EE640 SPRING 2007

Stochastic Systems

CLASS SCHEDULE AND HOMEWORK

Three problems each calss and due next week [HW Solutions1] [MidTerm Solutions] [MidTerm II Problems] [MidTerm II Solutions] [HW Solutions2]

LECTURE NOTES

Lecture 1: Introduction

Lecture 2: Set and Probability

Lecture 3: Bernoulli Trials and Binomial Probability

Lecture 4: Random Variables

Lecture 5: Expected Values

Lecture 6: Characteristic and Moment Generating Functions

Lecture 7: Random Vectors

Lecture 8: Bivariate Gaussians

Lecture 9: Functions of Random Variables

Lecture 10: Central Limit Theorem

Lecture 11: Bounds and Convergence

Lecture 12: Random Process Statistics

Lecture 13: Classes of Random Processes

Lecture 14: Types of Stationarity

Lecture 15: Correlation and Power Spectral Density

Lecture 16: LTI Systems and Matched Filters

Lecture 17: Series Expansion, Sampling and Quantization

Lecture 18: Markov Chains

Lecture 19: Winner Filter and Kalman Filter

Lecture 20: Signal Detection and Discrimination

Lecture 21: Fisher's Discriminant

Lecture 22: Optimum Decision Boundaries

Lecture 23: Multi-Variant Detection

PROJECTS

PART A: SYNTHESIS (EE640_Project_1A.pdf)

PART B: ANALYSIS (EE640_Project_1B.pdf)

PART A: DETECTION & DISCRIMINATION (EE640_Project_1C.pdf)

PART S: SUPPLEMENTARY (EE640_Project_1S.pdf, Target Data, Clutter Data)

Acknowledgement: the lecture notes are based on Prof. Laurence Hassebrook's notes for EE640 2006.
Updated 1-24-07