Signal analysis with continuous wavelet transform

Fluctuating signal analysis is not easy to do with computational systems. Initially, the Fourier transform was used to analyze signals on the basis of sine and cosine waves. In its development, the Fourier transform cannot be used to analyze signals that change with local time frequencies—developed as an update of the Fourier transform. Wavelet transform can construct a signal with a local representation of time and frequency. Continuous wavelet transform (CWT) is a technique for analyzing signals that produce local signal representations in the time and frequency domains. CWT converts the original signal into a wavelet domain to analyze signals at high and low frequencies. In this study, a review of CWT was carried out. The important thing in performing a continuous wavelet transform is to determine the wavelet used and determine the scale and position used for dilatation and translation of the signal. The result of the continuous wavelet transform is a scalogram. In analyzing the results of wavelet transformation, a continuous wavelet transform is used. The result of this research is the continuous wavelet transform for signal analysis.