NDLI: Maximum length sequence encoded Hadamard measurement paradigm for compressed sensing

Content Provider	IEEE Xplore Digital Library
Author	Shujia Qin Sheng Bi Ning Xi
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Sci. & Eng., South China Univ. of Technol., Guangzhou, China (Sheng Bi) \|\| State Key Lab. of Robot., Shenyang Inst. of Autom., Shenyang, China (Shujia Qin) \|\| Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA (Ning Xi)
Abstract	The development of compressed sensing technology has greatly facilitated its applications in many fields, such as medical imaging, multi-sensor and distributed sensing, coding theory, hyper-spectral imaging, and machine learning. Among these applications, the permuted Walsh-Hadamard matrices are frequently chosen for modeling real-world measurements that are limited to binary entry states by special structure (one typical example is the digital mirror device in singlepixel cameras) because the fast Walsh-Hadamard transform can efficiently calculate its multiplications; however, for a large-scale problem, the Walsh-Hadamard matrix would become unacceptably large to be stored in advance. To eliminate this defect, this paper proposes a maximum length sequence encoded Hadamard measurement paradigm that can be simply realized on chip without any usage of external memory, and proves this method can degenerate to a special permutation of the sequence ordered Walsh-Hadamard matrix so that the fast Walsh-Hadamard transform keeps feasible. Simulations show that compared with the conventional permuted Walsh-Hadamard matrix, the proposed one can emerge from the limit of external memory without losing much randomness performance in the measurement basis required by compressed sensing.
Starting Page	1151
Ending Page	1156
File Size	1406165
Page Count	6
File Format	PDF
e-ISBN	9781479957361
DOI	10.1109/AIM.2014.6878236
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-08
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biomedical measurement Vectors Cameras Pollution measurement Educational institutions Optical sensors Lenses
Content Type	Text
Resource Type	Article

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