By Saeed V. Vaseghi(auth.)
Electronic sign processing performs a crucial function within the improvement of recent communique and data processing structures. the idea and alertness of sign processing is worried with the identity, modelling and utilisation of styles and constructions in a sign procedure. The statement signs are frequently distorted, incomplete and noisy and as a result noise relief, the removing of channel distortion, and substitute of misplaced samples are vital elements of a sign processing process.
The fourth variation of Advanced electronic sign Processing and Noise Reduction updates and extends the chapters within the past variation and comprises new chapters on MIMO structures, Correlation and Eigen research and autonomous part research. the big variety of themes lined during this e-book comprise Wiener filters, echo cancellation, channel equalisation, spectral estimation, detection and elimination of impulsive and brief noise, interpolation of lacking facts segments, speech enhancement and noise/interference in cellular communique environments. This booklet offers a coherent and dependent presentation of the speculation and functions of statistical sign processing and noise relief equipment.
new chapters on MIMO platforms, correlation and Eigen research and self sustaining part research
accomplished assurance of complicated electronic sign processing and noise relief tools for communique and knowledge processing platforms
Examples and functions in sign and data extraction from noisy information
- Comprehensive yet available insurance of sign processing idea together with likelihood versions, Bayesian inference, hidden Markov types, 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 conversation communities.Content:
Chapter 1 advent (pages 1–33):
Chapter 2 Noise and Distortion (pages 35–50):
Chapter three info conception and chance types (pages 51–105):
Chapter four Bayesian Inference (pages 107–146):
Chapter five Hidden Markov versions (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 types (pages 227–255):
Chapter nine Eigenvalue research and relevant 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 removing (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 resources for Advanced Digital Signal Processing and Noise Reduction, Fourth Edition
22) where m is the discrete-time index. 21 illustrates a discrete-time signal and its continuous-time envelope. Many signals such as speech, music, image, video, radar, sonar and bio-signals and medical signals are analogue, in that in their original form they appear to vary continuously with time (and/or space) and are sensed by analogue sensors such as microphones, optical devices and antennas. Other signals such as stock market prices are inherently discrete-time and/or discrete amplitude signals.
21 illustrates a discrete-time signal and its continuous-time envelope. Many signals such as speech, music, image, video, radar, sonar and bio-signals and medical signals are analogue, in that in their original form they appear to vary continuously with time (and/or space) and are sensed by analogue sensors such as microphones, optical devices and antennas. Other signals such as stock market prices are inherently discrete-time and/or discrete amplitude signals. Continuous signals are termed analogue signals because their ﬂuctuations with time are analogous to the variations of the signal source.
In a neural network several layers of parallel processing elements are interconnected with a hierarchically structured connection network. The connection weights are trained to ‘memorise patterns’ and perform a signal processing function such as prediction or classiﬁcation. Neural networks are particularly useful in non-linear partitioning of a signal space, in feature extraction and pattern recognition, and in decision-making systems. In some hybrid pattern recognition systems neural networks are used to complement Bayesian inference methods.
Advanced Digital Signal Processing and Noise Reduction, Fourth Edition by Saeed V. Vaseghi(auth.)