PLAN B

3:25pm-4:55pm

3:25pm-4:55pm

Month

Tuesday

Thursday

1=January

(13) Lecture: Course description, Set Theory

1

(18) Lecture: Sets & Conditional Prob.

(20) No class

1

(25) Lecture: Combinatorial Probability, binomial and bernoulli distributions

HW#1A Due: 1.16=ternary channel, 1.19=Defective Parts, 1.41=MATLAB

(27)Lecture: Chapter 2, pdf and cdf. Gaussian and uniform r.v.

HW#1B Due

1.24=Missile targeting, 1.35=packet trans., 1.39=ring network

2=February

(1) Lecture: Continuous r.v., dirac delta function, conditional, joint and marginal.

HW #2A Due: 1.11 Set Probability, 1.15 Conditional Prob. Of Finite Set, 1.34 Independent Prob., 137 Complimentary Prob.

(3) Lecture: Continuous r.v. continued, marginal r.v., independent, r.v.s, examples of Rayleigh, exponential, joint, conditional and marginal r.v.s

HW #2B Due: Project 1, part A, item 7 Numerical generation of binary and Gaussian sequences

2

(8) Lecture: conditional pdfs and the poisson process.

HW #3A Due: 2.1 binomial distribution, 2.4 Prob. Density Func. (pdf), 2.5 Gaussian thermal noise, 2.15 Conditional pdf of a comm. Channel.

(10) Lecture: Discussion of MATLAB project, random surface modeling and functions of one r.v. (Chapter 3).

HW #3B Due: Handout for MATLAB generation of "non-stationary colored Gaussian noise image."

2

(15) Lecture: Functions of more than one r.v.

HW #4A Due: 2.18 Joint Gaussian r.v., 2.19 binomial distr. of bar code failure, 2.21 Joint pdf, 3.5 Functions of a r.v.

(17) Lecture Expected values. (Chapter 4).

HW#4B Due: 3.17 Functions of two r.v.s, 3.24 Functions of two r.v.s, MATLAB problems, Use surfl.m and shading.m to illuminate colored noise image in HW 3B, Use flattop1.m to visualize first 10 bits from the binary signal in HW#2B with 32 samples per bit. Turn in plots for both MATLAB items.

2

(22) Lecture expected values

(24) Lecture Moments and Characteristic functions.

HW#5 Due: 4.13 Variance, 4.21 Moments of r.v.s, 4.31 pdf of a sum of r.v.s, 4.34 Moments of an autoregression.

Project1b: Problem 1, plot B-1 and plot B-6

2/3

(29) Lecture: characteristic function, CLT, Cov estimate.

HW #6A: 4.16 Conditional Expected Value, 4.20 Moments of a Gaussian r.v., 4.37 Failure analysis, 4.38 Sum of Bernoulli r.v.s and the CLT

(2) Lecture: unbiased estimate of variance. MATLAB demonstrations.

HW #6B: Project 1, Part B: Problem 2 (Kt only), Project 1, Part C: Problem 3a (first case) and find peak location only. (No SNR or PACE)

3=March

(7) Lecture: Prove unbiased estimate of variance with E{}, MLR for single and multiple r.v., MATLAB Demos of Project 1.

(9) Lecture: Finish chapter 4, moment generating function, Chebyshev inequality, weak law of large numbers. Demo on Project 1.

Project 1 Due Friday.

3

(14) SPRING BREAK

(16) SPRING BREAK

3

(21) Lecture: Review for exam.

HW #7A 5.4 matrix outer product rank, 5.7 multivariate 2nd moment, 5.15 multivariate mean vector and covariance matrix.

(23) MIDTERM EXAM (chapters 1 through 4) Open Book, Open Notes

3

(28) Lecture: Mandelbrott set, Fisher Discriminant and Prewhitening.

(30) Lecture:

MATLAB: Generate Mandelbrott Set.

HW #7B 5.11 multivariate whitening.

HW #8A 6.2 variance estimate given known mean, 6.3 mean and variance estimates, 6.16 multivariate discrimination

4=April

(4) Lecture: Multivariant Linear Systems

Project 2: Part A, Problem 1.a and 1b., Problem 2 (apply to Mandelbrott images).

HW #8B 7.10 CCD with leaky cells, 7.14 linear system with additive noise input.

(6) Lecture: Ramdom Processes, white noise, ergodicity.

Project 2: Part B (set up one leg of a binary detector).

4

HW #9 8.7 Poisson r.p., 8.10 Markov Chain, 9.3 ensemble mean and variance of the time average, 9.10 optimum binary detector, 9.11 ergodic mean of WWS Gaussian

HW #10 8.13 WSS modulated r.p., 10.6 PSD of WSS r.p., 10.22 PSD of linear system, 10.23 modulated r.p.

4

(18) FINAL EXAM

Project 2 Due

4. Dead Week

(25) No Class

(27) No Class

5. Finals Week

(2) No test

(4) No test