Hi guys, I'm a newby of Matlab and i'm practising it through necessity, thus the question may sound stupid for those of you expert with this program.. I have a sinusoidal function that is unfortunately not regular, what i mean is that the peaks that it presents are not reached in a steady way, but among the biggest oscillation in which i have to pose my interest, there are many others much more smaller in amplitude that i would like to cut away from the results. I need to isolate the biggest amplitude peaks and leave off the smaller intermediate ones, i have already tried with Excel to elaborate this function but it recognizes not only this "real" peaks, but also the small slight oscillation that the function does to reach the bigger tops. I am lost completely so anyone that has even a tiny idea of how to go through this issue please let me know. Dunno if i have to link some images of the function elaborated with Excel but for clarity i will, right here below. The red dots are the calculated peaks, that for the first part of the function that is regular are rightly found. The problem comes after the vertical bar, where the function becomes unstable in his "growth". Thx so much once again to those who will help..
MATLAB: How can i calculate the zero of a sinusoidal irregular function
peakssinusoid
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The easiest way to see what’s going on with it is to use fvtool to look at it, and to give it a short random signal to look at the output:
signal = rand(1, 1E+4);[y,df] = bandstop(signal, [4500 950000], 5e9);fvtool(df)figureplot(y)It appears to be a well-designed filter, and works with a random signal. I suspect that something about your ‘signal’ vector could be part of the problem, possibly its amplitude, since the output of the random signal (with an amplitude between 0 and 1) produces an output on the order of 
. The normalised frequency of the stopband center frequency is about 
. (In my experience, this is a bit extreme.)
. The normalised frequency of the stopband center frequency is about . (In my experience, this is a bit extreme.) In situations like this, it’s sometimes worthwhile to design both a lowpass filter and a highpass filter, and then add the output vectors. (There is no way to cascade them, since the output of one filter will not overlap with the output of the other filter.)
The problem does not appear to be with the filter itself, since the fvtool analysis suggests that it’s stable and works well.
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