EE640 Spring 2006 Class Schedule

Last updated: 4-26-06

Bolded, Underlined, Italic Text is NOT YET UPDATED.

Room RMS 323	11:00am-12:15pm	11:00am-12:15pm
Month	Tuesday	Thursday
1=January		(12) Lecture: Course description, organization
1	(17) Lecture: Set Theory, Sets & Conditional Prob.	(19) Lecture: Combinatorial Probability, binomial and bernoulli distributions
1	(25) Lecture: random variables, pdf and cdf. Gaussian and uniform r.v. HW#1A Due: 2.2 Prob. And set theory, 2.3= P w/o replacement, 2.5 conditional prob. Problems of interest: 2.1,2.2, 2.3,2.4,2.5		(26) Lecture: Expected Value, Continuous r.v., dirac delta function, conditional, joint and marginal. HW#1B Due: 2.6= cond., 2.8=switching network, 2.17 mass func. & cond. Prob. Problems of interest: 2.6,2.7,2.8, 2.9, 2.12, 2.17
2=February	(31) Lecture: characteristic function and moment generating. HW #2A Due:2.13=expected value of binomial, 2.16=bit error rate, 2.18=expected value. Problems of interest 2.13, 2.14, 2.15, 2.16, 2.18, 2.23	(2) Lecture: Multivariate Gaussian Random vectors, mean vector, covariance matrix and function of one random variable. HW #2B Due: 2.22=Schwartz inequality, 2.19=expected value, 2.29=condional pdf. MATLAB VISUALIZATION Form 4 images, each is 128x128. The first matrix is a filled with values from a uniform distribution U(0,1). The second is a binarized matrix from the first with the threshold at 0.5 value. The third matrix is binarized from the first with a threshold 0.20 and the fourth matrix is a Gausian distribution with mean 0 and variance 1. Problems of interest 2.19, 2.20, 2.22, 2.29
2	(7) Lecture: Bivariate Gaussian and correlation coefficient. HW #3A Due: 2.25 Gaussian moments, 2.27 Moments, 2.28 Characteristic function. Problems of interest 2.25, 2.26, 2.27,2.28, 2.29, 2.36 , 2.32, 2.33	(9) Lecture: Functions of more than one r.v. Functions of more than one r.v. continuded. Jacobian and auxillary variables. HW #3B Due: 2.42 mean and covariance, 2.44 covariance proof, 2.47 bivariate Gaussian Problems of interest 2.31,2.42, 2.43, 2.44, 2.45, 2.46, 2.47
2	(14) Lecture: Functions of more than one r.v. Central Limit Theorem. HW #4A: Project 1, part A, item 2, just do g1, Numerical generation of pseudo random Gaussian sequences (you can use the 2004 version)	(16) Lecture: Bounds and convergence Lecture: Random Process Introduction. HW #4A Due: 2.38 func of 2 rv, 2.39 func of 2 r.v., 2.40 func of a N rv (Solve for N=1 only). HW #4B Due: 2.35 func of r.v. (do part a and b only), 2.37 func of 2 r.v. Problems of interest 2.35,2.38,2.39, 2.40,2.45
2	(21) Random Process Statistics HW #5A: 2.48 bound proof, 2.49 Tchebycheff and Chernoff bounds, 2.51 Union Bound VISUALIZATION: Stationary Noise Visualization (see main web page for description). Problems of interest 2.48, 2.49, 2.50, 2.51, 2.58, 2.59	(23) Types of R.P. Types of stationarity HW #5B: 2.31 conditional Gaussian, 2.46 covariance, 2.55 l.i.m. convergence. Problems of interest 2.31, 2.46, 2.47, 2.55, 2.56, 2.62
3	(28) ) Wiener-Kitchene Theorem, PSD and cross-cov, cross-correlation. Stochastic differentiation and integration. Lecture: KL expansion, quantization noise and whitening. HW #6A: 3.6 Wiener process and Martingale, 3.7 random walk, 3.8 Random walk is Martingale. Problems of interest 3.3, 3.4, 3.5, 3.6, 3.7, 3.8	(2) Lecture: Stochastic Integration and Differentiation, Linear Systems HW# 6B: 3.13 autocorrelation, 3.15 autocorrelation properties. Visualization : Non-Stationary Colored Noise
3=March Midterm	(7) Lecture: Review for Exam I	(9) EXAM I: covers chapter 2, open book, open notes, NO communication devices.
3	(14) SPRING BREAK	(16) SPRING BREAK
3	(21) Lecture: Binary Hypothesis and Maximum Likelihood Ratio, discussion of next visualization and project 1B. HW# 7A: 3.17 PSD of WSS, 3.19 trinary sequence, 3.23 effective bandwidth, 3.41 time average. Problems of interest 3.9,3.14, 3.16, 3.17, 3.18, 3.19, 3.21, 3.23	(23) Lecture by Wei Su related to Project 1S Fisher Discriminant
3	(28) All of Project 1A, Question/Task 1 from Project 1S.	(30) Lecture: HW #8A: 4.3 LTIVC, 4.4 LTIVC, 4.6 LTIVC with WSS input.
4=April	(4) Lecture: Mandelbrott set demonstration. Power Spectral Density of r.p.s. HW #8B: 4.8 System PSD, 4.12 PSD of integrator, 4.17 response from PSD	(6) Lecture: Stochastic systems, AWGN model, SNR of integrate and dump demodulator.
4	(11) Lecture: Random Processes HW #9A: 6.2 MAP decision, 6.6 cost minimization, 6.11 Neyman-Pearson Problems of interest: 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.8, 4.9, 4.12, 4.14, 4.17, 4.18, 4.20	(13) Lecture: LPCCF Due Friday: Project 1B
4	(18) NO CLASS Binary Detector and orthogonal decision space Problems of interest: 6.1, 6.2, 6.3, 6.4, 6.6, 6.10, 6.11, 6.12, 6.14, 6.18, 6.19, 6.20	(20) NO CLASS
4. Dead Week	(26) HW#9B: 6.12 ROC, 6.14 minimum Probability of error, 6.18 M-ary MPE. Problems of interest: 7-8, 7-40, 7-41	(27) Due Friday: Project 1C
5. Finals Week	(2) FINAL EXAM (Tuesday 5-2-06, 10:30am to 12:30pm, open book, open notes	(4)