EE640 Spring 2005 Class Schedule (3-24-05) Bolded, Underlined, Italic Text is NOT YET UPDATED.
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Room FPAT 255 |
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12:30pm-1:45pm |
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Month |
Tuesday |
Thursday |
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1=January |
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(13) Lecture: Course description, organization |
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1 |
(18) Lecture: Set Theory, Sets & Conditional Prob. |
(20) Lecture: Combinatorial Probability, binomial and bernoulli distributions |
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1 |
(25) Lecture: random variables, pdf and cdf. Gaussian and uniform r.v. HW#1A Due: 2.1 Boolean Algebra, 2.4= P w/o replacement, 2.5 conditional prob. Problems of interest: 2.2, 2.3 |
(27)Lecture: Expected Value, Continuous r.v., dirac delta function, conditional, joint and marginal. HW#1B Due: 2.7= cond., 2.8=switching network, 2.9=cond. Problems of interest: 2.6, 2.9, 2.12 |
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2=February |
(1) Lecture: characteristic function and moment generating. HW #2A Due:2.14=mean and variance, 2.15 (only part a)=mean and variance, 2.18=expected value. Problems of interest 2.13, 2.16, 2.23 |
(3) Lecture: Multivariate Gaussian Random vectors, mean vector, covariance matrix and function of one random variable. HW #2B Due: 2.19=expected value, 2.22=Schwartz inequality, 2.20=conditional probability. 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 2.22, 2.19, 2.29 |
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2 |
(8) Lecture: Functions of more than one r.v. Functions of more than one r.v. continuded. Jacobian and auxillary variables. HW #3A Due: 2.25 Gaussian moments, 2.29 joint and cond. pdfs, 2.26 Characteristic function. Problems of interest 2.27,2.28, 2.36 , 2.32, 2.33 |
(10) Lecture: Functions of more than one r.v.
Central Limit Theorem. HW #3B Due: 2.43 conditional mean and covariance, 2.45 covariance matrix, 2.46 n-variate Gaussian. Problems of interest 2.31,2.42, 2.43,2.44, 2.45,2.46,2.47 |
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2 |
(15) Lecture: Bounds and convergence HW #4A: Project 1, part A, item 2, just do g1, Numerical generation of pseudo random Gaussian sequences (you can use the 2004 version) |
(17) Lecture: Random Process Introduction. HW #4B Due: 2.30 func. of 2 r.v., 2.35 func of r.v., 2.37 func of 2 r.v. Problems of interest 2.35,2.38,2.39, 2.40,2.45 |
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2 |
(22) Types of R.P. Types of
stationarity HW #5A: 2.49 Tchebycheff and Chernoff bounds, 2.50 Tchebycheff and Chernoff bounds, 2.59 CLT Problems
of interest 2.48, 2.49, 2.50, 2.51, 2.58, 2.59 |
(24) Wiener-Kitchene Theorem, PSD and cross-cov, cross-correlation. Stochastic differentiation and integration. HW #5B: 2.55 l.i.m. convergence, 2.56 convergence, 2.62 convergence. Project: All of Project 1A Due Problems
of interest 2.47, 2.55 |
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3 |
(1) Lecture: KL expansion, quantization noise and whitening. HW #6A: 3.3 Gaussian r.p., 3.4 Markov
Problems
of interest 3.4, 3.5, 3.6, 3.7 |
(3) Lecture: HW #6B: 3.5 Markov, 3.6 Wiener process and Martingale |
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3=March Midterm |
(8) Lecture: Review for Exam I and Mandelbrott set, Stationarity. HW# 7A: 3.9 Random walk is Martingale, 3.12 autocorrelation, 3.14 autocorrelation properties. VISUALIZATION: Stationary Noise Visualization (see main web page for description). Problems of interest 3.8, 3.9, 3.12, 3.13, 3.14, 3.15 |
(10) EXAM I: covers chapter 2, open book, open notes |
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3 |
(15) SPRING BREAK |
(17) SPRING BREAK |
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3 |
(22) Lecture: Binary Hypothesis and Maximum Likelihood Ratio, discussion
of next visualization and project 1B. |
(24) Fisher Discriminant Visualization : Non-Stationary Colored Noise |
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3 |
(29) NO CLASS |
(31) Lecture: HW#7B: 3.16 PSD of WSS, 3.18 AutoCorr of PSD. HW #8A: 4.2 SSS proof, 6.1 MAP decision, 6.3 MAP minimization Problems of interest 3.16, 3.17, 3.18, 3.19, 3.21, 3.23 Problems of interest: 6.1, 6.2, 6.3, 6.4, 6.6, 6.10. 6.11 |
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4=April |
(5)
Lecture: Mandelbrott set demonstration. Power
Spectral Density of r.p.s. HW #8B: 4.5 Differentiator, 4.7 difference equation, 6.5 MAP. |
(7)
Lecture: Stochastic systems, AWGN model, SNR of integrate and dump
demodulator. Due Friday: Project 1B |
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4 |
(12) Lecture: Random Processes HW #9A: 4.14 PSD, 4.20 LTIV SNR, 6.11 N-P. Problems of interest: 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.8, 4.9, 4.12, 4.17, 4.18 |
(14) Lecture: LPCCF Due Friday: Project 1C |
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4 |
(19) Binary Detector and
orthogonal decision space HW #9B: 6.12 ROC, 6.19 MPE, 6.20
MPE Problems of interest: 6.2, 6.3,
6.4, 6.6, 6.10, 6.11, 6.12, 6.14, 6.18, 6.19, 6.20 |
(21) EXAM II (Chapter 3 and part
of 4 and part of 6) |
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4. Dead Week |
(26) HW#10: 7-8 MMSE, 7-40 Wiener, 7-41
Wiener versus Kalman |
(28) Due Friday: Project 2A |
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5. Finals Week |
(3) |
(5) |
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