2026 (Current Year) Faculty Courses School of Engineering Department of Information and Communications Engineering Graduate major in Information and Communications Engineering
Advanced Communication System Engineering
- Academic unit or major
- Graduate major in Information and Communications Engineering
- Instructor(s)
- Undecided
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Class
- -
- Course Code
- ICT.C412
- Number of credits
- 200
- Course offered
- 2026
- Offered quarter
- 3Q
- Syllabus updated
- Mar 5, 2026
- Language
- Japanese
Syllabus
Course overview and goals
Modern information and communication technology has been developing together with rapidly evolving data science and AI technology. In this lecture, we invite front-line researchers in leading industries as lecturers to outline the current status and issues of the latest R & D in these integrated areas.
Course description and aims
"1) Students will understand advanced technologies developed in the information and communication industry and future trends.
2) Students will understand the outline and basic principles of acoustic/speech processing, language processing, image processing, and image recognition, and how they are applied in modern human society.
3) Students will understand the outline and basic principles of artificial intelligence, machine learning, and data science, and how they are transforming society and daily life."
Keywords
Pattern recognition, coding, video information compression, MPEG, acoustic/speech processing, language processing, deep learning, human internal state understanding, object fingerprint, face recognition, person recognition, Artificial intelligence, machine learning, data mining, discovery science, simulation, optimization
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The first half of this course will be presented by instructors from the NEC central research laboratories. The second half of this course will be presented by instructors from the Fujitsu laboratories. In the class, the teaching material is projected on the screen. Through the Questions and Answers, the correct and deep understanding is enhanced.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Pattern recognition and its applications |
Understand an overview of pattern recognition technology and recent application examples |
| Class 2 | Video processing technologies for human recognition and their application |
Learn about video-based person recognition technology, mainly face recognition, and understand its challenges and application examples. |
| Class 3 | Real world recognition technology and its applications |
Learn about real-world recognition technology that digitizes the status of specified people and things from images and videos, and understand its challenges and application examples. |
| Class 4 | Deep learning using sensing technology and its applications |
Learn the basics of image sensing technology and deep learning, and understand their application examples |
| Class 5 | Human internal state estimation technology and its applications |
Understand technologies for estimating physiological and affective states and their real-world applications. |
| Class 6 | Acoustic/speech/language processing technology and its applications |
Understand acoustic/speech/language processing technology and its application examples |
| Class 7 | Video compression coding technology |
Understand the basic principles of video information compression and transmission, focusing on the international standard MPEG technology |
| Class 8 | Overview of the artificial intelligence |
Understand the overview of of the artificial inteligence |
| Class 9 | Overview of the data mining and the discovery science |
Understand the overview of the data mining and the discovery science |
| Class 10 | Overview of the machine learning |
Understand the idea behind the machine learning and its applications specially based on the Deep learning |
| Class 11 | Overview of the natural language processing |
Understand the overview of natural language processing and its applications |
| Class 12 | Overview of the simulation AI |
Understand the overview of the simulation AI and applications |
| Class 13 | Overview of the mathematical optimization 1 |
Understand the overview of mathematical optimization and its applications in data science |
| Class 14 | Overview of the mathematical optimization 2 |
Understand recent advancements of mathematical optimizations and their algorithms |
Study advice (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)
Not specified
Reference books, course materials, etc.
All course materials will be provided in each class.
Evaluation methods and criteria
Learning achievement is evaluated by the quality of the written reports on specific themes.
Related courses
- ICT.C205 ： Communication Theory (ICT)
- ZUS.M303 ： Digital Communications
- ICT.S403 ： Multidimensional Information Processing
- ICT.S206 ： Signal and System Analysis
- ICT.S414 ： Advanced Signal Processing (ICT)
- ICT.C201 ： Introduction to Information and Communications Engineering
- ICT.A402 ： Communications and Computer Engineering I
- ICT.H318 ： Foundations of Artificial Intelligence (ICT)
- ICT.S311 ： Machine Learning (ICT)
- ICT.S302 ： Functional Analysis and Inverse Problems
Prerequisites
Not specified
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
Nishio (contact person): [nishio[at]ict.eng.isct.ac.jp](mailto:nishio[at]ict.eng.isct.ac.jp)
Office hours
Students may approach the instructors at the end of class upon securing an appointment through e-mail.