By Saeed V. Vaseghi(auth.)
Electronic sign processing performs a primary function within the improvement of recent conversation and data processing structures. the speculation and alertness of sign processing is anxious with the id, modelling and utilisation of styles and constructions in a sign method. The commentary signs are frequently distorted, incomplete and noisy and for that reason noise aid, the elimination of channel distortion, and substitute of misplaced samples are vital elements of a sign processing procedure.
The fourth version of Advanced electronic sign Processing and Noise Reduction updates and extends the chapters within the prior variation and contains new chapters on MIMO platforms, Correlation and Eigen research and self sufficient part research. the big variety of issues lined during this e-book comprise Wiener filters, echo cancellation, channel equalisation, spectral estimation, detection and elimination of impulsive and temporary noise, interpolation of lacking info segments, speech enhancement and noise/interference in cellular conversation environments. This e-book offers a coherent and dependent presentation of the idea and functions of statistical sign processing and noise aid tools.
new chapters on MIMO platforms, correlation and Eigen research and autonomous part research
finished assurance of complicated electronic sign processing and noise aid tools for conversation and knowledge processing structures
Examples and functions in sign and data extraction from noisy info
- Comprehensive yet obtainable assurance of sign processing thought together with likelihood versions, Bayesian inference, hidden Markov versions, adaptive filters and Linear prediction versions
Advanced electronic sign Processing and Noise Reduction is a useful textual content for postgraduates, senior undergraduates and researchers within the fields of electronic sign processing, telecommunications and statistical facts research. it is going to even be of curiosity to specialist engineers in telecommunications and audio and sign processing industries and community planners and implementers in cellular and instant communique communities.Content:
Chapter 1 creation (pages 1–33):
Chapter 2 Noise and Distortion (pages 35–50):
Chapter three details thought and likelihood versions (pages 51–105):
Chapter four Bayesian Inference (pages 107–146):
Chapter five Hidden Markov types (pages 147–172):
Chapter 6 Least sq. errors Wiener?Kolmogorov Filters (pages 173–191):
Chapter 7 Adaptive Filters: Kalman, RLS, LMS (pages 193–225):
Chapter eight Linear Prediction versions (pages 227–255):
Chapter nine Eigenvalue research and primary part research (pages 257–270):
Chapter 10 energy Spectrum research (pages 271–294):
Chapter eleven Interpolation – alternative of misplaced Samples (pages 295–320):
Chapter 12 sign Enhancement through Spectral Amplitude Estimation (pages 321–339):
Chapter thirteen Impulsive Noise: Modelling, Detection and elimination (pages 341–358):
Chapter 14 brief Noise Pulses (pages 359–369):
Chapter 15 Echo Cancellation (pages 371–390):
Chapter sixteen Channel Equalisation and Blind Deconvolution (pages 391–421):
Chapter 17 Speech Enhancement: Noise aid, Bandwidth Extension and Packet substitute (pages 423–466):
Chapter 18 Multiple?Input Multiple?Output platforms, self sufficient part research (pages 467–490):
Chapter 19 sign Processing in cellular conversation (pages 491–508):
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Additional info for Advanced Digital Signal Processing and Noise Reduction, Fourth Edition
ICA is also used for beam forming in multiple-input multiple-output (MIMO) telecommunication. The ICA problem is formulated as follows. The observed signal vector x is assumed to be a linear mixture of M independent source signals s. 3) 10 Introduction The matrix A is known as the mixing matrix or the observation matrix. In many practical cases of interest all we have is the sequence of observation vectors [x(0), x(1), . . , x(N − 1)]. The mixing matrix A is unknown and we wish to estimate a demixing matrix W to obtain an estimate of the original signal s.
The higher frequencies that convey quality and sensation Applications of Digital Signal Processing 21 have relatively low energy, and can be degraded even by a low amount of noise. For example when a signal is recorded on a magnetic tape, the tape ‘hiss’ noise affects the quality of the recorded signal. On playback, the higher-frequency parts of an audio signal recorded on a tape have smaller signal-to-noise ratio than the low-frequency parts. Therefore noise at high frequencies is more audible and less masked by the signal energy.
The term linear array implies that the array of sensors is spatially arranged in a straight line and with equal spacing d between the sensors. 18. 20 Introduction The array of sensors samples the incoming wave as it propagates in space. 14) c where c is the speed of propagation of the wave in the medium. 15) T0 c τ= where T0 is the period of the sine wave. By inserting appropriate corrective time delays in the path of the samples at each sensor, and then averaging the outputs of the sensors, the signals arriving from the direction θ will be time-aligned and coherently combined, whereas those arriving from other directions will suffer cancellations and attenuations.
Advanced Digital Signal Processing and Noise Reduction, Fourth Edition by Saeed V. Vaseghi(auth.)