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Choi, Jaesik
Statistical Artificial Intelligence Lab
Research Interests
  • Artificial intelligence, machine learning, deep learning, robotics, automatic statistician, semantic segmentation, fault detection

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Low-complexity compressive sensing with downsampling

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Title
Low-complexity compressive sensing with downsampling
Author
Lee, DongeunChoi, JaesikShin, Heonshik
Keywords
Compressive sensing; Downsampling; Low-complexity; Sparse random matrix; Sparse signal recovery
Issue Date
201401
Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
Citation
IEICE ELECTRONICS EXPRESS, v.11, no.3, pp.1 - 6
Abstract
Compressive sensing (CS) with sparse random matrix for the random sensing basis reduces source coding complexity of sensing devices. We propose a downsampling scheme to this framework in order to further reduce the complexity and improve coding efficiency simultaneously. As a result, our scheme can deliver significant gains to a wide variety of resource-constrained sensors. Experimental results show that the computational complexity decreases by 99.95% compared to other CS framework with dense random measurements. Furthermore, bit-rate can be saved up to 46.29%, by which less bandwidth is consumed.
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DOI
http://dx.doi.org/10.1587/elex.11.20130947
ISSN
1349-2543
Appears in Collections:
ECE_Journal Papers
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