Howtosignalprocessing
- Is wavelet transform is suitable for stationary signal?
- What is the difference between continuous wavelet transform and discrete wavelet transform?
- What are the types of wavelet?
- Which is the best wavelet?
Is wavelet transform is suitable for stationary signal?
In such a situation wavelet transforms are primarily useful for processing non stationary signals. Applications demanding time and frequency information simultaneously wavelets are considered as a potential tool to provide tangible results.
What is the difference between continuous wavelet transform and discrete wavelet transform?
The CWT and the discrete wavelet transforms differ in how they discretize the scale parameter. The CWT typically uses exponential scales with a base smaller than 2, for example 21/12 . The discrete wavelet transform always uses exponential scales with the base equal to 2.
What are the types of wavelet?
There are two types of wavelet transforms: the continuous wavelet transform (CWT) and the discrete wavelet transform (DWT). Specifically, the DWT provides an efficient tool for signal coding.
Which is the best wavelet?
An orthogonal wavelet, such as a Symlet or Daubechies wavelet, is a good choice for denoising signals. A biorthogonal wavelet can also be good for image processing.
