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p = 4; % number of plots to be drawn
k = 0;
SNR = 0;

for k=0:p-1
    SNR = SNR + 5; % start SNR
% create monoexponential data (e.g. MSE)
    M0  = 500;
    T2  = 30e-3;
    Messpunkte=20;
    TE  = linspace(5e-3,200e-3,Messpunkte); % create array of echo-times
    temp_signal=M0*exp(-TE/T2);
    temp_signal=awgn(temp_signal,SNR,'measured'); %add gaussian noise
    temp_signal(2:end)=temp_signal(2:end) + M0*exp(-TE(2:end)/T2)*0.2;
    TE=TE(2:end);
    temp_signal=temp_signal(2:end);
% set up inequality set to find a constrained minimum of a scalar
% function of several variables starting at an initial estimate
    AT2 = [-eye(2); eye(2)];
    bT2 = [-1e-12*ones(1,2) 10e3  1];
    initial=[1,1];
    fun = @(param) norm(temp_signal - (param(1)*exp(-TE/param(2))) );
% constrained nonlinear optimization
    f_param = fmincon(fun, initial, AT2, bT2, [],[],[],[],[]);
% show fitted parameters
    fitted_plot=f_param(1)*exp(-TE/f_param(2));
    subplot(1,p,k+1)
    plot(TE, temp_signal, 'Color',[0,0.7,0.9], 'LineWidth', 1); hold all
    plot(TE, fitted_plot, 'r', 'LineWidth', 1);
    title({'monoexp fit'; ['T_2=' num2str(f_param(2)) 's' ...
           ' (M_0=' num2str(f_param(1)) ')']})
end