Sparse source recovery with graph in network coding

Sung Bok Yu; Yoonsik Choe

doi:10.1109/APSIPA.2016.7820844

Sparse source recovery with graph in network coding

Sung Bok Yu, Yoonsik Choe

Department of Electrical and Electronic Engineering

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

Abstract

In network coding, the successive original video frame data can be transmitted at once. However, if insufficient number of innovative packets are transmitted due to the packet loss or delay, network coding system is to be underdetermined. Thus, since network coding matrix (random coefficient matrix) is not invertible, original data cannot be recovered by matrix inversion. To solve this problem, in this paper, a new compressive sensing method with graph Laplacian regularizer is proposed, which exploits correlation between successive original video frame data. Experimental results demonstrate the effectiveness of proposed algorithm, implemented by the alternative direction multiplier method (ADMM) and show that PSNR values from reconstructed images are above 33dB with coding matrix Φ C^M×N of which measurement M = 0.75N and 22dB with measurement M = 0.66N, respectively.

Original language	English
Title of host publication	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9789881476821
DOIs	https://doi.org/10.1109/APSIPA.2016.7820844
Publication status	Published - 2017 Jan 17
Event	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 - Jeju, Korea, Republic of Duration: 2016 Dec 13 → 2016 Dec 16

Publication series

Name	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016

Other

Other	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
Country/Territory	Korea, Republic of
City	Jeju
Period	16/12/13 → 16/12/16

Bibliographical note

Publisher Copyright:
© 2016 Asia Pacific Signal and Information Processing Association.

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Science Applications
Information Systems
Signal Processing

Access to Document

10.1109/APSIPA.2016.7820844

Cite this

Yu, S. B., & Choe, Y. (2017). Sparse source recovery with graph in network coding. In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 [7820844] (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPA.2016.7820844

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title = "Sparse source recovery with graph in network coding",

abstract = "In network coding, the successive original video frame data can be transmitted at once. However, if insufficient number of innovative packets are transmitted due to the packet loss or delay, network coding system is to be underdetermined. Thus, since network coding matrix (random coefficient matrix) is not invertible, original data cannot be recovered by matrix inversion. To solve this problem, in this paper, a new compressive sensing method with graph Laplacian regularizer is proposed, which exploits correlation between successive original video frame data. Experimental results demonstrate the effectiveness of proposed algorithm, implemented by the alternative direction multiplier method (ADMM) and show that PSNR values from reconstructed images are above 33dB with coding matrix Φ CM×N of which measurement M = 0.75N and 22dB with measurement M = 0.66N, respectively.",

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Yu, SB & Choe, Y 2017, Sparse source recovery with graph in network coding. in 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016., 7820844, 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016, Institute of Electrical and Electronics Engineers Inc., 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016, Jeju, Korea, Republic of, 16/12/13. https://doi.org/10.1109/APSIPA.2016.7820844

Sparse source recovery with graph in network coding. / Yu, Sung Bok; Choe, Yoonsik.

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7820844 (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016).

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

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AU - Yu, Sung Bok

AU - Choe, Yoonsik

PY - 2017/1/17

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N2 - In network coding, the successive original video frame data can be transmitted at once. However, if insufficient number of innovative packets are transmitted due to the packet loss or delay, network coding system is to be underdetermined. Thus, since network coding matrix (random coefficient matrix) is not invertible, original data cannot be recovered by matrix inversion. To solve this problem, in this paper, a new compressive sensing method with graph Laplacian regularizer is proposed, which exploits correlation between successive original video frame data. Experimental results demonstrate the effectiveness of proposed algorithm, implemented by the alternative direction multiplier method (ADMM) and show that PSNR values from reconstructed images are above 33dB with coding matrix Φ CM×N of which measurement M = 0.75N and 22dB with measurement M = 0.66N, respectively.

AB - In network coding, the successive original video frame data can be transmitted at once. However, if insufficient number of innovative packets are transmitted due to the packet loss or delay, network coding system is to be underdetermined. Thus, since network coding matrix (random coefficient matrix) is not invertible, original data cannot be recovered by matrix inversion. To solve this problem, in this paper, a new compressive sensing method with graph Laplacian regularizer is proposed, which exploits correlation between successive original video frame data. Experimental results demonstrate the effectiveness of proposed algorithm, implemented by the alternative direction multiplier method (ADMM) and show that PSNR values from reconstructed images are above 33dB with coding matrix Φ CM×N of which measurement M = 0.75N and 22dB with measurement M = 0.66N, respectively.

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T3 - 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016

BT - 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016

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Yu SB, Choe Y. Sparse source recovery with graph in network coding. In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc. 2017. 7820844. (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016). https://doi.org/10.1109/APSIPA.2016.7820844

Sparse source recovery with graph in network coding

Abstract

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All Science Journal Classification (ASJC) codes

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