COM 5110-0

Brief

This course will cover the fundamental theory of random processes with emphasis on applications to signal processing and communications. This is a basic yet important course for students pursuing studies in these fields. The course will include basic concepts on random processes, stationarity, convergence of random sequences, law of large numbers, and examples of important random processes such as Poisson processes, Gaussian processes, and Markov chains/processes. With applications to communications and signal processing, we will also cover topics related to linear systems, spectral analysis, estimation, Karhunen-Loeve expansion etc.

Description

Course keywords: probability, random process, Poisson, Markov chain, Gaussian 一、課程說明(Course Description) This course will cover the fundamental theory of random processes with emphasis on applications to signal processing and communications. This is a basic yet important course for students pursuing studies in these fields. The course will include basic concepts on random processes, stationarity, convergence of random sequences, law of large numbers, and examples of important random processes such as Poisson processes, Gaussian processes, and Markov chains/processes. With applications to communications and signal processing, we will also cover topics related to linear systems, spectral analysis, estimation, Karhunen-Loeve expansion etc. 二、指定用書(Text Books) Robert G. Gallager, Stochastic Processes: Theory for Applications, Cambridge University Press, 2017. 三、參考書籍(References) Athanasios Papoulis and S. Unnikrishna Pillai, "Probability, Random Variables, and Stochastic Processes," McGraw Hill, 2002. 四、教學方式(Teaching Method) 3+ hours of weekly lectures plus reading assignments and homework. 五、教學進度(Syllabus) 1. Brief Review of Probability and Random Variables 2. Random Vectors, Random Sequences, and Random (Stochastic) Processes 3. Poisson Processes 4. Gaussian Random Vectors and Processes Midterm 5. Random Processes in Communications 6. Finite-State and Countable State Markov Chains 7. Markov Processes Final Exam 六、成績考核(Evaluation) 30% Homework 35% Midterm 35% Final Exam. 七、可連結之網頁位址 https://eeclass.nthu.edu.tw/

Timetable

Scores

Average Percentage 86.5

Std. Deviation 8.82

Remarks

本課程為16週課程。每週上課150分鐘，其餘時間由教授彈性運用

Restrictions

Compulsory

Elective

Cross Discipline

First Specialization

Second Specialization