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

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SUMMARY

This discussion focuses on the application of Parseval's Theorem in calculating noise amplitude in signal processing. The theorem establishes that the integral of the square of a function in the time domain is equal to the integral of the square of its Fourier transform in the frequency domain. The key conclusion drawn is that if the average noise amplitude is 2 mV, then the integral of the square of the input signal in the time domain equals 2, based on the relationship derived from the theorem. The discussion emphasizes the proportionality of power to the square of the amplitude, confirming that the noise power is reduced by a factor of four when considering a narrower frequency range.

PREREQUISITES
  • Understanding of Parseval's Theorem in signal processing
  • Familiarity with Fourier transforms and their properties
  • Knowledge of noise amplitude and power calculations
  • Basic concepts of signal processing and frequency domain analysis
NEXT STEPS
  • Study the implications of Parseval's Theorem in various signal processing applications
  • Learn about noise power spectral density and its significance
  • Explore the relationship between RMS values and noise amplitude in signal analysis
  • Investigate methods for calculating power in different frequency ranges
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This discussion is beneficial for electrical engineers, signal processing specialists, and researchers involved in noise analysis and amplitude calculations in communication systems.

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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.
 

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