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Permanent link (DOI): https://doi.org/10.7939/R38S4JX6D

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Parallel Sampling and Reconstruction with Permutation in Multidimensional Compressed Sensing Open Access

Descriptions

Other title
Subject/Keyword
multidimensional signal processing
permutation
compressed sensing
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Fang, Hao
Supervisor and department
Vorobyov, Sergiy A. (Electrical and Computer Engineering)
Jiang, Hai (Electrical and Computer Engineering)
Examining committee member and department
Nikolaidis, Ioanis (Computing Science)
Zhao, Vicky (Electrical and Computer Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Signal and Image Processing
Date accepted
2013-06-25T11:44:49Z
Graduation date
2013-11
Degree
Master of Science
Degree level
Master's
Abstract
The advent of compressed sensing provides a new way to sample and compress signals. In this thesis, a parallel compressed sensing architecture is proposed, which samples a two-dimensional reshaped multidimensional signal column by column using the same sensing matrix. Compared to architectures that sample a vector-reshaped multidimensional signal, the sampling device in the parallel compressed sensing architecture stores a smaller-sized sensing matrix and has lower computational complexity. Besides, the reconstruction of the multidimensional signal can be conducted in parallel, which reduces the computational complexity and time for reconstruction at the decoder side. In addition, when parallel sampling is not required but analog compressed sensing is desired, an alternative architecture proposed in this thesis, named parallel compressed sensing reconstruction architecture, can be used. In both proposed architectures, permutation is introduced and shown to enable the reduction of the required number of measurements for a given desired reconstruction error performance.
Language
English
DOI
doi:10.7939/R38S4JX6D
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
H. Fang, S. A. Vorobyov, H. Jiang, and O. Taheri, “2D signal compression via parallel compressed sensing with permutations,” in Proc. 46th Annual Asilomar Conf. Signals, Systems, and Computers, Pacific Grove, California, USA, Nov. 4–7, 2012, pp. 1925– 1929.

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