emäaaltofunktioita

Emäaaltofunktioita, often translated as mother wavelets, are the fundamental building blocks in wavelet analysis. They are a set of basis functions used to decompose a signal into different frequency components. Unlike Fourier analysis, which uses sines and cosines that extend infinitely, wavelets are localized in both time and frequency. This localization allows for the analysis of signals that change over time, such as speech or seismic data.

The term "mother wavelet" refers to the original, prototype wavelet function. All other wavelets used in a specific wavelet family are derived from this mother wavelet through scaling and translation operations. Scaling changes the frequency resolution, allowing for the analysis of high-frequency or low-frequency components. Translation shifts the wavelet along the time axis, enabling the examination of signal features at different points in time.

The choice of mother wavelet depends on the characteristics of the signal being analyzed and the specific application. Different mother wavelets have different properties, such as smoothness, symmetry, and the number of vanishing moments, which influence their suitability for particular tasks. Examples of commonly used mother wavelets include the Haar wavelet, the Daubechies wavelets, and the Mexican hat wavelet. The development and understanding of mother wavelets have been crucial for the advancement of signal processing, image compression, and various other scientific and engineering fields.