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The principal idea behind my recommended approach is to identify characteristic points in your cycle. You can then make a mapping from time within the cycle (as it is occuring) and the location of that point within your representative cycle. Once you have a set of mapping points, create a best fit function. This will then give you a mapping function of ...


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You may be able to use a modified transform to compute change in a time stretch parameter (I will use $\zeta$) versus time (t). Your success in being able to do this depends on the cross correlation properties of your pattern with time stretched versions of itself. Let me explain: First notice that the Fourier Transform is a correlation over all possible ...


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