Hi, differentiate the signal using the diff command. The derivative is a high pass filter which will remove the sine signale (low freq) and will amplify the rapid variations. You should get a sequence of peaks (negative and positive). Try to count them using the function findpeaks

Hi, 

 You can divide the total time by the pulse's duration.

Hi,

a 'sizzling hot' tip: seems you have a bad or missing ground connection (the sine which is overlayed by the pulse train). First try to get rid of the sine - this will make pulse counting much easier.

Looked another time at the images: sine with variable frequency ? Or do you have different sampling rates. If variable frequency: motor inverter ?

Regards

Hi Jamaleddin

below you can find the matlab code to solve your problem. I wrote the 1st part to reproduce in some way the signal you have. The second part is to recover the number of pulses. This last part is based on the concept I gave you as first answer. Hope that will help yuo.

close all;

clear all;

% 1st part used to generate the signal with pukses

t=8700:0.01:9100;

%

% sine wave

A=200;

f1=0.02;

x=A*cos(2*pi*f1*t);

%

% square wave

B=60;

f2=0.3;

y=B*square(2*pi*f2*t);

%

% NOISE

C=1;

r=C*rand(1,length(t));

%

% MIX them

z=x+y+r;

% 2nd part your code starts here replace z with your signal

% and B with the amplitude of the square wave e.g. 60

%

% getting filter signal

d=diff(z);

%

% find peaks

[p,i]=findpeaks(d,'MINPEAKHEIGHT',B);

%

% plots

plot(t,z,'b');

hold on;

plot(t(i),z(i),'ro');

grid on;

title(['Number of pulses: ',num2str(length(p))]);

legend('signal with pulses','pulse detected');

Hi,

Do you want in real time or in saved file?

@ Nivaldo Schiefler: I need to count pulses in save file. 

Well, you can do in many ways.

One option is you check sample by sample.

This way:

1ª - Looking for the first highest sample.

2ª - Save count in one var (count_peak = 1)

3ª – Now you need to find the next higher sample. Note that this time you need do pass cross-zero twice. After that you need like the 1ª.

4ª - Save count in one var (count_peak = 2….

5ª – repeat 3ª and 4ª until read all file and the final you will have the peaks.

If you didn't get, send me the file with your sample saved.

Nivaldo

@ Jamaleddin Mostafavi: HI, Did you take a look at my code? It solves completely your problem. If yoo don't want to use all the code just type:

d=diff(z);

[p,i]=findpeaks(d,'MINPEAKHEIGHT',B);

NUM_PULSES=length(p)

where z is your signal and  NUM_PULSES are the number of pulses inside it. Let me know. Bye

David

Yes, a differentiating filter will remove the interfering (low-freq) sinusoid, but it may also introduce a lot of noise. So you might get peaks due to high freq ripples that are not due to pulses from your encoder.

I think a better approach would be to make a near-dc notch filter. The natural roll-off (roll-on?) of this filter will also will also remove the interference, but the other higher-freq info should be unchanged.

The easiest way to design this dc notch filter would be just to compute the running average, over a finite (sliding) window of M samples (for M odd). This would be an FIR filter. You could realize this filter recursively, but you may get accumulated rounding errors after a (long) while. Subtract the computed mean from the sample at the centre of your sliding window, for an exactly linear-phase filter. This difference is the output of your filter. It should be easier to see and count the pulses in that filtered signal, using simple threshold-crossing logic.   

The width of the dc notch increases, as M is decreased. As you can imagine, the accuracy of the mean computation increases as M increases, which makes the notch, or the dc-null, narrower.  

Alternatively, you could compute a running exponential average recursively, using an exponentially decaying weight, for an IIR filter, with a recursive realization, without potential rounding error problems. The rate of exponential decay for this IIR replaces the M parameter for the FIR filter. That is, it defines the temporal selectivity and the width of the dc notch.

A lot of my recent research has been looking into extensions, analysis, and applications of these ideas: using higher-order polynomials, with different shapes of weighting functions.