課程資訊
課程名稱
 隨機訊號分析
Random Signal Analysis 
開課學期
 99-2 
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
 星期一6,7,8(13:20~16:20) 
上課地點
 知206 
備註
 本課程以英語授課。
總人數上限：12人 
Ceiba 課程網頁
 http://ceiba.ntu.edu.tw/992random_signal 
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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
 Athanasios Papoulis, S. Unnikrishna Pillai, Probability, random variables, and stochastic processes, 4th Edition, McGraw-Hill, c2002.  
參考書目
 Athanasios Papoulis, S. Unnikrishna Pillai, Probability, random variables, and stochastic processes, 4th Edition, McGraw-Hill, c2002.
H. Stark and J. W. Woods, Probability and Random Processes with Applications to Signal Processing 3rd Edition, Prentice-Hall, 2002. 
評量方式
(僅供參考)
    
課程進度
週次
日期
單元主題
Week 1
 02/21  Introduction  
Week 2
 02/28  228 Memorial Holiday (No Class) 
Week 3
 03/07  Probability theory 
Week 4
 03/14  Probability theory 
Week 5
 03/21  N-dimensional PMF and imaging system 
Week 6
 03/28  Vector, mattrix and operator 
Week 7
 04/04  Study break (No class) 
Week 8
 04/11  Imaging example + random process 
Week 9
 04/18  Midterm exam 
Week 10
 04/25  Random processes and noise power spectrum 
Week 11
 05/02/2011  Noise power spectrum and sampling theory 
Week 12
 5/9/2011  Estimation theory 
Week 13
 5/16/2011  Cramo Rao Lower Bound 
Week 14
 05/23/2011  Decision theory 
Week 15
 5/30/2011  Markov Chain 
Week 16
 6/6/2011  Dragon boat festival (No class)