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Hello everyone, I am looking for frequency (fft magnitude) peaks for specific activity. In order to confirm my theory, I have performed experiments in many different situations. So, what I need to do is to get specific peaks for my desired activities. Once I confirm those peaks do not happen in any other situation(noise, power supply ripple..) my theory is correct. So now, I have a hard time how to distinguish those peaks. Is there any specific method (algorithm) that exists, or should I just look for unions, and differences in my data. Hope I explained enough, wg
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ana wrote: > Hello everyone, > > I am looking for frequency (fft magnitude) peaks for specific activity. > In order to confirm my theory, I have performed experiments in many > different situations. So, what I need to do is to get specific peaks for > my desired activities. Once I confirm those peaks do not happen in any > other situation(noise, power supply ripple..) my theory is correct. > Hi, just for clarification: Are you looking for a way to find the local maxima of a two dimensional curve, consisting of measured values? Or...  ... in more than two dimensions?  ... with constraints regarding the detection of maxima?  ... under hard (time/space/whatever) conditions?  ... using interpolation, to find the "real" value?  ... do you want to skip the fft?  ... ? > So now, I have a hard time how to distinguish those peaks. Umm, of course the most simple algorithm to find a maximum in 2d may be:
if (x[n] > x[n1]) && (x[n] > x[n+1]) then print "maximum found." 
It runs in situ and in linear time, so there is basically nothing wrong with it. When the problem becomes more complicated there may be a bunch of methods you can try, depending on the concrete constraints; e.g.  using gradients  a linear programming solver  the wide field of genetic programming  mathmatical methods like Lagrange multipliers  and surely a lot more...