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Digital Communications (Rating: 3.57 - 2144 votes)

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Title:Digital Communications
Format Type:Ebook
Author:
Publisher:Wiley
ISBN:0471629472
ISBN 13:
Number of Pages:624
Category:Manga

Digital Communications by Simon Haykin

PDF, EPUB, MOBI, TXT, DOC Digital Communications Offers the most complete up to date coverage available on the principles of digital communications Focuses on basic issues relating theory to practice wherever possible Numerous examples worked out in detail have been included to help the reader develop an intuitive grasp of the theory Topics covered include the sampling process digital modulation techniques error control coding robust quantization for pulse code modulation coding speech at low bit radio information theoretic concepts coding and computer communication Because the book covers a broad range of topics in digital communications it should satisfy a variety of backgrounds and interests and offers a great deal of flexibility for teaching the course The author has included suggested course outlines for courses at the undergraduate or graduate levels

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Kalman Filtering and Neural Networks, Signals and Systems, Adaptive Filter Theory, Communication Systems, An Introduction to Analog and Digital Communications, Digital Communications, Neural Networks And Learning Machines, Least-Mean-Square Adaptive Filters, Neural Networks: A Comprehensive Foundation, Modern Wireless Communications
The second edition of this accessible book provides readers with an introductory treatment of communication theory as applied to the transmission of information bearing signals While it covers analog communications the emphasis is placed on digital technology It begins by presenting the functional blocks that constitute the transmitter and receiver of a communication system Readers will next learn about electrical noise and then progress to multiplexing and multiple access techniques, No description available, Introducing students to the many facets of neural networks this text provides many case studies to illustrate their real life practical applications, b State of the art coverage of Kalman filter methods for the design of neural networks b This self contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks Although the traditional approach to the subject is almost always linear this book recognizes and deals with the fact that real problems are most often nonlinear br br The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory Rauch Tung Striebel smoother and the extended Kalman filter Other chapters cover br br br An algorithm for the training of feedforward and recurrent multilayered perceptrons based on the decoupled extended Kalman filter DEKF Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes The dual estimation problem Stochastic nonlinear dynamics the expectation maximization EM algorithm and the extended Kalman smoothing EKS algorithm The unscented Kalman filter Each chapter with the exception of the introduction includes illustrative applications of the learning algorithms described here some of which involve the use of simulated and real life data Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems br br An Instructor s Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley Makerting Department