wavelet vol ii b cover front

Wavelet Advanced Methods

Wavelet Advanced Methods

Multiresolution Analysis, Custom Wavelets, and Feature Engineering

Volume II-B advances beyond the applied discrete wavelet methods presented earlier in the series to examine wavelet representations that address limitations of standard decimated transforms. Emphasis is placed on shift-invariant analysis, phase-aware representations, continuous time–frequency methods, and the design of wavelets tailored to specific signal characteristics and analytical objectives.

This volume treats wavelets as representational tools rather than fixed algorithms, focusing on how advanced transforms and custom constructions can be selected, compared, and evaluated for complex data and learning-oriented tasks. Through reproducible Python workflows and carefully chosen examples, the chapters demonstrate how advanced wavelet methods support multiscale interpretation, improved stability, and feature extraction in modern signal analysis and machine learning contexts.

This volume covers:

  • Maximal Overlap Discrete Wavelet Transform (MODWT) and multiresolution analysis
  • MODWT Multiresolution Analysis (MODWTMRA)
  • Dual-Tree Complex Wavelet Transform (DT-CWT)
  • Continuous Wavelet Transform (CWT) from one-dimensional to multidimensional data
  • Design and implementation of custom wavelets
  • Wavelet-based feature extraction for machine learning applications
  • Reproducible Python workflows for advanced wavelet analysis
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Publication Details

About the Author

Shouke Wei

Shouke Wei, PhD, is a researcher, scientist, and entrepreneur specializing in intelligent IoT systems, robotics, big data analytics, modeling and forecasting, early-warning systems, and edge computing. With academic and industry experience across Europe, North America, and Asia, Dr. Wei is recognized for bridging advanced theory with real-world, production-ready systems.

Dr. Wei earned his Ph.D. in Environmental and Resource Management from the Department of Environmental Informatics at Brandenburg University of Technology Cottbus–Senftenberg (Germany). He conducted postdoctoral research at the Swiss Federal Institute of Aquatic Science and Technology (Eawag), where he also served as a doctoral supervisor, and held research positions at the University of British Columbia (Canada). He has held distinguished and adjunct professorships at multiple institutions, including Yantai University, Ludong University, and Jining University. He has served as a graduate supervisor and distinguished professor in computer science, control engineering, and applied mathematics.

Dr. Wei’s work focuses on making advanced computational methods—particularly wavelet-based signal processing—accessible, practical, and impactful for researchers and practitioners worldwide. [More About the Author]

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