EE640 Spring 2003 Class Schedule (
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Room 255 AH |
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Month |
Tuesday |
Thursday |
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1=January |
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(16) Lecture: Course description, organization. C1,C2, review of probability and set theory. |
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1 |
(21) C3 review of probability measure review |
(23) Lecture: HW#1: 2-1 Boolean Algebra, 2-5 probability Proof, 2-9 conditional probability, 2-12 prob. of a time interval, 2-18 probability measure, 2-19 prob. Measure, 2-24 conditional prob., 2-26 binomial coefficient. Task: Send email from your email address with "EE640" in subject. |
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1 |
(28) Lecture: C4 review of a random variable |
(30) Lecture: HW#2: 3-1 binomial, 3-4 coin toss, 3-8 Bernoulli conditional prob, 3-9 card statistics, 3-10 Gamber's ruin problem, 4-6 uniform and normal r.v. |
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2=February |
(4) Lecture: C5 review functions of a random variable |
(6) Lecture: HW#3: 4-17 Rayleigh, 4-19 Conditional cdf proof, 4-26 exponential failure rate, 4-30 poisson?, 4-34 particle physics problem, 4-35 multi-nomial proof. Visualization 3: 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. |
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2 |
(11) Lecture: C6 functions of two random variables |
(13) Lecture: HW#4: 5-1 normal distr., 5-6 natural log function, 5-18 uniform to chi, 5-26 Poisson, 5-33 magnitude of Gaussian, 5-38 characteristic func. |
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2 |
(18) Lecture: C7 sequences of r.v.s |
(20) Lecture:
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2 |
(25) Lecture: HW#5: 6-1 pdfs,6-13 rayleigh, 6-20 exponential, 6-37 joint pdf, 6-63 covariance V5: "Stationary Colored Noise Field" (see EE640 main web page to download instructions). |
(27) Lecture:
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3=March |
(4) Lecture: C8 Statistics |
(6) Lecture: HW #6: 7-7 conditional E{}, 7-19 conditional E{}, 7-23 correlation matrix, 7-30 CLT, 7-32 characteristic function of complex r.v.s. V6: “Non-stationary Colored Noise Field” (see EE640 main web page to download instructions). |
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3 Midterm |
(11) Lecture: |
(13) Lecture: |
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3 |
(18) SPRING BREAK |
(20) SPRING BREAK |
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3 |
(25) Review and whitening/diagonalization of covariance. HW #7: confidence intervals 8.3, 8.7, 8.9, 8.12, 8.13, ML estimate, 8-21, 8-23, hypothesis testing 8-34. |
(27) No class but will be available for recitation either in office 473, lab 455 or class room 255. Friday March 28, 6pm to 8pm, room 255 AH MIDTERM EXAM Open Book, Open Notes |
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4=April |
(1) Lecture: Stochastic Processes |
(3) Lecture: |
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4 |
(8) Lecture: AutoCorrelation and PSD of linear systems |
(10) Lecture: differentiation and stochastic integration. C10 matched filter. HW #8: complex WSS 9.15, white noise 9.27, nonstationary white noise 9.28, PSD 9-34. Project 1a due |
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4 |
(15) Lecture: |
(17) Lecture: HW#9: thermal noise 10.3, Wiener process 10.6, show its WSS 10.12, SNR
10.25. Project 1b due |
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4 |
(22) Conference |
(24) Conference |
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4. Dead Week 5=May |
(29) Project 1c due |
(1) HW and Project 2a, due Monday May 5. HW #10: (optional) 12-3,12-4,12-9 and 12-15. If
submitted, will replace lowest hw score. Project 2a: Color Image encryption Due. |
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5. Finals Week |
(6) |
(8) (FRIDAY) FINAL PROJECT 2b due |
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