Mikaela_sf asked . 2021-01-19

Need Help about FFT and STFT

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?

matlab , signal , signal processing

Expert Answer

Kshitij Singh answered . 2024-12-21 12:24:58

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

Not satisfied with the answer ?? ASK NOW

Recent Matlab Projects

Classification of Covid and Non-Covid Lungs CT-Scan using Deep Learning with MATLAB

Matlab simulation on Wind Energy system

Frequently Asked Questions

MATLAB offers tools for real-time AI applications, including Simulink for modeling and simulation. It can be used for developing algorithms and control systems for autonomous vehicles, robots, and other real-time AI systems.

MATLAB Online™ provides access to MATLAB® from your web browser. With MATLAB Online, your files are stored on MATLAB Drive™ and are available wherever you go. MATLAB Drive Connector synchronizes your files between your computers and MATLAB Online, providing offline access and eliminating the need to manually upload or download files. You can also run your files from the convenience of your smartphone or tablet by connecting to MathWorks® Cloud through the MATLAB Mobile™ app.

Yes, MATLAB provides tools and frameworks for deep learning, including the Deep Learning Toolbox. You can use MATLAB for tasks like building and training neural networks, image classification, and natural language processing.

MATLAB and Python are both popular choices for AI development. MATLAB is known for its ease of use in mathematical computations and its extensive toolbox for AI and machine learning. Python, on the other hand, has a vast ecosystem of libraries like TensorFlow and PyTorch. The choice depends on your preferences and project requirements.

You can find support, discussion forums, and a community of MATLAB users on the MATLAB website, Matlansolutions forums, and other AI-related online communities. Remember that MATLAB's capabilities in AI and machine learning continue to evolve, so staying updated with the latest features and resources is essential for effective AI development using MATLAB.

Without any hesitation the answer to this question is NO. The service we offer is 100% legal, legitimate and won't make you a cheater. Read and discover exactly what an essay writing service is and how when used correctly, is a valuable teaching aid and no more akin to cheating than a tutor's 'model essay' or the many published essay guides available from your local book shop. You should use the work as a reference and should not hand over the exact copy of it.