases; iv) and Ising surfaces where our method identified the critical temperature and also proved to be stable.
Facultad de Ciencias Exactas
Materia
Ciencias Exactas
Física
Transición de Fase
Probabilidad
Cristales Líquidos
Entropía
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/29649

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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Complexity-entropy causality plane as a complexity measure for two-dimensional patternsRibeiro, Haroldo V.Zunino, Luciano JoséLenzi, Ervin K.Santoro, Perseu A.Mendes, Renio S.Ciencias ExactasFísicaTransición de FaseProbabilidadCristales LíquidosEntropíaComplexity measures are essential to understand complex systems and there are numerous definitions to analyze one-dimensional data. However, extensions of these approaches to two or higher-dimensional data, such as images, are much less common. Here, we reduce this gap by applying the ideas of the permutation entropy combined with a relative entropic index. We build up a numerical procedure that can be easily implemented to evaluate the complexity of two or higher-dimensional patterns. We work out this method in different scenarios where numerical experiments and empirical data were taken into account. Specifically, we have applied the method to i) fractal landscapes generated numerically where we compare our measures with the Hurst exponent; ii) liquid crystal textures where nematic-isotropic-nematic phase transitions were properly identified; iii) 12 characteristic textures of liquid crystals wher the different values show that the method can distinguish different phases; iv) and Ising surfaces where our method identified the critical temperature and also proved to be stable.Facultad de Ciencias Exactas2012info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/29649enginfo:eu-repo/semantics/altIdentifier/issn/1932-6203info:eu-repo/semantics/altIdentifier/pmid/22916097info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0040689info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/2.5/ar/Creative Commons Attribution 2.5 Argentina (CC BY 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:57:12Zoai:sedici.unlp.edu.ar:10915/29649Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:57:13.119SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Complexity-entropy causality plane as a complexity measure for two-dimensional patterns
title Complexity-entropy causality plane as a complexity measure for two-dimensional patterns
spellingShingle Complexity-entropy causality plane as a complexity measure for two-dimensional patterns
Ribeiro, Haroldo V.
Ciencias Exactas
Física
Transición de Fase
Probabilidad
Cristales Líquidos
Entropía
title_short Complexity-entropy causality plane as a complexity measure for two-dimensional patterns
title_full Complexity-entropy causality plane as a complexity measure for two-dimensional patterns
title_fullStr Complexity-entropy causality plane as a complexity measure for two-dimensional patterns
title_full_unstemmed Complexity-entropy causality plane as a complexity measure for two-dimensional patterns
title_sort Complexity-entropy causality plane as a complexity measure for two-dimensional patterns
dc.creator.none.fl_str_mv Ribeiro, Haroldo V.
Zunino, Luciano José
Lenzi, Ervin K.
Santoro, Perseu A.
Mendes, Renio S.
author Ribeiro, Haroldo V.
author_facet Ribeiro, Haroldo V.
Zunino, Luciano José
Lenzi, Ervin K.
Santoro, Perseu A.
Mendes, Renio S.
author_role author
author2 Zunino, Luciano José
Lenzi, Ervin K.
Santoro, Perseu A.
Mendes, Renio S.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Exactas
Física
Transición de Fase
Probabilidad
Cristales Líquidos
Entropía
topic Ciencias Exactas
Física
Transición de Fase
Probabilidad
Cristales Líquidos
Entropía
dc.description.none.fl_txt_mv Complexity measures are essential to understand complex systems and there are numerous definitions to analyze one-dimensional data. However, extensions of these approaches to two or higher-dimensional data, such as images, are much less common. Here, we reduce this gap by applying the ideas of the permutation entropy combined with a relative entropic index. We build up a numerical procedure that can be easily implemented to evaluate the complexity of two or higher-dimensional patterns. We work out this method in different scenarios where numerical experiments and empirical data were taken into account. Specifically, we have applied the method to i) fractal landscapes generated numerically where we compare our measures with the Hurst exponent; ii) liquid crystal textures where nematic-isotropic-nematic phase transitions were properly identified; iii) 12 characteristic textures of liquid crystals wher the different values show that the method can distinguish different phases; iv) and Ising surfaces where our method identified the critical temperature and also proved to be stable.
Facultad de Ciencias Exactas
description Complexity measures are essential to understand complex systems and there are numerous definitions to analyze one-dimensional data. However, extensions of these approaches to two or higher-dimensional data, such as images, are much less common. Here, we reduce this gap by applying the ideas of the permutation entropy combined with a relative entropic index. We build up a numerical procedure that can be easily implemented to evaluate the complexity of two or higher-dimensional patterns. We work out this method in different scenarios where numerical experiments and empirical data were taken into account. Specifically, we have applied the method to i) fractal landscapes generated numerically where we compare our measures with the Hurst exponent; ii) liquid crystal textures where nematic-isotropic-nematic phase transitions were properly identified; iii) 12 characteristic textures of liquid crystals wher the different values show that the method can distinguish different phases; iv) and Ising surfaces where our method identified the critical temperature and also proved to be stable.
publishDate 2012
dc.date.none.fl_str_mv 2012
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/29649
url http://sedici.unlp.edu.ar/handle/10915/29649
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1932-6203
info:eu-repo/semantics/altIdentifier/pmid/22916097
info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0040689
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/2.5/ar/
Creative Commons Attribution 2.5 Argentina (CC BY 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/2.5/ar/
Creative Commons Attribution 2.5 Argentina (CC BY 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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