Discrete Fourier Transform
Using DFT, discrete time data is converted into a discrete frequency representation. The experiment focussed on finding the DFT of input signal x[n]. In the first case, the input signal was a N point sequence, so the output of DFT was also a N point sequence. In the second case, the length of input signal was increased by zero padding. The result showed that, frequency spacing for output signal decreased. As a result, approximation error decreased.
DFT gives an approximate spectrum and is computationally slow.
DFT also increases the resolution of the spectrum
ReplyDeleteZero padding at the end of the signal helps in increasing the resolution.
DeleteDFT is periodic in nature because of periodic nature of twiddle factor
ReplyDeleteIn DFT, we assume signal to be periodic.
DeleteDFT is discrete version of DTFT.
ReplyDeleteIt is sampling of DTFT in F-domain.
DeleteDFT requires a lot of calculations hence we use FFT.DFT is used for Fourier analysis of signals
ReplyDeleteAs FFT has more parallelism than DFT, it is faster.
DeleteInput signal is assumed to be periodic for DFT computation.
ReplyDeleteYes and hence its response is discrete in nature.
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