By Stergios Stergiopoulos
Advances in electronic sign processing algorithms and computing device expertise have mixed to supply real-time structures with functions some distance past these of simply few years in the past. Nonlinear, adaptive equipment 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 instruction manual is helping you meet that problem. past delivering a superb advent to the foundations and purposes of complex sign processing, it develops a prevalent 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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Additional resources for Advanced Signal Processing Handbook Theory And Implementation For Radar, Sonar, And Medical Imaging Real-Time
Smoothing differs from filtering in that information about the quantity of interest need not be available at time t, and data measured later than time t can be used in obtaining this information. This means that in the case of smoothing there is a delay in producing the result of interest. Since * The material presented in this chapter is based on the author’s two textbooks: (1) Adaptive Filter Theory (1996) and (2) Neural Networks: A Comprehensive Foundation (1999), Prentice-Hall, Englewood Cliffs, NJ.
In the context of adaptive signal-processing applications, neural networks offer the following advantages: • Nonlinearity, which makes it possible to account for the nonlinear behavior of physical phenomena responsible for generating the input data • The ability to approximate any prescribed input-output mapping of a continuous nature • Weak statistical assumptions about the environment, in which the network is embedded • Learning capability, which is accomplished by undertaking a training session with input-output examples that are representative of the environment • Generalization, which refers to the ability of the neural network to provide a satisfactory performance in response to test data never seen by the network before • Fault tolerance, which means that the network continues to provide an acceptable performance despite the failure of some neurons in the network • VLSI implementability, which exploits the massive parallelism built into the design of a neural network ©2001 CRC Press LLC This is indeed an impressive list of attributes, which accounts for the widespread interest in the use of neural networks to solve signal-processing tasks that are too difficult for conventional (linear) adaptive filters.
10. C. S. D. E. Trahey, A comparative evaluation of several algorithms for phase aberration correction, IEEE Trans. Ultrasonics, Ferroelectrics Frequency Control, 41(5), 631–643, 1994. 11. K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, Englewood Cliffs, NJ, 1990. 12. 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.
Advanced Signal Processing Handbook Theory And Implementation For Radar, Sonar, And Medical Imaging Real-Time by Stergios Stergiopoulos