Data collection in a laboratory does not usually provide you with a clean signal. in other words, most often the captured signal is the summation of the desired (clean) signal and noise (i. e., background noise, instrumental noise, in a more complicated case, the noise and clean signal may be combined together with a transformation other than addition, which is beyond the scope of this course for this project a clean1 and noisy ecg signals are provided to you. the background noise is a very weak white gaussian noise which has been manually added to a clean ecg project. since the background noise is significantly weaker than the desired signal (snr> 20 db), an easy approach to smooth the short-term fluctuations caused by the noise is using a "moving average" system (a type of filter). this system can be represented as follows signal for the purpose of this x[n] y[n] dt system where y[n] = nak-1x(n-k]. therefore, each sample of the output is the average of n samples of the input signal, where the value of n can be set accordingly 1. write a matlab script and perform the following steps a. load both clean and noisy ecg signals b. using the input-output relationship of the system, compute the output (yln) when the input signal is the noisy ecg find the impulse response of this system (by hand), then define it in matlae c. compute the output of the system by convolving the noisy ecg signal and the impulse response of the system generate a figure with 4 subplots, where 1) subplot 1 is the clean ecg signal 2) subplot 2 is the noisy ecg signal. 3) subplot 3 is the output you computed in part b. 4) subplot 4 is the output you computed in part d note: sketch all the above signals for 2 seconds using stem function note: consider 4 different values of 2, 3, 5, and 10 for n. therefore, 4 different figures each with 4 subplots are required. check if you have found same results from parts b and d.