By Saeed V. Vaseghi(auth.)
Electronic sign processing performs a valuable function within the improvement of recent conversation and data processing platforms. the speculation and alertness of sign processing is worried with the identity, modelling and utilisation of styles and constructions in a sign procedure. The remark indications are usually distorted, incomplete and noisy and accordingly noise aid, the removing of channel distortion, and substitute of misplaced samples are vital components of a sign processing process.
The fourth variation of Advanced electronic sign Processing and Noise Reduction updates and extends the chapters within the prior variation and comprises new chapters on MIMO platforms, Correlation and Eigen research and self sufficient part research. the big variety of issues lined during this ebook comprise Wiener filters, echo cancellation, channel equalisation, spectral estimation, detection and removing of impulsive and brief noise, interpolation of lacking info segments, speech enhancement and noise/interference in cellular verbal exchange environments. This ebook offers a coherent and established presentation of the idea and functions of statistical sign processing and noise relief tools.
new chapters on MIMO platforms, correlation and Eigen research and autonomous part research
entire assurance of complicated electronic sign processing and noise relief equipment for verbal exchange and knowledge processing platforms
Examples and purposes in sign and knowledge extraction from noisy facts
- Comprehensive yet obtainable assurance of sign processing idea together with likelihood types, Bayesian inference, hidden Markov versions, adaptive filters and Linear prediction types
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 information 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 creation (pages 1–33):
Chapter 2 Noise and Distortion (pages 35–50):
Chapter three info concept and chance versions (pages 51–105):
Chapter four Bayesian Inference (pages 107–146):
Chapter five Hidden Markov types (pages 147–172):
Chapter 6 Least sq. mistakes 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 important 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 alternative (pages 423–466):
Chapter 18 Multiple?Input Multiple?Output structures, autonomous part research (pages 467–490):
Chapter 19 sign Processing in cellular conversation (pages 491–508):
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Extra info for Advanced Digital Signal Processing and Noise Reduction, Fourth Edition
Digital compression techniques, such as MP3, can be used to compress a digital signal. When combined with error-control coding and efﬁcient A Review of Sampling and Quantisation (3) (4) (5) (6) (7) (8) (9) 25 digital modulation methods the required overall bandwidth is less than that of say an FM-modulated analogue signal of similar noise robustness and quality. There is a similar reduction in storage requirement. Power savings. Power saving depends on the compression rate and the modulation method.
At 8 bits per sample, A-law and u-law quantisation methods can achieve the equivalent quality of 13-bit uniform quantisation. 5 Summary This chapter began with a deﬁnition of signal, noise and information and provided a qualitative explanation of their relationship. A broad categorisation of the various signal processing methodologies was provided. We considered several key applications of digital signal processing in biomedical signal processing, adaptive noise reduction, channel equalisation, pattern classiﬁcation/recognition, audio signal coding, signal detection, spatial processing for directional reception of signals, Dolby noise reduction, radar and watermarking.
20 Illustration of a radar system. 20) This shift in frequency is known as the Doppler frequency. If the object is moving towards the radar then the distance r(t) is decreasing with time, r˙ is negative, and an increase in the frequency is observed. Conversely if the object is moving away from the radar then the distance r(t) is increasing, r˙ is positive, and a decrease in the frequency is observed. Thus the frequency analysis of the reﬂected signal can reveal information on the direction and speed of the object.
Advanced Digital Signal Processing and Noise Reduction, Fourth Edition by Saeed V. Vaseghi(auth.)