Shuffled multi-channel sparse signal recovery.
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| Title: | Shuffled multi-channel sparse signal recovery. |
|---|---|
| Authors: | Koka, Taulant1 (AUTHOR) taulant.koka@tu-darmstadt.de, Tsakiris, Manolis C.2 (AUTHOR) manolis@amss.ac.cn, Muma, Michael1 (AUTHOR) michael.muma@tu-darmstadt.de, Béjar Haro, Benjamín3 (AUTHOR) benjamin.bejar@psi.ch |
| Source: | Signal Processing. Nov2024, Vol. 224, pN.PAG-N.PAG. 1p. |
| Subjects: | Signal reconstruction, Vital signs, Evaluation methodology, Calcium, Signals & signaling |
| 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 address this issue systematically, we frame it as a signal reconstruction problem where correspondences between samples and channels are lost. Assuming a sensing matrix for the signals, we show the problem's equivalence to a highly structured unlabeled sensing problem and establish conditions for unique recovery. This is crucial since existing unlabeled sensing theory is inapplicable and results for reconstructing shuffled multi-channel signals do not yet exist. Our results extend to continuous-time sparse signals, and we derive conditions for reconstructing shuffled sparse signals. For the two-channel case, we provide a first reconstruction method, which combines sparse signal recovery with robust linear regression, outperforming existing unlabeled sensing methods in numerical experiments. Additionally, we showcase its effectiveness in a real-world application involving calcium imaging traces. Our theory marks a significant initial step in addressing this challenging signal reconstruction problem, with potential extensions to diverse signal representations encountered in real-world problems with imprecise measurement or channel assignment. • Formalization of the shuffled multi-channel signal reconstruction problem. • Derivation of unique recovery conditions for cross-channel unlabeled sensing problem. • Extension of recovery results to sparse signals with unknown sensing matrix. • Proposal of two-step approach for shuffled sparse signal reconstruction. • Evaluation of the method in simulations and a practical application in neuroscience. [ABSTRACT FROM AUTHOR] |
| Copyright of Signal Processing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 178884685 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Shuffled multi-channel sparse signal recovery. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Koka%2C+Taulant%22">Koka, Taulant</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> taulant.koka@tu-darmstadt.de</i><br /><searchLink fieldCode="AR" term="%22Tsakiris%2C+Manolis+C%2E%22">Tsakiris, Manolis C.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> manolis@amss.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Muma%2C+Michael%22">Muma, Michael</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> michael.muma@tu-darmstadt.de</i><br /><searchLink fieldCode="AR" term="%22Béjar+Haro%2C+Benjamín%22">Béjar Haro, Benjamín</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> benjamin.bejar@psi.ch</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Signal+Processing%22">Signal Processing</searchLink>. Nov2024, Vol. 224, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Signal+reconstruction%22">Signal reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Vital+signs%22">Vital signs</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+methodology%22">Evaluation methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Calcium%22">Calcium</searchLink><br /><searchLink fieldCode="DE" term="%22Signals+%26+signaling%22">Signals & signaling</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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 address this issue systematically, we frame it as a signal reconstruction problem where correspondences between samples and channels are lost. Assuming a sensing matrix for the signals, we show the problem's equivalence to a highly structured unlabeled sensing problem and establish conditions for unique recovery. This is crucial since existing unlabeled sensing theory is inapplicable and results for reconstructing shuffled multi-channel signals do not yet exist. Our results extend to continuous-time sparse signals, and we derive conditions for reconstructing shuffled sparse signals. For the two-channel case, we provide a first reconstruction method, which combines sparse signal recovery with robust linear regression, outperforming existing unlabeled sensing methods in numerical experiments. Additionally, we showcase its effectiveness in a real-world application involving calcium imaging traces. Our theory marks a significant initial step in addressing this challenging signal reconstruction problem, with potential extensions to diverse signal representations encountered in real-world problems with imprecise measurement or channel assignment. • Formalization of the shuffled multi-channel signal reconstruction problem. • Derivation of unique recovery conditions for cross-channel unlabeled sensing problem. • Extension of recovery results to sparse signals with unknown sensing matrix. • Proposal of two-step approach for shuffled sparse signal reconstruction. • Evaluation of the method in simulations and a practical application in neuroscience. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Signal Processing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.sigpro.2024.109579 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Signal reconstruction Type: general – SubjectFull: Vital signs Type: general – SubjectFull: Evaluation methodology Type: general – SubjectFull: Calcium Type: general – SubjectFull: Signals & signaling Type: general Titles: – TitleFull: Shuffled multi-channel sparse signal recovery. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Koka, Taulant – PersonEntity: Name: NameFull: Tsakiris, Manolis C. – PersonEntity: Name: NameFull: Muma, Michael – PersonEntity: Name: NameFull: Béjar Haro, Benjamín IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 01651684 Numbering: – Type: volume Value: 224 Titles: – TitleFull: Signal Processing Type: main |
| ResultId | 1 |
