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.
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.
To demonstrate the convolution theorem in MATLAB, you can use the following example code. First, define two signals, such as x = [1, 2, 3] and h = [0.5, 1]. Compute their convolution using the conv function, and then verify the theorem by transforming both signals into the frequency domain using the Fast Fourier Transform (FFT), multiplying the results, and then applying the inverse FFT. Here's a simple implementation: x = [1, 2, 3]; h = [0.5, 1]; conv_result = conv(x, h); % Convolution in time domain % Frequency domain approach X = fft(x); H = fft(h, length(x) + length(h) - 1); % Zero-padding for proper multiplication Y = X .* H; % Multiply in frequency domain freq_conv_result = ifft(Y); % Inverse FFT to get back to time domain disp([conv_result; freq_conv_result']); % Display results This code illustrates that the convolution of the two signals in the time domain equals the inverse FFT of their product in the frequency domain.
no disadvantage
Advantage is the opposite of disadvantage.
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Zoom FFT is a diagnostic tool for the detection of blood clots and other diseases. It is less costly than other techniques. Zoom FFT uses a low frequency to measure blood flow in order to find clots. A transmitter passes an ultrasonic wave through the blood vessels. A reflected signal is passed to a DSP processor. (This is similar to the Doppler technique.) The process is achieved with one DSP chip in order to keep the costs lower.
because they have a high speed compared to 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.
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.
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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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.