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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/29649
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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) |
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openAccess |
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http://creativecommons.org/licenses/by/2.5/ar/ Creative Commons Attribution 2.5 Argentina (CC BY 2.5) |
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application/pdf |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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