As the name suggested FFT went on to prove itself to be computationally faster than its discrete counterpart. This was the very result we verified in our third lab session.Two cases were considered, , one with a four point input and other with an eight point input.The number of real additions and multiplications (Computations) were considerably lesser compared to the DFT computations.This very well justified the computational efficiency of the Fast Fourier Transform.
Fast Fourier Transform is more effective in a practical approach when code is executed and implemented
ReplyDeleteFast Fourier Transform is more effective in a practical approach when code is executed and implemented
ReplyDeleteFFTs basically rely on their parallel processing algorithms for computationally faster results.
DeleteWell expained.
ReplyDeleteYes, thank you so much
DeleteBy how much did the calculations reduce?
ReplyDeleteConsidering a 4 point signal (N = 4), FFT reduced the number of real multiplications by 48 and real additions by 24 which was verified practically.
DeleteBecause of parallel processing FFT is more faster than DFT
ReplyDeleteThe thing to consider here is that we cannot apply FFT to real time signals because in such cases the whole real time signal wont be available at a time at the system input which beats the point of parallel processing.
DeleteNumber of computations in FFT is less than that of DFT. This makes FFT computationally faster.
ReplyDeleteFFT is faster
ReplyDelete