Decoding Anomalous Diffusion Using Higher-Order Spectral Analysis and Multiple Signal Classification

[EN] Anomalous diffusion is characterized by nonlinear growth in the mean square displacement of a trajectory. Recent advances using statistical methods and recurrent neural networks have made it possible to detect such phenomena, even in noisy conditions. In this work, we explore feature extraction...

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Autores: Iglesias-Martínez, Miguel E., Garibo-i-Orts, Óscar, Conejero, J. Alberto|||0000-0003-3681-7533
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/220408
Acceso en línea:https://riunet.upv.es/handle/10251/220408
Access Level:acceso abierto
Palabra clave:Anomalous diffusion
Subdiffusion
Superdiffusion
Higher-order spectral analysis
Multiple signal classification
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spelling Decoding Anomalous Diffusion Using Higher-Order Spectral Analysis and Multiple Signal ClassificationIglesias-Martínez, Miguel E.Garibo-i-Orts, ÓscarConejero, J. Alberto|||0000-0003-3681-7533Anomalous diffusionSubdiffusionSuperdiffusionHigher-order spectral analysisMultiple signal classification[EN] Anomalous diffusion is characterized by nonlinear growth in the mean square displacement of a trajectory. Recent advances using statistical methods and recurrent neural networks have made it possible to detect such phenomena, even in noisy conditions. In this work, we explore feature extraction through parametric and non-parametric spectral analysis methods to decode anomalously diffusing trajectories, achieving reduced computational costs compared with other approaches that require additional data or prior training. Specifically, we propose the use of higher-order statistics, such as the bispectrum, and a hybrid algorithm that combines kurtosis with a multiple-signal classification technique. Our results demonstrate that the type of trajectory can be identified based on amplitude and kurtosis values. The proposed methods deliver accurate results, even with short trajectories and in the presence of noise.M.E.I.M. was funded by the postdoctoral research scholarship "Ayudas para la recualificacion del sistema universitario espanol 2021-2023. Modalidad: Margarita Salas", UPV, Ministerio de Universidades, Plan de Recuperacion, Transformacion y Resiliencia, Spain, funded by the European Union-Next Generation EU. & Ograve;.G.-i.-O. and J.A.C. are supported by by European Union-NextGenerationEU, ANDHI project CPP2021-008994.MDPI AGDepartamento de Matemática AplicadaInstituto Universitario de Matemática Pura y AplicadaEscuela Técnica Superior de Ingeniería InformáticaEuropean CommissionAgencia Estatal de InvestigaciónRepositorio Institucional de la Universitat Politècnica de València Riunet20252025-02-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/220408reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 CPP2021-008994 ANDHI - ANomalous Diffusion of Harmful Informationopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2204082026-06-13T07:49:27Z
dc.title.none.fl_str_mv Decoding Anomalous Diffusion Using Higher-Order Spectral Analysis and Multiple Signal Classification
title Decoding Anomalous Diffusion Using Higher-Order Spectral Analysis and Multiple Signal Classification
spellingShingle Decoding Anomalous Diffusion Using Higher-Order Spectral Analysis and Multiple Signal Classification
Iglesias-Martínez, Miguel E.
Anomalous diffusion
Subdiffusion
Superdiffusion
Higher-order spectral analysis
Multiple signal classification
title_short Decoding Anomalous Diffusion Using Higher-Order Spectral Analysis and Multiple Signal Classification
title_full Decoding Anomalous Diffusion Using Higher-Order Spectral Analysis and Multiple Signal Classification
title_fullStr Decoding Anomalous Diffusion Using Higher-Order Spectral Analysis and Multiple Signal Classification
title_full_unstemmed Decoding Anomalous Diffusion Using Higher-Order Spectral Analysis and Multiple Signal Classification
title_sort Decoding Anomalous Diffusion Using Higher-Order Spectral Analysis and Multiple Signal Classification
dc.creator.none.fl_str_mv Iglesias-Martínez, Miguel E.
Garibo-i-Orts, Óscar
Conejero, J. Alberto|||0000-0003-3681-7533
author Iglesias-Martínez, Miguel E.
author_facet Iglesias-Martínez, Miguel E.
Garibo-i-Orts, Óscar
Conejero, J. Alberto|||0000-0003-3681-7533
author_role author
author2 Garibo-i-Orts, Óscar
Conejero, J. Alberto|||0000-0003-3681-7533
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Matemática Aplicada
Instituto Universitario de Matemática Pura y Aplicada
Escuela Técnica Superior de Ingeniería Informática
European Commission
Agencia Estatal de Investigación
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Anomalous diffusion
Subdiffusion
Superdiffusion
Higher-order spectral analysis
Multiple signal classification
topic Anomalous diffusion
Subdiffusion
Superdiffusion
Higher-order spectral analysis
Multiple signal classification
description [EN] Anomalous diffusion is characterized by nonlinear growth in the mean square displacement of a trajectory. Recent advances using statistical methods and recurrent neural networks have made it possible to detect such phenomena, even in noisy conditions. In this work, we explore feature extraction through parametric and non-parametric spectral analysis methods to decode anomalously diffusing trajectories, achieving reduced computational costs compared with other approaches that require additional data or prior training. Specifically, we propose the use of higher-order statistics, such as the bispectrum, and a hybrid algorithm that combines kurtosis with a multiple-signal classification technique. Our results demonstrate that the type of trajectory can be identified based on amplitude and kurtosis values. The proposed methods deliver accurate results, even with short trajectories and in the presence of noise.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-02-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/220408
url https://riunet.upv.es/handle/10251/220408
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 CPP2021-008994 ANDHI - ANomalous Diffusion of Harmful Information
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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