一、課程說明 (Course Desc<x>ription)
<br>This course introduces discrete-time signal processing with an emphasis on rigor. 
<br>Topics including sampling and aliasing, discrete-time Fourier transform, z-
<br>Transform, FIR/IIR filter design, filter structures, and discrete and short-time 
<br>Fourier transform. 
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<br>Pre-requisite: Calculus, Linear Algebra, Probability (to a certain extent), 
<br>Signals and Systems (or equivalent, upon lecturer's consent).
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<br>二、指定用書 (Textbook)
<br>Discrete-Time Signal Processing, A. V. Oppenheim, and R. W. Schafer, 3rd Ed., 
<br>Pearson, 2009.
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<br>三、參考書籍 (References)
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<br>四、教學方式 (Teaching Method)
<br>This course will be taught **in English** by the lecturer using combination of 
<br>slides and the blackboard. There will also be homework discussion sessions where 
<br>students will be given bonus points if they volunteer to demonstrate their 
<br>solution to the problem. 
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<br>五、教學進度 (Syllabus)：
<br>Please refer to the university elearning system "eeclass" for a detailed 
<br>syllabus. 
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<br>Lectures will cover sampling and signal reconstruction, analysis of LTI systems, 
<br>FIR and IIR filter design, discrete Fourier transforms, short-time Fourier 
<br>transform (above are covered in textbook Chapter 4-10).
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<br>六、成績考核 (Evaluation) 
<br>Problem-ba<x>sed computer homework will account for at least 30% of the grades. 
<br>Otherwise, we have a midterm exam and a final exam. Details will be announced 
<br>later.
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<br>Exams will be close-book while allowing an A4 double-sided cheat-sheet
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<br>七、可連結之網頁位址 (Course li<x>nk)
<br>http://eeclass.nthu.edu.tw/  [To be announced]
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<br>八、Rules regarding the usage of generative AI tools:
<br>Students are allowed to used generative AI for all the homework.