Discrete Fourier Transform
Discrete Fourier transform (DFT) takes a finite number of samples of a signal and transforms them into a finite number of frequency samples of that signal. We have used C programming and analysed three cases. A finite 4 point sequence was taken as input. The length of the output signal was same as input signal. When the signal is zero padded to an 8 point sequence, we observe that the resolution of the output magnitude spectrum improved. Now the input sequence is expanded to an 8 point sequence by adding alternate zeros. This compresses the spectrum in frequency domain. Also, DFT produces periodic results.
Originally posted at: https://chaitanya33.wordpress.com/2017/03/13/discrete-fourier-transform/
Originally posted at: https://chaitanya33.wordpress.com/2017/03/13/discrete-fourier-transform/
DFT has widespread applications in SpectralAnalysis of systems, LTI systems, Calculating convolution of signals,multiplication of large polynomials, noise removal et
ReplyDeleteYes. Thank you for mentioning the applications
DeleteBy appending more zeroes, the missing values in less point DFT are present in the DFT with more point.
ReplyDeleteYes. Frequency spacing reduces and hence approximation error reduces
DeleteDFT is easier to implement and finds its application in FSM method
ReplyDeleteGood explanation
ReplyDeleteThank you
DeleteIt is used for Fourier analysis of a signal
ReplyDeleteYes
DeleteDFT is periodic in nature because of periodic nature of twiddle factor
ReplyDeleteTrue
DeleteThe approximation error reduces by appending the input sequence by zeroes.
ReplyDeleteYes because the Frequency spacing reduces
DeleteResults of DFT can be stored in memory
ReplyDeleteYes because the DFT spectrum is discrete
DeleteDFT uses sampling of frequency response.
ReplyDelete