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Author Sadri, Amir, author.
Title Application of norm optimization in compressive sensing / by Amir Sadri.
Published [Northridge, California] : California State University, Northridge, 2013.
LOCATION CALL # STATUS
 Electronic Book  TA153 .Z953 2013 S23eb    ONLINE
  
Description 1 online resource (vi, 64 pages) : illustrations, some color + 5 computer application files.
Content Type text
still image
Format online resource
File Characteristics text file PDF
Thesis M.S. California State University, Northridge 2013.
Bibliography Includes bibliographical references (pages 32-34).
Note Description based on online resource; title from PDF title page (viewed on Aug. 8, 2013).
Summary There has been a lot of interest in the research community in recent years in Compressed Sensing for solving under-determined systems of equations or reconstruction of sparse signals from highly inadequate samples (in the original or in a transformed domain such as Fourier transform or Wavelet transform). To solve these problems, many techniques have been suggested and developed, a lot of which have focused on optimization. Among different optimization techniques l1 norm optimization has been source of much investigation. But later it was suggested that lp norm optimization with 0<p<1 can result in more accurate signal reconstruction. In this project a review of Compressed Sensing is offered and some approaches to signal reconstruction are discussed. In particular, methods involving lp norm optimization are evaluated. To this end, Matlab scripts have been developed to solve the lp norm optimization problem (0<p<1). They will be discussed in details and their results will be compared with results of l1 norm optimization.
Subject Signal processing.
Local Subject Dissertations, Academic -- CSUN -- Engineering -- Electrical and Computer Engineering.
OCLC number 855545348