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## What is meant by wavelet?

A wavelet is a wave-like oscillation with an amplitude that begins at zero, increases, and then decreases back to zero. It can typically be visualized as a “brief oscillation” like one recorded by a seismograph or heart monitor.

## Is wavelet transform frequency domain?

While the Fourier transform creates a representation of the signal in the frequency domain, the wavelet transform creates a representation of the signal in both the time and frequency domain, thereby allowing efficient access of localized information about the signal.

## Why do we use wavelet transform?

In contrast to STFT having equally spaced time-frequency localization, wavelet transform provides high frequency resolution at low frequencies and high time resolution at high frequencies.

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## Where are wavelets used?

The most common use of wavelets is in signal processing applications. For example: Compression applications. If we can create a suitable representation of a signal, we can discard the least significant” pieces of that representation and thus keep the original signal largely intact.

## Why DWT is used?

The discrete wavelet transform has a huge number of applications in science, engineering, mathematics and computer science. Most notably, it is used for signal coding, to represent a discrete signal in a more redundant form, often as a preconditioning for data compression.

## What is wavelet method?

The wavelet transform is a mathematical technique which can decompose a signal into multiple lower resolution levels by controlling the scaling and shifting factors of a single wavelet function (mother wavelet) (Foufoula-Georgiou and Kumar, 1995; Lau and Weng, 1995; Torrence and Compo, 1998; Percival and Walden, 2000).

## Why is Stft used?

The Short-time Fourier transform (STFT), is a Fourier-related transform used to determine the sinusoidal frequency and phase content of local sections of a signal as it changes over time. This reveals the Fourier spectrum on each shorter segment.

## What is the difference between wavelet and Wavefront?

A wavefront is the locus of all the particles which are in phase. All the points on the circular ring are in phase, such a ring is called a wavefront. A wavelet is an oscillation that starts from zero, then the amplitude increases and later decreases to zero.

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## What is DWT briefly?

A discrete wavelet transform (DWT) is a transform that decomposes a given signal into a number of sets, where each set is a time series of coefficients describing the time evolution of the signal in the corresponding frequency band.

## Why DWT is better than DCT?

Both techniques have its’ own advantages and disadvantage. Like DWT gives better compression ratio [1,3] without losing more information of image but it need more processing power. While in DCT need low processing power but it has blocks artifacts means loss of some information.

## What is the difference between FFT and STFT?

STFT stands for short time Fourier transform, the emphasis is “short time”. It evaluates the Fourier transform over a short time window. The purpose is to provide the information regarding the fluctuation of the frequency contents over time. FFT is an algorithm for computing a FFT which is the digital version of STFT.

## How to transform time domain to wavelet domain?

Using DWT, we can transform m signals from the time domain to the wavelet domain (see Scheme 2a ): Scheme 2. Schematic representation of (a) discrete wavelet transform of data set, X, from time domain to wavelet domain, W, and (b) matrix Wsorted containing wavelet coefficients sorted according to their contribution to the data variance.

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## Is the wavelet transform good for all kinds of data?

Wavelet compression is not good for all kinds of data: transient signal characteristics mean good wavelet compression, while smooth, periodic signals are better compressed by other methods, particularly traditional harmonic compression (frequency domain, as by Fourier transforms and related).

## How are wavelet coefficients used in the wavelet domain?

A similar procedure can be performed in the wavelet domain [27 ]. In this case, there is no need for adding noisy variables, but instead, wavelet coefficients associated with the data noise can be used to calculate the threshold value of the stability of the b-coefficients.

## How are wavelets used to analyze dynamic signals?

Wavelets are a better way of analyzing these dynamic signals because they have a relatively higher resolution in both time and frequency domain. Wavelet Transform tells us about the frequencies present as well as the time in which these frequencies were observed. This is done by working with different scales.