Adaptive Filtering

MATLAB^® Functions

[m,z]=hist(x,a)	m = counts in each bin; z = coordinates of the a bins; w = max(x)/length(z); bp = m/(w*a) = probability per bin;
[r,p,k]=residue(nu,de)	Example: F (z)= z2/z2−0.04, nu = [1 0 0], de =[1 0 −0.04], r = [0.1000 −0.1000], p= [0.2000 −0.2000],k = 1, hence F(z) = 0.1(z/(z − 0.2))− 0.1(z/(z + 0.2)) + 1, the inverse is f(n) = 0.1(0.2)n − 0.1(−0.2)n + δ(n); r= residues, p = poles
[r]=xcorr(x,y,‘biased’)	Produces a biased cross-correlation; if x = y, r = autocorrelation; the r is an N − symmetric function with respect to 0
[r]=xcorr(x,y,‘unbiased’)	Produces an unbiased cross-correlation; if x = y, r = autocorrelation
\omega	Produces the lowercase omega, the same for the rest of the Greek letters
\Omega	Produces ...