| Course title | |||||
| 視覚信号処理特論Ⅱ [Advanced Visual Signal Processing II] | |||||
| Course category | courses for the doctral program | Requirement | Credit | 1 | |
| Department | Year | ～ | Semester | 3rd | |
| Course type | 3rd | Course code | 125028 | ||
| Instructor(s) | |||||
| 田中 雄一 [TANAKA Yuichi] | |||||
| Facility affiliation | Faculty of Engineering | Office | afjgxte/L1151 | Email address | |
| Course description |
|
This 1-credit course introduces theory and applications of emerging tools for signal processing on graphs, including a review of spectral graph theory, filtering of graph signals, downsampling, and wavelets and filter banks. This course corresponds to the specialized applied courses of the curriculum of the Department of Bio-Functions and Systems Science. |
| Expected Learning |
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Learners who successfully complete this course will be able to: 1. Understand the motivation of graph signal processing 2. Understand basic spectral graph theory 3. Understand and implement a few operations of graph signal processing Corresponding criteria in the Diploma Policy: See the Curriculum maps. |
| Course schedule |
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1. Introduction of graph signal processing 2. Review of spectral graph theory and linear algebra 3. Adjacency matrix and graph Laplacian 4. Graph signals and graph Fourier transform 5. Graph filtering 6. Downsampling and oversampling of graph signals 7. Applications for image processing 8. Summary |
| Prerequisites |
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Students entering this class are assumed to have had linear algebra and signal processing in undergraduate level. A basic knowledge of graph theory is plus. Basic skills of programming language, e.g, MATLAB, python, or C++ (OpenCV) are required. In addition to the 16 hours of class time, refer to handouts and other materials, prepare and review for the standard number of hours set by the university |
| Required Text(s) and Materials |
| Handouts are provided. |
| References |
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G. Strang, Linear algebra and its applications, 4th ed., Cengage Learning, 2005. F. R. Chung, Spectral graph theory, volume92, AMS Bookstore, 1997. D. K. Hammond, P. Vandergheynst, and R. Gribonval. Wavelets on graphs via spectral graph theory. Applied and Computational Harmonic Analysis , 30(2):129-150, 2011. D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. Signal Processing Magazine, IEEE , 30(3):83-98, 2013. D. Spielman, Spectral graph theory, Lecture Notes, Yale University, 2009. |
| Assessment/Grading |
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Midterm report: 30% Final report: 70% |
| Message from instructor(s) |
| Course keywords |
| Office hours |
| Email for appointment |
| Remarks 1 |
| Remarks 2 |
| Related URL |
| Lecture Language |
| Japanese |
| Language Subject |
| Last update |
| 3/25/2020 10:28:20 AM |