Out of these two methods, which one is computationally intensive and why?
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I have a signal, let's say A6. It has 300 sample points. There are two different methods to estimate the magnitude of the signal. I want to know which method is computationally intensive.
The steps of the first method are as followed,
Step1: A = A6 - mean(A6);
Step2: B = A .* A;
Step3: C = cumsum(B);
Step4: D = trapz(C);
D represents the magnitude of the signal in terms of area under the curve.
The steps of the second method are as followed,
Step1: A = A6 .* A6;
Step2: B = cumsum(A);
Step3: Mean Square Error (MSE) between B and fitted straight line.
The value of the MSE represents the magnitude of the signal.
Thanks,
Irtaza
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Walter Roberson
2017년 12월 25일
trapz should have a lower constant of proportion because it is sum(C) - (C(1)+C(end)) /2 whereas mse requires sqrt(sum((C-B).^2)). Both are linear in size but one requires squaring as well as sum.
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Walter Roberson
2017년 12월 26일
Unless you have a formula for the curve, there would be no way to calculate the area without examining each value at least once, which is all that trapz requires.
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