Sine wave change zero crossing and increase amplification

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David Jones 2020-9-7

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评论： Star Strider 2020-9-10

I have attached a picture of a sine wave, can it be modified ie all centred around zero, all peaks and troughs the same size?

Any help would be appreciated.

David

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Johannes Fischer 2020-9-7

What exactly do you want to extract from the data, after you have manipulated as you describe? Have you considered a Fourier analysis of the data?

Image Analyst 2020-9-7

Please attach your data in a .mat file if you want people to try anything to help you.

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Star Strider 2020-9-7

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First, use the fft function to do a Fourier transform to see what the signal frequency components are.

After that, it should be straightforward to design a highpass filter to eliminate both the D-C offset and baseline drift, so the result will be close to what you want.

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Star Strider 2020-9-8

在 MATLAB Online 中打开

Johannes Fischer — Thank you for your Comment!

One way to compute the Fourier transform is:

L = 200000;
Fs = 2500000;
Fn = Fs/2;                                              % Nyquist Frequency
t = linspace(0, 200000, L)/Fs;                          % Create Time Vector
s = sin(2*pi*2*t)+0.124;                                % Create Signal Vector
sm = mean(s);
FTs = fft(s - sm)/L;
Fv = linspace(0, 1, fix(L/2)+1)*Fn;                     % Frequency Vector
Iv = 1:numel(Fv);                                       % Index Vector
figure
plot(Fv, abs(FTs(Iv))*2)
grid
title('Fourier Transform - Original Signal')
xlabel('Frequency (Hz)')
ylabel('Amplitude (?)')
xlim([0 1E3])                                           % Optional

There should be at least 2 peaks, one (or more) for the very slow baseline variation and another for the signal itself. It will likely be necessary to limit the frequency axis (as I did here) to see them clearly. Set the cutoff frequency of the highpass filter to a value between the lower-frequency peaks in the Fourier transform plot and the signal frequency peak. The purpose of subtracting the mean of the signal before taking the Fourier transform is to eliminate the baselkine offset, making the signal peaks more apparent.