Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2025 349312 Digital Signal Processing 1 3.0 3 ★ Ying-Ren Chien ECE 13 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2025	349312	Digital Signal Processing	1	3.0	3	★	Ying-Ren Chien	ECE	13	0

Course Plan：

Instructor Ying-Ren Chien　Office Hours

Email yrchien@ntut.edu.tw

Last Updated 2025-05-25 00:56:37

Course Objective This course provides an in-depth study of digital signal processing (DSP) principles and techniques. Some ideas are built using MATLAB codes. Topics include discrete-time signals and systems, Fourier analysis, Z-transform, filter design and implementation, sampling rate conversion, random signal processing, and spectral analysis. The course emphasizes both theoretical foundations and practical applications through assignments, projects, and research paper studies. By the end of this course, students will be able to: 1. Analyze and process discrete-time signals and systems. 2. Apply the Fourier and Z-transform techniques to signal analysis. 3. Design and implement FIR and IIR digital filters. 4. Perform spectral analysis and sampling rate conversion. 5. Critically review and present recent research papers in DSP. 6. Complete a comprehensive final project demonstrating mastery of course concepts.

Course Schedule Week 1: Introduction Week 2: Discrete-Time Signals and Systems Week 3: Discrete-Time Fourier Analysis Week 4: Z-Transform Week 5-6: Discrete Fourier Transform Week 7-8: Implementation of Discrete-Time Filter Week 9: Midterm Week 10: FIR Filter Design Week 11: IIR Filter Design Week 12: Sampling Rate Conversion Week 13: Random Signal Processing Week 14-15 Spectral Analysis Techniques Week 16-17 Paper Study Week 18: Final Project Presentation

Evaluation and grading policy Attendance 10% Midterm 30% Final Project Reports 30% Final Project Presentations 30%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
1. Digital Digital signal processing using MATLAB V.4 / Vinay K. Ingle, John G. Proakis. (1997) 2. Applied digital signal processing : theory and practice / Dimitris G. Manolakis, Vinay K. Ingle.(2012)

The access to curricular consultation You can meet me either face-to-face or online by making appointments

The course corresponds to the SDGs SDG4：優質教育（Quality Education）

Does the course incorporate AI AI1：使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）
AI2：鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）

Note
　

Instructor	Ying-Ren Chien　Office Hours
Email	yrchien@ntut.edu.tw
Last Updated	2025-05-25 00:56:37
Course Objective	This course provides an in-depth study of digital signal processing (DSP) principles and techniques. Some ideas are built using MATLAB codes. Topics include discrete-time signals and systems, Fourier analysis, Z-transform, filter design and implementation, sampling rate conversion, random signal processing, and spectral analysis. The course emphasizes both theoretical foundations and practical applications through assignments, projects, and research paper studies. By the end of this course, students will be able to: 1. Analyze and process discrete-time signals and systems. 2. Apply the Fourier and Z-transform techniques to signal analysis. 3. Design and implement FIR and IIR digital filters. 4. Perform spectral analysis and sampling rate conversion. 5. Critically review and present recent research papers in DSP. 6. Complete a comprehensive final project demonstrating mastery of course concepts.
Course Schedule	Week 1: Introduction Week 2: Discrete-Time Signals and Systems Week 3: Discrete-Time Fourier Analysis Week 4: Z-Transform Week 5-6: Discrete Fourier Transform Week 7-8: Implementation of Discrete-Time Filter Week 9: Midterm Week 10: FIR Filter Design Week 11: IIR Filter Design Week 12: Sampling Rate Conversion Week 13: Random Signal Processing Week 14-15 Spectral Analysis Techniques Week 16-17 Paper Study Week 18: Final Project Presentation
Evaluation and grading policy	Attendance 10% Midterm 30% Final Project Reports 30% Final Project Presentations 30%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 1. Digital Digital signal processing using MATLAB V.4 / Vinay K. Ingle, John G. Proakis. (1997) 2. Applied digital signal processing : theory and practice / Dimitris G. Manolakis, Vinay K. Ingle.(2012)
The access to curricular consultation	You can meet me either face-to-face or online by making appointments
The course corresponds to the SDGs	SDG4：優質教育（Quality Education）
Does the course incorporate AI	AI1：使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） AI2：鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
Note