1
GATE ECE 2020
Numerical
+1
-0
The random variable
$$ Y=\int_{-\infty}^{\infty} W(t) \phi(t) d t, \quad \text { where } \phi(t)=\left\{\begin{array}{cc} 1, & 5 \leq t \leq 7 \\ 0, & \text { otherwise } \end{array}\right. $$
and $W(t)$ is a real white Gaussian noise process with two-sided power spectral density $S_W(f)=3 \mathrm{~W} / \mathrm{Hz}$, for all $f$. The variance of $Y$ is $\_\_\_\_$ .
Your input ____
2
GATE ECE 2017 Set 2
Numerical
+1
-0
Consider the random process
x(t) = U + Vt.
Where U is a zero mean Gaussian random variable and V is a random variable uniformly distributed between 0 and 2. Assume that U and V are statistically independent. The mean value of the random process at t = 2 is _________________
x(t) = U + Vt.
Where U is a zero mean Gaussian random variable and V is a random variable uniformly distributed between 0 and 2. Assume that U and V are statistically independent. The mean value of the random process at t = 2 is _________________
Your input ____
3
GATE ECE 2014 Set 4
MCQ (Single Correct Answer)
+1
-0.3
If calls arrive at a telephone exchange such that the time of arrival of any call is independent of the time of arrival of earlier or future calls, the probability distribution function of the total number of calls in a fixed time interval will be
4
GATE ECE 2012
MCQ (Single Correct Answer)
+1
-0.3
The power spectral density of a real process X(t) for positive frequencies is shown below. The value of $$E\,\left[ {{X^2}\,(t)} \right]$$ and $$E\,\left[ {X\,(t)} \right]$$, respectively, are


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Control Systems
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Discrete Fourier Transform and Fast Fourier Transform Discrete Time Signal Fourier Series Fourier Transform Continuous Time Signal Laplace Transform Fourier Transform Representation of Continuous Time Signal Fourier Series Transmission of Signal Through Continuous Time LTI Systems Miscellaneous Sampling Continuous Time Linear Invariant System Discrete Time Linear Time Invariant Systems Discrete Time Signal Z Transform Transmission of Signal Through Discrete Time Lti Systems
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