Linkedin Learning – Wavelet Analysis Concepts with Wolfram Language

Linkedin Learning – Wavelet Analysis Concepts with Wolfram Language
English | Tutorial | Size: 106.20 MB

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.

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