Contents
How is DFT different from FFT?
FFT is a much efficient and fast version of Fourier transform whereas DFT is a discrete version of Fourier transform. DFT is a mathematical algorithm which transforms time-domain signals to frequency domain components on the other hand FFT algorithm consists of several computation techniques including DFT.
Which is better among DFT FFT?
The Fast Fourier Transform (FFT) is an implementation of the DFT which produces almost the same results as the DFT, but it is incredibly more efficient and much faster which often reduces the computation time significantly. It is just a computational algorithm used for fast and efficient computation of the DFT.
What does the difference between DFT and FFT mean?
If you change the size of a DFT or FFT window so that it is an exact integer multiple of the period of a purely periodic input waveform, then the windowing artifacts (or leakage) just happens to be all zeros. DFT stands for Discrete Fourier Transform and FFT stands for Fast Fourier Transform.
Is it assumed that 20 sample points repeat in DFT?
In both algorithm, it has been assumed that the signal is periodic (so my understanding is that, if you have a 20 sample point signal, and you do DFT or FFT, the algorithm assumes 20 sample points repeat). Now here is the question, someone told me that DFT can take odd number of samples, and spectral leakage could be avoid if use DFT.
Can a factor 2 be used in an odd length FFT?
They produce the exact same results, except for numerical noise. The factor 2 is common, but 3 and 5 are also small primes that can be used in a very efficient odd number length FFT implementation. Many newer FFT libraries (Accelerate/vDSP, FFTW, etc.) thus support odd length FFTs, not just powers of 2.
How to calculate the number of FFT points?
Assuming that you don’t zero-pad the FFT input vector, N is the number of samples you should acquire and then feed into the FFT. If you use M samples, with M < N you can append N − M zeros to the FFT input which will result in an interpolation of the “missing” frequency bins.