Advanced Signal Processing and Digital Noise Reduction

Saeed V. Vaseghi

ISBN 10: 3322927741 / ISBN 13: 9783322927743

Published by Vieweg+Teubner Verlag, 2012

About this Item

reprint edition. 410 pages. German language. 9.61x6.69x0.94 inches. In Stock.

Seller Inventory # x-3322927741

Bibliographic Details

• Title: Advanced Signal Processing and Digital Noise Reduction
• Publisher: Vieweg+Teubner Verlag
• Publication Date: 2012
• Language: German
• ISBN 10: 3322927741
• ISBN 13: 9783322927743
• Binding: Paperback
• Condition: Brand New

About this title

Synopsis

Stochastic signal processing plays a central role in telecommunication and information processing systems, and has a wide range of applications in speech technology, audio signal processing, channel equalisation, radar signal processing, pattern analysis, data forecasting, decision making systems etc. The theory and application of signal processing is concerned with the identification, modelling, and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy. Hence, noise reduction and the removal of channel distortions is an important part of a signal processing system. The aim of this book is to provide a coherent and structured presentation of the theory and applications of stochastic signal processing and noise reduction methods. This book is organised in fourteen chapters. Chapter 1 begins with an introduction to signal processing, and provides a brief review of the signal processing methodologies and applications. The basic operations of sampling and quantisation are reviewed in this chapter. Chapter 2 provides an introduction to the theory and applications of stochastic signal processing. The chapter begins with an introduction to random signals, stochastic processes, probabilistic models and statistical measures. The concepts of stationary, non-stationary and ergodic processes are introduced in this chapter, and some important classes of random processes such as Gaussian, mixture Gaussian, Markov chains, and Poisson processes are considered. The effects of transformation of a signal on its distribution are considered.