EE640 SPRING 2009
STOCHASTIC SYSTEMS
INFORMATION
Updated 4-14-09
VISUALIZAIONS
Correlation Detection in Additive Non-Stationary Colored Noise
PROJECTS
PROJECT 1A:
PART A: SYNTHESIS (EE640_Project_1A09.pdf)
Target Data: Target.zip
Clutter Data: Clutter.zip
PROJECT 1B:
PART B: ANALYSIS (EE640_Project1_B09.pdf)
PROJECT 1C:
PART C: DETECTION AND DISCRIMINATION (EE640_Project1_C09.pdf)
(NOT READY FOR 2009) PROJECT 1S: Supplemental
PART S: Supplemental:EE640_Project_1S.pdf
Journal References for project 1
VISUALIZAIONS (not updated from 2005 yet)
Example of Stationary Colored Noise versus a non-stationary image, both having same PSD
Data Whitening and the Covariance Matrix
LECTURE NOTES
Lecture 1: Historical Perspective and Overview of Probability and Stochastic Processes
Lecture 2: Set Theory and Probability
Lecture 5X: Non-biased variance estimate
Lecture 6: Characteristic Function and Moment Generating Function
Lecture 7: Random Vectors, Covariance Matrix and Correlation Matrix
Lecture 8: Bi-variate Gaussian r.v and Correlation Coefficient.
Lecture 9: Functions of Random Variables
Lecture 9A: Auxiliary Variable
Lecture 10: Central Limit Theorem
Lecture 11: Bounds and Convergence
Lecture 13: R.P.: TYPES OF STATIONARITY
Lecture 13 Example: Quantization Noise Model
Lecture 14: R.P.: POWER SPECTRAL DENSITY, AUTOCORRELATION AND MEAN SQARE CALCULAS
Lecture 15: LINEAR TIME-INVARIANT STOCHASTIC SYSTEMS
Lecture 16: STOCHASTIC SERIES EXPANSION
Lecture 17:DETECTION AND DISCRIMINATION
Lecture 18: OPTIMUM DECISION BOUNDARIES
Lecture 19: TYPES OF DECISION BOUNDARIES
Lecture 21: QUADRATURE MODULATION AND DEMODULATION
Lecture 22: SIGNAL SPACE DECISION BOUNDARIES
Lecture 23: OPTIMUM DETECTION FILTER BANKS
Lecture 24: DETECTION FILTER PERFORMANCE MEASURES
Lecture 25: ESTIMATION OF PROBABILITY DENSITY FUNCTION
Lecture 26: ESTIMATION, MINIMUM MEAN SQUARED ERROR
Lecture 27: ESTIMATION, MAXIMUM LIKELIHOOD
Lecture 28: STOCHASTIC FILTER PREDICTORS
Lecture 29: MINIMUM AVERAGE CORRELATION ENERGY FILTER DERIVATION
