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      KCI등재 SCOPUS

      Block Sparse Signals Recovery Algorithm for Distributed Compressed Sensing Reconstruction

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      https://www.riss.kr/link?id=A106175020

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      다국어 초록 (Multilingual Abstract)

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      Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linearmeasurements. Various studies about DCS have been carried out recently. In many practical applications, thereis no prior information except for standard sparsity on signals. The typical example is the sparse signals haveblock-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usuallyunavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptiveorthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. Incontrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMPresorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, whichconsists of forward selection and backward removal stages in each iteration. An advantage of this method isthat perfect reconstruction performance can be achieved without prior information on the block-sparsitystructure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.
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      Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linearmeasurements. Various studies about DCS have been carried out recently. In many practical applications, thereis no prior information except for stan...

      Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linearmeasurements. Various studies about DCS have been carried out recently. In many practical applications, thereis no prior information except for standard sparsity on signals. The typical example is the sparse signals haveblock-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usuallyunavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptiveorthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. Incontrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMPresorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, whichconsists of forward selection and backward removal stages in each iteration. An advantage of this method isthat perfect reconstruction performance can be achieved without prior information on the block-sparsitystructure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.

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      참고문헌 (Reference)

      1 X. T. Yuan, "Visual classification with multitask joint sparse representation" 21 (21): 4349-4360, 2012

      2 K. Lee, "Subspace methods for joint sparse recovery" 58 (58): 3613-3641, 2012

      3 S. F. Cotter, "Sparse channel estimation via matching pursuit with application to equalization" 50 (50): 374-377, 2002

      4 J. A. Tropp, "Simultaneous sparse approximation via greedy pursuit" 2005

      5 E. Candes, "Robust uncertainty principles : exact signal reconstruction from highly incomplete frequency information" 52 (52): 489-509, 2006

      6 M. B. Wakin, "Recovery of jointly sparse signals from few random projections" 18 : 1435-1440, 2005

      7 B. X. Huang, "Recovery of block sparse signals by a block version of StOMP" 106 : 231-244, 2015

      8 F. Parvaresh, "Recovering sparse signals using sparse measurement matrices in compressed DNA microarrays" 2 (2): 275-285, 2008

      9 L. Vidya, "RBF network based sparse signal recovery algorithm for compressed sensing reconstruction" 63 : 66-78, 2015

      10 A. L. Goldberger, "PhysioBank, PhysioToolkit, and PhysioNet : components of a new research resource for complex physiologic signals" 101 (101): e215-e220, 2000

      1 X. T. Yuan, "Visual classification with multitask joint sparse representation" 21 (21): 4349-4360, 2012

      2 K. Lee, "Subspace methods for joint sparse recovery" 58 (58): 3613-3641, 2012

      3 S. F. Cotter, "Sparse channel estimation via matching pursuit with application to equalization" 50 (50): 374-377, 2002

      4 J. A. Tropp, "Simultaneous sparse approximation via greedy pursuit" 2005

      5 E. Candes, "Robust uncertainty principles : exact signal reconstruction from highly incomplete frequency information" 52 (52): 489-509, 2006

      6 M. B. Wakin, "Recovery of jointly sparse signals from few random projections" 18 : 1435-1440, 2005

      7 B. X. Huang, "Recovery of block sparse signals by a block version of StOMP" 106 : 231-244, 2015

      8 F. Parvaresh, "Recovering sparse signals using sparse measurement matrices in compressed DNA microarrays" 2 (2): 275-285, 2008

      9 L. Vidya, "RBF network based sparse signal recovery algorithm for compressed sensing reconstruction" 63 : 66-78, 2015

      10 A. L. Goldberger, "PhysioBank, PhysioToolkit, and PhysioNet : components of a new research resource for complex physiologic signals" 101 (101): e215-e220, 2000

      11 G. Coluccia, "Operational rate-distortion performance of single-source and distributed compressed sensing" 62 (62): 2022-2033, 2014

      12 R. Qi, "On recovery of block sparse signals via block generalized orthogonal matching pursuit" 153 : 34-46, 2018

      13 R. G. Baraniuk, "Model-based compressive sensing" 56 (56): 1982-2001, 2010

      14 D. Sundman, "Greedy pursuits for compressed sensing of jointly sparse signals" 368-372, 2011

      15 Y. Zhang, "Forward-backward pursuit method for distributed compressed sensing" 76 (76): 20587-20608, 2017

      16 D. L. Donoho, "For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution" 59 (59): 797-829, 2006

      17 M. F. Duarte, "Distributed compressed sensing of jointly sparse signals" 1537-1541, 2005

      18 D. Baron, "Distributed compressed sensing"

      19 L. Zelnik-Manor, "Dictionary optimization for block-sparse representations" 60 (60): 2386-2395, 2012

      20 H. Palangi, "Convolutional deep stacking networks for distributed compressive sensing" 131 : 181-189, 2017

      21 D. Needell, "CoSaMP : iterative signal recovery from incomplete and inaccurate samples" 26 (26): 301-321, 2009

      22 Y. C. Eldar, "Block-sparse signals : Uncertainty relations and efficient recovery" 58 (58): 3042-3054, 2010

      23 A. Kamali, "Block subspace pursuit for block-sparse signal reconstruction" 37 (37): 1-16, 2013

      24 Rui Qi, "Block Sparse Signals Recovery via Block Backtracking- Based Matching Pursuit Method" 한국정보처리학회 13 (13): 360-369, 2017

      25 Y. J. Zhang, "Backtracking-based matching pursuit method for distributed compressed sensing" 76 (76): 14691-14710, 2017

      26 Y. C. Eldar, "Average case analysis of multichannel sparse recovery using convex relaxation" 56 (56): 505-519, 2010

      27 Q. Wang, "A robust and efficient algorithm for distributed compressed sensing" 37 (37): 916-926, 2011

      28 Y. Oktar, "A review of sparsity-based clustering methods" 148 : 20-30, 2018

      29 X. Li, "A gradient-based approach to optimization of compressed sensing systems" 139 : 49-61, 2017

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2012-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2011-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2009-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.09 0.09 0.09
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.07 0.06 0.254 0.59
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