Experimental Process:In this experiment, we divided the signal into two parts, the values of the signal x[n] when n is even and when n is odd. The even signal was computed using the the standard procedure while the odd signal values were computed by multiplying the signal with the twiddle factor.
Conclusions Inferred:
When a computation counter was added to the program, it was seen that the number of mathematical calculations were significantly lesser in number, when compared to the number of calculations required to evaluate the same length signals using the standard methods. This made us understand how FFT is computationally faster that standard discrete fourier transform.
While the time difference in computing signals of such small magnitude seems insignificant, while computing a longer, more complex signal, fast fourier transform will save valuable execution time.
FFT is faster than DFT as it combines common calculations around input samples. Thus, a fixed pattern is established for performing these calculations for input samples.
ReplyDeleteFFT is computationally faster than DFT
ReplyDeleteFFT does parallel computing
ReplyDeleteNumber of computations required are less, thus speed increases.
ReplyDeleteFFT is computationally faster that standard discrete fourier transform
ReplyDeletewell written
ReplyDelete