Compressive Sensing with Optical Chaos
نویسندگان
چکیده
منابع مشابه
Compressive Sensing with Optical Chaos
Compressive sensing (CS) is a technique to sample a sparse signal below the Nyquist-Shannon limit, yet still enabling its reconstruction. As such, CS permits an extremely parsimonious way to store and transmit large and important classes of signals and images that would be far more data intensive should they be sampled following the prescription of the Nyquist-Shannon theorem. CS has found appl...
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Michael B. Wakin is the Ben L. Fryrear Associate Professor in the Department of Electrical Engineering and Computer Science at the Colorado School of Mines (CSM). Dr. Wakin received a B.S. in electrical engineering and a B.A. in mathematics in 2000 (summa cum laude), an M.S. in electrical engineering in 2002, and a Ph.D. in electrical engineering in 2007, all from Rice University. He was an NSF...
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Compressive sensing is a new type of sampling theory, which predicts that sparse signals and images can be reconstructed from what was previously believed to be incomplete information. As a main feature, efficient algorithms such as l1-minimization can be used for recovery. The theory has many potential applications in signal processing and imaging. This chapter gives an introduction and overvi...
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ژورنال
عنوان ژورنال: Scientific Reports
سال: 2016
ISSN: 2045-2322
DOI: 10.1038/srep35206