© 2013

Multiscale Signal Analysis and Modeling

  • Xiaoping Shen
  • Ahmed I. Zayed

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Sampling

    1. Front Matter
      Pages 1-1
    2. Frank Stenger, Maha Youssef, Jenny Niebsch
      Pages 25-49
    3. H. R. Fernández-Morales, A. G. García, G. Pérez-Villalón
      Pages 51-80
    4. M. Zuhair Nashed, Qiyu Sun
      Pages 81-104
    5. P. P. Vaidyanathan, Piya Pal
      Pages 105-137
    6. Daniel Alpay, Palle Jorgensen, Izchak Lewkowicz, Itzik Marziano
      Pages 161-182
  3. Multiscale Analysis

    1. Front Matter
      Pages 209-209
    2. Jianbo Gao, Jing Hu, Wen-wen Tung
      Pages 211-231
    3. En-Bing Lin, Megan Haske, Marilyn Smith, Darren Sowards
      Pages 233-255
    4. Dale H. Mugler, Anandi Mahadevan
      Pages 257-274
    5. Bradley Marchand, Naoki Saito
      Pages 275-294
  4. Statistical Analysis

    1. Front Matter
      Pages 295-295
    2. Norbert Reményi, Brani Vidakovic
      Pages 317-346
    3. Shuai Lu, Sergiy Pereverzyev Jr., Sivananthan Sampath
      Pages 347-366
  5. Back Matter
    Pages 367-378

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.

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. 


Bayesian Shrinkage Strategies ECG signals Sinc convolution Sparse sampling techniques Time series analysis Wavelet analysis Wavelet filters Wavelet frame waveform design

Editors and affiliations

  • Xiaoping Shen
    • 1
  • Ahmed I. Zayed
    • 2
  1. 1., Department of MathematicsOhio UniversityAthensUSA
  2. 2., Department of Mathematical SciencesDePaul UniversityChicagoUSA

Bibliographic information

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