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 |
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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 |
課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
課程大綱
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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.
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課程要求 |
預修科目:工數、信號處理 |
預期每週課後學習時數 |
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Office Hours |
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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) |
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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) |
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