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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
- Graduate major in Information and Communications Engineering
- Instructor(s)
- Tsukada Masato Anai Hirokazu Oami Ryoma Ishiyama Rui Aoki Hirofumi Tsujikawa Masanori Toizumi Takahiro Umematsu Terumi Asai Tatsuya Kato Takashi Yamada Hiroaki Kimura Keiji
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue7-8() Fri7-8()
- Group
- -
- Course number
- ICT.C412
- Credits
- 2
- Academic year
- 2021
- Offered quarter
- 3Q
- Syllabus updated
- 2021/10/5
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
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.
Student learning outcomes
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 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 understand how society and life are being transformed.
Keywords
Color image processing, coding, video information compression, MPEG, audio processing, deep learning, internal understanding, object fingerprint, face recognition, person recognition, Artificial intelligence, machine learning, data mining, discovery science, simulation, optimization
Competencies that will be developed
| ✔ 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. Using lots of related pictures and photographs, each technology is explained according to the course schedule. Through the Questions and Answers, the correct and deep understanding is enhanced. Real products and samples will be shown in the class if necessary for better understanding.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Image processing technology and its applications | Understand the basic color image processing and noise reduction, and their applications in the fields. |
| Class 2 | Affective computing - Forecasting tomorrow's stress | Understand an overview of "Affective computing" and some current researches which are stress estimating and forecasting, and their applications. |
| Class 3 | Individual Object Identification and Authentication using Fingerprint of Things Image Recognition Tech. | Understand an overview of object identification and authentication technologies and their applications such as securities, manufacturing, retail and daily use of general items. |
| Class 4 | Deep learning and its applications | Understand the basic architectures of deep neural networks, and their applications |
| Class 5 | Video processing technologies for human recognition and their application | Understand the video processing technologies used for human recognition with a focus on facial identification, as well as the challenges and their applications |
| Class 6 | Acoustic Signal Processing for Audio Terminals | Understand the acoustic problems and the signal processing technologies to solve them for audio terminals or voice communication terminals |
| Class 7 | Video compression coding technology | Understand the basic principles of video compression focusing on MPEG international standards |
| Class 8 | Overview of the artificial intelligence | Understand the overview of of the artificial inteligence |
| Class 9 | Overview of the machine learning | Understand the idea behind the machine learning and its applications specially based on the Deep learning |
| Class 10 | Overview of the data mining and the discovery science 1 | Understand the overview of the data mining and the discovery science |
| Class 11 | Overview of the simulation | Understand the overview of the simulation |
| Class 12 | Overview of the mathematical optimization | Understand the overview of the optimization in data science and AI technology. |
| Class 13 | Overview of the data mining and the discovery science 2 | Understand the overview of the data mining and the discovery science |
| Class 14 | Laboratory tour | Take tours of research laboratories in leading industries. |
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)
Not specified
Reference books, course materials, etc.
All course materials will be provided in each class.
Assessment criteria and methods
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 (i.e., required knowledge, skills, courses, etc.)
Not specified
Contact information (e-mail and phone) Notice : Please replace from "[at]" to "@"(half-width character).
isao[at]ict.e.titech.ac.jp
Office hours
Students may approach the instructors at the end of class upon securing an appointment through e-mail.
