Revision as of 21:46, 29 March 2015 by Wu112 (Talk | contribs)


ECE Ph.D. Qualifying Exam

Communication, Networking, Signal and Image Processing (CS)

Question 1: Probability and Random Processes

August 2011



Question

Part 1. 25 pts


 $ \color{blue}\text{ Let } \mathbf{X}\text{, }\mathbf{Y}\text{, and } \mathbf{Z} \text{ be three jointly distributed random variables with joint pdf } f_{XYZ}\left ( x,y,z \right )= \frac{3z^{2}}{7\sqrt[]{2\pi}}e^{-zy} exp \left [ -\frac{1}{2}\left ( \frac{x-y}{z}\right )^{2} \right ] \cdot 1_{\left[0,\infty \right )}\left(y \right )\cdot1_{\left[1,2 \right]} \left ( z \right) $

$ \color{blue}\left( \text{a} \right) \text{Find the joint probability density function } f_{YZ}(y,z). $

$ \color{blue}\left( \text{b} \right) \text{Find } f_{x}\left( x|y,z\right ). $

$ \color{blue}\left( \text{c} \right) \text{Find } f_{Z}\left( z\right ). $

$ \color{blue}\left( \text{d} \right) \text{Find } f_{Y}\left(y|z \right ). $

$ \color{blue}\left( \text{e} \right) \text{Find } f_{XY}\left(x,y|z \right ). $


Click here to view student answers and discussions

Part 2. 25 pts


 $ \color{blue} \text{Show that if a continuous-time Gaussian random process } \mathbf{X}(t) \text{ is wide-sense stationary, it is also strict-sense stationary.} $


Click here to view student answers and discussions

Part 3. 25 pts


 $ \color{blue} \text{Show that the sum of two jointly distributed Gaussian random variables that are not necessarily statistically independent is a Gaussian random variable.} $


Click here to view student answers and discussions

Part 4. 25 pts


 $ \color{blue} \text{Assume that } \mathbf{X}(t) \text{ is a zero-mean continuous-time Gaussian white noise process with autocorrelation function} $

               $ \color{blue} R_{\mathbf{XX}}(t_1,t_2)=\delta(t_1-t_2). $

 $ \color{blue} \text{Let } \mathbf{Y}(t) \text{ be a new random process ontained by passing } \mathbf{Y}(t) \text{ through alinear time-invariant system with impulse response } h(t) \text{ whose Fourier transform} H(\omega) \text{ has the ideal low-pass characteristic} $

               $ \color{blue} H(\omega) = \begin{cases} 1, & \mbox{if } |\omega|<\Omega,\\ 0, & \mbox{elsewhere,} \end{cases} $

 $ \color{blue} \text{where } \Omega>0. $

 $ \color{blue} \text{a) Find the mean of } \mathbf{Y}(t). $

 $ \color{blue} \text{b) Find the autocorrelation function of } \mathbf{Y}(t). $

 $ \color{blue} \text{c) Find the joint pdf of } \mathbf{Y}(t_1) \text{ and } \mathbf{Y}(t_2) \text{ for any two arbitrary sample time } t_1 \text{ and } t_2. $

 $ \color{blue} \text{d) What is the minimum time difference } t_1-t_2 \text{ such that } \mathbf{Y}(t_1) \text{ and } \mathbf{Y}(t_2) \text{ are statistically independent?} $

Click here to view student answers and discussions

Back to ECE Qualifying Exams (QE) page

Alumni Liaison

Abstract algebra continues the conceptual developments of linear algebra, on an even grander scale.

Dr. Paul Garrett