Saturday 25 April 2015

convolution - Reduce Signal Size to Compare Them


I have multiple experiments and each of them produce several ($k$ for example) binary signals; some artificial example next:binary signal



I have a metric to compare experiment results but I need vectors of equal size to do it.


The problem is that signal length can differ from one experiment to another so I need to reduce the signal dimension somehow.


What I tried/ideas:



  1. Calculate area under each signal. Result is vector of dimension $k$. The problem is that this method cannot differentiate signals with the same area.

  2. Split signal on $m$ bins and calculate an area in each bin. Result is a vector of dimension $k \times m$.

    1. Take Fourier series coefficients. Result is vector of dimension $(a+b)\times k$. The problem is that the spectrum is too large and Fourier cannot work nice (am I correct?)

    2. Some wavelet transforms (like Haar for example). But I don't understand how to do it correctly.





I will be grateful for advices or any method that can help me.




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