Wavelet Analysis: Concepts with Wolfram Language
49mIntermediate2024-01-04
Authors
Wolfram Research
Course details
Wavelets decompose a signal into approximations and details at different scales, making them useful for applications such as data compression, detecting features and removing noise from signals. This course from Wolfram Research explains some of the theory behind continuous, discrete, and stationary wavelet transforms and demonstrates how the Wolfram Language and its built-in functions can be used to construct, compute, visualize, and analyze wavelet transforms and related functions.
Skills covered
Wolfram LanguageWolfram ResearchComputational DesignData AnalysisAECProduct and ManufacturingData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOne-Off
Concepts
0. Introduction
- 01 - Introduction
1. Continuous Wavelet Transform
- 02 - Continuous wavelet transform
2. Discrete Wavelets
- 03 - Discrete wavelets
3. Wavelet Filter Bank
- 04 - Wavelet filter bank
4. Discrete Wavelet Data
- 05 - Discrete wavelet data
5. Wavelet Best Basis
- 06 - Wavelet best basis
6. Wavelet Thresholding
- 07 - Wavelet thresholding
References
- 08 - References