function out = addNoise(sig,mean,sd,Plot)
%addNoise
%adds noise with given mean and sd to the signal
    rand=randn(1,1000)*sd+mean;
    out=sig+rand;
    if(Plot==1)
        plot(1:1000,out,1:1000,sig);
    end
end

function out = sample(sd,plot,sig)
%sample
%samples a manually constructed signal, and adds gaussian noise to it
%with a standard deviation that is provided
out=fft(addNoise(sig,0,sd,plot));
end

function [out,samp]=init(sig,sd)
    %averages the signal and the noise over a number of samples to make the
    %noise level manageable
    out=sig+randn(1,1000).*sd;
    %optimize number of samples
    if sd<76
        val=6.25;
    else
        val=9;
    end
    samp=floor((ceil(sd))^2/(val));
    for n=2:samp
        out=(out.*(n-1)+sig+randn(1,1000).*sd)/n;
    end
    out=fft(out);
end

function [mask, NSig,runT] = simpleIterate(sigMask,threshold,run,n,sd,sig)
%simpleIterate(sigMask,threshold,run,n)
%computes an iteration of the thresholding, with a running average of run,
%on iteration n, with the current signal mask of sigMask
%returns the new signal mask, the current signal(non-masked) NSig and the
%running average of the signal runT
    siz=size(sigMask);
    temp=zeros(1,siz(2));
    for i=1:4
        NSig=sample(sd,0,sig).*sigMask;
        if(n==1)
            runT=NSig;
        else
            runT=(run.*(n-1)+NSig)/n;
        end
        %temp=temp+(max(abs(real(NSig)),abs(imag(NSig)))>threshold);
        temp=temp+(max(abs(real(runT)),abs(imag(runT)))>threshold);
        %temp=temp+(abs(NSig)>threshold);
    end
    mask=zeros(1,siz(2));
    for l=1:siz(2)
        if(temp(l)<2)
            mask(l)=0;
        else
            mask(l)=sigMask(l);
        end
    end
end

function [flag,samples,time]=testArbitrary(sig,sd)
%Simulates the transmission of a signal in the library, and tests whether
%or not it can be recovered.
    siglib=cat(1,sin(0:pi/500:(1000*pi-1)/500),sin(0:pi/250:(2000*pi-1)/500),sin(0:pi/125:(4000*pi-1)/500),sin(0:pi/50:(10000*pi-1)/500),sin(0:pi/25:(20000*pi-1)/500));
    siglib=cat(1,siglib,sin(0:pi/500:(1000*pi-1)/500)+sin(0:pi/50:(10000*pi-1)/500),cos(0:pi/500:(1000*pi-1)/500),cos(0:pi/250:(2000*pi-1)/500),cos(0:pi/125:(4000*pi-1)/500),cos(0:pi/50:(10000*pi-1)/500));
    siglib=cat(1,siglib,cos(0:pi/25:(20000*pi-1)/500),cos(0:pi/25:(20000*pi-1)/500)+sin(0:pi/500:(1000*pi-1)/500),sin(0:pi/500:(1000*pi-1)/500)+sin(0:pi/125:(4000*pi-1)/500)+cos(0:pi/25:(20000*pi-1)/500));
    sigmax=max(abs(fft(siglib(sig,:))));
    threshhold=sigmax-3*max(abs(real(fft(randn(1,1000)))));
    tolerance=.5;
    A=ones(1,1000);
    flag=0;
    tic
    [C,samples]=init(siglib(sig,:),sd);
    for i=1:10000
        [A,B,C]=simpleIterate(A,threshhold,C,i+samples,sd,siglib(sig,:));
        for j=1:size(siglib)
            if(abs(ifft(A.*C)-siglib(j,:))<tolerance)
                flag=j;
                break;
            end
        end
        if(flag>0)
            break;
        end
    end
    samples=samples+i;
    time=toc;
end

function accepted = Controller(enteredpassword,sd)
%Tests whether or not a transmission of a password will activate the system
%This simulates the noise and processing as well as the values
    actualpassword=cat(1,13,5,10,4,2,8);
    accepted=1;
    redundancy=3;
    for i=1:size(actualpassword);
        flag=0;
        %while flag==0
        for j=1:redundancy
            [flag,runs]=testArbitrary(enteredpassword(i),sd);
        end
        if(flag~=actualpassword(i))
            accepted=-i;
            break;
        end
    end
end

function Controller2()
%Helper function used to graph trends
    sig=1;
    for sd=0:30
        passed=0;
        for reps=1:50
            if(testArbitrary(sig,3+sd/10)==sig)
                passed=passed+1;
            end
        end
        temp(sd*10-29)=passed
    end
    subplot(1,1,1);
    plot(temp);
end