62 lines
1.6 KiB
Matlab
62 lines
1.6 KiB
Matlab
clear all
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close all
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% loads signal and its characteristics
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signal = csvread('unknownsignal.csv');
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%%%%%SIGNAL CHARACTERISTICS%%%%%
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% sets sampling frequency
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fps = 200; % -> freqMax of the signal should be < 150 Hz (Shannon-Nyquisit theorem), in practice freqMax < 60 Hz would be better
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% computes the duration of the signal
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duration = length(signal) / fps; % in s
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disp(duration);
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% estimates its original frequency resolution
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resolution = fps / length(signal); % in Hz
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%Then we study the stationarity
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% temporal plot
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figure;
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plot(signal);
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xticks(0:0.2*fps:length(signal)*fps);
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xticklabels(0:0.2:length(signal)/fps);
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xlabel('Time (s)');
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ylabel('Amplitude (a.u.)');
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% spectrogram
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step_size = 50; %ms
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window_size = 1000; %ms
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spectrogram(signal, fps, step_size, window_size);
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% We conclude that the signal must be cut in 2 stationary parts
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%Thus we cut it in 2 parts of equal length
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% First part: [0 1s]
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signal_1 = signal(1:end/2);
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% Second part: [1s 2s]
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signal_2 = signal(end/2+1:end);
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%%%%%SPECTRAL ANALYSIS (RECTANGULAR WINDOW)%%%%%
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%plots power spectrum with rectangular window
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% 1st part of the signal with 0.5 Hz resolution
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frequencySpectrum(signal_1, fps, 0.5);
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% 2nd part of the signal with 0.5 Hz resolution
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frequencySpectrum(signal_2, fps, 0.5);
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%%%%%SPECTRAL ANALYSIS (BLACKMAN WINDOW)%%%%%
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%plots power spectrum with blackman window
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signal_1_win = blackmanWin(signal_1);
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% 1st part of the signal with 0.5 Hz resolution
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frequencySpectrum(signal_1_win, fps, 0.5);
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signal_2_win = blackmanWin(signal_2);
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% 2nd part of the signal with 0.5 Hz resolution
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frequencySpectrum(signal_2_win, fps, 0.5);
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