ABSTRACT
This chapter describes certain examples of wavelets and elementary examples of continuous wavelet transforms Wavelet transform is a technique for local analysis of signals. This transform is an alternative but not a replacement of the Fourier transform. The building blocks in wavelet analysis are derived by translation and dilation of a mother function. It uses wavelets instead of long waves. These wavelets are localized functions. Instead of oscillating forever, as in the case of the basis functions used in Fourier analysis, wavelets eventually drop to zero. Wavelet transforms can be either continuous or discrete. The wavelet transform is a mapping of a function defined in time domain, into a function which has a time-scale representation. That is, the wavelet transformation is a two-dimensional representation of a one-dimensional function. Regularity of a wavelet is a measure of its smoothness.
