課程名稱 |
隨機訊號分析 Random Signal Analysis |
開課學期 |
100-2 |
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 |
631 M3140 |
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207 |
備註 |
總人數上限:12人 |
Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1002random_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 |
第1週 |
2/20 |
No class |
第2週 |
3/3 |
Introduction |
第3週 |
3/5 |
Review of probability theory |
第4週 |
3/12 |
Random variables and random functions |
第5週 |
3/19 |
Characteristic function and random functions |
第6週 |
3/26 |
Random functions in matrix form |
第7週 |
4/2 |
Example: imaging system |
第8週 |
4/9 |
Imaging problem 2 |
第9週 |
4/16 |
Midterm exam |
第10週 |
4/23 |
Stochastic process |
第13週 |
5/11 |
Noise power spectrum |
第14週 |
5/28 |
Estimation theory |
第15週 |
6/8 |
Mean square error |
第16週 |
6/11 |
Decision theory |
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