2021 Advanced Communication System Engineering

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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  Yamada Isao 
Course component(s)
Lecture    (ZOOM)
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.

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