In this experiment the aim was to study and understand Discrete Fourier Transform (DFT) using C language and manual verification.
Discrete Fourier Transform is used to transform time domain signals into frequency domain signals by sampling. More is the number of samples the greater is the degree of realisation of the original signal in the transfer domain.
We observed the magnitude spectrum of 4 pt and 8 pt signals. Also, by adding zeros to the 4 pt signal, the magnitude spectrum of the signal was more defined as we had more samples to plot.
We also observed that the transformed signal achieved from the time domain is compressed and also that DFT is computationally slow as the number of real and imaginary additions and multiplications is high.
Discrete Fourier Transform is used to transform time domain signals into frequency domain signals by sampling. More is the number of samples the greater is the degree of realisation of the original signal in the transfer domain.
We observed the magnitude spectrum of 4 pt and 8 pt signals. Also, by adding zeros to the 4 pt signal, the magnitude spectrum of the signal was more defined as we had more samples to plot.
We also observed that the transformed signal achieved from the time domain is compressed and also that DFT is computationally slow as the number of real and imaginary additions and multiplications is high.
How is it slow?
ReplyDeleteThe total number of real and complex addition and multiplications are more than that of FFT
DeleteIt requires more computations than FFT.
ReplyDeleteAnd hence FFT is more efficient and faster
DeleteThe DFT is also used to efficiently solve partial differential equations, and to perform other operations such as convolutions or multiplying large integers
ReplyDeleteYes, very well explained
DeleteDFT is said to be a frequency domain representation of the original input sequence
ReplyDeleteYes, DFT is frequency sampled output of DTFT
Delete