Revision as of 07:57, 27 June 2012

ECE Ph.D. Qualifying Exam: COMMUNICATIONS, NETWORKING, SIGNAL AND IMAGE PROESSING (CS)- Question 1, August 2000

Question

Part 1.

a) The Laplacian density function is given by $f\left(x\right)=\frac{A}{2}e^{-A\left|x\right|}\text{ where }A>0.$ Determine its characteristic function.

b) Determine a bound on the probability that a RV is within two standard deviations of its mean.

Click here to view student answers and discussions

Part 2.

$\mathbf{X}\left(t\right)$ is a WSS process with its psd zero outside the interval $\left[-\omega_{max},\ \omega_{max}\right]$ . If $R\left(\tau\right)$ is the autocorrelation function of $\mathbf{X}\left(t\right)$ , prove the following: $R\left(0\right)-R\left(\tau\right)\leq\frac{1}{2}\omega_{max}^{2}\tau^{2}R\left(0\right).$ (Hint: $\left|\sin\theta\right|\leq\left|\theta\right|$ ).

Click here to view student answers and discussions

Part 3.

Inquiries arrive at a recorded message device according to a Poisson process of rate 15 inquiries per minute. Find the probability that in a 1-minute period, 3 inquiries arrive during the first 10 seconds and 2 inquiries arrive during the last 15 seconds.

Click here to view student answers and discussions

Part 4.

A RV is given by $\mathbf{Z}=\sum_{n=0}^{8}\mathbf{X}_{n}$ where $\mathbf{X}_{n}$ 's are i.i.d. RVs with characteristic function given by $\Phi_{\mathbf{X}}\left(\omega\right)=\frac{1}{1-j\omega/2}.$

(a) Determine the characteristic function of $\mathbf{Z}$ .

(b) Determine the pdf of $\mathbf{Z}$ . You can leave your answer in integral form.

Click here to view student answers and discussions

Back to ECE Qualifying Exams (QE) page

@@ Line 34: / Line 34: @@
 '''(b)''' Determine the pdf of <math class="inline">\mathbf{Z}</math> . You can leave your answer in integral form.
 :'''Click [[ECE_PhD_QE_CNSIP_2000_Problem1.4|here]] to view student [[ECE_PhD_QE_CNSIP_2000_Problem1.4|answers and discussions]]'''
-----
-=Solution 1 (retrived from [[ECE600_QE_2000_August|here]])=
-''(a)'''
-<math class="inline">\Phi_{\mathbf{X}}\left(\omega\right)=E\left[e^{i\omega\mathbf{X}}\right]=\int_{-\infty}^{\infty}\frac{A}{2}e^{-A\left|x\right|}\cdot e^{i\omega x}dx=\frac{A}{2}\left[\int_{-\infty}^{0}e^{x\left(A+i\omega\right)}dx+\int_{0}^{\infty}e^{x\left(-A+i\omega\right)}dx\right]</math><math class="inline">=\frac{A}{2}\left[\frac{e^{x\left(A+i\omega\right)}}{A+i\omega}\biggl|_{-\infty}^{0}+\frac{e^{x\left(-A+i\omega\right)}}{-A+i\omega}\biggl|_{0}^{\infty}\right]=\frac{A}{2}\left[\frac{1}{A+i\omega}-\frac{1}{-A+i\omega}\right]</math><math class="inline">=\frac{A}{2}\cdot\frac{A-i\omega+A+i\omega}{A^{2}+\omega^{2}}=\frac{A^{2}}{A^{2}+\omega^{2}}.</math>
-'''(b)'''
-<math class="inline">P\left(\left\{ \left|\mathbf{X}-\overline{\mathbf{X}}\right|\leq2\sigma\right\} \right)=1-P\left(\left\{ \left|\mathbf{X}-\overline{\mathbf{X}}\right|>2\sigma\right\} \right).</math>  By [[ECE 600 Chebyshev Inequality|Chebyshev Inequality]], <math class="inline">P\left(\left\{ \left|\mathbf{X}-\overline{\mathbf{X}}\right|>2\sigma\right\} \right)\leq\frac{\sigma^{2}}{\left(2\sigma\right)^{2}}=\frac{1}{4}</math> .
-<math class="inline">P\left(\left\{ \left|\mathbf{X}-\overline{\mathbf{X}}\right|\leq2\sigma\right\} \right)\geq\frac{3}{4}.</math>
-'''2.'''
-ref. pds means the power spectral density [[ECE 600 General Concepts of Stochastic Processes The Power Spectrum|(More information on the Power Spectrum)]].
-If <math class="inline">\mathbf{X}\left(t\right)</math>  is real, then <math class="inline">R_{\mathbf{X}}\left(\tau\right)</math>  is real and even function.
-<math class="inline">S_{\mathbf{X}}\left(\omega\right)=\int_{-\infty}^{\infty}R_{\mathbf{X}}\left(\tau\right)e^{-i\omega\tau}d\tau=\int_{-\infty}^{\infty}\left(R_{\mathbf{X}}\left(\tau\right)\cos\left(\omega\tau\right)-R_{\mathbf{X}}\left(\tau\right)i\sin\left(\omega\tau\right)\right)d\tau</math><math class="inline">=2\int_{0}^{\infty}R_{\mathbf{X}}\left(\tau\right)\cos\left(\omega\tau\right)d\tau\Longrightarrow\;\therefore S_{\mathbf{X}}\left(\omega\right)\text{ is real and even function.}</math>
-<math class="inline">R_{\mathbf{X}}\left(\tau\right)=\frac{1}{2\pi}\int_{-\infty}^{\infty}S_{\mathbf{X}}\left(\omega\right)e^{i\omega\tau}d\omega=\frac{1}{\pi}\int_{0}^{\omega_{max}}S_{\mathbf{X}}\left(\omega\right)\cos\left(\omega\tau\right)d\omega.</math>
-<math class="inline">R_{\mathbf{X}}\left(0\right)=\frac{1}{2\pi}\int_{-\infty}^{\infty}S_{\mathbf{X}}\left(\omega\right)e^{i\omega0}d\omega=\frac{1}{\pi}\int_{0}^{\omega_{max}}S_{\mathbf{X}}\left(\omega\right)d\omega.</math>
-<math class="inline">R_{\mathbf{X}}\left(0\right)-R_{\mathbf{X}}\left(\tau\right)=\frac{1}{\pi}\int_{0}^{\omega_{max}}S_{\mathbf{X}}\left(\omega\right)\left(1-\cos\left(\omega\tau\right)\right)d\omega=\frac{1}{\pi}\int_{0}^{\omega_{max}}S_{\mathbf{X}}\left(\omega\right)\left(2\sin^{2}\left(\frac{\omega\tau}{2}\right)\right)d\omega</math><math class="inline">\leq\frac{2}{\pi}\left|\int_{0}^{\omega_{max}}S_{\mathbf{X}}\left(\omega\right)\sin^{2}\left(\frac{\omega\tau}{2}\right)d\omega\right|\leq\frac{2}{\pi}\int_{0}^{\omega_{max}}\left|S_{\mathbf{X}}\left(\omega\right)\right|\left|\sin\left(\frac{\omega\tau}{2}\right)\right|^{2}d\omega</math><math class="inline">\leq\frac{2}{\pi}\int_{0}^{\omega_{max}}\left|S_{\mathbf{X}}\left(\omega\right)\right|\left(\frac{\omega^{2}\tau^{2}}{4}\right)d\omega\leq\frac{\omega_{max}^{2}\tau^{2}}{2\pi}\int_{0}^{\omega_{max}}\left|S_{\mathbf{X}}\left(\omega\right)\right|d\omega</math><math class="inline">\leq\frac{\omega_{max}^{2}\tau^{2}}{2\pi}\pi R_{\mathbf{X}}\left(0\right)=\frac{\omega_{max}^{2}\tau^{2}}{2}R_{\mathbf{X}}\left(0\right).</math>
-<math class="inline">\therefore R_{\mathbf{X}}\left(0\right)-R_{\mathbf{X}}\left(\tau\right)\leq\frac{1}{2}\omega_{max}^{2}\tau^{2}R_{\mathbf{X}}\left(0\right).</math>
-<math class="inline">\because\cos\left(\omega\tau\right)=\cos^{2}\left(\frac{\omega\tau}{2}\right)-\sin^{2}\left(\frac{\omega\tau}{2}\right)=1-2\sin^{2}\left(\frac{\omega\tau}{2}\right).</math>
-'''3.'''
-<math class="inline">\lambda=\frac{15}{60\text{ sec}}=\frac{1}{4}\text{ sec}^{-1}.</math>
-<math class="inline">P\left(\left\{ N\left(t_{1},t_{2}\right)=k\right\} \right)=\frac{\left(\left(\lambda\left(t_{2}-t_{1}\right)\right)^{k}e^{-\lambda\left(t_{2}-t_{1}\right)}\right)}{k!}.</math>
-<math class="inline">P\left(\left\{ N\left(0,10\right)=3\right\} \cap\left\{ N\left(45,60\right)=2\right\} \right)=P\left(\left\{ N\left(0,10\right)=3\right\} \right)P\left(\left\{ N\left(45,60\right)=2\right\} \right)</math><math class="inline">=\frac{\left(\frac{1}{4}\times10\right)^{3}e^{-\frac{1}{4}\times10}}{3!}\times\frac{\left(\frac{1}{4}\times15\right)^{2}e^{-\frac{1}{4}\times15}}{2!}</math><math class="inline">=\frac{1}{12}\cdot\left(\frac{5}{2}\right)^{3}\left(\frac{15}{4}\right)^{2}e^{-\frac{25}{4}}.</math>
-'''4.'''
-'''(a)'''
-<math class="inline">\Phi_{\mathbf{Z}}\left(\omega\right)=E\left[e^{i\omega\mathbf{Z}}\right]=E\left[e^{i\omega\sum_{n=0}^{8}\mathbf{X}_{n}}\right]=E\left[\prod_{n=0}^{8}e^{i\omega\mathbf{X}_{n}}\right]=\prod_{n=0}^{8}E\left[e^{i\omega\mathbf{X}_{n}}\right]=\left(\frac{1}{1-j\omega/2}\right)^{9}.</math>
-'''(b)'''
-<math class="inline">f_{\mathbf{Z}}\left(z\right)=\frac{1}{2\pi}\int_{-\infty}^{\infty}\Phi_{\mathbf{Z}}\left(\omega\right)e^{-i\omega z}d\omega=\frac{1}{2\pi}\int_{-\infty}^{\infty}\left(\frac{1}{1-j\omega/2}\right)^{9}e^{-i\omega z}d\omega.</math>
-----
-==Solution 2==
-Write it here.
 ----
 [[ECE_PhD_Qualifying_Exams|Back to ECE Qualifying Exams (QE) page]]

Difference between revisions of "ECE PhD QE CNSIP 2000 Problem1" - Rhea

Revision as of 07:57, 27 June 2012

ECE Ph.D. Qualifying Exam: COMMUNICATIONS, NETWORKING, SIGNAL AND IMAGE PROESSING (CS)- Question 1, August 2000

Question

Alumni Liaison