EE640 Spring 2004 Class Schedule (
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Room RMB 323 Switched to C053 Raymond |
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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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(15) Lecture: Course description, organization |
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1 |
(20) Lecture: Set Theory, Sets & Conditional Prob. |
(22) Lecture: Combinatorial Probability, binomial and bernoulli distributions |
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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. |
(29)Lecture: Expected Value, Continuous r.v., dirac delta function, conditional, joint and marginal. HW#1B Due: 2.6= cond., 2.8=switching network, 2.17 mass func. & cond. Prob. |
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2=February |
(3) Lecture: Continuous r.v. continued, marginal r.v.,
independent, joint, conditional and marginal r.v.s.
Expected value of joint r.v. HW #2A Due:2.13=expected value of binomial, 2.16=bit error rate, 2.18=expected value. Problems of interest 2.23 |
(5) Lecture: characteristic
function and moment generating. 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 0.25 and the fourth matrix is a Gausian distribution with mean 0 and variance 1. Problems of interest 2.32, 2.33 |
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2 |
(10) Lecture: Multivariate Gaussian
Random vectors, mean vector, covariance matrix and function of one random
variable. HW #3A Due: 2.25 Gaussian moments, 2.27 Moments, 2.28 Characteristic function. Problems of interest 2.28, 2.36 |
(12) Lecture: Functions of more
than one r.v. Functions of more than one r.v. continuded. Jacobian and auxillary
variables. HW #3B Due: 2.42 mean and covariance, 2.44 covariance proof, 2.47 bivariate Gaussian. Problems of interest 2.43, 2.45 |
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2 |
(17) Lecture: Lecture: Bounds and
convergence HW #4A: Project 1, part A, item 2, just do g1, Numerical generation of pseudo random Gaussian sequences |
(19) Lecture: Convergence and
Random Process Introduction. Random
Processes HW #4B Due: 2.40 func of a N rv., 2.38 func of 2 rv, 2.39 func of 2 r.v. Problems of interest 2.35,2.45 |
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2 |
(24) No class: Illness HW #5A: 2.48 bound proof, 2.49 Tchebycheff and Chernoff bounds, 2.51 Union Bound Problems of interest 2.50,2.58,2.59 |
(26) Types of RP, Wiener-Kitchene Theorem, PSD and cross-cov,
cross-correlation. HW #5B: 2.31 conditional Gaussian, 2.46 covariance, 2.55 l.i.m. convergence. Problems of interest 2.42, 2.47 |
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3 |
(2) Lecture |
(4) Lecture: |
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3=March Midterm |
(9) Lecture: HW #6: 3.6 Wiener process and Martingale, 3.7 random walk. VISUALIZATION: Stationary Noise Visualization (see main web page for description). |
(11) Lecture: Project: All of Project 1A Due Problems of interest 3.4,3.5 |
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3 |
(16) SPRING BREAK |
(18) SPRING BREAK |
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3 |
(23) Lecture: Review for test and Mandelbrott set, Stationarity. |
(25) MIDTERM EXAM Part A(chapters 1 and 2) Open Book, Open Notes NOTE: The exam has been split into Part A and Part B. Both Parts will have
3 equally weighted problems but they will be graded as 1 exam and the lowest problem
score is dropped (ie. Best 5 out of 6). |
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3 |
(30) Lecture: Fisher Discriminant. HW# 7A: 3.8 Random walk is Martingale, 3.13 autocorrelation, 3.15 autocorrelation properties. Problems of interest 3.9, 3.14 |
(1) Lecture: HW#7B: 3.17 PSD of WSS, 3.19 trinary sequence. Visualization 7: Non-Stationary Colored Noise Problems of interest 3.16, 3.18,
3.21, 3.23 |
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4=April |
(6) Lecture: Mandelbrott
set demonstration. Power Spectral Density of r.p.s. HW #8A: 4.3 LTIVC, 4.4 LTIVC, 4.6 LTIVC with WSS input . Problems of interest: 4.1, 4.2, 4.5 |
(8) Lecture: Stochastic systems,
AWGN model, SNR of integrate and dump demodulator. Due Friday: Project 1B |
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4 |
(13) Lecture: Random Processes HW #8B: 4.8 System PSD, 4.12 PSD of integrator, 4.17 response from PSD Problems of interest: 4.9, 4.12,
4.18 |
(15) Lecture: LPCCF Due Friday: Project 1C |
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4 |
(20) Binary Detector and orthogonal decision space HW #9A: 6.2 MAP decision, 6.6 cost
minimization, 6.11 Neyman-Pearson Problems of interest: 6.3, 6.4, 6.10 |
(22) EXAM Part B (Chapter 3
and part of 4 and part of 6) |
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4. Dead Week |
(27) HW#9B: 6.12 ROC, 6.14 minimum
Probability of error, 6.18 M-ary MPE. Problems of interest 6.12, 6.19, 6.20 |
(29) Due Friday: Project 2A |
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5. Finals Week |
(4) |
(6) Project 2B: Due |
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