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Statistical mechanics of 1-bit Compressed Sensing

Grant number: 14/22258-2
Support Opportunities:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): March 01, 2015
Effective date (End): July 31, 2015
Field of knowledge:Physical Sciences and Mathematics - Physics - General Physics
Principal Investigator:Renato Vicente
Grantee:Paulo Victor Camargo Rossi
Supervisor: Yoshiyuki Kabashima
Host Institution: Instituto de Física (IF). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Research place: Tokyo Institute of Technology, Yokohama (Tokyo Tech), Japan  
Associated to the scholarship:14/00792-7 - Statistical mechanics applied to compressed sensing, BP.DR

Abstract

Compressed Sensing (CS) é a Family of techniques where signal measurement and signal compression happen simultaneously. Given a signal represented by a N-dimensional vector x0 and a measuring matrix F of dimension MxN, we make M linear measurements y=F x0. Previous work by Sompolinsky et al. and Kabashima et al. where Statistical Mechanics techniques were used indicated that when x0 is k-sparse (i.e., when it has at most k non-zero entries), there is a regime where linear programming algorithms can reconstruct the original signal x0 from the knowledge of F and y even when M

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
ROSSI, PAULO V.; KABASHIMA, YOSHIYUKI; INOUE, JUN-ICHI. Bayesian online compressed sensing. Physical Review E, v. 94, n. 2, . (14/22258-2)
ROSSI, PAULO V.; KABASHIMA, YOSHIYUKI; INOUE, JUN-ICHI. Bayesian online compressed sensing. PHYSICAL REVIEW E, v. 94, n. 2, p. 8-pg., . (14/22258-2)

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