EE640 SPRING 2008

STOCHASTIC SYSTEMS

INFORMATION

Updated 4-22-08

VISUALIZAIONS

Stationary Colored Noise

Non-Stationary Colored Noise

PROJECTS

PROJECT 1A:

PART A: SYNTHESIS (EE640_Project_1A08.pdf)

Target Data: Target.zip

Clutter Data: Clutter.zip

PROJECT 1B:

PART B: ANALYSIS (EE640_Project1_B08.pdf)

PROJECT 1C:

PART C: DETECTION AND DISCRIMINATION (EE640_Project1_C08.pdf)

PROJECT 1S: Supplemental (Won’t be used for 2008)

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 12: RANDOM PROCESSES

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 20: MULTI-VARIANT MLR

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