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What are limitations of z transform?
1.z transform can not aaply in continious signal. 2.z transform can not analyse analog filter by - prakash kr
Why Gaussian kernels are used wavelet transforms?
Oh, dude, Gaussian kernels are used in wavelet transforms because they have a smooth and bell-shaped curve that helps in capturing both low and high-frequency components of a signal. It's like they're the cool kids at the party who can mingle with everyone. So, when we want to analyze signals with varying frequencies, we invite Gaussian kernels to the wavelet transform shindig because they know how to handle the crowd.
What are the limitation of fourier series?
what are the limitations of forier series over fourier transform
What are the limitations of z-transform?
The main limitation is that the z-transform is appropriate only if the underlying data are normally distributed. It is also important that the estimates for the mean and variance are sufficiently accurate.
What is the difference between the fourier laplace transform?
They are similar. In many problems, both methods can be used. You can view Fourier transform is the Laplace transform on the circle, that is |z|=1. When you do Fourier transform, you don't need to worry about the convergence region. However, you need to find the convergence region for each Laplace transform. The discrete version of Fourier transform is discrete Fourier transform, and the discrete version of Laplace transform is Z-transform.
What is the difference between wavelet transform and wavelet packet transform?
in wavelet transform only approximate coeffitients are further decoposed into uniform frequency subbands while in that of wavelet packet transform both approximate and detailed coeffitients are deomposed further into sub bands.
What is the difference between Fourier transform and Wavelet transform?
Fourier transform analyzes signals in the frequency domain, representing the signal as a sum of sinusoidal functions. Wavelet transform decomposes signals into different frequency components using wavelet functions that are localized in time and frequency, allowing for analysis of both high and low frequencies simultaneously. Wavelet transform is more suitable than Fourier transform for analyzing non-stationary signals with localized features.
What is the main objective of Discrete wavelet transform?
It allows you to store the information of a signal in a small number of coefficients.
What is curvelet transform?
multiscale and multidirectional transform just like Fourier and wavelet but more sparse and redundant....useful in representing 2-D discontinuities in image
What are the limitations of laplace transform?
Laplace will only generate an exact answer if initial conditions are provided
What has the author Arto Kaarna written?
Arto Kaarna has written: 'Multispectral image compression using the wavelet transform' -- subject(s): Image processing, Wavelets (Mathematics)
What are limitations of z transform?
1.z transform can not aaply in continious signal. 2.z transform can not analyse analog filter by - prakash kr
Why Gaussian kernels are used wavelet transforms?
Oh, dude, Gaussian kernels are used in wavelet transforms because they have a smooth and bell-shaped curve that helps in capturing both low and high-frequency components of a signal. It's like they're the cool kids at the party who can mingle with everyone. So, when we want to analyze signals with varying frequencies, we invite Gaussian kernels to the wavelet transform shindig because they know how to handle the crowd.
What is the diminutive of wave?
The diminutive of wave is wavelet.
What are the limitation of fourier series?
what are the limitations of forier series over fourier transform
How do you perform wavelet transformation?
Wavelet transformation is a mathematical technique used in signal processing. To perform wavelet transformation, you need to convolve the input signal with a wavelet function. This process involves decomposing the signal into different frequency components at various scales. The output of wavelet transformation provides information about the signal's frequency content at different resolutions.
What is diminutive of wave?
The diminutive of wave is wavelet.
