Overview
- Discusses recently developed signal modeling techniques, such as the multiscale method for complex time series modeling, multiscale positive density estimations, Bayesian Shrinkage Strategies, and algorithms for data adaptive statistics
- Introduces new sampling algorithms for multidimensional signal processing
- Provides comprehensive coverage of wavelets with presentations on waveform design and modeling, wavelet analysis of ECG signals and wavelet filters
- Reviews features extraction and classification algorithms for multiscale signal and image processing using Local Discriminant Basis (LDB)
- Develops multi-parameter regularized extrapolating estimators in statistical learning theory
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About this book
Multiscale Signal Analysis and Modeling presents recent advances in multiscale analysis and modeling using wavelets and other systems. This book also presents applications in digital signal processing using sampling theory and techniques from various function spaces, filter design, feature extraction and classification, signal and image representation/transmission, coding, nonparametric statistical signal processing, and statistical learning theory.
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Keywords
Table of contents (15 chapters)
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Sampling
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Multiscale Analysis
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Statistical Analysis
Editors and Affiliations
Bibliographic Information
Book Title: Multiscale Signal Analysis and Modeling
Editors: Xiaoping Shen, Ahmed I. Zayed
DOI: https://doi.org/10.1007/978-1-4614-4145-8
Publisher: Springer New York, NY
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Science+Business Media New York 2013
Hardcover ISBN: 978-1-4614-4144-1Published: 18 September 2012
Softcover ISBN: 978-1-4899-9684-8Published: 15 October 2014
eBook ISBN: 978-1-4614-4145-8Published: 18 September 2012
Edition Number: 1
Number of Pages: XVIII, 378
Topics: Signal, Image and Speech Processing, Mathematical Modeling and Industrial Mathematics, Computational Science and Engineering