Koka, Taulant ; Tsakiris, Manolis C. ; Muma, Michael ; Haro, Benjamín Béjar (2023)
Shuffled Multi-Channel Sparse Signal Recovery.
doi: 10.48550/arXiv.2212.07368
Report, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
Mismatches between samples and their respective channel or target commonly arise in several real-world applications. For instance, whole-brain calcium imaging of freely moving organisms, multiple-target tracking or multi-person contactless vital sign monitoring may be severely affected by mismatched sample-channel assignments. To systematically address this fundamental problem, we pose it as a signal reconstruction problem where we have lost correspondences between the samples and their respective channels. Assuming that we have a sensing matrix for the underlying signals, we show that the problem is equivalent to a structured unlabeled sensing problem, and establish sufficient conditions for unique recovery. To the best of our knowledge, a sampling result for the reconstruction of shuffled multi-channel signals has not been considered in the literature and existing methods for unlabeled sensing cannot be directly applied. We extend our results to the case where the signals admit a sparse representation in an overcomplete dictionary (i.e., the sensing matrix is not precisely known), and derive sufficient conditions for the reconstruction of shuffled sparse signals. We propose a robust reconstruction method that combines sparse signal recovery with robust linear regression for the two-channel case. The performance and robustness of the proposed approach is illustrated in an application related to whole-brain calcium imaging. The proposed methodology can be generalized to sparse signal representations other than the ones considered in this work to be applied in a variety of real-world problems with imprecise measurement or channel assignment.
| Typ des Eintrags: | Report |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Koka, Taulant ; Tsakiris, Manolis C. ; Muma, Michael ; Haro, Benjamín Béjar |
| Art des Eintrags: | Bibliographie |
| Titel: | Shuffled Multi-Channel Sparse Signal Recovery |
| Sprache: | Englisch |
| Publikationsjahr: | 24 Juli 2023 |
| Verlag: | arXiv |
| Reihe: | Electrical Engineering and Systems Science |
| Kollation: | 13 Seiten |
| Auflage: | 3.Version |
| DOI: | 10.48550/arXiv.2212.07368 |
| URL / URN: | https://arxiv.org/abs/2212.07368 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Mismatches between samples and their respective channel or target commonly arise in several real-world applications. For instance, whole-brain calcium imaging of freely moving organisms, multiple-target tracking or multi-person contactless vital sign monitoring may be severely affected by mismatched sample-channel assignments. To systematically address this fundamental problem, we pose it as a signal reconstruction problem where we have lost correspondences between the samples and their respective channels. Assuming that we have a sensing matrix for the underlying signals, we show that the problem is equivalent to a structured unlabeled sensing problem, and establish sufficient conditions for unique recovery. To the best of our knowledge, a sampling result for the reconstruction of shuffled multi-channel signals has not been considered in the literature and existing methods for unlabeled sensing cannot be directly applied. We extend our results to the case where the signals admit a sparse representation in an overcomplete dictionary (i.e., the sensing matrix is not precisely known), and derive sufficient conditions for the reconstruction of shuffled sparse signals. We propose a robust reconstruction method that combines sparse signal recovery with robust linear regression for the two-channel case. The performance and robustness of the proposed approach is illustrated in an application related to whole-brain calcium imaging. The proposed methodology can be generalized to sparse signal representations other than the ones considered in this work to be applied in a variety of real-world problems with imprecise measurement or channel assignment. |
| Freie Schlagworte: | Signal Processing (eess.SP), Machine Learning (cs.LG), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences |
| Zusätzliche Informationen: | Titel der Publikation mit der 3.Version geändert, 1.-2.Version: Reconstruction of Multivariate Sparse Signals from Mismatched Samples |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Robust Data Science |
| Hinterlegungsdatum: | 26 Jul 2023 08:51 |
| Letzte Änderung: | 19 Dez 2024 11:38 |
| PPN: | 509977642 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Reconstruction of Multivariate Sparse Signals from Mismatched Samples. (deposited 19 Dez 2022 09:59)
- Shuffled Multi-Channel Sparse Signal Recovery. (deposited 26 Jul 2023 08:51) [Gegenwärtig angezeigt]
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