Curriculum System
MEIP
Curriculum System
Deepen foundation
Broaden horizons
Based on a solid foundation in mathematics and physics, we aim to nurture students with a challenging spirit who are creative and adaptable. The curriculum is structured so that students deepen their understanding by thinking for themselves, working with their hands, and explaining in their own words.
The five pillars of the Mathematical and Information Engineering course are algebra, analysis, geometry, probability, and arithmetic and mathematical engineering, while the five basic subjects of the Systems and Information Engineering course are measurement, circuits, control, signal processing, and systems. Many subjects are common to both courses, and programs are offered to promote interactions between courses in experiments and graduation thesis research. A broad curriculum for application to advanced science and technology is also offered.
1-2 Years
General education course
- Mathematical science
- Mathematics I Calculus
- Mathematics II Linear Algebra
- Material science
- Dynamics
- (Chemistry) Thermodynamics
- Electrodynamics
- Structural chemistry
- Physical chemistry
- Integrated subject
- Modern engineering
- Mathematical science
- Informatics
- Other
- Foreign language
- Humanities
- Social science
General education course
- Basic mathematics
- Brush up and reinforce what has been learned (set theory, graphs, topology, analysis, linear algebra, etc.) in order to solidify the foundation of mathematics necessary for engineering.
- Electromagnetics I
- Computers, robots, measuring instruments... You can't talk about engineering without electromagnetics. Step up from the basics to the applications.
- Circuit and System Fundamentals
- This course provides an introduction to the rudiments of systems theory and signal theory, which form the basis of circuits, control, and regulation. The course will focus on electric circuits with emphasis on the physical aspects of the phenomena.
- General Theory of Measurement C
- Measurement is the starting point of science. This lecture will focus on the basic concepts of measurement and the principles of measuring various physical quantities.
- Optimization technique
- Many engineering problems can be attributed to optimization problems. In this section, you will learn examples of important optimization problems in engineering and optimization methods for solving optimization problems.
- Fundamentals of Cognitive Behavioral Systems
- The fundamentals of computer science and robotics will be organized from a mathematical perspective. At the same time, basic principles of computational systems, cognitive systems, and behavioral systems will be lectured.
- Mathematics 1D/Mathematics and Mechanics Exercise I
- To acquire the basics of ordinary differential equations, vector analysis, and variational calculus through lectures and exercises.
College and Course Decisions
3-4 Years
Courses common to both courses
- Math 2D
- Math 3
- Mathematical Methods
- Electromagnetics II
- Quantum mechanics II
- Nanoscience
- Introduction to Brain Science
- Optics
- Solid state physics
- Statistical mechanics
- Economic Engineering I, II
- Special Lectures on Mathematical Engineering
- Practical exercise
The goal of the Mathematical and Information Engineering course is to make free use of mathematics to deeply penetrate real-world problems. The foundation for this is laid thoroughly in the five basic subjects and exercises. In addition, subjects related to various fields of engineering (statistics, computing, information, operations research, biology, etc.) are offered.
Fundamentals of Mathematical Information
- Analytical and Mathematical Engineering
- Based on the fundamentals of analysis acquired in Komaba's Mathematics I, such as continuity, convergence, and calculus, students will study measurement theory, Lebesgue integrals, and functional analysis, and consider their application to real-world problems.
- Geometric and Mathematical Engineering
- The interest of geometry lies in being able to image it. Students learn to master one-step advanced geometry such as tensor analysis and topology, and to capture the images mathematically in engineering.
- Algorithmic Mathematics and Engineering
- Computation is the basis of science. It is an important issue how much "effort" it takes to perform a calculation. This course will teach you how to estimate the amount of calculation and how to design an efficient calculation method.
- Algebraic Mathematics and Engineering
- Students will learn about the structure of typical operations such as groups, rings, and fields, and develop the ability to look at engineering systems from a cross-sectional perspective, focusing on the structure of operations.
- Probability Mathematical Engineering
- Probabilistic and statistical models make it possible to extract information hidden in uncertain phenomena. This course will study the mathematics underlying such probabilistic/statistical methods.
More advanced mathematical information
Through lectures on mathematical informatics as engineering, students will experience the "living mathematics" necessary to solve real-world problems and consider what they should do as engineers.
- mathematical programming
- Information theory
- Bioinformatics
- Program Mathematics
- Advanced Mathematical Information Engineering
- Mathematics of Machine Learning
- Applied Spatial Theory
- Applied statistics
- Computational quantity theory
Graduation Thesis [Sample Research Topic]
- Chaos engineering
- Mathematical Model of the Brain
- Mathematical Models of Social Behavior
- Non-linear engineering
- Operations Research
- Optimization and mathematical programming
- Algorithm Theory
- Numerical analysis
- Numerical simulation
- Information theory
- Cryptography
- Complex network
- Applied mechanics
- (the study of) statistics
- Time series analysis
- Financial engineering
- Risk analysis
- Computer science
- Natural language processing
- Machine learning
- Data mining
The Systems and Information Engineering course offers a systematic and wide-ranging curriculum related to computer-based cognitive and behavioral systems, with the five pillars of measurement, circuits, control, signal processing, and systems. The course aims to nurture individuals who can solve new problems from a broad perspective, and individuals who can pose problems on their own and pioneer new fields.
Fundamentals of System Information
- Control Theory I, II
- Students will learn the basic concepts of control engineering under a coherent system, focusing on control theory, which is said to be the most beautiful and well-organized in engineering.
- Signal Processing Theory I, II
- Students will learn the mathematical fundamentals and algorithms of both digital and analog signal processing and its applications to audio, acoustics, image processing and fault detection.
- Circuit Theory I and II
- In the first course, semiconductor devices and their circuits and analog integrated circuits including sensor circuits, and in the second course, distributed constant circuits and wave information processing such as microwaves and light.
- Computational Systems Theory I, II
- The entire picture of computing systems, from logic mathematics to computer architecture, is described, focusing on hardware from the fundamentals to practice.
- Cognitive Behavioral Systems Theory I, II
- "The fundamentals of machine systems, such as robots, that recognize external situations and act intelligently based on them will be discussed.
Cybernetics and artificial reality systems in which humans and machines act organically as one will also be discussed."
More advanced system information
Through more advanced lectures on cognitive and behavioral systems, students will study a wide range of topics in systems and information engineering and gain a deep understanding of the current state of the new discipline.
- Sensor and Actuator Engineering
- Image Processing Theory
- applied acoustics
- Advanced System Information Engineering
- Biometrics
Graduation Thesis [Sample Research Topic]
- VLSI Design
- Processor Development
- Massively parallel process
- Systems Control Theory and Applications
- Robust control
- Modeling
- Adaptation and Learning
- Artificial reality
- Autonomous decentralized system
- Cybernetics
- Robotics
- Biological neural network
- Sensor Fusion
- Intelligent integrated sensor
- Image processing
- Pattern recognition
- Visual, auditory, and tactile information processing
- Speech and Music Information Processing
- Brain Function Measurement
- Human interface
- Inverse problem