EE640 Spring 2009 Class Schedule

Last updated: 4-14-09

Bolded, Underlined, Italic Text is NOT YET UPDATED.

Room RMS 323	11:00am-12:15pm	11:00am-12:15pm
Month	Tuesday	Thursday
1=January		(15) Lecture: Course description, organization. Historical perspective of probability and Stochastics
1	(20) Lecture: Set Theory, Sets & Conditional Prob.	(22) Lecture: Combinatorial Probability, binomial and bernoulli distributions
1	(27) 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		(29) Lecture: Expected Value, Continuous r.v., dirac delta function, conditional, joint and marginal. HW#1B Due: 2.6= cond., 2.8=switching network, 2.11 mass func. Problems of interest: 2.6,2.7,2.8, 2.9, 2.12
2	(3) Lecture: characteristic function and moment generating. HW #2A Due:2.13=stats of binomial, 2.16=conditional prob., 2.18=expected value. Problems of interest 2.13, 2.14, 2.15, 2.16, 2.18	(5) Lecture: Multivariate Gaussian Random vectors, mean vector, covariance matrix and function of one random variable. HW #2B Due: 2.22=expected value,2.25 Gaussian moments, 2.27=characteristic function. MATLAB VISUALIZATION Form 4 images, each is 256x256. 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 0.10 and the fourth matrix is a Gaussian distribution with mean 0 and variance 1. Problems of interest 2.19, 2.20, 2.22, 2.23, 2.28
2=February	(10) Lecture: Bivariate Gaussian and correlation coefficient. HW #3A Due: 2.28 characteristic func., 2.30 joint r.v., 2.31 joint Gaussian. Problems of interest 2.29, 2.30, 2.32	(12) Lecture: Functions of more than one r.v. Functions of more than one r.v. continuded. Jacobian and auxillary variables. HW #3B Due: 2.34 functions of N r.v.,2.35 functions of r.v.s, 2.39 functions of 2 r.v.s Problems of interest 2.33, 2.342.36,2.41
2	(17) Lecture: Functions of more than one r.v. Central Limit Theorem. HW #4A Due: 2.42 Covariance matrix, 2.43 Conditional covariance, 2.44 Covariance matrix. Problems of interest 2.42, 2.43, 2.44, 2.45, 2.46, 2.47	(19) Lecture: Bounds and convergence Lecture: Random Process Introduction. HW #4B Due: 2.46 eigenvalues, 2.47 multivariate Gaussian. VISUALIZATION: Stationary Noise Visualization (see main web page for description).
2	(24) Random Process Statistics HW #5A: 2.48 bound, 2.50 TI and CB, 2.51 Union Bound Problems of interest 2.48, 2.49, 2.50, 2.51, 2.58, 2.59	(26) Types of R.P. Types of stationarity HW #5B: 2.55 l.i.m. convergenc, 2.56 Gaussian Approx, 2.58 practical. Project 1A, part A.1 Problems of interest 2.31, 2.46, 2.47, 2.55, 2.56, 2.62
3	(3 ) Wiener-Kitchene Theorem, PSD and cross-cov, cross-correlation. Stochastic differentiation and integration. Lecture: KL expansion, quantization noise and whitening. HW #6A: 3.5 Markov, 3.6 Wiener process and Martingale, 3.8 Random walk is Martingale. Problems of interest 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9	(5) Stochastic Integration and Differentiation, Linear Systems HW# 6B: 3.13 WSS, 3.14 autocorrelation. Visualization : Non-Stationary Colored Noise Problems of interest 3.11,3.14,3.15
3=March Midterm	(10) Lecture:Review for Exam 1:	(12) EXAM I: covers chapter 2, open book, open notes, NO communication devices.
3	(17) SPRING BREAK	(19) SPRING BREAK
3	(24) Lecture: Stochastic Series Expansion and Binary Hypothesis Project 1A, part A.2	(26) Lecture: Maximum Likelihood Ratio, discussion of next visualization. Fisher Discriminant Project 1A, part A.3
3	(31) HW# 7A: 3.15 AutoCorr of WSS, 3.17 PSD function, 3.23 bandwidth. Problems of interest 3.16, 3.17, 3.18, 3.19, 3.21, 3.23, 3.24, 3.41, 3.43, 3.46	(2) Lecture: HW# 7B: 3.37 Time Averages, 3.43 Basis Vectors, 3.45 Sampling.
4=April	(7) NO CLASS: LGH will be at the KSTC conference in Louisville Lecture: Mandelbrott set demonstration. Power Spectral Density of r.p.s.	(9) Lecture: Stochastic systems, AWGN model, SNR of integrate and dump demodulator. HW #8A: 4.3 LTIVC, 4.4 LTIVC, 4.8 System PSD. HW #8B: 4.12 integrator, 4.17 PSD bandwidth, 4.20 response from PSD 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
4	(14) Lecture: Random Processes HW #9: 6.2 MAP decision, 6.8 cost optimization, 6.11 Neyman-Pearson Problems of interest: 6.1, 6.2, 6.3, 6.4, 6.6, 6.8,6.10, 6.11, 6.12, 6.14, 6.18, 6.19, 6.20	(16) NO CLASS Lecture: LPCCF HW#9B: 6.12 ROC, 6.15 minimum Probability of error, 6.19 M-ary MPE.
4	(21) Binary Detector and orthogonal decision space HW#10A: Visualization: Correlation Detection Performance in Non-stationary Colored Noise Problems of interest:7-6, 7-8, 7-10,7-36,7-40, 7-41	(23) EXAM II Open Book, Open Notes, closed Comm. Devices.
4. Dead Week	(28) HW#10B: 7-6, 7-10 MMSE, 7-36 Wiener Due Wednesday: Project 1B	(30) Project 1C: Due Friday
5. Finals Week	(5)	(May 7)