![]() If you want to understand why there is an abs, or what relevant info you are losing by not representing the phase of the fft, you may want to read a bit more about the DFT transform to understand exactly what you get. Create a Counter Input Channel Create a DataAcquisition, and add a counter input channel with PulseWidth measurement type. ![]() If by frequency you meant the frequency representation of your signal, then to a first approximation, you just want to plot the abs of the FFT to get an idea of where the energy is: plot(abs(fft)) A sensor is used to measure distance from a point: the width of the pulse is correlated with the measured distance. I need to generate a signal at any required crank angle say 200.3. UPDATE: I realize that I assumed you meant by "frequency" of your signal the pitch or base harmonic or frequency with the most energy, however you want to look at it. In Simulink, I am getting a signal (a crank angle signal from an IC Engine) at a varying rate with in cycle of 720 degrees. 95%, 99%, or some other number would depend on how much noise corrupts your signal. So to account for that noise, you would take the absolute max of the autocorrelation (autocorrelation(length(autocorrelation)/2+1), and then find where the autocorrelation is larger than, say, 95% of that maximum value for the first time in the second half of the signal. Since the signal shifted by a multiple of its period will always look like itself, you need to make sure that the maximum you find indeed corresponds to the period of the signal and not one of its multiples.īecause of the noise in your signal, the absolute maximum could very well occur at a multiple of your period instead of the period itself. (The autocorrelation will be symmetric with its maximum in the middle.) By finding that maximum, you find the first place where the shifted signal looks more or less like itself. This example shows how to analyze pulses and transitions and compute metrics including rise time, fall time, slew rate, overshoot, undershoot, pulse width, duty cycle, and pulse period. The signal frequency will then be: frequency = indexMax * Fs / L Īlternatively, faster and working fairly well too depending on the signal you have, take the autocorrelation of your signal: autocorrelation = xcorr(signal) Īnd find the first maximum occurring after the center point of the autocorrelation. Measurement of Pulse and Transition Characteristics. Note: to get from indexMax to the actual frequency of interest, you will need to know the length L of the fft (same as the length of your signal), and the sampling frequency Fs. Where indexMax is the index where the max fft value can be found. I won't know if there is a One 1 or Two 1 in a Pulse. So it is not possible to check number of 1s, because the duration of each pulse is random. Last, if your signal has an offset, as is the case with the one you show, you want to get rid of that offset before taking the fft so that you do not get a max at the origin representing the DC component.Įverything I described put in one line would be: = max(abs(fft(signal-mean(signal)))) The timing of the 'Data' is uncontrolled. The index will correspond to the normalized frequency with maximum energy. Since the fft gives you the frequency representation of the signal, you want to look for the maximum, and since the fft is a complex signal, you will want to take the absolute value first. Imshow(app.ImageFile, 'Parent', app.One way to go is indeed to use an fft. Īpp.ImageFile = imresize(app.ImageFile, ) % Button pushed function: CaricaimmagineSRButtonįunction CaricaimmagineSRButtonPushed(app, event)Īnswer = questdlg('do you wanna crop?'. I want call getPhoto() into the app and getPhoto() is a function into another. Measurement of Pulse and Transition Characteristics.
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