Course title
L00150002
Signal Processing

 MANABE Hiroyuki
Course description
Students acquire the mathematical knowledge and skills necessary for signal processing, and learn the basic concepts of analog signal processing and digital signal processing, which are necessary for understanding digital signal processing. Students will also acquire the skills to put them into practice through programming.
Specifically, students will learn to understand and compute Fourier series expansion, Fourier transform, and convolution integral for continuous-time signals, and to implement programs for DCT, DFT, and FFT for discrete-time signals and apply them to actual signals.

Note taht the language used in this course is only Japanese. Students who want to take this course need to be able to listen and read Japanese.
Purpose of class
Signal processing concepts play a central role in many fields, such as communications, acoustics, medicine, robotics, and finance, and are used in a variety of applications, including noise reduction, signal restoration, and feature extraction. Basic signal processing concepts are often useful in new technical fields such as deep learning and neural networks. This course not only explains the concepts of signal processing, but also enables students to perform calculations by themselves and further deepen their understanding through program implementation and application to actual signals.
Goals and objectives
  1. Students can explain the mathematical knowledge and techniques required for signal processing.
  2. Students understand and can operationalize basic analog signal processing concepts necessary to understand digital signal processing.
  3. Students understand basic concepts of digital signal processing and can implement DCT, DFT, and FFT in C.
  4. Students can apply basic signal processing to digital signals.
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 report or quiz midterm exam final exam Total.
1. 5% 15% 0% 20%
2. 5% 15% 0% 20%
3. 10% 0% 20% 30%
4. 5% 0% 25% 30%
Total. 25% 30% 45% -
Evaluation method and criteria
Evaluation will be based on report or in-class quiz, mid-term exam, and final exam.
Refer to the Japanese syllabus for details.
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. Guidance, introduction Review of previous mathematical knowledge 120minutes
2. Review of mathematical knowledge Prepare with handouts. 120minutes
3. Continuous-time signal analysis 1 Prepare with handouts. 120minutes
4. Continuous-time signal analysis 2 Prepare with handouts. 120minutes
5. Analysis of continuous-time systems Prepare with handouts. 120minutes
6. Review of continuous-time signal processing Prepare with handouts. 360minutes
7. Summary and mid-term exam Review of the classes. 120minutes
8. Discrete-time signal analysis 1 Prepare with handouts. 120minutes
9. Discrete-time signal analysis 2 Prepare with handouts. 120minutes
10. Discrete-time system analysis Prepare with handouts. 120minutes
11. Discrete Signal Processing Algorithms Implementation using C language 300minutes
12. Digital signal processing for 1D signals Implementation using C language 300minutes
13. Digital signal processing for 2D signals Implementation using C language 300minutes
14. Summary and final exam Review of the classes. 360minutes
Total. - - 2700minutes
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Feedback in the class
Textbooks and reference materials
reference book：よくわかる信号処理　浜田望（オーム社）．
Prerequisites
It is assumed that the student has an understanding of high school mathematics (especially calculus and trigonometric functions) and can program at an elementary level in the C language.
In addition, it is desirable for students to have studied the basics of calculus and linear algebra at the university level.
Office hours and How to contact professors for questions
  • To make an appointment in advance by e-mail.
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
  • Course that cultivates a basic problem-solving skills
Active-learning course
More than one class is interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
Applicable Students can learn from a wide range of perspectives, not only within a specific field, but also by considering applied cases and their impact on the real world.
Education related SDGs:the Sustainable Development Goals
  • 4.QUALITY EDUCATION
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
  • 12.RESPONSIBLE CONSUMPTION & PRODUCTION
Last modified : Thu Mar 06 10:21:19 JST 2025