Download Cognitive Networked Sensing and Big Data by Robert Qiu, Michael Wicks PDF

By Robert Qiu, Michael Wicks

Cognitive Networked Sensing and massive info defines high-dimensional information processing within the context of instant disbursed computing and cognitive sensing. This publication provides the demanding situations which are designated to this zone resembling synchronization because of the excessive mobility of the nodes. The authors talk about the combination of software program outlined radio implementation and testbed improvement. The e-book additionally bridges new examine effects and contextual studies. also, the authors offer an exam of huge cognitive radio community; testbed; disbursed sensing; and dispensed computing.

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Extra info for Cognitive Networked Sensing and Big Data

Example text

8 Random Vector and Jensen’s Inequality T A random vector x = (X1 , . . , Xn ) ∈ Rn is a collection of n random variables Xi on a common probability space. Its expectation is the vector T Ex = (EX1 , . . , EXn ) ∈ Rn . 30 1 Mathematical Foundation A complex random vector z = x + jy ∈ Cn is a special case of a 2n-dimensional real random vector (x, y) ∈ R2n . A collection of random vectors x1 , . . , xN ∈ Cn is called (stochastic ally) independent if for all measurable subsets A1 , . . , AN ⊂ Cn , P (x1 ∈ A1 , .

If f1 and f2 are both convex, then so is their sum f1 + f2 . Combining nonnegative scaling and addition [48, p. 78) is convex. Similarly, a nonnegative weighted sum of concave functions is concave. A linear function is of course convex. Let Sn×n stand for the set of the n × n symmetric matrix. Any linear function f : Sn×n → R can be represented in the form f (X) = Tr (CX) , C ∈ Sn×n . 20 Lieb’s Theorem Lieb’s Theorem is the foundation for studying the sum of random matrices in Chap. 2. We present a succinct proof this theorem, following the arguments of Tropp [49].

72) This formula determines the logarithm on the positive definite cone. 5]. 73) where 0 denotes the zero matrix whose entries are all zeros. 74) for all positive definite A, H and τ ∈ [0, 1]. Operator monotone functions and operator convex functions are depressingly rare. In particular, the matrix exponential does not belong to either class [23, Chap. V]. Fortunately, the trace inequalities of a matrix-valued function can be used as limited substitute. For a survey, see [40]which is very accessible.

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