課程資訊
課程名稱
 隨機訊號分析
RANDOM SIGNAL ANALYSIS 
開課學期
 97-2 
授課對象
 生物資源暨農學院  生物產業機電工程學研究所  
授課教師
 周呈霙 
課號
 BME7402 
課程識別碼
 631EM3140 
班次
  
學分
 3 
全/半年
 半年 
必/選修
 選修 
上課時間
 星期三6,7,8(13:20~16:20) 
上課地點
 知武會議室 
備註
 本課程以英語授課。上課教室在知武館二樓206室
總人數上限：20人 
Ceiba 課程網頁
 http://ceiba.ntu.edu.tw/972random_signal 
課程簡介影片
 
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課程概述

Course objectives: To become familiar with the theory of random sequences and stochastic processes. To learn the mathematical tools available for the analysis and estimation of random phenomena. To see how stochastic modeling is used in practice. 

課程目標
 1. Review of basic probability: probability spaces, sample description spaces, events, probability measure, random variable, PDFs, PMFs, and pdfs, function of one, two, N random variables, expectations and moments, correlation coefficient, characteristic functions.
2. Random sequences: The meaning of a random sequence, stationary sequences, auto- and cross-correlation functions. Wide-sense stationary sequences; Power spectral density (psd); The Wiener – Khinchin Theorem.
3. Continuous-time random processes: The meaning of a random process; sample functions and sequences of random variables; the random telegraph signal; the binary switching sequence; the Markov random process; white noise; the independent-increment process; power spectrum and correlation functions; Input/output computations; power-spectral estimation.
4. Signal processing: The orthogonality principle; optimum interpolation; hidden Markov process; applications to speech processing; Wiener and Kalman filter.
 
課程要求
 預修科目：工數、信號處理 
預期每週課後學習時數
  
Office Hours
  
指定閱讀
  
參考書目
 H. Stark and J. W. Woods, Probability and Random Processes with Applications to Signal Processing 3rd
Edition, Prentice-Hall, 2002.
S. M. Kay, Fundamentals of statistical signal processing: estimation theory, Prentice Hall, 1993.
A. Papoulis, and S. U. Pillai, Probability, Random Variables and Stochastic Processes, McGraw Hill,
2002.
W. C. van Etten, Introduction to Random Signals and Noise, Wiley, 2006.
S. Kay, Intuitive Probability and Random Processes using MATLAB, Springer, 2006.



 
評量方式
(僅供參考)
  
No.
項目
百分比
說明
1. 
 Homework and quizzes 
40% 
  
2. 
 Mid-term exam 
30% 
  
3. 
 Final exam 
30% 
  
 
課程進度
週次
日期
單元主題
第1週
 2/18  Course overview and Review of probability theory  
第2週
 02/25  Random variables, probability distribution and density functions 
第3週
 03/04  Functions of random variables 
第4週
 03/11  Characterizatoin of random variables 
第5週
 03/18  Characteristic function and moments 
第6週
 03/25  Stochastic process 
第7週
 04/01  Examples  
第8週
 04/08  Spectrum 
第9週
 04/15  Midterm 
第10週
 04/22  Estimation theory 
第11週
 04/29  Estimation theory 
第12週
 05/06  Mean Square Estimation & CRLB 
第13週
 05/13  Decision theory 
第14週
 05/20  Decision theory 
第15週
 05/27  Markov chains & Matrix Representation 
第16週
 06/03  期末報告 & Convolution 
第17週
 06/10  作業複習