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 Jeff Fessler - University of Michigan
Course objectives: 1. to teach students the concepts of discrete-time signals, including mathematical representations; 2. to teach students the concepts of linear time-invariant discrete-time systems; 3. to introduce the concepts of filter design.
by Fausto Pedro García Marquez - InTech
Digital filters are the most versatile, practical and effective methods for extracting the information necessary from the signal. This book presents the most advanced digital filters including different case studies and the most relevant literature.
by Paolo Prandoni, Martin Vetterli - EFPL Press
The book is less focused on the mathematics and more on the concepts, allowing students to think about the subject at a higher conceptual level, thus building the foundations for more advanced topics and helping students solve real-world problems.
by Sophocles J. Orfanidis
In this edition the emphasis is on real-time adaptive signal processing, eigenvector methods of spectrum estimation, and parallel processor implementations of optimum filtering and prediction algorithms, and including several new developments.