By Paulo S. R. Diniz
This booklet offers a finished evaluation of either the basics of wavelet research and comparable instruments, and of the main energetic fresh advancements in the direction of functions. It deals a cutting-edge in numerous energetic parts of study the place wavelet rules, or extra normally multiresolution principles have proved relatively potent. the most purposes lined are within the numerical research of PDEs, and sign and picture processing. lately brought options comparable to Empirical Mode Decomposition (EMD) and new traits within the restoration of lacking info, similar to compressed sensing, also are provided. purposes variety for the reconstruction of noisy or blurred photographs, trend and face attractiveness, to nonlinear approximation in strongly anisotropic contexts, and to the class instruments according to multifractal research
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Additional info for Adaptive filtering: algorithms and practical implementation
P. Strobach, Linear Prediction Theory, Springer Verlag, New York, NY, 1990. 14. B. Widrow and S. D. Stearns, Adaptive Signal Processing, Prentice Hall, Englewood Cliffs, NJ, 1985. 15. J. R. Treichler, C. R. , and M. G. Larimore, Theory and Design of Adaptive Filters, John Wiley & Sons, New York, NY, 1987. 16. B. Farhang-Boroujeny, Adaptive Filters: Theory and Applications, John Wiley & Sons, New York, NY, 1998. 17. S. Haykin, Adaptive Filter Theory, Prentice Hall, Englewood Cliffs, NJ, 4th edition, 2002.
A. 87) i=0 where x(k) = [x0 (k) x1 (k) . . xN (k)]T and w(k) = [w0 (k) w1 (k) . . wN (k)]T are the input signal and the adaptive-filter coefficient vectors, respectively. In many applications, each element of the input signal vector consists of a delayed version of the same signal, that is: x0 (k) = x(k), x1 (k) = x(k − 1), . . , xN (k) = x(k − N ). Note that in this case the signal y(k) is the result of applying an FIR filter to the input signal x(k). Since most of the analyses and algorithms presented in this book apply equally to the linear combiner and the FIR filter cases, we will mostly consider the latter case throughout the rest of the book.
W. Schaffer, Digital Processing of Speech Signals, Prentice Hall, Englewood Cliffs, NJ, 1978. 20. D. H. Johnson and D. E. Dudgeon, Array Signal Processing, Prentice Hall, Englewood Cliffs, NJ, 1993. 12 Chapter 1 Introduction to Adaptive Filtering 21. T. Kailath, Linear Systems, Prentice Hall, Englewood Cliffs, NJ, 1980. 22. D. G. Luenberger, Introduction to Linear and Nonlinear Programming, Addison Wesley, Reading, MA, 2nd edition, 1984. 23. A. -S. Lu, Practical Optimization: Algorithms and Engineering Applications, Springer, New York, NY, 2007.
Adaptive filtering: algorithms and practical implementation by Paulo S. R. Diniz