I am not certain what result you want.
Here, findpeaks finds 21 distinct spikes (and their locations)
LD = load('buzzerData.mat');
time = LD.time;
voltage = LD.voltage;
[pks,locs] = findpeaks(voltage, 'MinPeakProminence',4.5);
mdl = mean(diff(locs));
figure
plot(time, voltage)
hold on
plot(time(locs), pks, '^r')
hold off
xlabel('time')
ylabel('voltage')
ylim([-1 6])
figure
histogram(diff(locs), 50)
The histogram is optional, and simply looks at how the differences in the ‘locs’ values are clustered.
The ‘time’ vector has irregular sampling intervals, so it will not be possible to do any sort of signal processing on these data as they currently exist. One way to deal with that is to use the resample funciton, however this results in strange ‘voltage’ values. However if you want to use it, one approach would be:
Fs = 1/mean(diff(time));
[voltagers,timers] = resample(voltage,time,Fs);
The approximate sampling frequency (calcualted from the mean of the differences in the ‘time’ vector) is about 83.9925 Hz (assuming the ‘time’ units are seconds).
This works in R2022a, and should procduce reliable results in R2018a.
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