Syllabus data

Teacher name ： KYOCHI Seisuke
Academic year
2025Year
Term
First Semester
Course title
Signal Analysis
Class type
Lecture
Course title (ENG)
Signal Analysis
Class code・Class name・Teaching forms
Z1900037 Signal Analysis
Instructor
KYOCHI Seisuke
Credits
2.0Credits
Day and Time
Wed.4Period
Campus
Shinjuku Remote
Location

Relationship between diploma policies and this course
A) A high degree of specialized expertise　100%
B) The skills to use science and technology　0%
C) The ability to conduct research independently, knowledge pertaining to society and occupations, and sense of ethics required of engineers and researchers　0%
D) Creative skills in specific areas of specialization　0%
Goals and objectives
1) Students will explain the essence of what the frequency spectrum is.​
2) Students will explain each signal analysis method from the viewpoint of the linear combination in linear algebra.​
Prerequisites
Basic knowledge of linear algebra and signal processing is required, such as Fourier transform, FIR/IIR filters, convolution.
MATLAB is used for programming exercises.
Method Using AL・ICT
Discussion Debate／Interactive classes using ICT

Class schedule
1. Guidance and review of linear algebra
2. Review of basic signal processing (Fourier transform)
3. Block transform (discrete Fourier transform, discrete cosine transform)
4. Orthogonal basis and orthogonal transform
5. Block transform (principal component analysis: theory)
6. Block transform (principal component analysis: application)
7. Relationship between block transforms and filter banks
8. Two-channel filter banks and wavelet transform
9. Sparse representation by frame
10. Dictionary learning
11. Deep neural network (theory)
12. Deep neural network (application)
13. Graph signal processing
14. Conclusion

Evaluation
Final report: 6​0​%​
Excercise, ​quiz: ​4​0​%​
Feedback for students
Feedback will be given from KU-LMS and Email.

Textbooks
なし
Reference materials
- M. Vetterli, J. Kovačević, and V. Goyal (2014). Foundations of Signal Processing. Cambridge: Cambridge University Press.
- I. Goodfellow, Y. Bengio, and A. Courville. 2016. Deep Learning. The MIT Press.

Office hours and How to contact teachers for questions
13:40-15:25 on Monday.

If you have any questions, you can freely contact me by email.
Email: jt13685@g.kogakuin.jp
Message for students
This course will provide many mathematical discussions. For a deeper understanding, students are expected to do a lot of practical exercises and MATLAB programming.
For those who want to study further contents, please see the references.

In some cases, lectures may be live-streamed with GoogleMeet.

Course by professor with work experience
Not applicable
Work experience and relevance to the course content if applicable

Teaching profession course
Informatics Program