Friday, 22 April 2016

Convolution and Correlation Algorithms


EXPERIMENT 1:  Convolution and Correlation Algorithms
Day one of DSPP practicals. Although we were given an idea about how things worked during those two hours, we did not know what to expect. Ours was just the second batch of the week since we had our lab time on Tuesday mornings. We tried to satisfy our curiosity by asking for feedback from the students of the previous batch. But the tired and frustrated faces told us little and did nothing to lift our hopes. The suspense continued.
At sharp 9 a.m. on Tuesday we entered the lab with our sleepy eyes and jittery nerves. Opening the lab manual given to us, we read the title of the first experiment. That brought a smile to our faces because this was related to something we studied even in the previous semester. We sighed with relief and sat ourselves in front of our respective computers.
The aim of this experiment was to study mathematical operation such as Linear convolution, Circular convolution, Linear convolution using circular convolution.
We developed a function to find Linear Convolution and Circular Convolution
We calculated Linear Convolution, Circular Convolution and Linear Convolution using Circular Convolution and verified the results using mathematical formulation.
We then concluded on aliasing effect in Circular convolution.
The aim of the second part of the experiment was to study mathematical operation of correlation and measure degree of similarity between two signals.
We wrote a function to find correlation operation.                                                                               We calculated correlation of a DT signals and verify the results using mathematical formulae. We also measured the degree of similarity using Carl’s Correlation Coefficient formula in time domain.
All done in a day's work, we congratulated ourselves. With beaming smiles on our faces and hope in our heart, we moved onto the next experiment.
Program code: Correlation




4 comments:

  1. If both x(n) and h(n) are causal then y(n) is also causal.

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  2. real time applications of correlation includes radar and signal analysis

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  3. Correlation is the optimal technique for detecting a known waveform in random noise. It has applications such as radar.

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