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:

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:


Calculate area under each signal. Result is vector of dimension $k$. The problem is that this method cannot differentiate signals with the same area.
Split signal on $m$ bins and calculate an area in each bin. Result is a vector of dimension $k \times m$.


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?)
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.
It seems your needs matches the Dynamic Time Warping algorithm.
You should try it as a metric to compare them.

The idea of changing the size is trickier as you may loose / change data which is important for your comparison.  

Maybe what you should do is alter the "Distance" metric in the Dynamic Time Warping algorithm to something which suits your needs.