Till now in the course, we look at the samples of a signal s[n] as realizations of a random variable S. For a sinusoidal signal (s[n]=cos(Omega_0 n)), calculate the probability density function (pdf) of this random variable (S). Assume that Omega_0 is not a rational number.
[y1,y2]= separate(x1,x2);
x1 and x2, each one of is mixture of two signals. The function takes them, and gives us y1 and y2, which have to be separated signals. To test the function, you create in MATLAB two signals (s1 and s2), mix them (for example using x1=0.8*s1+0.2*s2; x2=0.2*s1+ 0.8*s2) and then give x1 and x2 to the above function to get y1 and y2. Then, measure the difference between separated and original images using SNR in dB, defined as (assuming that you have no permutation, that is, y1 is an estimation of s1 not an estimation of s2):
SNR = 10 * log10 (mean(s1.^2) / mean( (s1-y1).^2 ) )
and include these SNR's in your report.