By Stergios Stergiopoulos
Advances in electronic sign processing algorithms and laptop know-how have mixed to provide real-time structures with functions a long way past these of simply few years in the past. Nonlinear, adaptive tools for sign processing have emerged to supply larger array achieve functionality, although, they lack the robustness of traditional algorithms. The problem continues to be to strengthen an idea that exploits the benefits of both-a scheme that integrates those equipment in sensible, real-time systems.
The complex sign Processing guide is helping you meet that problem. past delivering an excellent creation to the foundations and functions of complicated sign processing, it develops a frequent processing constitution that takes benefit of the similarities that exist between radar, sonar, and clinical imaging structures and integrates traditional and nonlinear processing schemes.
Read Online or Download Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real Time Systems (Electrical Engineering & Applied Signal Processing Series) PDF
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Advances in electronic sign processing algorithms and desktop expertise have mixed to supply real-time structures with services a ways past these of simply few years in the past. Nonlinear, adaptive equipment for sign processing have emerged to supply greater array achieve functionality, although, they lack the robustness of traditional algorithms.
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Additional info for Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real Time Systems (Electrical Engineering & Applied Signal Processing Series)
D. Kundur and D. Hatzinakos, Blind image deconvolution, Signal Processing Magazine, 13, 43–64, May 1996. 40. , Chelmsford, MA, 1998. 41. S. Stergiopoulos, R. Alterson, D. Havelock, and J. Grodski, Acoustic Tomography Methods for 3D Imaging of Shallow Buried Objects, 139th Meeting of the Acoustical Society of America, Atlanta, GA, May 2000. 1 The Filtering Problem The term “filter” is often used to describe a device in the form of a piece of physical hardware or software that is applied to a set of noisy data in order to extract information about a prescribed quantity of interest.
Synaptic weights and bias levels) of a neural network are adapted through a continuing process of stimulation by the environment in which the network is embedded. The type of learning is determined by the manner in which the parameter changes take place. Specifically, learning machines may be classified as follows: • Learning with a teacher, also referred to as supervised learning • Learning without a teacher This second class of learning machines may also be subdivided into • Reinforcement learning • Unsupervised learning or self-organized learning In the subsequent sections of this chapter, we will describe the important aspects of these learning machines and highlight the algorithms involved in their designs.
2 assume the use of complex signals. The adaptive filtering algorithm so described is said to be in complex form. The important virtue of complex adaptive filters is that they preserve the mathematical formulation and elegant structure of complex signals encountered in the aforementioned areas of application. If the signals to be processed are real, we naturally use the real form of the adaptive filtering algorithm of interest. Given the complex form of an adaptive filtering algorithm, it is straightforward to deduce the corresponding real form of the algorithm.
Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real Time Systems (Electrical Engineering & Applied Signal Processing Series) by Stergios Stergiopoulos