Characteristics Of Wavelet Transforms

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4.1. Wavelet Transforms (WT) 4.1.1. Wavelet Definition A ‘wavelet’ is a small wave which has its energy concentrated in time. It has an oscillating wavelike characteristic but also has the ability to allow simultaneous time and frequency analysis and it is a suitable tool for transient, non-stationary or time-varying phenomena. (a) (b) Fig: 4.0.1 Representation of a wave (a) and a wavelet (b) 4.1.2. Wavelet Characteristics The difference between wave (sinusoids) and wavelet is shown in figure 3.1. Waves are smooth, predictable and everlasting, whereas wavelets are of limited duration, irregular and may be asymmetric. Waves are used as deterministic basis functions in Fourier analysis for the expansion of functions (signals), which are time-invariant, or stationary. The important characteristic of wavelets is that they can serve as deterministic or non-deterministic basis for generation and analysis of the most natural signals to provide better time-frequency representation, which is not possible with waves using conventional Fourier analysis. 4.1.3. Wavelet Analysis …show more content…

Temporal analysis is performed with a contracted, high frequency version of the prototype wavelet, while frequency analysis is performed with a dilated, low frequency version of the same wavelet. Mathematical formulation of signal expansion using wavelets gives Wavelet Transform pair, which is analogous to the Fourier Transform (FT) pair. Discrete-time and discrete-parameter version of WT is termed as Discrete Wavelet Transform. DWT can be viewed in a similar framework of Discrete Fourier Transform (DFT) with its efficient implementation through fast filterbank algorithms similar to Fast Fourier Transform (FFT)

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