Hidden Markov Models: Estimation and Control
by R. J. Elliott, L. Aggoun, J. B. Moore
Publisher: Springer 1995
Number of pages: 373
The aim of this book is to present graduate students with a thorough survey of reference probability models and their applications to optimal estimation and control. These new and powerful methods are particularly useful in signal processing applications where signal models are only partially known and are in noisy environments.
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by Sophocles J. Orfanidis - Prentice Hall
An applications-oriented introduction to digital signal processing. The author covers all the basic DSP concepts, such as sampling, DFT/FFT algorithms, etc. The book emphasizes the algorithmic, computational, and programming aspects of DSP.
by Brad Osgood - Stanford University
This text is appropriate for science and engineering students. Topics include: Periodicity and Fourier series; The Fourier transform and its basic properties; Convolution and its applications; Distributions and their Fourier transforms; etc.
by William A. Gardner - McGraw-Hill
A first course on random processes for graduate engineering and science students, particularly those with an interest in the analysis and design of signals and systems. The book includes detailed coverage of minimum-mean-squared-error estimation.
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The book about the mathematical basis of signal processing and its many areas of application for graduate students. The text emphasizes current challenges, new techniques adapted to new technologies, and recent advances in algorithms and theory.