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I'll try a bandpass

this is not for school, the green line is a completely different instrument... advice is welcome...

the green line is the correct representation of the motion

Here is most of the code clear clc home load ACCfilter load ElapsedTime %definitions Fs = 32; t = ElapsedTime; % sampling range yno = ACCfilter; [B,A] = butter(5,0.5/(Fs/2),'high'); y = filter(B,A,yno); %plot in time domain subplot(2, 1, 1); % plot(t, y,'r'); grid on % plot with grid...

Here is the code clear clc home load ACCfilter load ElapsedTime %definitions Fs = 32; t = ElapsedTime; % sampling range yno = ACCfilter; [B,A] = butter(5,0.5/(Fs/2),'high'); y = filter(B,A,yno); %plot in time domain subplot(2, 1, 1); % plot(t, y,'r'); grid on % plot with grid...

Here is a more high res photohttp://i.imgur.com/YtK0X.png

I am using fast fourier transforms to convert raw acceleration to displacement. I am able to get extremely close to expected results; however, I am having issues at the beginning of the dataset. I believe there may be an issue with the filter I am using. Here is the code for the butterworth...

I am using fast fourier transforms to convert raw acceleration to displacement. I am able to get extremely close to expected results; however, I am having issues at the beginning of the dataset. I believe there may be an issue with the filter I am using. Here is the code for the butterworth...

Thanks for the help guys, but the input is just a regular sin wave that I'm making noisier and nosier. I've called it acceleration for now before I actually start putting in data from the source. I don't think a high pass filter would help in this situation because after the FFT the results (ie...

thread384-296403 The aforementioned thread presents a code that deals with a normal sin wave quite well; however, I introduced noise into the code by adding a 2.5hz sin wave, 5hz sin wave and 25hz sin wave, which produced some unexpected results. See the attached graph for further details, the...