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Signal Processing Methods for Audio, Images and Telecommunications [Kõva köide]

Series edited by (The Engineering Practice), Edited by (The Ohio State University), Series edited by (University of Texas, Austin), Edited by (Illinois Institute of Technology), Series edited by (CRC for Sensor Signal and Information Processing, Signal Processing Research Institute, Technology Park)
  • Formaat: Hardback, 452 pages, kõrgus x laius: 229x152 mm, kaal: 830 g
  • Sari: Signal Processing and Its Applications
  • Ilmumisaeg: 02-Jun-1995
  • Kirjastus: Academic Press Inc
  • ISBN-10: 0121757900
  • ISBN-13: 9780121757908
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Signal Processing Methods for Audio, Images and TelecommunicationsSuurem pilt
  • Formaat: Hardback, 452 pages, kõrgus x laius: 229x152 mm, kaal: 830 g
  • Sari: Signal Processing and Its Applications
  • Ilmumisaeg: 02-Jun-1995
  • Kirjastus: Academic Press Inc
  • ISBN-10: 0121757900
  • ISBN-13: 9780121757908
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780121757908.html
Märksõnad:
In recent years, rapid advances in computer hardware technology, including the development of specialized digital signal processors, have facilitated the development of algorithms whose applications would have been unthinkable only a short time ago. These algorithms allow for real-time application, make use of prior knowledge, can adapt in response to a changing environment, and are designed to achieve near-optimum performance under a broad range of operating conditions. This book examines the application of such algorithms to audio, video, and telecommunications.
The book is divided into four parts: methods, applications to audio, video, and telecommunications. Topics covered include wavelet transforms, adaptive filter design, neural networks, order statistic filters and projection methods.
Each chapter has been written by a leading expert in the field. Signal Processing Methods for Audio, Images and Telecommunications will be of great interest to students, researchers, and engineers alike, in all areas of signal and image processing.

Key Features
* Wavelet transforms
* Adaptive filter design
* Neural networks
* Order statistic filters
* Projections methods

Arvustused

"This book can be of great value and interest to graduate students, researchers and engineers in all areas of signal and image processing. It can also be a valuable reference book for final year undergraduates engaged in signal processing projects. I found the book rich and compact, and a very special and useful signal processing book." --INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING EDUCATION

Muu info

Key Features * Wavelet transforms * Adaptive filter design * Neural networks * Order statistic filters * Projections methods
Orthogonal Wavelets and Signal Processing: Multiresolution Analysis. Two
Orthogonal Wavelet Bases. Discrete Wavelets and the Daubechies Construction.
The Discrete Wavelet Transform Algorithm. Wavelets and Multiscale
EdgeDetection. Wavelets and Non-Stationary Signal Analysis. Signal
Compression Using Wavelets. Adaptive Filtering Using Vector Spaces of
Systems: Fundamentals of Adaptive Filtering. The Vector Space Adaptive
Filter. Algorithm Convergence and AsymptoticPerformance. Choosing the Vector
Space. Choosing the Basis. Examples. Order Statistics and Adaptive Filtering:
Median and Order Statistic Filters. Adaptive Filters and Order Statistics. OS
Filters and Robustness. Rapid Adaptation--An Ad-Hoc Estimator. Multi-Layer
Perceptron Neural Networks with Application to Speech Recognition: The
Multi-Layer Perceptron. Signal Classification Design Examples. Perceptron
Architecture and Learning. Statistical Training of Multi-Layer Perceptrons.
Combining Multi-Layer Perceptrons for Speech Recognition. Auditory
Localization Using Spectral Information: A Localization Model Based on HRTFs.
Template Matching and a Matching Measure. Normalized Correlation Matching.
Optimal DMM Matching. Matching Using Backpropagation Neural Networks.
Experiments. Signal Processing by Projection Methods: Applications to Color
Matching, Resolution Enhancement, and Blind Deconvolution: The Method of
Projections Onto Convex Sets. Color Matching Problems. Resolution
Enhancement. Generalized Projections. Projection-Based Blind Deconvolution.
Projection Based Image Reconstruction from Compressed Data: Principles of the
Proposed Recovery Approach. Constraint Set Based on the Transmitted Data.
Constraint Sets Based on Prior Knowledge. The Recovery Algorithm. A
Simplified Algorithm. Computational Complexity Analysis. Experiments.
Non-Orthogonal Expansion for Template Matching and Edge Detection: The
Correlation Approach: A Brief Review. The Discriminative Signal-to-Noise
Ratio and Expansion Matching. EXM Related to Minimum Squared Error
Restoration. EXM Related to Non-Orthogonal Expansion. Experimental Results.
EXM-based Optimal DSNR Edge Detection. Locally Optimum Detection and Its
Application to Communicationsand Signal Processing: Derivation of the
Memoryless Locally Optimum Detector. Derivation of the Locally Optimum
Detector with Memory. Detector Implementation Methods. LO Detection Applied
to a Spread Spectrum System. Estimation of Probability Density Functions
Using Projections Onto Convex Sets: Constraint Sets for Probability Density
Function Estimation. Determining the Parameters in Constraint Sets. Competing
Algorithms. Numerical Results. Subject Index.