By Stergios Stergiopoulos

Advances in electronic sign processing algorithms and machine expertise have mixed to supply real-time platforms with functions a long way past these of simply few years in the past. Nonlinear, adaptive equipment for sign processing have emerged to supply greater array achieve functionality, even though, they lack the robustness of traditional algorithms. The problem is still to improve an idea that exploits some great benefits of both-a scheme that integrates those equipment in useful, real-time systems.

The complex sign Processing guide is helping you meet that problem. past delivering an excellent advent to the rules and purposes of complicated sign processing, it develops a well-known processing constitution that takes benefit of the similarities that exist between radar, sonar, and clinical imaging structures and integrates traditional and nonlinear processing schemes.

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Extra info for Advanced Signal Processing Handbook Theory And Implementation For Radar, Sonar, And Medical Imaging Real-Time

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Punch bowl) with a uniquely defined bottom or minimum point. 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).

6. J. Scudder, Introduction to computer aided tomography, Proc. IEEE, 66(6), 628–637, 1978. 7. C. Kak and M. Slaney, Principles of Computerized Tomography Imaging, IEEE Press, New York, 1992. 8. D. C. Kak, Ultrasonic Diffraction Imaging, TR-EE 82–80, Department of Electrical Engineering, Purdue University, West Lafayette, IN, August 1982. 9. W. Flax and M. O’Donnell, Phase-aberration correction using signals from point reflectors and diffuse scatterers: basic principles, IEEE Trans. Ultrasonics, Ferroelectrics Frequency Control, 35(6), 758–767, 1988.

The stochastic gradient approach may also be pursued in the context of a lattice structure. The resulting adaptive filtering algorithm is called the gradient adaptive lattice (GAL) algorithm. In their own individual ways, the LMS and GAL algorithms are just two members of the stochastic gradient family of linear adaptive filters, although it must be said that the LMS algorithm is by far the most popular member of this family. 2 Least-Squares Estimation The second approach to the development of linear adaptive filtering algorithms is based on the method of least squares.

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