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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Undergraduate major in Mechanical Engineering
- Instructor(s)
- Sato Chiaki
- Class Format
- Lecture / Exercise (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Wed3-4(S222)
- Group
- -
- Course number
- MEC.B331
- Credits
- 1
- Academic year
- 2022
- Offered quarter
- 1Q
- Syllabus updated
- 2022/3/18
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
This course focuses on signal processing, and covers the fundamentals of Fourier series and Fourier transform.
This approach/method is not only useful for solving partial differential equations found in fields of mechanical engineering, electrical engineering, information and communication engineering, but is applicable to many other areas.
Student learning outcomes
Fourier series, Fourier transform, a sampling theorem, a discrete Fourier transform, a fast Fourier transform and a frequency filter can be understood and ready to apply.
Keywords
Fourier series, Fourier transform, Sampling theorem, Discrete Fourier transform, Fast Fourier Transform, Frequency filter
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Students will be given exercise problems at every lecture.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Linear systems and Fourier series | Exercise related to Fourier series |
| Class 2 | Complex Fourier series | Exercise related to Complex Fourier series |
| Class 3 | Application of Fourier Series | Exercise related to Application of Fourier series |
| Class 4 | Fourier transform and convolution integral | Exercise related to Fourier transform and convolution integral |
| Class 5 | Discrete Fourier transform and delta function | Exercise related to Discrete Fourier transform |
| Class 6 | Sampling theorem and spectrum | Exercise related to Sampling theorem |
| Class 7 | Filtering of discrete data, Frequency filter and convolution, Fast Fourier Transform | Exercise related to Fast Fourier Transform |
Out-of-Class Study Time (Preparation and Review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Upload on OCWi
Reference books, course materials, etc.
Handouts will be provided as needed.
Assessment criteria and methods
Exercises(100%)
Related courses
- MEC.B212 ： Complex Function Theory
- LAS.M101 ： Calculus I / Recitation
- LAS.M107 ： Calculus Recitation II
- MEC.B211 ： Ordinary Differential Equations
- LAS.M105 ： Calculus II
- MEC.B213 ： Partial Differential Equations
Prerequisites (i.e., required knowledge, skills, courses, etc.)
No prerequisites
