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...
| Autores: | ; ; |
|---|---|
| Tipo de documento: | Artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Lenguaje: | Inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/220408 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/220408 |
| Nivel de acceso: | Acceso abierto |
| Palabra clave: | Anomalous diffusion Subdiffusion Superdiffusion Higher-order spectral analysis Multiple signal classification |
| _version_ | 1878735687429128192 |
|---|---|
| author | Iglesias-Martínez, Miguel E. |
| author2 | Garibo-i-Orts, Óscar Conejero, J. Alberto|||0000-0003-3681-7533 |
| author2_role | author author |
| author_browse | Conejero, J. Alberto|||0000-0003-3681-7533 Garibo-i-Orts, Óscar Iglesias-Martínez, Miguel E. |
| author_facet | Iglesias-Martínez, Miguel E. Garibo-i-Orts, Óscar Conejero, J. Alberto|||0000-0003-3681-7533 |
| author_role | author |
| collection | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_81a67dc2fa11c923d77b6677cdfcfcd2 |
| institution | Universitat Politècnica de València (UPV) |
| instname_str | Universitat Politècnica de València (UPV) |
| language | eng |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:riunet.upv.es:10251/220408 |
| publishDate | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI AG |
| publisherStr | MDPI AG |
| reponame_str | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| spelling | Decoding Anomalous Diffusion Using Higher-Order Spectral Analysis and Multiple Signal Classification Iglesias-Martínez, Miguel E. Garibo-i-Orts, Óscar Conejero, J. Alberto|||0000-0003-3681-7533 Anomalous diffusion Subdiffusion Superdiffusion Higher-order spectral analysis 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 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 AG https://riunet.upv.es/handle/10251/220408 |
| 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 | 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_short | 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 |
| topic | Anomalous diffusion Subdiffusion Superdiffusion Higher-order spectral analysis Multiple signal classification |
| topic_facet | Anomalous diffusion Subdiffusion Superdiffusion Higher-order spectral analysis Multiple signal classification |
| url | https://riunet.upv.es/handle/10251/220408 |