Discrete Fourier Transform
In this experiment, we calculated the discrete fourier transform of a N point sequence in time domain to obtain an N point sequence in Frequency domain. Hence, using DFT, discrete time data sets are converted into discrete frequency spectrum. DFT gives an approximate spectrum and is computationally slow.
In the first case, the input signal was x[n] was an N point sequence. The output of DFT gave X[k] which was also an N point sequence.
In the third case, the input signal was expanded by adding zeros in between the signal values as shown.
All these cases showed that, as the length of signal increases by zero padding: Frequency spacing decreases, approximation error decreases and resolution of the spectrum increases. Hence, Expansion of a signal in time domain gives a compression of signal in frequency domain.


As the length of the signal is increased by zero padding the quality of magnitude spectrum is improved.
ReplyDeleteBy appending more zeroes, the missing values in less point DFT are present in the DFT with more point.
ReplyDeleteIncrease in resolution results in more accuracy
ReplyDeleteDFT is discrete version of DTFT
ReplyDelete