AN OVERVIEW OF WAVELET BASED SIGNAL PROCESSING
Tuba Özge Onur · 2015
Wavelets are mathematical functions that cut up data into different frequency components, and then study each component with a resolution matched to its scale. They have advantages over traditional Fourier methods in analyzing physical situations where the signal contains discontinuities and sharp spikes. Wavelets were developed independently in the fields of mathematics, quantum physics, electrical engineering, and seismic geology. Interchanges between these fields during the last ten years have led to many new wavelet applications such as image compression, turbulence, human vision, radar, and earthquake prediction. This paper introduces wavelets to the interested technical person outside of the digital signal processing field. The history of wavelets is described by beginning with Fourier transform, short time Fourier transform, continuous wavelet transform, discrete wavelet transform and comparing wavelet transforms with Fourier transforms. Then, an application for comparing wavelet transforms is performed for sinusoidals with additional noise and CWT is investigated.