Wavelet Analysis Concepts: Wolfram U Class

How to construct, compute, visualize and analyze wavelet transforms with the Wolfram Language. Video class also explains some of the theory behind continuous, discrete and stationary wavelet transforms.

Summary
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 class 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. Familiarity with Fourier transforms and data smoothing methods is recommended for this class.

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You'll Learn To

Overcome limitations of traditional Fourier analysis by breaking down a signal into smaller components
Perform continuous wavelet transforms using the Wolfram Language
Construct and plot discrete wavelet and scaling functions
Compute lowpass and highpass filter coefficients and frequency response functions
Use WaveletBestBasis with discrete wavelet packet transforms
Compare named automatic thresholding methods
Apply stationary wavelet transforms for image detection