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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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 |
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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.
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評量方式 (僅供參考) |
No. |
項目 |
百分比 |
說明 |
1. |
Homework and quizzes |
40% |
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2. |
Mid-term exam |
30% |
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3. |
Final exam |
30% |
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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 |
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