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

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Lebenszyklus und Umweltanalyse,ng as emanated in the first few works on the subject. The first part of this chapter is therefore a concise exposition to compressed sensing which requires no prior background. The second half of this chapter slightly extends the theory and discusses its applicability to filtering of dynamic sparse signals.

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Unternehmensführung & Controlling large fraction of its components is zero-valued. We discuss algorithms for signal estimation in the described scenario, analyze their complexity, and demonstrate their near-optimal performance even in the case where sensors transmit a single bit (i.e., the sign of innovation) to the fusion center.

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Book 2014 popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within

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Introduction to Compressed Sensing and Sparse Filtering,ng as emanated in the first few works on the subject. The first part of this chapter is therefore a concise exposition to compressed sensing which requires no prior background. The second half of this chapter slightly extends the theory and discusses its applicability to filtering of dynamic sparse signals.

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,Estimation of Time-Varying Sparse Signals in Sensor Networks, large fraction of its components is zero-valued. We discuss algorithms for signal estimation in the described scenario, analyze their complexity, and demonstrate their near-optimal performance even in the case where sensors transmit a single bit (i.e., the sign of innovation) to the fusion center.

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Alexander Knöss,Markus Kreßmanned for. All extensions preserve the computational efficiency of the classic algorithms, and most of the extensions are illustrated with numerical examples, which are part of an open source Kalman smoothing Matlab/Octave package.

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Alexander Knöss,Markus Kreßmannroblem at hand. The obtained bounds establish the relation between the complexity of the autoregressive process and the attainable estimation accuracy through the use of a novel measure of complexity. This measure is suggested herein as a substitute to the generally incomputable restricted isometric property.

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