Friday, 22 April 2016

Experiment 1: Correlation & Convolution

We performed Linear Convolution, Circular concolution, Auto Correlation, Cross Correlation and Linear Convolution using Circular Convolution.
Linear Convolution : The length of the linear convolution output was equal to
                                  [(length of 1st input signal) + (length of 2nd input signal) -1]
Circular Convolution : The length of the circular convolution output was equal to
                                   [Maximum(length of 1st input signal , length of 2nd input signal)]
Auto Correlation : The two input sequences are the same and the output sequence was
                              symmetrical.
                              In Auto Correlation of Delayed input the output was the same as that
                              without delayed input signal.
Cross Correlation : The two input sequences are different and length of the output was
                               equal to
                               [(length of 1st input signal) + (length of 2nd input signal) -1]
                               Cross Correlation with one delayed input gives a delayed output.
Linear Convolution using : The length of the output is equal to or greater than
Circular Convolution        
[(length of 1st input signal) + (length of 2nd input signal) -1]

When both the inputs in Convolution are Causal the the output is also Causal.



Linear Convolution : https://drive.google.com/open?id=0BwzFGc0wvjNveFVIYUVjY0JreFkCircular Convolution : https://drive.google.com/open?id=0BwzFGc0wvjNvbjYtcnhVSE9CbGM
Correlation : https://drive.google.com/open?id=0BwzFGc0wvjNvdjJnZmNSSHluZzQ

6 comments:

  1. If both x[n] and h[n] are causal then the resultant y[n] is also causal.

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  2. Application of correlation?

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  3. Auto correlation can be used in light related applications like laser to measure short duration light pulses.

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  4. Were the effects of zero padding and aliasing effect observed?

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  5. Aliasing is when the last value of M-1 of y(n) wraps around and gets added with the first M-1 of y(n).

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  6. Zero padding is required to avoid aliasing effect.

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