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| SIGNAL PROCESSING METHODS FOR AUDIO, IMAGES AND TELECOMMUNICATIONS
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To order this title, and for more information, click here
Edited By
Henry Stark, Illinois Institute of Technology
Richard Green, The Engineering Practice
Peter Clarkson, The Ohio State University
Doug Gray, CRC for Sensor Signal and Information Processing, Signal Processing Research Institute, Technology Park
Edward Powers, University of Texas, Austin
Included in series
Signal Processing and its Applications,
Description
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.
Audience
Advanced students, researchers, and engineers in the areas of telecommunications, digital signal processing, image processing, acoustics and applied mathematics.
Contents
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.
| Bibliographic details |
Hardbound, 452 pages, publication date: JUN-1995
ISBN-13: 978-0-12-175790-8
ISBN-10: 0-12-175790-0
Imprint: ACADEMIC PRESS
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Last update: 4 Sep 2009
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