The aim of this experiment was to perform Fast Fourier Transform(FFT) of N point signal. We found out the FFT of 4 point and 8 point input sequence using DITFFT. We also performed Inverse Fast Fourier Transform(IFFT) to verify our original input signal.
We observed that, input sequence order and output sequence order in Radix-2 FFT algorithm is in bit reversal manner. We compared the real and complex additions as well as the real and complex multiplications of FFT with DFT and came to a conclusion that FFT improves operational efficiency by parallel processing. Thus, FFT is computationally fast algorithm.
We observed that, input sequence order and output sequence order in Radix-2 FFT algorithm is in bit reversal manner. We compared the real and complex additions as well as the real and complex multiplications of FFT with DFT and came to a conclusion that FFT improves operational efficiency by parallel processing. Thus, FFT is computationally fast algorithm.
Radix 2 FFT is computationally fastest algorithm.
ReplyDeleteYes this is because decimation reduces calculation.
Deleteorder of ip and op is inverse in dit-fft and dif-fft
ReplyDeleteFFT makes use of parallel processing
ReplyDeleteNumber of computations in FFT is less than that of DFT. This makes FFT computationally faster.
ReplyDeleteDescriptive and informative!
ReplyDeleteBecause of parallel processing FFT is more faster than DFT
ReplyDeleteOther radices are also possible
ReplyDeleteFFT is faster than DFT
ReplyDeletethe signal is divided into 2 parts, so the computation is carried out only for N/2 signal values
ReplyDelete