Advanced Signal Processing and Digital Noise Reduction

Name: Advanced Signal Processing and Digital Noise Reduction
Brand: Vieweg+teubner Verlag
SKU: 9783322927743
Price: 254.87 PLN
Availability: InStock

Autor: Saeed V. Vaseghi

Język:

Oprawa: Miękka

Wydawca: Vieweg+teubner Verlag

Dostępność: Dostępna u dostawcy

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Stochastic signal processing plays a central role in telecommunication and information processing sy...

Informacje o książce

Autor

Saeed V. Vaseghi

Język

Niemiecki

Oprawa

Książka - Miękka

Data wydania

2012

strony

397

EAN

9783322927743

ISBN

3322927741

Enbook ID

02723813

Wydawca

Vieweg+teubner Verlag

Waga

658

Wymiary

170 x 244 x 22

Pełny opis

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.

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