We have started with the basics of DSPP that is convolution and correlation. We have encoded the methods and data in C language and have calculated the precise values which are approximately closer to the theoretical values.
Initially, we entered the length of input x(n) and impulse response h(n). Then we made the function to calculate the output signal y(n).
We concluded that, in linear convolution, if both the input signals are causal, then the resultant output signal is also causal.
Correlation gives an output signal which is both sided and gives us the degree of similarity between the inputs. Correlation of a signal with itself is called auto correlation its output is always a palindrome.
https://drive.google.com/drive/folders/0B3FKfBjwMZ_EOVJyVHBsOUlKcEE
Initially, we entered the length of input x(n) and impulse response h(n). Then we made the function to calculate the output signal y(n).
We concluded that, in linear convolution, if both the input signals are causal, then the resultant output signal is also causal.
Correlation gives an output signal which is both sided and gives us the degree of similarity between the inputs. Correlation of a signal with itself is called auto correlation its output is always a palindrome.
https://drive.google.com/drive/folders/0B3FKfBjwMZ_EOVJyVHBsOUlKcEE
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