Distributed compressive sensing for correlated information sources

Jeonghun Park; Seunggye Hwang; Janghoon Yang; Kitae Bae; Hoon Ko; Dong Ku Kim

doi:10.1007/978-3-319-58967-1_15

Distributed compressive sensing for correlated information sources

Jeonghun Park, Seunggye Hwang, Janghoon Yang, Kitae Bae, Hoon Ko, Dong Ku Kim

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The abstract should summarize the contents of the paper and should Distributed Compressive Sensing (DCS) improves the signal recovery performance of multi signal ensembles by exploiting both intra- and inter-signal correlation and sparsity structure. In this paper, we propose a novel algorithm, which improves detection performance even without a priori-knowledge on the correlation structure for arbitrarily correlated sparse signal. Numerical results verify that the propose algorithm reduces the required number of measurements for correlated sparse signal detection compared to the existing DCS algorithm.

Original language	English
Title of host publication	Big Data Technologies and Applications - 7th International Conference, BDTA 2016, Proceedings
Editors	Jason J. Jung, Pankoo Kim
Publisher	Springer Verlag
Pages	130-137
Number of pages	8
ISBN (Print)	9783319589664
DOIs	https://doi.org/10.1007/978-3-319-58967-1_15
Publication status	Published - 2017
Event	7th International Conference on Big Data Technologies and Applications, BDTA 2016 - Seoul, Korea, Republic of Duration: 2016 Nov 17 → 2016 Nov 18

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	194 LNICST
ISSN (Print)	1867-8211

Other

Other	7th International Conference on Big Data Technologies and Applications, BDTA 2016
Country/Territory	Korea, Republic of
City	Seoul
Period	16/11/17 → 16/11/18

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (2015R1C1A1A02037515), and (2012R1A2A2A01047554).

Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.

All Science Journal Classification (ASJC) codes

Computer Networks and Communications

Access to Document

10.1007/978-3-319-58967-1_15

Cite this

Park, J., Hwang, S., Yang, J., Bae, K., Ko, H., & Kim, D. K. (2017). Distributed compressive sensing for correlated information sources. In J. J. Jung, & P. Kim (Eds.), Big Data Technologies and Applications - 7th International Conference, BDTA 2016, Proceedings (pp. 130-137). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 194 LNICST). Springer Verlag. https://doi.org/10.1007/978-3-319-58967-1_15

Park, Jeonghun ; Hwang, Seunggye ; Yang, Janghoon et al. / Distributed compressive sensing for correlated information sources. Big Data Technologies and Applications - 7th International Conference, BDTA 2016, Proceedings. editor / Jason J. Jung ; Pankoo Kim. Springer Verlag, 2017. pp. 130-137 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

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abstract = "The abstract should summarize the contents of the paper and should Distributed Compressive Sensing (DCS) improves the signal recovery performance of multi signal ensembles by exploiting both intra- and inter-signal correlation and sparsity structure. In this paper, we propose a novel algorithm, which improves detection performance even without a priori-knowledge on the correlation structure for arbitrarily correlated sparse signal. Numerical results verify that the propose algorithm reduces the required number of measurements for correlated sparse signal detection compared to the existing DCS algorithm.",

author = "Jeonghun Park and Seunggye Hwang and Janghoon Yang and Kitae Bae and Hoon Ko and Kim, {Dong Ku}",

note = "Funding Information: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (2015R1C1A1A02037515), and (2012R1A2A2A01047554). Publisher Copyright: {\textcopyright} ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.; 7th International Conference on Big Data Technologies and Applications, BDTA 2016 ; Conference date: 17-11-2016 Through 18-11-2016",

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doi = "10.1007/978-3-319-58967-1_15",

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series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

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Park, J, Hwang, S, Yang, J, Bae, K, Ko, H & Kim, DK 2017, Distributed compressive sensing for correlated information sources. in JJ Jung & P Kim (eds), Big Data Technologies and Applications - 7th International Conference, BDTA 2016, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 194 LNICST, Springer Verlag, pp. 130-137, 7th International Conference on Big Data Technologies and Applications, BDTA 2016, Seoul, Korea, Republic of, 16/11/17. https://doi.org/10.1007/978-3-319-58967-1_15

Distributed compressive sensing for correlated information sources. / Park, Jeonghun; Hwang, Seunggye; Yang, Janghoon et al.
Big Data Technologies and Applications - 7th International Conference, BDTA 2016, Proceedings. ed. / Jason J. Jung; Pankoo Kim. Springer Verlag, 2017. p. 130-137 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 194 LNICST).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Distributed compressive sensing for correlated information sources

AU - Park, Jeonghun

AU - Hwang, Seunggye

AU - Yang, Janghoon

AU - Bae, Kitae

AU - Ko, Hoon

AU - Kim, Dong Ku

N1 - Funding Information: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (2015R1C1A1A02037515), and (2012R1A2A2A01047554). Publisher Copyright: © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.

PY - 2017

Y1 - 2017

N2 - The abstract should summarize the contents of the paper and should Distributed Compressive Sensing (DCS) improves the signal recovery performance of multi signal ensembles by exploiting both intra- and inter-signal correlation and sparsity structure. In this paper, we propose a novel algorithm, which improves detection performance even without a priori-knowledge on the correlation structure for arbitrarily correlated sparse signal. Numerical results verify that the propose algorithm reduces the required number of measurements for correlated sparse signal detection compared to the existing DCS algorithm.

AB - The abstract should summarize the contents of the paper and should Distributed Compressive Sensing (DCS) improves the signal recovery performance of multi signal ensembles by exploiting both intra- and inter-signal correlation and sparsity structure. In this paper, we propose a novel algorithm, which improves detection performance even without a priori-knowledge on the correlation structure for arbitrarily correlated sparse signal. Numerical results verify that the propose algorithm reduces the required number of measurements for correlated sparse signal detection compared to the existing DCS algorithm.

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Park J, Hwang S, Yang J, Bae K, Ko H, Kim DK. Distributed compressive sensing for correlated information sources. In Jung JJ, Kim P, editors, Big Data Technologies and Applications - 7th International Conference, BDTA 2016, Proceedings. Springer Verlag. 2017. p. 130-137. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-319-58967-1_15

Distributed compressive sensing for correlated information sources

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