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== Solution: == | == Solution: == | ||
− | a) | + | a) Since <math> Y_{x} </math> is Poisson random variable, <math> E[Y_{x}]=\lambda_{x}</math>. |
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− | <math> | + | |
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− | + | ||
− | E[Y_{x}]=\lambda_{x} | + | |
− | </math> | + | |
(b) For Poisson r.v., <math>E[Y_{x}]=Var[Y_{x}]\\ | (b) For Poisson r.v., <math>E[Y_{x}]=Var[Y_{x}]\\ |
Revision as of 19:26, 2 December 2015
ECE Ph.D. Qualifying Exam in Communication Networks Signal and Image processing (CS)
Question 5, August 2012, Part 2
- Part 1 , 2
Solution:
a) Since $ Y_{x} $ is Poisson random variable, $ E[Y_{x}]=\lambda_{x} $.
(b) For Poisson r.v., $ E[Y_{x}]=Var[Y_{x}]\\ \Rightarrow Var[Y_{x}]=\lambda_{x} $
(c) The attenuation of photons obeys:
$ \frac{\partial \lambda_{x}}{\partial x}=-\mu(x)\lambda_{x} $
(d) The solution is:
$ \lambda_{x}=\lambda_{0}e^{-\int_0^x \mu(t)\partial t} $ (e) Based on the result of (d)
$ \lambda_{T}=\lambda_{0}e^{-\int_0^T \mu(t)\partial t}\\ \Rightarrow \frac{\lambda_{T}}{\lambda_{0}}=e^{-\int_0^T \mu(t)\partial t}\\ \Rightarrow \int_0^T \mu(t)\partial t=-ln{\frac{\lambda_{T}}{\lambda_{0}}}=ln{\frac{\lambda_{0}}{\lambda_{T}}} $