EE640 Spring 2007 Class Schedule
Last updated:
Bolded, Underlined, Italic Text is NOT YET UPDATED.
|
Room RMS 323 |
11:00am-12:15pm |
11:00am-12:15pm |
|
Month |
Tuesday |
Thursday |
|
Jan. |
|
(11) Lecture: Course description, organization |
|
Jan. |
(16) Lecture: Set Theory, Conditional Prob., & Bayes’s theorem HW#1A: 2.1 Prob. And set theory. Problems of interest: 2.1,2.2, 2.3,2.4,2.5 |
(18) Lecture: Combinatorial Probability, binomial and bernoulli distributions HW#1B: 2.7 cond. prob.. 2.9 Bayes’ theorem 2.12 comb. Prob. Problems of interest: 2.6,2.7,2.8, 2.9, 2.12, 2.17 |
|
Jan. |
(23) Lecture: random variables, pdf and cdf. Gaussian and uniform r.v. HW #2A: 2.14 Expected value of Poisson, 2.23 Expected value of Uniform, 2.16 Bit error rate. Problems of interest 2.13, 2.14, 2.15, 2.16, 2.18, 2.23
|
(25) Lecture: Expected Value, Continuous r.v., dirac delta function,
conditional, joint and marginal. HW #2B: 2.22 Schwartz inequality, 2.20 Conditional expectation, 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 |
|
Feb. |
(30) Lecture: characteristic function and moment generating HW #3A: 2.26 Characteristic function, 2.28 Characteristic function, 2.33 Joint pdf Problems of interest 2.25, 2.26, 2.27,2.28, 2.29, 2.36 , 2.32, 2.33 |
(1) Lecture: Multivariate Gaussian Random vectors, mean vector, covariance matrix and function of one random variable. HW #3B: 2.31 Bivariate Gaussian, 2.43 random vector, 2.46 Linear
transformation of Gaussian r.v. Problems of interest 2.31,2.42, 2.43, 2.44, 2.45, 2.46, 2.47 |
|
Feb. |
(6) Lecture: Bivariate Gaussian and correlation coefficient. HW #4A: Project 1A Due: Feb. 15 |
(8) Lecture: Functions of more than one r.v. Functions of more than one r.v. continuded. Jacobian and auxillary variables. HW #4B 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 #4C 2.35 func of r.v. (do part a and b only), 2.37 func of 2 r.v. Problems of interest 2.35, 2.37, 2.38, 2.39, 2.40, 2.45 |
|
Feb. |
(13) Lecture: Functions of more than one r.v. Central Limit Theorem. HW #5A: 2.48 bound proof, 2.49 Tchebycheff and Chernoff bounds, 2.59 central limit theorem Problems of interest 2.48, 2.49, 2.50, 2.51, 2.58, 2.59 |
(15) Lecture: Bounds and convergence HW #5B: 2.31 conditional Gaussian, 2.46 covariance, 2.55 l.i.m. convergence. VISUALIZATION: Stationary Colored Noise Visualization (Link). Problems of interest 2.31, 2.46, 2.47, 2.55, 2.56, 2.62 |
|
Feb. |
(20) Lecture: Random Process Statistics. 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 |
(22) Lecture: Types of R. P. HW# 6B: 3.13 autocorrelation, 3.15 autocorrelation properties. Visualization : Non-Stationary Colored Noise (Link) |
|
Mar. |
(27) Types of stationarity. Ergodicity. Wiener-Kitchene Theorem, PSD
|
(1) AutoCorrelation, Power Spectral Density and Mean Squared Integration. HW# 7: 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 |
|
Mar. |
(6) Lecture: Stochastic Integration and Differentiation, Linear Systems, matched filters. HW #8A: 4.3 LTIVC, 4.4 LTIVC, 4.6 LTIVC with WSS input. |
(8) Lecture: KL expansion, sampling, and quantization noise. HW #8B: 4.8 System PSD, 4.12 PSD of integrator, 4.17 response from PSD |
|
Mar. |
(13) SPRING BREAK |
(15) SPRING BREAK |
|
Mar. |
(20) Lecture: Markov Chain. Review for Exam I. HW #9A: 5.32 State Diagram, 5.34 Homogeneous MC, 5.35 Two-state MC |
(22) EXAM I: covers chapter 2, open book, open notes, NO communication devices.
|
|
Mar. |
(27)Lecture: Discussion on Midterm |
(29) Lecture: Review for Exam II. Wiener Filter and Kalman filter HW #9B: 7.30 Kalman Filter, 7.31 Steady state Kalman Filter, 7.35 Wiener Filter |
|
Apr. |
(3) Lecture: Signal detection and discrimination. Due: Apr. 17 |
(5) Lecture: Optimum Decision Boundaries. Fisher Discriminant. HW#10B: 6.12 ROC, 6.14 minimum Probability of error, 6.18 M-ary MPE. Due: Apr. 19 |
|
Apr. |
(10) Lecture: Optimum Decision Boundaries. MAP, Neyman Pearson, Lagrange Multipliers. HW #11A: Project 1B Due: Apr. 24 |
(12) Lecture: Multi-variant MLR HW #11B: Project 1C Due: Apr. 26 |
|
Apr. |
(17) Lecture: Optimal Detector Filter Banks |
(19) Lecture: Detection Filter Performance Measures |
|
Apr. Dead Week |
(24) Lecture: Advanced Topics, Review for the final exam |
(26) Lecture: Advanced Topics, Review for the final exam |
|
May |
(1) Final |
(3) |