📘 Wavelet Transform in Practice — Volume I was released on February 12, 2026
Wavelet transforms are powerful tools for analyzing signals, images, and complex data across multiple scales. Yet many resources focus heavily on theory while offering limited guidance on practical implementation.
Wavelet Transform in Practice — Volume I bridges this gap by combining mathematical foundations with reproducible Python workflows.
This volume introduces:
• Mathematical foundations of wavelet theory
• Scaling functions and orthonormal wavelet bases
• Multiresolution Analysis (MRA)
• Discrete Wavelet Transform (DWT)
• Stationary Wavelet Transform (SWT)
• Wavelet Packet Transform (WPT)
• Practical implementations using PyWavelets
• Visualization and interpretation of wavelet coefficients
• Reproducible scientific Python workflows
The book is designed for researchers, data scientists, engineers, and students interested in signal processing, machine learning, and time-series analysis.
It is the first volume in the Wavelet Transform in Practice series.
