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==Question==
 
==Question==
'''Part 1. '''
+
'''1. (20 pts.)'''
  
Write Statement here
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A probability space <math class="inline">\left(\mathcal{S},\mathcal{F},\mathcal{P}\right)</math>  has a sample space consisting of all pairs of positive integers: <math class="inline">\mathcal{S}=\left\{ \left(k,m\right):\; k=1,2,\cdots;\; m=1,2,\cdots\right\}</math> . The event space <math class="inline">\mathcal{F}</math>  is the power set of <math class="inline">\mathcal{S}</math> , and the probability measure <math class="inline">\mathcal{P}</math>  is specified by the pmf <math class="inline">p\left(k,m\right)=p^{2}\left(1-p\right)^{k+m-2},\qquad p\in\left(0,1\right)</math>.
 +
 
 +
(a)
 +
 
 +
Find <math class="inline">P\left(\left\{ \left(k,m\right):\; k\geq m\right\} \right)</math> .
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 +
(b)
 +
 
 +
Find <math class="inline">P\left(\left\{ \left(k,m\right):\; k+m=r\right\} \right)</math> , for <math class="inline">r=2,3,\cdots</math> .
 +
 
 +
'''(c)'''
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 +
Find <math class="inline">P\left(\left\{ \left(k,m\right):\; k\text{ is an odd number}\right\} \right)</math> .
  
 
:'''Click [[ECE_PhD_QE_CNSIP_2004_Problem1.1|here]] to view student [[ECE_PhD_QE_CNSIP_2004_Problem1.1|answers and discussions]]'''
 
:'''Click [[ECE_PhD_QE_CNSIP_2004_Problem1.1|here]] to view student [[ECE_PhD_QE_CNSIP_2004_Problem1.1|answers and discussions]]'''
 
----
 
----
'''Part 2.'''
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'''2. (20 pts.)'''
  
Write question here.
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Let <math class="inline">\mathbf{X}</math>  and <math class="inline">\mathbf{Y}</math>  be two independent identically distributed exponential random variables having mean <math class="inline">\mu</math> . Let <math class="inline">\mathbf{Z}=\mathbf{X}+\mathbf{Y}</math> . Find <math class="inline">f_{\mathbf{X}}\left(x|\mathbf{Z}=z\right)</math> , the conditional pdf of <math class="inline">\mathbf{X}</math>  given the event <math class="inline">\left\{ \mathbf{Z}=z\right\}</math>  .
  
 
:'''Click [[ECE_PhD_QE_CNSIP_2004_Problem1.2|here]] to view student [[ECE_PhD_QE_CNSIP_2004_Problem1.2|answers and discussions]]'''
 
:'''Click [[ECE_PhD_QE_CNSIP_2004_Problem1.2|here]] to view student [[ECE_PhD_QE_CNSIP_2004_Problem1.2|answers and discussions]]'''
 
----
 
----
'''Part 3.'''
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'''3. (25 pts.)'''
  
Write question here.
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Let <math class="inline">\mathbf{X}_{1},\cdots,\mathbf{X}_{n}</math>  be independent identically distributed (i.i.d. ) random variables uniformaly distributed over the interval <math class="inline">\left[0,1\right]</math> .
 +
 
 +
'''(a)'''
 +
 
 +
Find the probability density function of <math class="inline">\mathbf{Y}=\max\left\{ \mathbf{X}_{1},\cdots,\mathbf{X}_{n}\right\}</math> .
 +
 
 +
'''(b)'''
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 +
Find the probability density function of <math class="inline">\mathbf{Z}=\min\left\{ \mathbf{X}_{1},\cdots,\mathbf{X}_{n}\right\}</math> .  
  
 
:'''Click [[ECE_PhD_QE_CNSIP_2004_Problem1.3|here]] to view student [[ECE_PhD_QE_CNSIP_2004_Problem1.3|answers and discussions]]'''
 
:'''Click [[ECE_PhD_QE_CNSIP_2004_Problem1.3|here]] to view student [[ECE_PhD_QE_CNSIP_2004_Problem1.3|answers and discussions]]'''
 
----
 
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'''Part 4.'''
 
  
Write question here.
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'''4. (35 pts.)'''
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 +
Assume that <math class="inline">\mathbf{X}\left(t\right)</math>  is a zero-mean, continuous-time, Gaussian white noise process with autocorrelation function <math class="inline">R_{\mathbf{XX}}\left(t_{1},t_{2}\right)=\frac{N_{0}}{2}\delta\left(t_{1}-t_{2}\right).</math> Let <math class="inline">\mathbf{Y}\left(t\right)</math>  be a new random process defined as the output of a linear time-invariant system with impulse response <math class="inline">h\left(t\right)=\frac{1}{T}e^{-t/T}\cdot u\left(t\right),</math>  where <math class="inline">u\left(t\right)</math>  is the unit step function and <math class="inline">T>0</math> .
 +
 
 +
'''(a)'''
 +
 
 +
What is the mean of <math class="inline">\mathbf{Y\left(t\right)}</math> ?
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'''(b)'''
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 +
What is the autocorrelation function of <math class="inline">\mathbf{Y}\left(t\right)</math> ?
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'''(c)'''
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 +
Write an expression for the <math class="inline">n</math> -th order characteristic function of <math class="inline">\mathbf{Y}\left(t\right)</math>  sampled at time <math class="inline">t_{1},t_{2},\cdots,t_{n}</math> . Simplify as much as possible.
 +
 
 +
'''(d)'''
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 +
Write an expression for the second-order pdf <math class="inline">f_{\mathbf{Y}\left(t_{1}\right)\mathbf{Y}\left(t_{2}\right)}\left(y_{1},y_{2}\right)</math>  of <math class="inline">\mathbf{Y}\left(t\right)</math> . simplify as much as possible.
 +
 
 +
'''(e)'''
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 +
Find the minium mean-square estimate of <math class="inline">\mathbf{Y}\left(t_{2}\right)</math>  given that <math class="inline">\mathbf{Y}\left(t_{1}\right)=y_{1}</math> . Simplify your answer as much as possible.
  
 
:'''Click [[ECE_PhD_QE_CNSIP_2004_Problem1.4|here]] to view student [[ECE_PhD_QE_CNSIP_2004_Problem1.4|answers and discussions]]'''
 
:'''Click [[ECE_PhD_QE_CNSIP_2004_Problem1.4|here]] to view student [[ECE_PhD_QE_CNSIP_2004_Problem1.4|answers and discussions]]'''
 
----
 
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Revision as of 00:17, 10 March 2015


ECE Ph.D. Qualifying Exam

Communication, Networking, Signal and Image Processing (CS)

Question 1: Probability and Random Processes

August 2004



Question

1. (20 pts.)

A probability space $ \left(\mathcal{S},\mathcal{F},\mathcal{P}\right) $ has a sample space consisting of all pairs of positive integers: $ \mathcal{S}=\left\{ \left(k,m\right):\; k=1,2,\cdots;\; m=1,2,\cdots\right\} $ . The event space $ \mathcal{F} $ is the power set of $ \mathcal{S} $ , and the probability measure $ \mathcal{P} $ is specified by the pmf $ p\left(k,m\right)=p^{2}\left(1-p\right)^{k+m-2},\qquad p\in\left(0,1\right) $.

(a)

Find $ P\left(\left\{ \left(k,m\right):\; k\geq m\right\} \right) $ .

(b)

Find $ P\left(\left\{ \left(k,m\right):\; k+m=r\right\} \right) $ , for $ r=2,3,\cdots $ .

(c)

Find $ P\left(\left\{ \left(k,m\right):\; k\text{ is an odd number}\right\} \right) $ .

Click here to view student answers and discussions

2. (20 pts.)

Let $ \mathbf{X} $ and $ \mathbf{Y} $ be two independent identically distributed exponential random variables having mean $ \mu $ . Let $ \mathbf{Z}=\mathbf{X}+\mathbf{Y} $ . Find $ f_{\mathbf{X}}\left(x|\mathbf{Z}=z\right) $ , the conditional pdf of $ \mathbf{X} $ given the event $ \left\{ \mathbf{Z}=z\right\} $ .

Click here to view student answers and discussions

3. (25 pts.)

Let $ \mathbf{X}_{1},\cdots,\mathbf{X}_{n} $ be independent identically distributed (i.i.d. ) random variables uniformaly distributed over the interval $ \left[0,1\right] $ .

(a)

Find the probability density function of $ \mathbf{Y}=\max\left\{ \mathbf{X}_{1},\cdots,\mathbf{X}_{n}\right\} $ .

(b)

Find the probability density function of $ \mathbf{Z}=\min\left\{ \mathbf{X}_{1},\cdots,\mathbf{X}_{n}\right\} $ .

Click here to view student answers and discussions

4. (35 pts.)

Assume that $ \mathbf{X}\left(t\right) $ is a zero-mean, continuous-time, Gaussian white noise process with autocorrelation function $ R_{\mathbf{XX}}\left(t_{1},t_{2}\right)=\frac{N_{0}}{2}\delta\left(t_{1}-t_{2}\right). $ Let $ \mathbf{Y}\left(t\right) $ be a new random process defined as the output of a linear time-invariant system with impulse response $ h\left(t\right)=\frac{1}{T}e^{-t/T}\cdot u\left(t\right), $ where $ u\left(t\right) $ is the unit step function and $ T>0 $ .

(a)

What is the mean of $ \mathbf{Y\left(t\right)} $ ? (b)

What is the autocorrelation function of $ \mathbf{Y}\left(t\right) $ ? (c)

Write an expression for the $ n $ -th order characteristic function of $ \mathbf{Y}\left(t\right) $ sampled at time $ t_{1},t_{2},\cdots,t_{n} $ . Simplify as much as possible.

(d)

Write an expression for the second-order pdf $ f_{\mathbf{Y}\left(t_{1}\right)\mathbf{Y}\left(t_{2}\right)}\left(y_{1},y_{2}\right) $ of $ \mathbf{Y}\left(t\right) $ . simplify as much as possible.

(e)

Find the minium mean-square estimate of $ \mathbf{Y}\left(t_{2}\right) $ given that $ \mathbf{Y}\left(t_{1}\right)=y_{1} $ . Simplify your answer as much as possible.

Click here to view student answers and discussions

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Ph.D. 2007, working on developing cool imaging technologies for digital cameras, camera phones, and video surveillance cameras.

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