Springer Multiscale Signal Analysis and Modeling

This book surveys advances in multiscale analysis using wavelets and other systems: sinc functions, discrete Hermite functions and more, and offers applications in filter design, sampling, signal and image representation/transmission and other methods. From the Back Cover 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. This book also: 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 Multiscale Signal Analysis and Modeling is an ideal book for graduate students and practitioners, especially those working in or studying the field of signal/image processing, telecommunication and applied statistics. It can also serve as a reference book for engineers, researchers and educators interested in mathematical and statistical modeling.