EE640 SPRING 2010

STOCHASTIC SYSTEMS

INFORMATION

Updated 3-22-10

VISUALIZAIONS

Stationary Colored Noise

Non-Stationary Colored Noise

Correlation Detection in Additive Non-Stationary Colored Noise

Reference Fingerprint

Test Fingerprint

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 2010) 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 3: Combinatorics

Lecture 4: Random Variables

Lecture 5: Expected Values

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 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

OTHER REFERENCES:

For Project 1C: Encryption of image data into host image: K. T. Lin, “Information hiding based on binary encoding methods and pixel scrambling techniques,” Applied Optics, Vol 49, No. 2, pp 220-228, January (2010).

HOW TO INPUT BMP INTO MATLAB

B_bmp=double(imread('example.bmp')); % load example.bmp image

Br=B_bmp(:,:,1);

Bg=B_bmp(:,:,2);

Bb=B_bmp(:,:,3);

Bbw=(Br+Bg+Bb)/3;