Stergios Stergiopoulos's Advanced signal processing handbook : theory and PDF

Imaging Systems

By Stergios Stergiopoulos

ISBN-10: 0849336910

ISBN-13: 9780849336911

Advances in electronic sign processing algorithms and desktop know-how have mixed to supply real-time structures with functions some distance past these of simply few years in the past. Nonlinear, adaptive tools for sign processing have emerged to supply greater array achieve functionality, notwithstanding, they lack the robustness of traditional algorithms. The problem is still to increase an idea that exploits some great benefits of both-a scheme that integrates those tools in sensible, real-time systems.

The complicated sign Processing guide is helping you meet that problem. past delivering an exceptional advent to the foundations and functions of complex sign processing, it develops a popular processing constitution that takes good thing about the similarities that exist between radar, sonar, and scientific imaging structures and integrates traditional and nonlinear processing schemes

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Sample text

A systolic array architecture, as described herein, offers the desirable features of modularity, local interconnections, and highly pipelined and synchronized parallel processing; the synchronization is achieved by means of a global clock. 4 have a common property: all three of * The systolic array was pioneered by Kung and Leiserson (1978). In particular, the use of systolic arrays has made it possible to achieve a high throughput, which is required for many advanced signal-processing algorithms to operate in real time.

An introductory discussion of linear adaptive filters would be incomplete without saying something about their tracking behavior. In this context, we note that stochastic gradient algorithms such as the LMS algorithm are model independent; generally speaking, we would expect them to exhibit good tracking behavior, which indeed they do. In contrast, RLS algorithms are model dependent; this, in turn, means that their tracking behavior may be inferior to that of a member of the stochastic gradient family, unless care is taken to minimize the mismatch between the mathematical model on which they are based and the underlying physical process responsible for generating the input data.

Moreover, non-Gaussian processes are quite common in many signal processing applications encountered in practice. The use of a Wiener filter or a linear adaptive filter to extract signals of interest in the presence of such non-Gaussian processes will therefore yield suboptimal solutions. We may overcome this limitation by incorporating some form of nonlinearity in the structure of the adaptive filter to take care of higher order statistics. Although, by so doing, we no longer have the Wiener filter as a frame of reference and so complicate the mathematical analysis, we would expect to benefit in two significant ways: improving learning efficiency and a broadening of application areas.

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Advanced signal processing handbook : theory and implementation for radar, sonar, and medical imaging real time systems by Stergios Stergiopoulos

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