Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/29390
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dc.contributor.authorTausiesakul B.
dc.contributor.authorAsavaskulkiet K.
dc.contributor.otherSrinakharinwirot University
dc.date.accessioned2023-11-15T02:08:30Z-
dc.date.available2023-11-15T02:08:30Z-
dc.date.issued2023
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85169291656&doi=10.1109%2fJCSSE58229.2023.10202042&partnerID=40&md5=6a5d605cc94472d756f36f3c59d9ef6d
dc.identifier.urihttps://ir.swu.ac.th/jspui/handle/123456789/29390-
dc.description.abstractFOCal Underdetermined System Solver (FOCUSS) is an estimation method for finding a n unknown vector that potentially has a sparse structure. The application of this estimation technique can be found in several areas, e.g., sparse signal recovery in image reconstruction, wireless communications, etc. The convergence analysis performance and order of convergence of this technique are the focuses of this study. In this work, we investigate its estimation error performance on the second order, in terms of error variance or mean squared error. Since the computation in this algorithm is nonlinear, an exact form of the error performance seems infeasible. Therefore, we derive a closed-form expression that approximates the mean squared error of the FOCUSS. Numerical simulation was conducted to illustrate the closeness of our prediction to the real estimation error. © 2023 IEEE.
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectCompressive sensing
dc.subjectfocal underdetermined system solver
dc.subjectmean squared error
dc.titleAn Approximation of FOCUSS Mean Squared Error
dc.typeConference paper
dc.rights.holderScopus
dc.identifier.bibliograpycitationProceedings of JCSSE 2023 - 20th International Joint Conference on Computer Science and Software Engineering. Vol , No. (2023), p.231-236
dc.identifier.doi10.1109/JCSSE58229.2023.10202042
Appears in Collections:Scopus 2023

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