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

不易燃

破裂

閑逛

高調(diào)

Sums of Matrix-Valued Random Variables applications. Although powerful, the methods are elementary in nature. It is remarkable that some modern results on matrix completion can be simply derived, by using the framework of sums of matrix-valued random matrices.

HEED

使腐爛

Covariance Matrix Estimation in High Dimensionspports the idea that sparsity can be exploited for statistical estimation, too. The treatment of this subject is very superficial, due to the limited space. This chapter is mainly developed to support the detection theory in Chap. 10.

勤勞

Spinal-Fusion

磨碎

conifer

Compressed Sensing and Sparse Recoverysparse signal, the relevant “information” is much less that what we thought previously. As a result, to recover the sparse signal, the required samples are much less than what is required by the traditional Shannon’s sampling theorem.