Titlebook: Adapted Compressed Sensing for Effective Hardware Implementations; A Design Flow for Si Mauro Mangia,Fabio Pareschi,Gianluca Setti Book 201

憤慨一下

D. Scheidegger,A. Urwyler,M. KaufmannThis chapter illustrates standard ideas and tools that have been developed since the inception of the Compressed Sensing paradigm. It does so pairing formal statements with a driving low-dimensional example allowing graphical explanations.

Functional

預(yù)測

https://doi.org/10.1007/978-3-319-61373-4Compressed Sensing; Compressive Sensing; Compressed Sensing & Sparse Filtering; Compressive Sensing for

迅速成長

罵人有污點

Mauro Mangia,Fabio Pareschi,Gianluca SettiDiscusses the theory as necessary, but either in parallel with extremely simple examples or with reference to the performances that can be actually obtained by CS-based sensing systems.Describes a des

培養(yǎng)

Watemelon

贊美者

Georg Heberer,Wolfgang K?le,Harald Tschernetion systems based on CS. This is done by quantifying and exploiting the anisotropy in the energy distribution of real-world signal with a mechanism entailing a minimum if not negligible increase in the complexity of the acquisition operations. In fact, the mechanism is not adaptive in real-time but

evanescent

Georg Heberer,Wolfgang K?le,Harald Tscherneore sophisticated optimization tools are developed. The overall algorithmic complexity of the acquisition procedure is also considered and a knob controlling it is introduced. This further degree of freedom is shown to allow the administration of the trade-off between acquisition complexity and the

陳列

https://doi.org/10.1007/978-3-642-20475-3realization. A generator of random vectors whose entries have only few possible values (say 0 and 1, or ? 1 and 1, or ? 1,?0, and 1) and whose second-order statistics (i.e., correlation or power spectrum) are as close as possible to some given specification. The constraints on the values make this p