Computing $\sigma_N(f;t)$ from $s_n(f;t)$

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In summary, we are discussing the relationship between the two equations: s_{n}(f;t) = \sum_{k=-n}^{n}\widehat{f}(k)e^{ikt} and \sigma_{N}(f;t)= \frac{1}{N+1}\sum_{n=0}^{N}s_{n}(f;t). The question is how to get from the first equation to the second equation, which involves reversing the order of summation. The solution involves rewriting the second equation as \sigma_{N}(f;t)=\frac{1}{N+1}\sum_{n=0}^{N}\sum_{k=-n}^{n}\widehat{f}(k)e^{ikt
  • #1
errordude
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suppose, [tex] s_{n}(f;t) = \sum_{k=-n}^{n}\widehat{f}(k)e^{ikt}[/tex]
and
[tex]\sigma_{N}(f;t)= \frac{1}{N+1}\sum_{n=0}^{N}s_{n}(f;t)[/tex].

how do i get from this [tex]\sigma_{N}(f;t)= \frac{1}{N+1}\sum_{n=0}^{N}s_{n}(f;t)[/tex].

to this


[tex]\sigma_{N}(f;t)= \sum_{n=-N}^{N}(1-\frac{|n|}{N+1})\widehat{f}(n)e^{int}[/tex]

obviously one starts with:

[tex]\sigma_{N}(f;t)=\frac{1}{N+1}\sum_{n=0}^{N}\sum_{k=-n}^{n}\widehat{f}(k)e^{ikt}[/tex]

thanks!
 
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  • #2
And what happens when you reverse the order of summation ... the sum on k outside, the sum on n inside?
 
  • #3
g_edgar said:
and what happens when you reverse the order of summation ... The sum on k outside, the sum on n inside?

?














?
 
  • #4
wow this must be slowest forum on the face of the planet
 
  • #6
errordude said:
wow this must be slowest forum on the face of the planet

Perhaps, but remember we're not all free to check forums 25 hours a day, 8 days a week. Two hours 40 for what looks like a hint seems pretty good to me. Have you tried it?
 
  • #7

1. What is the purpose of computing $\sigma_N(f;t)$ from $s_n(f;t)$?

The purpose of computing $\sigma_N(f;t)$ from $s_n(f;t)$ is to estimate the standard deviation of a signal $f(t)$ over a certain time period $t$. This allows for the characterization of the variability or noise present in the signal, which is useful in various scientific and engineering applications.

2. How is $\sigma_N(f;t)$ related to $s_n(f;t)$?

Mathematically, $\sigma_N(f;t)$ is the square root of the integral of the squared amplitude spectrum $|s_n(f;t)|^2$ over a certain frequency range. In other words, $\sigma_N(f;t)$ is a summary statistic of the spectral properties of $f(t)$ represented by $s_n(f;t)$.

3. What are the key factors that affect the accuracy of computing $\sigma_N(f;t)$?

The accuracy of computing $\sigma_N(f;t)$ depends on several factors, including the length of the time period $t$, the frequency resolution of $s_n(f;t)$, and the presence of any noise or interference in the signal. Additionally, the choice of algorithm or method used to compute $\sigma_N(f;t)$ can also impact its accuracy.

4. Can $\sigma_N(f;t)$ be calculated for non-periodic signals?

Yes, $\sigma_N(f;t)$ can be calculated for non-periodic signals. In this case, the time period $t$ may refer to a certain segment of the signal, and the calculation may be repeated for multiple segments to estimate the overall standard deviation of the signal.

5. How is the value of $\sigma_N(f;t)$ interpreted in practical applications?

The value of $\sigma_N(f;t)$ provides a measure of the variability or noise present in the signal $f(t)$. In practical applications, this can be used to determine the signal-to-noise ratio, evaluate the effectiveness of noise reduction techniques, and assess the overall quality of the signal. It can also be compared to the expected or theoretical value of $\sigma_N(f;t)$ for a given type of signal.

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