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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
Issue Date
2014-01
Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
Citation
IEICE ELECTRONICS EXPRESS, v.11, no.3, pp.20130947
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.
URI
https://scholarworks.unist.ac.kr/handle/201301/4307
URL
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84896863744
DOI
10.1587/elex.11.20130947
ISSN
1349-2543
Appears in Collections:
EE_Journal Papers
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