Last edited by Visar
Thursday, July 23, 2020 | History

1 edition of Multiscale Signal Analysis and Modeling found in the catalog.

Multiscale Signal Analysis and Modeling

by Xiaoping Shen

  • 276 Want to read
  • 30 Currently reading

Published by Springer New York, Imprint: Springer in New York, NY .
Written in English

    Subjects:
  • Mathematical Modeling and Industrial Mathematics,
  • Engineering,
  • Image and Speech Processing Signal,
  • Computer science,
  • Computational Science and Engineering

  • Edition Notes

    Statementedited by Xiaoping Shen, Ahmed I. Zayed
    ContributionsZayed, Ahmed I., SpringerLink (Online service)
    Classifications
    LC ClassificationsTK5102.9, TA1637-1638, TK7882.S65
    The Physical Object
    Format[electronic resource] /
    ID Numbers
    Open LibraryOL27076307M
    ISBN 109781461441458

    General methodology Numerical techniques for multiscale dynamical systems with stochastic effects, Comm. Math. Sci. 1, () ; A computational strategy for multiscale chaotic systems with applications to Lorenz 96 model, with I. Fatkullin, J. Comp. Phys. , ().; Analysis of multiscale methods for stochastic differential equations, with W. E and D. Liu, Comm. Pure App. Multiscale Analysis of Complex Time Series fills this pressing need by presenting chaos and random fractal theory in a unified manner. Adopting a data-driven approach, the book covers: DNA sequence analysis; EEG analysis; Heart rate variability analysis; Neural information processing; Network traffic modeling; Economic time series analysis; And.

    wavelet and other multiscale transforms. In this book, we describe many such applications, and in this way illustrate the theory and practice of such transforms. We describe an ‘embedded systems’ approach to wavelets and multiscale transforms in this book, in that we introduce and appraise ap-. "Physical phenomena can be modeled at varying degrees of complexity and at different scales. Multiscale modeling provides a framework, based on fundamental principles, for constructing mathematical and computational models of such phenomena by examining the connection between models at different scales. This book, by one of the leading contributors to the field, is the first to provide a.

      where \vec{p} is the dipole moment of the radiation source (not to be confused with the polarization mismatch) and k is the wave vector for the medium.. One breakdown of the various regions for the electromagnetic field generated from an electrically small antenna. In this equation, there are three factors of 1/r 1/r 2 and 1/r 3 terms will be more significant near the source, while the 1. Link to the book and the chapter [CHO] A.A. Franco, Multiscale modeling of electrochemical devices for energy conversion and storage, book chapter in: Encyclopedia of Applied Electrochemistry, edited by R. Savinell, K.I. Ota, G. Kreysa (publisher: Springer, UK) (). Link to the chapter.


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Multiscale Signal Analysis and Modeling by Xiaoping Shen Download PDF EPUB FB2

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. 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.

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.

springer, 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.

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 Brand: Springer New York.

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 statisticsIntroduces new sampling algorithms for multidimensional signal processing Provides.

Constructions and a generalization of perfect autocorrelation sequences on Z.- Part II Multiscale Analysis.- A unified theory for multiscale analysis of complex time series.- Wavelet Analysis of ECG Signals.- Multiscale signal processing with discrete Hermite functions Signal Analysis, by Ronald L.

Allen and Duncan W. Mills is an excellent book for students taking a DSP course. The basic concepts are presented using simple and clear language and help the readers gain a strong foundation in Signal Analysis by: This is where multiscale modeling comes in.

By considering simultaneously models at different scales, we hope to arrive at an approach that shares the efficiency of the macro-scopic models as well as the accuracy of the microscopic models. This idea is far from being new. After all, there has been considerable efforts in trying to understand.

This thoroughly updated new edition presents state of the art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on Cited by: The book is divided into three parts: Part I is devoted to the mathematical theory of wavelets and features several papers on wavelet sets and the construction of wavelet bases in different settings.

Part II looks at the use of multiscale harmonic analysis for understanding the geometry of large data sets and extracting information from them. Additional Sources for Math Book Reviews; About MAA Reviews; Mathematical Communication; Information for Libraries; Author Resources; Advertise with MAA; Meetings.

MAA MathFest. Register Now; Registration Rates and Other Fees; Exhibitors and Sponsors; Abstracts; Mathematical Sessions.

Invited Addresses; Contributed Paper Sessions; Panel. Multiscale Analysis And Modeling Using Wavelets Article in Journal of Chemometrics 13() October with Reads How we measure 'reads'Author: Bhavik R. Bakshi. Anke Meyer-Baese, Volker Schmid, in Pattern Recognition and Signal Analysis in Medical Imaging (Second Edition), Multiscale-Analysis Spaces.

Multiscale signal analysis provides the key to the link between wavelets and pyramidal dyadic trees. A wavelet family is used to decompose a signal into scaled and translated copies of a basic function.

In engineering, mathematics, physics, chemistry, bioinformatics, computational biology, meteorology and computer science, multiscale modeling or multiscale mathematics is the field of solving problems which have important features at multiple scales of time and/or space.

Important problems include multiscale modeling of fluids, solids, polymers, proteins, nucleic acids as well as various. Electronic: Print: Centered around multiscale phenomena, Multiscale Modeling and Simulation (MMS) is an interdisciplinary journal focusing on the fundamental modeling and computational principles underlying various multiscale methods.

By its nature, multiscale modeling is highly interdisciplinary, with developments. Chaos and random fractal theory are two of the most important theories developed for data analysis. Until now, there has been no single book that encompasses all of the basic concepts necessary for researchers to fully understand the ever-expanding literature and apply novel methods to effectively solve their signal processing problems.

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 Brand: Springer New York. Book Title:Wavelets and Multiscale Signal Processing (Applied Mathematics) Since their appearance in mids, wavelets and, more generally, multiscale methods have become powerful tools in mathematical analysis and in applications to numerical analysis and signal processing.

This book is based on "Ondelettes et Traitement Numerique du Signal. multiscale signals. For this reason we have decided to include a variety of chapters covering different aspects and applications of multiscale analysis. The monograph is divided into three main parts: Part I is a collection of chapters on sampling theory while Parts II and III contain chapters on multiscale analysis and statistical analysis.

Anke Meyer-Baese, Volker Schmid, in Pattern Recognition and Signal Analysis in Medical Imaging (Second Edition), Multiscale-Analysis Spaces.

Multiscale signal analysis provides the key to the link between wavelets and pyramidal dyadic trees. A wavelet family is used to decompose a signal into scaled and translated copies of a basic.We introduce a novel approach to nonlinear signal analysis, which is referred to as supremal multiscale analysis.

The proposed approach provides a rigorous mathematical foundation for a class of nonlinear multiscale signal analysis schemes and leads to a decomposition that can effectively be used in signal processing and analysis. Moreover, it is related to the supremal scale-spaces proposed Cited by: Image Processing and Data Analysis: The Multiscale Approach.

An introduction to a range of techniques for the analysis of digital data, especially the manipulation of images. Contains real-world examples in astronomy, electrical engineering, physics, geophysics and medical imaging.