Convolution and its types were studied in the previous semester. This experiment was all about brushing up our old skils of c language and the topic itself.
Linear Convolution was done by taking the vaules of L and M as inputs and their respective x(n) and h(n) values. The answer was stored in y(n) and the length N was calculated using the formula N=L+M-1.
Circular Convolution was done by taking x(n) and h(n) both of length N. The different values of N were 4,5,6,7 and 8. The signal remained the same but it was done using zero padding.
Correlation was implemented by taking values of L,M, x(n) and y(n). Autocorrelation and correlation was done too.
https://drive.google.com/open?id=0B8F3pY6H1pIWU2lTeUFERWUzbk0
https://drive.google.com/open?id=0B8F3pY6H1pIWcHZESUs5amc3Nmc
https://drive.google.com/open?id=0B8F3pY6H1pIWMjROczRaWTZPWUU
http://preranasarode1995.blogspot.com/2016/04/linear-circular-corelation.html
Correlation gives a measure of similarity between two signals, which are compared and the degree of similarity between them is computed is using Correlation.
ReplyDeleteCorrelation gives a measure of similarity between two signals, which are compared and the degree of similarity between them is computed is using Correlation.
ReplyDeleteCorrelation gives a measure of similarity between two signals, which are compared and the degree of similarity between them is computed is using Correlation
ReplyDeleteAliasing effect occurs in circular convolution when N is less than L+M-1
ReplyDeleteI forgot to mention that correlation is used to find the degree of similarity between two signals.
ReplyDeleteFor N=L+M-1 linear convolution is equal to Circular Convolution
ReplyDeleteIn digital signal processing, frequency filtering can be simplified by convolving two functions (data with a filter) in the time domain, which is analogous to multiplying the data with a filter in the frequency domain
ReplyDelete