Please post Matlab code along with all deliverables mentioned in the problem

4. Signal Smoothing: A common example of a digital signal processing application is the removal of the noise component from a signal corrupted by additive noise. Let sn] be the signal corrupted by a random noise u [n] resulting in the noisy signal z [n] = s[n] + w[n]. The objective is to operate on rin] to generate a signal yin) which is a reasonable approximation to sinl. To this end. a simple approach is to generate an output sample by averaging a number of input samples around the sample at instant n. For example, a three-point moving average algorithm is given by, Let s[n] = 2n0 9n denote the signal which is corrupted by Gaussian noise u n) of zero mean and variance Note that w n] can be generated using the MATLAB function randn. Im- plement the three-point moving average algorithm described above on the corrupted signal x[n] = s[n] + w[n] to find an approximation for s[n]. Let the length for all the signals be n=0 : 100, Plot the signals s[n], w[n] and z[n] on one figure and s[n], w[nl and y[n] on another figure