How Does Parseval's Theorem Apply to Noise Amplitude Calculations?

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Parseval's theorem is applied to relate the energy in the time domain to the energy in the frequency domain, leading to the equation that connects the integrals of the squared amplitudes of two signals. The discussion highlights that the average noise amplitude of 2 mV in the time domain suggests a corresponding integral value of 2 in the frequency domain, as power is proportional to the square of the amplitude. It is noted that the noise power from 0 to 10 MHz is one-fourth of that from 0 to 40 MHz, simplifying the calculations. The conversation emphasizes that understanding the relationship between noise amplitude and power is crucial for accurate calculations. Overall, the application of Parseval's theorem aids in quantifying noise characteristics in signal processing.
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Homework Statement
You have a signal with a maximum frequency of 40 MHz. The average noise amplitude is 2 mV in the time domain. The noise is uniformly distributed from 0 to 40 MHz in the frequency domain. Now you apply a filter to remove all the frequency components above 10 MHz. What is the average amplitude of the noise in the filtered signal in the time domain?
Relevant Equations
##\int_\infty ^\infty {\left| f(x) \right|}^2 dx=\int_\infty ^\infty {\left| F(u) \right|}^2 du##
Before:
##\int_\infty ^\infty {\left| F_i(u) \right|}^2 du=\int_0 ^{40} a^2 du=40 a^2##

Therefore: ##a^2= \frac{1}{40}\int_\infty ^\infty {\left| F_i(u) \right|}^2 du##

After:
##\int_\infty ^\infty {\left| F_f(u) \right|}^2 du=\int_0 ^{10} a^2 du=10 a^2##

Therefore: ##a^2= \frac{1}{10}\int_\infty ^\infty {\left| F_f(u) \right|}^2 du##

Equating the two equations:
##\frac{1}{40}\int_\infty ^\infty {\left| F_i(u) \right|}^2 du=\frac{1}{10}\int_\infty ^\infty {\left| F_f(u) \right|}^2 du##

Therefore:
##\int_\infty ^\infty {\left| F_i(u) \right|}^2 du=4\int_\infty ^\infty {\left| F_f(u) \right|}^2 du##

using Parseval’s theorem which states that: ##\int_\infty ^\infty {\left| f(x) \right|}^2 dx=\int_\infty ^\infty {\left| F(u) \right|}^2 du##, we get:

##\int_\infty ^\infty {\left| f_i(x) \right|}^2 dx=4\int_\infty ^\infty {\left| f_f(x) \right|}^2 dx##

The question states that the average noise amplitude is 2 mV in the time domain. Does this mean that:
##\int_\infty ^\infty {\left| f_i(x) \right|}^2 dx=2## ?
 
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Sorry I know this is an old thread, but are you still looking for an answer to this?

MartynaJ said:
The question states that the average noise amplitude is 2 mV in the time domain. Does this mean that:
##\int_\infty ^\infty {\left| f_i(x) \right|}^2 dx=2## ?

I think the 2 mV refers to |f(x)|, so the power would be proportional to |f(x)|^2 just like P = \frac{V^2}{R}. I haven't seen the term 'noise amplitude' before to be honest, but it seems like they are asking for the rms value. We can say that the power is proportional to 4 of some arbitrary units (we don't need to worry about them given that the power spectral density is constant).

MartynaJ said:
Relevant Equations:: ##\int_\infty ^\infty {\left| f(x) \right|}^2 dx=\int_\infty ^\infty {\left| F(u) \right|}^2 du##

Before:
##\int_\infty ^\infty {\left| F_i(u) \right|}^2 du=\int_0 ^{40} a^2 du=40 a^2##

I think you can get to the end result sooner. We are told that the noise is uniformly distributed from 0 to 40 MHz (area of a rectangle, as you have found). Therefore, when only considering the noise from 0 to 10 MHz, you can immediately tell that the noise power is now 1/4 of what it previously was (there isn't necessarily a need to write out the integrals with a^2).

Once we have the power is 1/4 of the previous value, we can find the noise amplitudes. You were basically at the solution. We want to find the value that solves: x^2 \times 4 = 4

Hope that is of some help.
 
Question: A clock's minute hand has length 4 and its hour hand has length 3. What is the distance between the tips at the moment when it is increasing most rapidly?(Putnam Exam Question) Answer: Making assumption that both the hands moves at constant angular velocities, the answer is ## \sqrt{7} .## But don't you think this assumption is somewhat doubtful and wrong?

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