function xbincomm(u, p, pf, samples, seed) 
% xbincomm(u, p, pf, samples, seed)  Binary channel experiment: a Bernoulli 
%    signal with random noise.  The first plot is a parametric plot of the  
%    probability of a detection and the probability of a false detection as 
%    functions of the threshold value, as it goes from 0 to u.  The second  
%    plot is the error probability versus the threshold value.  Both plots  
%    are done against the theoretically predicted curves. 
% 
%       u       value for a positive on the Bernoulli signal 
%       p       probability of a positive on the Bernoulli signal 
%       pf      probability function for noise:  must have mean of 0 
%       samples number of experimental samples to use 
%       seed    seed for random number generator 
if u<=0 
   error('u must be > 0') 
end 
if samples<=0 
   error('samples must be > 0') 
end 
if ispf(pf)==0 
   error('not a valid probability function') 
end 
if inrange(mmean(pf), 0, 1e-4)==0 
   error('noise probability function must have mean 0') 
end 
t=[0:u/100:u]; 
PD=zeros(size(t)); 
PF=PD; 
PE=PD; 
PPD=PD; 
PPF=PD; 
PPE=PE; 
pff=pf; 
x=xvals(pf); 
pfd=pf; 
pfd(2:length(x)+1,1)=(x+u)'; 
cff=getcf(pff); 
cfd=getcf(pfd); 
N=rvgen(cff, 1, 2*samples, seed); 
NF=N(1:samples); 
ND=N(samples+1:2*samples)+u; 
for i=1:length(t) 
   f=find(NF>=t(i)); 
   PF(i)=length(f)/samples; 
   NF=NF(f); 
   f=find(ND>=t(i)); 
   PD(i)=length(f)/samples; 
   ND=ND(f); 
end 
PPD=1-pval(cfd, t); 
PPF=1-pval(cff, t); 
PE=p.*(1-PD)+(1-p).*PF; 
PPE=p.*(1-PPD)+(1-p).*PPF; 
f=find(PE==min(PE)); 
topt=t(f(1)); 
f=find(PPE==min(PPE)); 
ptopt=t(f(1)); 
subplot(1, 2, 1); 
plot(PF, PD, 'r'); 
hold on 
plot(PPF, PPD); 
hold off 
xlabel('PF'); 
ylabel('PD'); 
subplot(1, 2, 2); 
plot(t, PE, 'r'); 
hold on 
plot(t, PPE); 
hold off 
xlabel('threshold'); 
ylabel('PE'); 
s=sprintf('\"Best\" threshold was %1.4f - predict %1.4f', topt, ptopt); 
disp(s);