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

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Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks,. As a key technology, spectrum sensing enables cognitive radios to find spectrum holes and improve spectral utilization efficiency. To exploit more spectral opportunities, wideband spectrum sensing approaches should be adopted to search multiple frequency bands at a time. However, wideband spectrum

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,Estimation of Time-Varying Sparse Signals in?Sensor Networks,ch time interval, the fusion center transmits the predicted signal estimate and its corresponding error covariance to a selected subset of sensors. The selected sensors compute quantized innovations and transmit them to the fusion center. We consider the situation where the signal is sparse, i.e., a

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Sparsity and Compressed Sensing in Mono-Static and Multi-Static Radar Imaging,Rs). We provide a brief overview of how sparsity-driven imaging has recently been used in various radar imaging scenarios. We then focus on the problem of imaging from undersampled data, and point to recent work on the exploitation of compressed sensing theory in the context of radar imaging. We con

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Structured Sparse Bayesian Modelling for Audio Restoration,an example, a model to remove impulse and background noise from audio signals via their representation in time-frequency space using Gabor wavelets is presented. A number of prior models for the sparse structure of the signal in this space are introduced, including simple Bernoulli priors on each co