Advanced Digital Signal Processing and Noise Reduction by Saeed V. Vaseghi

By Saeed V. Vaseghi

Sign processing performs an more and more important position within the improvement of recent telecommunication and data processing platforms, with quite a lot of purposes in parts akin to multimedia expertise, audio-visual sign processing, mobile cellular conversation, radar structures and monetary information forecasting. the idea and alertness of sign processing bargains with the id, modelling and utilisation of styles and constructions in a sign technique. The remark signs are usually distorted, incomplete and noisy and accordingly, noise relief and the elimination of channel distortion is a crucial a part of a sign processing procedure. complex electronic sign Processing and Noise aid, 3rd variation, offers an absolutely up to date and based presentation of the speculation and functions of statistical sign processing and noise aid tools. Noise is the everlasting bane of communications engineers, who're continuously striving to discover new how you can enhance the signal-to-noise ratio in communications structures and this source can assist them with this activity.

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Extra resources for Advanced Digital Signal Processing and Noise Reduction

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The term shot noise arose from the analysis of random variations in the emission of electrons from the cathode of a vacuum tube. Discrete electron particles in a current flow arrive at random times, and therefore there will be fluctuations about the average particle flow. The fluctuations in the rate of particle flow constitute the shot noise. Other instances of shot noise arise in the flow of photons in a laser beam, the flow and recombination of electrons and holes in semiconductors, and the flow of photoelectrons emitted in photodiodes.

Chapter 4 describes the Bayesian inference methodology and the estimation of random processes observed in noise. 4 NEURAL NETWORKS Neural networks are combinations of relatively simple nonlinear adaptive processing units, arranged to have a structural resemblance to the transmission and processing of signals in biological neurons. In a neural network several layers of parallel processing elements are interconnected by a hierarchically structured connection network. The connection weights are trained to perform a signal processing function such as prediction or classification.

SIGNAL PROCESSING METHODS 3 In the past few decades, the theory and applications of digital signal processing have evolved to play a central role in the development of modern telecommunication and information technology systems. Signal processing methods are central to efficient communication, and to the development of intelligent man–machine interfaces in areas such as speech and visual pattern recognition for multimedia systems. In general, digital signal processing is concerned with two broad areas of information theory: (1) efficient and reliable coding, transmission, reception, storage and representation of signals in communication systems; and (2) extraction of information from noisy signals for pattern recognition, detection, forecasting, decision-making, signal enhancement, control, automation, etc.

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