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==Question==
 
==Question==
'''Part 1. '''
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1. (25 Points)
  
Write Statement here
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Consider a random experiment in which a point is selected at random from the unit square (sample space <math class="inline">S=\left[0,1\right]\times\left[0,1\right] )</math>. Assume that all points in <math class="inline">S</math>  are equally likely to be selected. Let the random variable <math class="inline">\mathbf{X}\left(\omega\right)</math>  be the distance from the outcome <math class="inline">\omega</math>  to the origin (the lower left corner of the unit square). Find the cumulative distribution function (cdf) <math class="inline">F_{\mathbf{X}}\left(x\right)=P\left(\left\{ \mathbf{X}\leq x\right\} \right)</math>  of the random variable <math class="inline">\mathbf{X}</math> . Make sure and specify your answer for all <math class="inline">x\in\mathbf{R}</math> .
  
 
:'''Click [[ECE_PhD_QE_CNSIP_2002_Problem1.1|here]] to view student [[ECE_PhD_QE_CNSIP_2002_Problem1.1|answers and discussions]]'''
 
:'''Click [[ECE_PhD_QE_CNSIP_2002_Problem1.1|here]] to view student [[ECE_PhD_QE_CNSIP_2002_Problem1.1|answers and discussions]]'''
 
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'''Part 2.'''
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2. (25 Points)
  
Write question here.
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Let <math class="inline">\mathbf{X}</math>  and <math class="inline">\mathbf{Y}</math>  be two jointly distributed Gaussian random variables. The random variable <math class="inline">\mathbf{X}</math>  has mean <math class="inline">\mu_{\mathbf{X}}</math>  and variance <math class="inline">\sigma_{\mathbf{X}}^{2}</math> . The correlation coefficient between <math class="inline">\mathbf{X}</math>  and <math class="inline">\mathbf{Y}</math>  is <math class="inline">r</math> . Define a new random variable <math class="inline">\mathbf{Z}</math>  by <math class="inline">\mathbf{Z}=a\mathbf{X}+b\mathbf{Y}</math>, where <math class="inline">a</math>  and <math class="inline">b</math>  are real numbers.
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(a) Prove that <math class="inline">\mathbf{Z}</math>  is a Gaussian random variable.
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(b) Find the mean of <math class="inline">\mathbf{Z}</math> . Express your answer in terms of the parameters <math class="inline">\mu_{\mathbf{X}}</math> , <math class="inline">\sigma_{\mathbf{X}}^{2}</math> , <math class="inline">\mu_{\mathbf{Y}}</math> , <math class="inline">\sigma_{\mathbf{Y}}^{2}</math> , <math class="inline">r</math> , <math class="inline">a</math> , and <math class="inline">b</math> .
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(c)
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Find the variance of <math class="inline">\mathbf{Z}</math> . Express your answer in terms of the parameters <math class="inline">\mu_{\mathbf{X}}</math> , <math class="inline">\sigma_{\mathbf{X}}^{2}</math> , <math class="inline">\mu_{\mathbf{Y}}</math> , <math class="inline">\sigma_{\mathbf{Y}}^{2}</math> , <math class="inline">r</math> , <math class="inline">a</math> , and <math class="inline">b</math> .
  
 
:'''Click [[ECE_PhD_QE_CNSIP_2002_Problem1.2|here]] to view student [[ECE_PhD_QE_CNSIP_2002_Problem1.2|answers and discussions]]'''
 
:'''Click [[ECE_PhD_QE_CNSIP_2002_Problem1.2|here]] to view student [[ECE_PhD_QE_CNSIP_2002_Problem1.2|answers and discussions]]'''
 
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'''Part 3.'''
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3. (25 Points)
  
Write question here.
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Let <math class="inline">\mathbf{X}\left(t\right)</math>  be a wide-sense stationary Gaussian random process with mean <math class="inline">\mu_{\mathbf{X}}</math>  and autocorrelation function <math class="inline">R_{\mathbf{XX}}\left(\tau\right)</math> . Let <math class="inline">\mathbf{Y}\left(t\right)=c_{1}\mathbf{X}\left(t\right)-c_{2}\mathbf{X}\left(t-\tau\right),</math> where <math class="inline">c_{1}</math>  and <math class="inline">c_{2}</math>  are real numbers. What is the probability that <math class="inline">\mathbf{Y}\left(t\right)</math>  is less than or equal to a real number <math class="inline">\gamma</math> ? Express your answer in terms of “phi-function”<math class="inline">\Phi\left(x\right)=\int_{-\infty}^{x}\frac{1}{\sqrt{2\pi}}e^{-z^{2}/2}dz.</math>
  
 
:'''Click [[ECE_PhD_QE_CNSIP_2002_Problem1.3|here]] to view student [[ECE_PhD_QE_CNSIP_2002_Problem1.3|answers and discussions]]'''
 
:'''Click [[ECE_PhD_QE_CNSIP_2002_Problem1.3|here]] to view student [[ECE_PhD_QE_CNSIP_2002_Problem1.3|answers and discussions]]'''
 
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'''Part 4.'''
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4. (25 Points)
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Assume that the distribution of stars within a galaxy is accurately modeled by a 3-dimensional homogeneous Poisson process for which the following two facts are known to be true:
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• The number of starts in a region of volume <math class="inline">V</math>  is a Poisson random variable with mean <math class="inline">\lambda V</math> , where <math class="inline">\lambda>0</math> .
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• The number of starts in any two disjoint regions are statistically independent.
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Assume you are located at an arbitrary position near the center of the galaxy.
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(a) Find the probability density function (pdf) of the distance to the nearest star.
  
Write question here.
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(b) Find the most likely distance to the nearest star.
  
 
:'''Click [[ECE_PhD_QE_CNSIP_2002_Problem1.4|here]] to view student [[ECE_PhD_QE_CNSIP_2002_Problem1.4|answers and discussions]]'''
 
:'''Click [[ECE_PhD_QE_CNSIP_2002_Problem1.4|here]] to view student [[ECE_PhD_QE_CNSIP_2002_Problem1.4|answers and discussions]]'''
 
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Revision as of 00:16, 10 March 2015


ECE Ph.D. Qualifying Exam

Communication, Networking, Signal and Image Processing (CS)

Question 1: Probability and Random Processes

August 2002



Question

1. (25 Points)

Consider a random experiment in which a point is selected at random from the unit square (sample space $ S=\left[0,1\right]\times\left[0,1\right] ) $. Assume that all points in $ S $ are equally likely to be selected. Let the random variable $ \mathbf{X}\left(\omega\right) $ be the distance from the outcome $ \omega $ to the origin (the lower left corner of the unit square). Find the cumulative distribution function (cdf) $ F_{\mathbf{X}}\left(x\right)=P\left(\left\{ \mathbf{X}\leq x\right\} \right) $ of the random variable $ \mathbf{X} $ . Make sure and specify your answer for all $ x\in\mathbf{R} $ .

Click here to view student answers and discussions

2. (25 Points)

Let $ \mathbf{X} $ and $ \mathbf{Y} $ be two jointly distributed Gaussian random variables. The random variable $ \mathbf{X} $ has mean $ \mu_{\mathbf{X}} $ and variance $ \sigma_{\mathbf{X}}^{2} $ . The correlation coefficient between $ \mathbf{X} $ and $ \mathbf{Y} $ is $ r $ . Define a new random variable $ \mathbf{Z} $ by $ \mathbf{Z}=a\mathbf{X}+b\mathbf{Y} $, where $ a $ and $ b $ are real numbers.

(a) Prove that $ \mathbf{Z} $ is a Gaussian random variable.

(b) Find the mean of $ \mathbf{Z} $ . Express your answer in terms of the parameters $ \mu_{\mathbf{X}} $ , $ \sigma_{\mathbf{X}}^{2} $ , $ \mu_{\mathbf{Y}} $ , $ \sigma_{\mathbf{Y}}^{2} $ , $ r $ , $ a $ , and $ b $ .

(c)

Find the variance of $ \mathbf{Z} $ . Express your answer in terms of the parameters $ \mu_{\mathbf{X}} $ , $ \sigma_{\mathbf{X}}^{2} $ , $ \mu_{\mathbf{Y}} $ , $ \sigma_{\mathbf{Y}}^{2} $ , $ r $ , $ a $ , and $ b $ .

Click here to view student answers and discussions

3. (25 Points)

Let $ \mathbf{X}\left(t\right) $ be a wide-sense stationary Gaussian random process with mean $ \mu_{\mathbf{X}} $ and autocorrelation function $ R_{\mathbf{XX}}\left(\tau\right) $ . Let $ \mathbf{Y}\left(t\right)=c_{1}\mathbf{X}\left(t\right)-c_{2}\mathbf{X}\left(t-\tau\right), $ where $ c_{1} $ and $ c_{2} $ are real numbers. What is the probability that $ \mathbf{Y}\left(t\right) $ is less than or equal to a real number $ \gamma $ ? Express your answer in terms of “phi-function”$ \Phi\left(x\right)=\int_{-\infty}^{x}\frac{1}{\sqrt{2\pi}}e^{-z^{2}/2}dz. $

Click here to view student answers and discussions

4. (25 Points)

Assume that the distribution of stars within a galaxy is accurately modeled by a 3-dimensional homogeneous Poisson process for which the following two facts are known to be true:

• The number of starts in a region of volume $ V $ is a Poisson random variable with mean $ \lambda V $ , where $ \lambda>0 $ .

• The number of starts in any two disjoint regions are statistically independent.

Assume you are located at an arbitrary position near the center of the galaxy.

(a) Find the probability density function (pdf) of the distance to the nearest star.

(b) Find the most likely distance to the nearest star.

Click here to view student answers and discussions

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