Titlebook: Compressed Sensing in Information Processing; Gitta Kutyniok,Holger Rauhut,Robert J. Kunsch Book 2022 The Editor(s) (if applicable) and Th

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Recovery Under Side Constraints,lexity. Finally, we address the measurement system design for linear and nonlinear measurements of sparse signals. To this end, we derive a new linear mixing matrix design based on coherence minimization. Then, we extend our focus to nonlinear measurement systems where we design parallel optimizatio

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Fast Radio Propagation Prediction with Deep Learning, how to estimate the pathloss function, given an urban environment, with comparable accuracy to ray-tracing methods, but orders of magnitude faster. The radio map estimation problem is seen as an image-to-image task, in which the map of the city and additional domain information are transformed to t

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Atmospheric Radar Imaging Improvements Using Compressed Sensing and MIMO,ave also used MIMO on simple ARI application, i.e., a single scattering target on a given range and frequency, in combination with compressed sensing to develop a novel multistatic radar system called SIMONe to study the mesosphere and lower thermosphere dynamics with unprecedented temporal and spat

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Sparse Recovery of Sound Fields Using Measurements from Moving Microphones,mates according to the compressed-sensing paradigm. The framework is based on a sound-field representation by notional grid points in space, and dynamic samples are interpreted as the result of bandlimited interpolation on that grid using sampled sinc-function approximations. This procedure leads to

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Compressed Sensing in the Spherical Near-Field to Far-Field Transformation,spherical near field, which are spherical harmonics and Wigner D-functions. Still, the reduction of measurement points does not automatically guarantee a reduction in measurement time, since considerations on the mechanical speed at which the data points can be acquired come into play. For this reas

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