Titlebook: Compressed Sensing and its Applications; MATHEON Workshop 201 Holger Boche,Robert Calderbank,Jan Vybíral Book 2015 Springer International P

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Sparse Model Uncertainties in Compressed Sensing with Application to Convolutions and Sporadic Commstable low-dimensional embeddings of the uncalibrated receive signals whereby addressing also efficient communication-oriented methods like . random demodulation. Exemplarily, we investigate in more detail sparse convolutions serving as a basic communication channel model. In using some recent resul

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https://doi.org/10.1007/978-3-8349-6646-9orm, mixed ../.. norm, etc.). While the LASSO algorithm has been around for 20 years and has enjoyed great success in practice, there has been relatively little analysis?of its performance. In this chapter, we will provide a full performance analysis and compute, in closed form, the mean-square-erro

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Führungskontext in China versteheny. Beyond NAH, this chapter shows how compressive sensing is being applied to other acoustic scenarios such as active sonar, sampling of the plenacoustic function, medical ultrasound imaging, localization of directive sources, and interpolation of plate vibration response.

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Strategisches Technologiemanagementamework for compressed sensing of higher-order tensors which preserves the intrinsic structure of tensorial data with reduced computational complexity at reconstruction. We demonstrate that GTCS offers an efficient means for representation of multidimensional data by providing simultaneous acquisiti