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When would one use the Fourier Transform?
Physics would be one of a few examples of fourier transform. One would also use it when they are using engineering so, yeah that is basically it as far as the fourier transform is concerned.
Is there away to sort an array of data using the fast Fourier transform and finding the highest lower or average value finding his value or even best his position?
The Fast Fourier Transform is an implementation of the Discrete Fourier Transform. The DFT is a method of processing a time-sampled signal (eg, an audio wave) into a series of sines and cosines. As such, it is not a sorting algorithm, so this question does not make any sense.
What is a quantitative EEG?
An extension of the EEG technique, called quantitative EEG (qEEG), involves manipulating the EEG signals with a computer using the fast Fourier transform algorithm.
What is involved in a quantitative electroencephalography?
manipulating the EEG signals with a computer using the fast Fourier transform algorithm. The result is then best displayed using a colored gray scale transposed onto a schematic map of the head
Convolution in matlab using for loop?
There are a lot of convolution functions in matlab, mostly in the signal processing toolbox, so it depends on what you want to do. Matlab has extensive help files available online.
What is the solution to the Heat equation using fourier transform?
The solution to the Heat equation using Fourier transform is given by the convolution of the initial condition with the fundamental solution of the heat equation, which is the Gaussian function. The Fourier transform helps in solving the heat equation by transforming the problem from the spatial domain to the frequency domain, simplifying the calculations.
When would one use the Fourier Transform?
Physics would be one of a few examples of fourier transform. One would also use it when they are using engineering so, yeah that is basically it as far as the fourier transform is concerned.
How can a composite signal be decomposed into its individual frequencies?
Fourier analysis Frequency-domain graphs
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.
Is there away to sort an array of data using the fast Fourier transform and finding the highest lower or average value finding his value or even best his position?
The Fast Fourier Transform is an implementation of the Discrete Fourier Transform. The DFT is a method of processing a time-sampled signal (eg, an audio wave) into a series of sines and cosines. As such, it is not a sorting algorithm, so this question does not make any sense.
What has the author Shunde Zhao written?
Shunde Zhao has written: 'The computation of detailed geoids using the fast Fourier transform method'
What is a sentence using convolution?
gyiuhkjhkj
What is a quantitative EEG?
An extension of the EEG technique, called quantitative EEG (qEEG), involves manipulating the EEG signals with a computer using the fast Fourier transform algorithm.
In Fourier transformation and Fourier series which one follows periodic nature?
The Fourier series can be used to represent any periodic signal using a summation of sines and cosines of different frequencies and amplitudes. Since sines and cosines are periodic, they must form another periodic signal. Thus, the Fourier series is period in nature. The Fourier series is expanded then, to the complex plane, and can be applied to non-periodic signals. This gave rise to the Fourier transform, which represents a signal in the frequency-domain. See links.
Difference between fourier series and z-transform?
Laplace = analogue signal Fourier = digital signal Notes on comparisons between Fourier and Laplace transforms: The Laplace transform of a function is just like the Fourier transform of the same function, except for two things. The term in the exponential of a Laplace transform is a complex number instead of just an imaginary number and the lower limit of integration doesn't need to start at -∞. The exponential factor has the effect of forcing the signals to converge. That is why the Laplace transform can be applied to a broader class of signals than the Fourier transform, including exponentially growing signals. In a Fourier transform, both the signal in time domain and its spectrum in frequency domain are a one-dimensional, complex function. However, the Laplace transform of the 1D signal is a complex function defined over a two-dimensional complex plane, called the s-plane, spanned by two variables, one for the horizontal real axis and one for the vertical imaginary axis. If this 2D function is evaluated along the imaginary axis, the Laplace transform simply becomes the Fourier transform.
What is involved in a quantitative electroencephalography?
manipulating the EEG signals with a computer using the fast Fourier transform algorithm. The result is then best displayed using a colored gray scale transposed onto a schematic map of the head
What is the difference between continuous and discrete convolution?
A convolution is a function defined on two functions f(.) and g(.). If the domains of these functions are continuous so that the convolution can be defined using an integral then the convolution is said to be continuous. If, on the other hand, the domaisn of the functions are discrete then the convolution would be defined as a sum and would be said to be discrete. For more information please see the wikipedia article about convolutions.
