1. Review of Random Variables (Papoulis, Chaps. 1-7, and class note) 2. Introduction to Random Processes: General Concepts and Spectral Analysis (Papoulis, Chap. 9, and class note) 3. Gaussian Random Vectors and Gaussian Random Processes (Larson & Shubert, class note) 4. Signal Representation — Karhunen-Love Expansion (Papoulis, Chap. 11, and class note) 5. Narrowband Processes and Bandpass Systems (Davenport and Root, and class note) 6. Poisson Processes (Larson & Shubert, Leon-Garcia, and class note) 7. Markov Processes and Markov Chains (Larson & Shubert, Leon-Garcia, and class note) 8. Queuing Systems (Leon-Garcia) 9. Random Walk Processes and Brownian Motion Processes (Leon-Garcia) The purpose of this course is to provide students with a solid and pertinent mathematical background for thoroughly understanding digital communications and communication networks. It is a prerequisite for advanced study of numerous communication applications, including wireless communications, mobile communications, communication networks, spread spectrum communications, satellite communications, optical communications, radar and sonar signal processing, signal synchronization, etc. The students majoring in communications and networks are strongly recommended to take this course. The course consists of lectures organized in class notes. College of Electrical Engineering & Computer Science Prerequisite: Probability and Statistics. Grading Policy: There will be six homeworks, one every three weeks, one midterm exam, and one final exam. The grading policy is “Homeworks: 30%; Midterm: 35%; Final: 35%”. CHAR-DIR CHUNG Friday 789 EE5041 3

1. Review of Random Variables (Papoulis, Chaps. 1-7, and class note) 2. Introduction to Random Processes: General Concepts and Spectral Analysis (Papoulis, Chap. 9, and class note) 3. Gaussian Random Vectors and Gaussian Random Processes (Larson & Shubert, class note) 4. Signal Representation — Karhunen-Love Expansion (Papoulis, Chap. 11, and class note) 5. Narrowband Processes and Bandpass Systems (Davenport and Root, and class note) 6. Poisson Processes (Larson & Shubert, Leon-Garcia, and class note) 7. Markov Processes and Markov Chains (Larson & Shubert, Leon-Garcia, and class note) 8. Queuing Systems (Leon-Garcia) 9. Random Walk Processes and Brownian Motion Processes (Leon-Garcia) The purpose of this course is to provide students with a solid and pertinent mathematical background for thoroughly understanding digital communications and communication networks. It is a prerequisite for advanced study of numerous communication applications, including wireless communications, mobile communications, communication networks, spread spectrum communications, satellite communications, optical communications, radar and sonar signal processing, signal synchronization, etc. The students majoring in communications and networks are strongly recommended to take this course. The course consists of lectures organized in class notes. College of Electrical Engineering & Computer Science Prerequisite: Probability and Statistics. Grading Policy: There will be six homeworks, one every three weeks, one midterm exam, and one final exam. The grading policy is “Homeworks: 30%; Midterm: 35%; Final: 35%”. CHAR-DIR CHUNG Friday 789 EE5041 3

In this class, I will cover the basic techniques for design and analysis of algorithms. I will also give a brief introduction to advanced topics such as approximate algorithms and randomized algorithms. 1 Introduce different algorithm design techniques. 2 Teach the students how to evaluate the performance of different algorithms. College of Electrical Engineering & Computer Science Grading: Homework: 40% Midterm: 30% Final exam: 30% HO-LIN CHEN Tuesday 234 EE5048 3

In this class, I will cover the basic techniques for design and analysis of algorithms. I will also give a brief introduction to advanced topics such as approximate algorithms and randomized algorithms. 1 Introduce different algorithm design techniques. 2 Teach the students how to evaluate the performance of different algorithms. College of Electrical Engineering & Computer Science Grading: Homework: 40% Midterm: 30% Final exam: 30% HO-LIN CHEN Tuesday 234 EE5048 3

Please see the Chinese version Please see the Chinese version College of Electrical Engineering & Computer Science Please see the Chinese version JAMES B KUO Monday ABC EE5078 3

Information Theory is a senior (undergraduate) level course designed for students who are interested in the quantitative fundamental limits of information. What is information and how to quantify information? What is the ultimate data compression rate and what is the ultimate transmission rate of communication? In this course, we introduce the fascinating theory originated from Claude E. Shannon, which addresses the above fundamental questions in communication theory. We will develop methods and coding techniques to achieve these fundamental limits. Finally, we will also demonstrate the application of information theory to other fields, including statistics (hypothesis testing and estimation) and statistical inferences. 1. Introduce basic topics in information theory, including measures of information, source coding theorem, channel coding theorem, and source-channel separation. 2. Develop methods and coding techniques to achieve these fundamental limits. 3. Show applications of information theory beyond communications, especially in high dimensional statistics and statistical inferences. College of Electrical Engineering & Computer Science Prerequisite: Probability, Linear Algebra, Optional: Random Processes, Communication Systems Homework (30%), Midterm (30%), Final (40%) I-HSIANG WANG Wednesday 234 EE5028 3

1. Introduction of Queueing Model and Review of Markov Chain 2. Simple Markovian Birth and Death Queueing Models (M/M/1, etc) 3. Advanced Markovian Queueing Models 4. Jackson Queueing Networks 5. Models with General Arrival or Service Pattern (M/G/1, G/M/1) 6. Discrete-Time Queues and Applications in Networking To provide the basic knowledge in queueing models and the analysis capability of the queueing models in telecommunications, computers, and industrial engineering College of Electrical Engineering & Computer Science Midterm 45% Final Exam 45% Homework (including programming and simulations) 10% ZSEHONG TSAI Wednesday 789 EE5039 3

1. INTRODUCTION TO LIGHTING 2. COLOR SCIENCE 3. INRODUCTION TO DIODES 4. CARRIER RECEOMBINATION 5. LED MATERIAL AND DEVICE 6. HIGH POWER LEDS 7. APPLICATION OF LEDS VARIOUS PROGRAM NONE! UNDERGRADUATE STUDENTS ARE EXTREMELY WELCOME TO TAKE THE COURSE! JIAN JANG HUANG Tuesday 789 OE5040 3

1. INTRODUCTION TO LIGHTING 2. COLOR SCIENCE 3. INRODUCTION TO DIODES 4. CARRIER RECEOMBINATION 5. LED MATERIAL AND DEVICE 6. HIGH POWER LEDS 7. APPLICATION OF LEDS College of Electrical Engineering & Computer Science NONE! UNDERGRADUATE STUDENTS ARE EXTREMELY WELCOME TO TAKE THE COURSE! JIAN JANG HUANG Tuesday 789 OE5040 3

1. INTRODUCTION TO LIGHTING 2. COLOR SCIENCE 3. INRODUCTION TO DIODES 4. CARRIER RECEOMBINATION 5. LED MATERIAL AND DEVICE 6. HIGH POWER LEDS 7. APPLICATION OF LEDS College of Electrical Engineering & Computer Science NONE! UNDERGRADUATE STUDENTS ARE EXTREMELY WELCOME TO TAKE THE COURSE! JIAN JANG HUANG Tuesday 789 OE5040 3

EACH LECTURE WILL BE TAILORED ACCORDING TO STUDENTS UNDERSTANDING. SUBJECTS TO BE COVERED FOR THIS COURSE ARE AS FOLLOWS: 1) THEORETICAL REVIEW OF ELECTROMAGNETISM 2) INTRODUCTION TO VARIOUS OPTICAL SIMULATION TECHNIQUES 3) MONTE CARLO TECHNIQUE 4) NUMERICAL SOLUTIONS OF MAXWELL’S EQUATIONS 5) APPLICATION OF THE TAYLOR’S EXPANSION 6) SCALAR WAVE EQUATION 7) THE FINITE-DIFFERENCE TIME-DOMAIN TECHNIQUE 8) PRAGMATIC SIMULATION OF OPTICAL PROBLEMS College of Electrical Engineering & Computer Science PREREQUISITES: – GENERAL PHYSICS – CALCULUS – ELECTROMAGNETISM – BASIC PROGRAMMING SKILLS (MATLAB, FORTRAN, OR C/C++) GRADING FACTORS: ASSIGNMENTS: 35% MIDTERM EXAM: 25% FINAL EXAM: 30% PARTICIPATION IN CLASS : 10% GRADING FACTORS INCLUDE AN ASSESSMENT OF STUDENTS’ UNDERSTANDING OF THE COURSE CONTENT, PARTICIPATION IN CLASS, AND THEIR ABILITY IN COMPLETING THE ASSIGNMENTS. SIMULATION ASSIGNMENTS ARE DESIGNED TO PREPARE STUDENTS WITH HANDS-ON EXPERIENCE OF LIGHT PROPAGATION SIMULATION. STUDENTS ARE EXPECTED TO BECOME FAMILIAR WITH MATLAB. MIDTERM AND FINAL EXAMS WILL SERVE THE PURPOSE TO EVALUATE STUDENTS’ LEARNING PROGRESS. GRADES THUS ARE GIVEN BASED UPON STUDENTS’ ABILITY IN CARRYING OUT THE ASSIGNMENTS AND THEIR PERFORMANCE IN THE MIDTERM AND FINAL EXAMS. Wednesday 789 OE5047 3

EACH LECTURE WILL BE TAILORED ACCORDING TO STUDENTS UNDERSTANDING. SUBJECTS TO BE COVERED FOR THIS COURSE ARE AS FOLLOWS: 1) THEORETICAL REVIEW OF ELECTROMAGNETISM 2) INTRODUCTION TO VARIOUS OPTICAL SIMULATION TECHNIQUES 3) MONTE CARLO TECHNIQUE 4) NUMERICAL SOLUTIONS OF MAXWELL’S EQUATIONS 5) APPLICATION OF THE TAYLOR’S EXPANSION 6) SCALAR WAVE EQUATION 7) THE FINITE-DIFFERENCE TIME-DOMAIN TECHNIQUE 8) PRAGMATIC SIMULATION OF OPTICAL PROBLEMS College of Electrical Engineering & Computer Science PREREQUISITES: – GENERAL PHYSICS – CALCULUS – ELECTROMAGNETISM – BASIC PROGRAMMING SKILLS (MATLAB, FORTRAN, OR C/C++) GRADING FACTORS: ASSIGNMENTS: 35% MIDTERM EXAM: 25% FINAL EXAM: 30% PARTICIPATION IN CLASS : 10% GRADING FACTORS INCLUDE AN ASSESSMENT OF STUDENTS’ UNDERSTANDING OF THE COURSE CONTENT, PARTICIPATION IN CLASS, AND THEIR ABILITY IN COMPLETING THE ASSIGNMENTS. SIMULATION ASSIGNMENTS ARE DESIGNED TO PREPARE STUDENTS WITH HANDS-ON EXPERIENCE OF LIGHT PROPAGATION SIMULATION. STUDENTS ARE EXPECTED TO BECOME FAMILIAR WITH MATLAB. MIDTERM AND FINAL EXAMS WILL SERVE THE PURPOSE TO EVALUATE STUDENTS’ LEARNING PROGRESS. GRADES THUS ARE GIVEN BASED UPON STUDENTS’ ABILITY IN CARRYING OUT THE ASSIGNMENTS AND THEIR PERFORMANCE IN THE MIDTERM AND FINAL EXAMS. Wednesday 789 OE5047 3