Compressed Sensing Meets Information Theory

Seventh Tech Talk Abstract presented October 2009 by Dror Baron, Visiting Scientist, Technion - Israel Institute of Technology. traditional techniques, band-limited analog signal detection test signals above the Nyquist rate, which is linked to higher frequency analog signal. Compressed Sensing (CS) is the revelation that optimization routines can reconstruct a sparse signal based on a small number of linear projections of the signal. Therefore, the CS-based techniques canpurchase and process signals scattered at much lower prices. CS offers an enormous potential in applications such as broadband analog-digital conversion if the Nyquist is on the state of the art. The theory has to offer many insights into CS, I will describe several studies in this direction. Before distributed compressed sensing (DCS) offers new distributed signal detection algorithms using both intra-and inter-signal correlation structures in multi-signalEnsemble. DCS is immediately applicable in sensor networks. Next, you use the remarkable success of reduction algorithms and graphic codes to design low complexity LDPC channel of CS reconstruction algorithms. linear measurements play a crucial role not only in compressed sensing, but in disciplines such as finance, where a large number of measurements needed to evaluate different statistical properties are noisy. In fact, try to many areas of science and technology to extract information from linear-...

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