Titlebook: Compressed Sensing & Sparse Filtering; Avishy Y. Carmi,Lyudmila Mihaylova,Simon J. Godsil Book 2014 Springer-Verlag Berlin Heidelberg 2014

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Frederik Ahlemann,Fedi El Arbi,Axel Heckhen discussed in Sect.?.. Special attention is paid to the use of Sub-Nyquist sampling and compressed sensing techniques for realizing wideband spectrum sensing. Finally, Sect.?. shows an adaptive compressed sensing approach for wideband spectrum sensing in cognitive radio networks.

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Kunal Mohan Dr.,Frederik Ahlemannacing emphasis on minimal input resources and blind identification whereby only output samples are available plus a–priori information on input characteristics. Based on this taxonomy a variety of algorithms, existing and new, are studied and evaluated by simulations.

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Strategisches Qualit?tscontrollingterns reflect spectral and spatial diversity trade-offs. Characterization of the expected quality of the reconstructed images in these scenarios prior to actual data collection is a problem of central interest in task planning for multi-mode radars. Compressed sensing theory argues that the mutual c

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1860-4862 btaining sparse solutions using fewer observations thanconventionally needed. The book emphasizes on the role of sparsity as a machinery for promoting low complexity representations and likewise its connections978-3-662-50894-7978-3-642-38398-4Series ISSN 1860-4862 Series E-ISSN 1860-4870

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Introduction to Compressed Sensing and Sparse Filtering,ing and scientific domains. Presently, there is a wealth of theoretical results that extend the basic ideas of compressed sensing essentially making analogies to notions from other fields of mathematics. The objective of this chapter is to introduce the reader to the basic theory of compressed sensi

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The Geometry of Compressed Sensing,ing a geometrical interpretation. This geometric point of view not only underlies many of the initial theoretical developments on which much of the theory of compressed sensing is built, but has also allowed ideas to be extended to much more general recovery problems and structures. A unifying frame

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Sparse Signal Recovery with Exponential-Family Noise,ng literature. Typically, the signal reconstruction problem is formulated as .-regularized . regression. From a statistical point of view, this problem is equivalent to maximum a posteriori probability (MAP) parameter estimation with Laplace prior on the vector of parameters (i.e., signal) and linea

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