plot(abs(fft(vectorname)))the FFT function returns a complex vector thus when you plot it, you get a complex graph. If you plot the absolute value of the FFT array, you will get the magnitude of the FFT.
A disadvantage of the Zoom FFT is that it can be computationally intensive, particularly for very high-resolution frequency analysis, as it may require multiple FFT computations to achieve the desired frequency precision. Additionally, it may introduce artifacts or reduce frequency resolution in regions outside the zoomed range, which can complicate the interpretation of results. Lastly, the need for careful parameter selection in the zooming process can make it less user-friendly for those unfamiliar with its intricacies.
clc clear all close all a = [1 2 3 4]; b = [6 7 8 9 10]; f=length(a); g=length(b); h=(f+g-1); i=[a,zeros(1,(h-f))]; j=[b,zeros(1,(h-g))]; y1 = fft(i); y2 = fft(j); z = y1.*y2; c = ifft(z); subplot(2,2,1); plot(a) title('a') subplot(2,2,2); plot(b) title('b') subplot(2,2,3); plot(c) title('Convolution of a,b')
The face value of 3 is 3: the value of 3 is 3000The face value of 5 is 5: the value of 5 is 500The face value of 3 is 3: the value of 3 is 3000The face value of 5 is 5: the value of 5 is 500The face value of 3 is 3: the value of 3 is 3000The face value of 5 is 5: the value of 5 is 500The face value of 3 is 3: the value of 3 is 3000The face value of 5 is 5: the value of 5 is 500
Then the measured value is larger than the actual value.
FFT is the frequency domain representation. In can be shown in Simulink with blocks. These blocks graphically show the domain or x value plotted against the frequency or y value.
because they have a high speed compared to fft
plot(abs(fft(vectorname)))the FFT function returns a complex vector thus when you plot it, you get a complex graph. If you plot the absolute value of the FFT array, you will get the magnitude of the FFT.
FFT reduces the computation since no. of complex multiplications required in FFT are N/2(log2N). FFT is used to compute discrete Fourier transform.
FT is needed for spectrum analysis, FFT is fast FT meaning it is used to obtain spectrum of a signal quickly, the FFT algorithm inherently is fast algorithm than the conventional FT algorithm
There's no need for it.
FFT is faster than DFT because no. of complex multiplication in DFT is N^2 while in FFT no. of complex multiplications are N/2(log2N). for example if N=8 no. of complex multiplications required in DFT are 64. while no. of complex multiplications required in FFT are 12 thus reduces computation time.
Fast Fourier Transform
Food For Thought
hi.... for DIT fft algorithm, refer to this link, it has c-code for that. http://cnx.org/content/m12016/latest/
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A Discrete Fourier Transform is simply the name given to the Fourier Transform when it is applied to digital (discrete) rather than an analog (continuous) signal. An FFT (Fast Fourier Transform) is a faster version of the DFT that can be applied when the number of samples in the signal is a power of two. An FFT computation takes approximately N * log2(N) operations, whereas a DFT takes approximately N^2 operations, so the FFT is significantly faster simple answer is FFT = Fast DFT