how to eliminate zeros in the output of fast fourier transform (matrix / array)?

I use simulink to find the frequency domain value of a sine signal that consists of two frequencies using fast fourier transform and then use findpeaks to find the value of each peak it gets. By using a zero order hold of 1e-3 and a buffer of 1000 and a time of 10 seconds so that the discrete matrix value becomes [11x1000] but there is an error so it can't be entered into findpeaks because I think it comes from the value t = 0 is all value 0 so that at [1x1000] there is a leading zero. I have used a lot of code but nothing works, is there any way to help me find out where the error is?

one example is I use the code below and I think because I omitted [1x1000] at the beginning so the rest becomes [10x1000] but it says an error because the number of elements doesn't match and I don't understand why.

in the way below is still the same, which part is lacking please I really need help

I also attach the workspace if possible it can be more helpful to understand

function y = fcn(u)
a = fftshift(u);
v = nonzeros(a');
y = reshape(v,1000,10);

function y = fcn(u)
y = u;
y(y==0) = [];

[Paul Hoffrichter]

I do not have simulink, so maybe that is why my load of 'file.mat' gives me only this:

out =
  1×6 uint32 row vector
   3707764736            2            1            1            1            3

[Naufal Arfani]

[Naufal Arfani]

I'm sorry I mean the contents are like this so it looks like at t = 0 the value is all 0, but I tried to convert it to NaN but there were still errors

Answer 1

[Mathieu NOE]

hello

FYI, this is a code to do averaged fft analysis + findpeaks demo + notch filter (extra bonus)

you can import the data from where you want

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% load signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%% dummy data
Fs = 1000;
samples = 25000;
t = (0:samples-1)'*1/Fs;
signal = cos(2*pi*50*t)+cos(2*pi*100*t)+1e-3*rand(samples,1);


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FFT parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
NFFT = 1000;    % 
Overlap = 0.75;
w = hanning(NFFT);     % Hanning window / Use the HANN function to get a Hanning window which has the first and last zero-weighted samples.


%% notch filter section %%%%%%
% H(s) = (s^2 + 1) / (s^2 + s/Q + 1)

fc = 50;       % notch freq
wc = 2*pi*fc;
Q = 5;         % adjust Q factor for wider (low Q) / narrower (high Q) notch
                % at f = fc the filter has gain = 0

w0 = 2*pi*fc/Fs;
alpha = sin(w0)/(2*Q);

            b0 =   1;
            b1 =  -2*cos(w0);
            b2 =   1;
            a0 =   1 + alpha;
            a1 =  -2*cos(w0);
            a2 =   1 - alpha;
            
% analog notch (for info)
num1=[1/wc^2 0 1];
den1=[1/wc^2 1/(wc*Q) 1];

% digital notch (for info)
num1z=[b0 b1 b2];
den1z=[a0 a1 a2];

freq = linspace(fc-1,fc+1,200);
[g1,p1] = bode(num1,den1,2*pi*freq);
g1db = 20*log10(g1);

[gd1,pd1] = dbode(num1z,den1z,1/Fs,2*pi*freq);
gd1db = 20*log10(gd1);

figure(1);
plot(freq,g1db,'b',freq,gd1db,'+r');
title(' Notch: H(s) = (s^2 + 1) / (s^2 + s/Q + 1)');
legend('analog','digital');
xlabel('Fréquence (Hz)');
ylabel(' dB')
 
% now let's filter the signal 
signal_filtered = filtfilt(num1z,den1z,signal);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display : averaged FFT spectrum before / after notch filter
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

[freq,fft_spectrum] = myfft_peak(signal, Fs, NFFT, Overlap);
sensor_spectrum_dB = 20*log10(fft_spectrum);% convert to dB scale (ref = 1)

% demo findpeaks
df = mean(diff(freq));
[pks,locs]= findpeaks(sensor_spectrum_dB,'SortStr','descend','NPeaks',2);

[freq,fft_spectrum_filtered] = myfft_peak(signal_filtered, Fs, NFFT, Overlap);
sensor_spectrum_filtered_dB = 20*log10(fft_spectrum_filtered);% convert to dB scale (ref = 1)

figure(2),semilogx(freq,sensor_spectrum_dB,'b',freq,sensor_spectrum_filtered_dB,'r');grid
title(['Averaged FFT Spectrum  / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(freq(2)-freq(1)) ' Hz ']);
legend('before notch filter','after notch filter');
xlabel('Frequency (Hz)');ylabel(' dB')
text(locs+.02,pks,num2str(freq(locs)))

function  [freq_vector,fft_spectrum] = myfft_peak(signal, Fs, nfft, Overlap)
% FFT peak spectrum of signal  (example sinus amplitude 1   = 0 dB after fft).
% Linear averaging
%   signal - input signal, 
%   Fs - Sampling frequency (Hz).
%   nfft - FFT window size
%   Overlap - buffer overlap % (between 0 and 0.95)
signal = signal(:);
samples = length(signal);
% fill signal with zeros if its length is lower than nfft
if samples<nfft
    s_tmp = zeros(nfft,1);
    s_tmp((1:samples)) = signal;
    signal = s_tmp;
    samples = nfft;
end

% window : hanning
window = hanning(nfft);
window = window(:);

%    compute fft with overlap 
 offset = fix((1-Overlap)*nfft);
 spectnum = 1+ fix((samples-nfft)/offset); % Number of windows
%     % for info is equivalent to : 
%     noverlap = Overlap*nfft;
%     spectnum = fix((samples-noverlap)/(nfft-noverlap));	% Number of windows

    % main loop
    fft_spectrum = 0;
    for i=1:spectnum
        start = (i-1)*offset;
        sw = signal((1+start):(start+nfft)).*window;
        fft_spectrum = fft_spectrum + (abs(fft(sw))*4/nfft);     % X=fft(x.*hanning(N))*4/N; % hanning only 
    end
    fft_spectrum = fft_spectrum/spectnum; % to do linear averaging scaling

% one sidded fft spectrum  % Select first half 
    if rem(nfft,2)    % nfft odd
        select = (1:(nfft+1)/2)';
    else
        select = (1:nfft/2+1)';
    end
fft_spectrum = fft_spectrum(select);
freq_vector = (select - 1)*Fs/nfft;
end

[Naufal Arfani]

Wow, it's really long, I really appreciate it, thank you very much, I'll definitely try it @Mathieu NOE

[Mathieu NOE]

You're welcome !

[Naufal Arfani]

sorry I ask for your help again @Mathieu NOE can you help make fft coding but with the input signal from simulink so maybe you don't need to use t and port because it will use t from simulink, can you help me?

[Mathieu NOE]

hello

I loaded the mat file but in don't understand what the data represents. I was waiting for a vector of same length as time vector. What are those 11 vectors that i plotted ? that has litte to do with a sine signal of 2 frequencies

>> load('file.mat')

>> out

out =

Simulink.SimulationOutput:

simout: [11x1000 double]

simout1: [1x1 double]

tout: [10001x1 double]

SimulationMetadata: [1x1 Simulink.SimulationMetadata]

ErrorMessage: [0x0 char]