@bul:* Wavelet Toolware
* Continuous Wavelt Transforms for a one dimensional signal
* Discrete Wavelet Transforms
* Short Time Fourier Transform
* Computation and display of Scaling Function
* Computation and graphical display of Wavelets
* Mapping between B-Splines and their duals
* Decomposition of a one-dimensional signal
* Reconstruction of a one-dimensional signal
* Noise removal using hard thresholding
* One-dimensional and two-dimensional wavelet packet decomposition and reconstruction
* Allows user to put in additional wavelts of their choice an delete the ones they don't need
@text:Wavelet Toolware is designed as a learning and experimentation toolk, not for solving high-precision computational problems; It works well as a self-contained package or in a learning environment in conjunction with An Introduction to Wavelets.
@introbul:*An Introduction to Wavelets
@bul* Market leader as a comprehensive, yet accessible introduction to the state-of-the-art
* Time-frequency localization
* Integral wavelet transforms
* Dyadic wavelets
* Orthonormal wavelet bases
* Wavelet packets
* Nonorthgonal, semi-orthogonal, and orthogonal wavelets
Wavelets are considered by many to be the most powerful mathematical tool for signal and image processing. This unique CD-ROM boxed set contains three useful items: a copy of the text An Introduction to Wavelets by Charles Chui, a CD containing well-designed wavelets and signal processing software by computer scientist Steve Liu, and a booklet that serves both as the manual for the software and a quick reference guide to wavelet basics by electrical engineer Andrew Chan. The software supports all essential wavelets and allows users to put in additional wavelets of their choice and delete the ones they don't need. It also includes custom computation routines (in C-language) for wavelet signal processing. A flexible programming interface through the DLL standard allows the user to plug in new and original processing routines easily. This self-learning package provides the tools for the user to compare short time fourier transform and wavelet transform as well as the trade-off between time/spatial and frequency domain signal analysis.
University researchers, engineers, and specialists in numerical applications other than signal and image processing.