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Hi!

I got a large amount of data, more than 5mb, so I can’t upload it. I would like to ask, my data is three xyz three-axis data collected from acceleratoter and it based on time, they are written in a .mat file.

I want to intercept a small part of the time from this .mat file. What should I do?

After that when i get the new data from that time ,how can i calculate the FFT Power Spectrum for a sliding window ? Can someone help me with this? Best Regards!

Mathieu NOE
on 13 Jan 2021

hello

see the code below for (averaged) fft analysis and spectrogram

you can easily incorporate a test to select samples that matches the condition time > t_min and time < t_max.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% load signal

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

load('signal15.mat') % time (tout) and data (simout)

% t = tout;

channel = 1;

signal = simout(:,channel);

samples = length(signal);

Fs = 1/mean(diff(tout));

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% FFT parameters

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

NFFT = 4096; %

OVERLAP = 0.75;

% spectrogram dB scale

spectrogram_dB_scale = 80; % dB range scale (means , the lowest displayed level is XX dB below the max level)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% options

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% if you are dealing with acoustics, you may wish to have A weighted

% spectrums

% option_w = 0 : linear spectrum (no weighting dB (L) )

% option_w = 1 : A weighted spectrum (dB (A) )

option_w = 0;

%% decimate (if needed)

% NB : decim = 1 will do nothing (output = input)

decim = 2;

if decim>1

signal = decimate(signal,decim);

Fs = Fs/decim;

end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% display 1 : averaged FFT spectrum

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

[freq, sensor_spectrum] = myfft_peak(signal,Fs,NFFT,OVERLAP);

% convert to dB scale (ref = 1)

sensor_spectrum_dB = 20*log10(sensor_spectrum);

% apply A weigthing if needed

if option_w == 1

pondA_dB = pondA_function(freq);

sensor_spectrum_dB = sensor_spectrum_dB+pondA_dB;

my_ylabel = ('Amplitude (dB (A))');

else

my_ylabel = ('Amplitude (dB (L))');

end

figure(1),plot(freq,sensor_spectrum_dB,'b');grid

title(['Averaged FFT Spectrum / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(freq(2)-freq(1)) ' Hz ']);

xlabel('Frequency (Hz)');ylabel(my_ylabel);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% display 2 : time / frequency analysis : spectrogram demo

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

[sg,fsg,tsg] = specgram(signal,NFFT,Fs,hanning(NFFT),floor(NFFT*OVERLAP));

% FFT normalisation and conversion amplitude from linear to dB (peak)

sg_dBpeak = 20*log10(abs(sg))+20*log10(2/length(fsg)); % NB : X=fft(x.*hanning(N))*4/N; % hanning only

% apply A weigthing if needed

if option_w == 1

pondA_dB = pondA_function(fsg);

sg_dBpeak = sg_dBpeak+(pondA_dB*ones(1,size(sg_dBpeak,2)));

my_title = ('Spectrogram (dB (A))');

else

my_title = ('Spectrogram (dB (L))');

end

% saturation of the dB range :

% saturation_dB = 60; % dB range scale (means , the lowest displayed level is XX dB below the max level)

min_disp_dB = round(max(max(sg_dBpeak))) - spectrogram_dB_scale;

sg_dBpeak(sg_dBpeak<min_disp_dB) = min_disp_dB;

% plots spectrogram

figure(2);

imagesc(tsg,fsg,sg_dBpeak);colormap('jet');

axis('xy');colorbar('vert');grid

title([my_title ' / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(fsg(2)-fsg(1)) ' Hz ']);

xlabel('Time (s)');ylabel('Frequency (Hz)');

function pondA_dB = pondA_function(f)

% dB (A) weighting curve

n = ((12200^2*f.^4)./((f.^2+20.6^2).*(f.^2+12200^2).*sqrt(f.^2+107.7^2).*sqrt(f.^2+737.9^2)));

r = ((12200^2*1000.^4)./((1000.^2+20.6^2).*(1000.^2+12200^2).*sqrt(1000.^2+107.7^2).*sqrt(1000.^2+737.9^2))) * ones(size(f));

pondA = n./r;

pondA_dB = 20*log10(pondA(:));

end

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

Mathieu NOE
on 18 Jan 2021

hello

If your window lenghth is nfft , the spectrum frequency resolution is df = Fs/nfft ; so there are compromise between frequency resolution and time resolution for a spectrogram plot.

I usually start with an overlap of 50 to 75% of nfft .

if I really need to heave a smoother and refined time resolution , I can go up to 95 / 97% of overlap.

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