Presentation 2013-05-23
A Study on Reconstruction Control in Compressive Sensing for Change Detection of Multi-dimensional Data
Hiroki FURUSAWA, Hiroyuki KASAI,
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Abstract(in English) One way to detect the change of multi-dimensional data is tensor decomposition. Sensing vectors obtained using the core tensor and Compressive Sensing. Efficient change detection methods t have been proposed while avoiding an increase of the additionnal processing load by treating instead the sensing vectors obtained using a core tensor Compressive Sensing. This paper proposes a efficient data restoration method that dynamically changs the number of samples of the sensing matrix. This adaptation folllows the distribution of the nonzero elements of the input signal. Final simulation experiments shows improvements in the higher rate of data restoration by comparing with the conventional method while avoiding compatation load increase.
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Keyword(in English) Tensor Decomposition / Compressive Sensing / Change detection / Multi-dimensional Data
Paper # MoNA2013-1
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Committee MoNA
Conference Date 2013/5/16(1days)
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Registration To Mobile Network and Applications(MoNA)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Study on Reconstruction Control in Compressive Sensing for Change Detection of Multi-dimensional Data
Sub Title (in English)
Keyword(1) Tensor Decomposition
Keyword(2) Compressive Sensing
Keyword(3) Change detection
Keyword(4) Multi-dimensional Data
1st Author's Name Hiroki FURUSAWA
1st Author's Affiliation Graduate School of information systems, The University of Electro-communications()
2nd Author's Name Hiroyuki KASAI
2nd Author's Affiliation Graduate School of information systems, The University of Electro-communications
Date 2013-05-23
Paper # MoNA2013-1
Volume (vol) vol.113
Number (no) 56
Page pp.pp.-
#Pages 6
Date of Issue