Noise and Random Processes

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Course Outline

Course Schedule


Lecture 1-Probability (Small) (Large)
Lecture 2-Random Variables (Small) (Large)

Lecture 3-Joint Probabilities (small) (Large)
Lecture 4-Functions of Random Variables (Small)(Large)
Lecture 5-Moments and Characteristic Functions (Small)(Large)
Lecture 6 - Repeated Trials and Law of Large Numbers (Small) (Large)
Lecture 7 - Poisson and Normal Distributions (Small)(Large)
Lecture 8-Photon Detection (Small) (Large)
Lecture 9 - Detective Quantum Efficiency (Small) (Large) (Corrected 4/16)
Lecture 10 -DQE of Image Intensifier (Small) (Large)
Lecture 11-Correlation in Random Processes (Small) (Large)

Lecture 12 - Introduction to Random Processes (Small) (Large)
Lecture 13 - Linear Filtering of Random Processes (Small) (Large)
Lecture 14 - Discrete Filter Representation of Random Processes (Small) (Large)
Lecture 15 - Power Spectrum Estimation (Small) (Large)

Lecture 16 - Handwritten notes distributed in class.
Lecture 17 - Random process generation and analysis (Small) (Large)

Chapter 1-Probability
Chapter 2-Random Variables
Chapter 3-Averages
Chapter 4-Repeated Trials
Chapter 5-Photon Detection
Chapter 6-Detective Quantum Efficiency
Chapter 7-Random Process
Chapter 7-Digital Filter


Midterm Exam Review Questions

Final Exam Review Outline and Questions

Paper on DQE in XRAY imaging system

DQE in Crystallography

Abstracts of papers by I A. Cunningham