Room 212 CB

12:30pm-1:45pm

12:30pm-1:45pm

Month

Tuesday

Thursday

1=January

(10) Lecture: Course description, organization

1

(15) Lecture: Set Theory, Sets & Conditional Prob.

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

1

(22) Lecture: random variables, pdf and cdf. Gaussian and uniform r.v.

HW#1A Due: 2.1= P w/o replacement, 2.2 set theory, 2.4 conditional prob.

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

HW#1B Due: 2.8=switching network, 2.9= cond., 2.12 cond. Prob.

1

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

conditional and marginal r.v.s

(31) Lecture: Expected value of joint r.v.

HW #2A Due: 2.18=linearity and expected value, 2.19=expected value, 2.23=uniform distribution.

2=February

(5) Lecture: characteristic function and moment generating.

HW #2B Due: 2.17=conditional probablility of comm channel, 2.32=conditional Gaussian, 2.33 joint r.v., 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.

(7) Lecture: Multivariate Gaussian Random vectors, mean vector, covariance matrix and function of one random variable.

2

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

HW #3A Due: 2.25 Gaussian moments, 2.28 Moments, 2.36 Characteristic function.

(14) Lecture: Functions of more than one r.v. continuded. Jacobian and auxillary variables.

HW #3B Due: 2.43 Conditional r.v. and covariance, 2.45 covariance matrix, 2.46 multi-variant whitening.

2

HW #3C: Project 1, part A, item 7, Numerical generation of binary and Gaussian sequences

MATLAB: Visualization of Stationary Noise Fields.

(21) Lecture: Bounds and convergence

HW #4A Due: 2.35 func of a rv., 2.38 func of 2 rv, 2.41 func of 2 r.v. 

2

(26) Lecture: Convergence and Random Process Introduction

HW #4B: 2.46 whitening, 2.42 multivariate Gaussian, 2.47 eigenvectors.

(28) Lecture: Random Processes

HW #5: 2.50 Tchebycheff and Chernoff bounds, 2.58 Gaussian approx., 2.59 CLT

3=March

Midterm

(5) No Class:

(7) Lecture:

HW #6: 3.1 stats of r.p., 3.4 Markov, 3.5 Chapman Kolmoroff Eq.

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

3

(12) SPRING BREAK

(14) SPRING BREAK

3

(19) Lecture: Review for test and MLR and Fisher Discriminant.

MATLAB: Visualization of non-Stationary Noise

(21) MIDTERM EXAM (chapters 1 and 2) Open Book, Open Notes

3

(26) Lecture: Fisher Discriminant.

(28) Lecture: Mandelbrott set, Stationarity.

All of Project 1 Due Friday.

4=April

(2) Lecture: Mandelbrott set demonstration. Power Spectral Density of r.p.s.

HW# 7A: 3.9 Markov is Martingale, 3.14 Ryy and Rxx for WSS input, 3.15 autocorrelation properties.

MATLAB: Generate Mandelbrott Set.

(4) Lecture: Stochastic systems, AWGN model, SNR of integrate and dump demodulator.

Due Friday:

HW#7B: 3.21 WSS and modulated joint Gaussian r.v., 3.16 PSD of WSS, 3.18 find Rxx from PSD, 3.23 Effective Bandwidth.

4

HW #8A: 4.1 PSD of Y, 4.2 LTIVC with SSS input, 4.5 LTIVC with WSS input

HW #8B: 4.9 System PSD, 4.12 PSD of integrator, 4.18 PSD of Butterworth Filter

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

4

4. Dead Week

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

5. Finals Week

(30)

Project 2 Due