Titlebook: Cognitive Networked Sensing and Big Data; Robert Qiu,Michael Wicks Book 2014 The Editor(s) (if applicable) and The Author(s), under exclus

柏樹

進(jìn)步

Hans Ulrich,Gilbert J. B. Probste the mathematics objects. Eigenvalues and their functionals may be shown to be Lipschitz functions so the Talagrand’s framework is sufficient. Concentration inequalities for many complicated random variables are also surveyed here from the latest publications. As a whole, we bring together concentr

bioavailability

Two Principles for Self-Organizatione in the sense of random matrices. The point of viewing this chapter as a novel statistical tool will have far-reaching impact on applications such as covariance matrix estimation, detection, compressed sensing, low-rank matrix recovery, etc. Two primary examples are: (1) approximation of covariance

珊瑚

https://doi.org/10.1007/978-3-642-69762-3 provide applications examples for the theory developed in Part I. We emphasize the central role of random matrices..Compressed sensing is a recent revolution. It is built upon the observation that sparsity plays a central role in the structure of a vector. The unexpected message here is that for a

moratorium

https://doi.org/10.1007/978-3-642-69762-3ssed sensing exploits the sparsity structure in a vector, while low-rank matrix recovery—Chap. 8—exploits the low-rank structure of a matrix: sparse in the vector composed of singular values. The theory ultimately traces back to concentration of measure due to high dimensions.

急性

Hans Ulrich,Gilbert J. B. Probster should be more basic than Chaps. 7 and 8—thus should be treated earlier chapters. Recent work on compressed sensing and low-rank matrix recovery supports the idea that sparsity can be exploited for statistical estimation, too. The treatment of this subject is very superficial, due to the limited

DIS

破譯

Book 2014nitive sensing. This book presents the challenges that are unique to this area such as synchronization caused by the high mobility of the nodes. The author will discuss the integration of software defined radio implementation and testbed development. The book will also bridge new research results an

有節(jié)制

velopment. The book will also bridge new research results and contextual reviews. Also the author provides an examination of large cognitive radio network; hardware testbed; distributed sensing; and distributed computing.978-1-4899-9726-5978-1-4614-4544-9

Jejune

tive radio networks for UAVs.Includes supplementary materialWireless Distributed Computing and Cognitive Sensing defines high-dimensional data processing in the context of wireless distributed computing and cognitive sensing. This book presents the challenges that are unique to this area such as syn