| << Chapter < Page | Chapter >> Page > |
The following is the MATLAB source code for each of the components of our project.
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);
endend
function out = sample(sd,plot,sig)
%sample%samples a manually constructed signal, and adds gaussian noise to it
%with a standard deviation that is providedout=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 manageableout=sig+randn(1,1000).*sd;
%optimize number of samplesif 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=1:4NSig=sample(sd,0,sig).*sigMask;
if(n==1)runT=NSig;
elserunT=(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);
endmask=zeros(1,siz(2));
for l=1:siz(2)if(temp(l)<2)
mask(l)=0;else
mask(l)=sigMask(l);end
endend
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;
endend
if(flag>0)
break;end
endsamples=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);
endif(flag~=actualpassword(i))
accepted=-i;break;
endend
end
function Controller2()
%Helper function used to graph trendssig=1;
for sd=0:30passed=0;
for reps=1:50if(testArbitrary(sig,3+sd/10)==sig)
passed=passed+1;end
endtemp(sd*10-29)=passed
endsubplot(1,1,1);
plot(temp);end
Notification Switch
Would you like to follow the 'Sparse signal recovery in the presence of noise' conversation and receive update notifications?
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|