Data in the Enterprise, Circuits, Digital Design, Technology, Computer Science 

"This book gives readers an intuitive appreciation for randomfunctions, plus theory and processes necessary for sophisticatedapplications. It covers probability theory, random processes,canonical representation, optimal filtering, and random models.Second in the SPIE/IEEE Series on Imaging Science &Engineering.It also presents theory along with applications, to help readersintuitively appreciate random functions.Included are special cases in which probabilistic insight is morereadily achievable. When provided, proofs are in the main body ofthe text and clearly delineated; sometimes they are either notprovided or outlines of conceptual arguments are given. The intentis to state theorems carefully and to draw clear distinctionsbetween rigorous mathematical arguments and heuristic explanations.When a proof can be given at a mathematical level commensurate withthe text and when it enhances conceptual understanding, it isusually provided; in other cases, the effort is to explainsubtleties of the definitions and properties concerning randomfunctions, and to state conditions under which a propositionapplies. Attention is drawn to the differences betweendeterministic concepts and their random counterparts, for instance,in the mean-square calculus, orthonormal representation, and linearfiltering. Such differences are sometimes glossed over in methodbooks; however, lack of differentiation between random anddeterministic analysis can lead to misinterpretation ofexperimental results and misuse of techniques.The author's motivation for the book comes from his experience inteaching graduate-level image processing and having to end upteaching random processes. Even students who have taken a course onrandom processes have often done so in the context of linearoperators on signals. This approach is inadequate for imageprocessing. Nonlinear operators play a widening role in imageprocessing, and the spatial nature of imaging makes itsignificantly different from one-dimensional signal processing.Moreover, students who have some background in stochastic processesoften lack a unified view in terms of canonical representation andorthogonal projections in inner product spaces." - from Amzon