Course Overview
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 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.
Featured Products & Technologies: Wolfram Language (available in Mathematica and Wolfram|One)
You'll Learn To
- Overcome limitations of traditional Fourier analysis by breaking down a signal into smaller components
- Perform continuous wavelet transforms using 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
