By Stergios Stergiopoulos
Advances in electronic sign processing algorithms and computing device know-how have mixed to supply real-time platforms with features 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, even though, they lack the robustness of traditional algorithms. The problem continues to be to enhance an idea that exploits some great benefits of both-a scheme that integrates those tools in functional, real-time systems.The complicated sign Processing guide is helping you meet that problem. past supplying a superb creation to the foundations and purposes of complicated sign processing, it develops a normal processing constitution that takes benefit of the similarities that exist between radar, sonar, and scientific imaging structures and integrates traditional and nonlinear processing schemes.
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Additional resources for Advanced signal processing handbook: theory and implementation for radar, sonar, and medical imaging real-time systems
As mentioned previously, we refer to this paraboloid as the error-performance surface; the tap weights corresponding to the minimum point of the surface define the optimum Wiener solution. To develop a recursive algorithm for updating the tap weights of the adaptive transversal filter, we proceed in two stages. , the matrix equation defining the optimum Wiener solution) through the use of the method of steepest descent, a well-known technique in * In the general definition of a function, we speak of a transformation from a vector space into the space of real (or complex) scalars (Luenberger, 1969; Dorny, 1975).
S. M. Henkelman, K-space description for the imaging of dynamic objects, Magn. Reson. , 29, 422–428, 1993. 13. L. W. Holdsworth, R. K. Rutt, Effects of physiologic waveform variability in triggered MR imaging: theoretical analysis, J. Magn. Reson. Imaging, 4(6), 853–867, 1994. 14. J. R. D. F. King, and Y. Kim, Correction of computed tomography motion artifacts using pixel-specific back-projection, IEEE Trans. Medical Imaging, 15(3), 333–342, 1996. 15. S. Stergiopoulos, Implementation of adaptive and synthetic-aperture processing schemes in integrated active-passive sonar systems, Proc.
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.