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A white noise signal having two sided spectral density of 5*10-6 V2/Hz is applied to a simple RC low pass filter whose 3 dB cutoff frequency is 5 kHz. The mean squared value of noise obtained at the output of filter would be
  • a)
    0.3927 V2
  • b)
    39.27*10-6 V2
  • c)
    39.27 V2
  • d)
    39.27*10-3 V2
Correct answer is option 'D'. Can you explain this answer?
Most Upvoted Answer
A white noise signal having two sided spectral density of 5*10-6 V2/H...
White noise with 2 sided PSD 5*10-6 V2/Hz.
RC low pass filter has 3 dB cutoff 5 kHz.
Mean Squared value of the output power can be found using following way,
E(y2)=Ry(0)
This will give the Mean squared output power to be 39.27*10-3 V2
Free Test
Community Answer
A white noise signal having two sided spectral density of 5*10-6 V2/H...
To find the mean squared value of noise at the output of the RC low pass filter, we need to consider the transfer function of the filter and the power spectral density of the input white noise signal.

1. Transfer Function of the RC Low Pass Filter:
The transfer function of an RC low pass filter is given by:

H(f) = 1 / (1 + j2πf/fcut)

where fcut is the 3 dB cutoff frequency of the filter.

2. Power Spectral Density of the Input Signal:
The power spectral density (PSD) of a white noise signal is constant across all frequencies. In this case, the two-sided PSD is given as 5*10^-6 V^2/Hz.

3. Mean Squared Value of Noise at the Output:
The mean squared value of the noise at the output of the filter can be found by integrating the PSD of the input signal multiplied by the squared magnitude of the transfer function of the filter over all frequencies.

Let's calculate this step by step:

- First, we square the magnitude of the transfer function:

|H(f)|^2 = 1 / (1 + (2πf/fcut)^2)

- Then, we multiply it by the PSD of the input signal:

PSD_output(f) = PSD_input(f) * |H(f)|^2
= (5*10^-6 V^2/Hz) * (1 / (1 + (2πf/fcut)^2))

- Now, we integrate the PSD_output(f) over all frequencies:

Mean Squared Value = ∫ PSD_output(f) df (from -∞ to +∞)

Since the PSD of the white noise signal is constant across all frequencies, we can take it out of the integral:

Mean Squared Value = PSD_input * ∫ |H(f)|^2 df (from -∞ to +∞)
= (5*10^-6 V^2/Hz) * ∫ 1 / (1 + (2πf/fcut)^2) df (from -∞ to +∞)

- Solving the integral, we get:

Mean Squared Value = (5*10^-6 V^2/Hz) * (π/fcut)

Substituting the value of fcut = 5 kHz = 5000 Hz:

Mean Squared Value = (5*10^-6 V^2/Hz) * (π/5000 Hz)
= (5*10^-6 V^2/Hz) * (3.14/5000)
= 3.14*10^-9 V^2
= 3.14*10^-3 * 10^-6 V^2
= 3.14*10^-3 * 10^-6 * 10^3 V^2
= 3.14*10^-9 * 10^3 V^2
= 3.14*10^-6 V^2
= 39.27*10^-6 V^2

Therefore, the mean squared value of the noise obtained at the output of the RC low pass filter is 39.27*10^-6 V^2, which is option D.
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A white noise signal having two sided spectral density of 5*10-6 V2/Hz is applied to a simple RC low pass filter whose 3 dB cutoff frequency is 5 kHz. The mean squared value of noise obtained at the output of filter would bea)0.3927 V2b)39.27*10-6 V2c)39.27 V2d)39.27*10-3 V2Correct answer is option 'D'. Can you explain this answer?
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