2022年度 シラバス大学院

IT教育研究領域 (応用情報工学)

2022/08/13  現在

科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
ヴィジェガス オロズコ ジュリアン アルベルト
担当教員名
/Instructor
ヴィジェガス オロズコ ジュリアン アルベルト, コーエン マイケル
推奨トラック
/Recommended track
先修科目
/Essential courses
This course is offered exclusively in English
更新日/Last updated on 2022/01/26
授業の概要
/Course outline
The purpose of this course is to study the fundamentals of audio signal processing and its application to music. Besides reviewing the underlying techniques, this course focuses in practical implementations of such techniques, so the course is intense in hands-on exercises, assignments, and projects mainly based on  Pure-data, C/C++, Matlab/Octave, and Faust.
授業の目的と到達目標
/Objectives and attainment
goals
Students who approve this course are expected to:

1. Understand some basic techniques used in digital audio effects, computer music,   and terminology on this topic.

2. Be able to create their own digital audio effect chain.
授業スケジュール
/Class schedule
1 Introductions
2 visual programming for audio
3 DFT, Causality and stability
4 Basic Filters
5 Time-varying effects I
6 Time-varying effects II
7 Modulation
8 GUI for DAFX in mobile platforms
9 Nonlinear FXs I
10 Nonlinear FXs II
11 Pitch and rhythm
12 Programming audio objects in C
13 Deploying audio projects
14 Final presentations
教科書
/Textbook(s)
• U. Zölzer, editor. DAFX – Digital Audio Effects. John Wiley & Sons, New York, NY, USA, 2nd edition, 2011.
• Various materials prepared by the instructors
成績評価の方法・基準
/Grading method/criteria
Exercises 40%
Final project 60%

Based on the techniques studied in class, student propose an audio chain to be deployed in a mobile platform. This chain is demonstrated in front of the class in the last session of the course.
履修上の留意点
/Note for course registration
Experience on Pure-data is desirable.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
• Theory and Techniques of Electronic Music (M. Puckette): http://msp.ucsd.edu/techniques.htm
• Julius Orion Smith III website: https://ccrma.stanford.edu/~jos/


The course instructor has practical working experience. He worked as an Ikerbasque researcher for about three years at the laboratory of phonetics in the Basque Country University.


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開講学期
/Semester
2022年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
朱 欣
担当教員名
/Instructor
朱 欣
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/19
授業の概要
/Course outline
Biomedical modeling and visualization is an important technology to extract useful information and discover the biomedical mechanisms buried in the huge amount of data produced in the basic biomedical researches and clinical medical practice. This course will introduce how to implement computer information technology in biomedical modeling and visualization. Main lecture contents include computer modeling and simulation of biological cells, organs, and systems, mathematical basis for biomedical modeling and simulation, physiological modeling and simulation, and biomedical visualization. Homework and projects will be assigned based on measured data in Biomedical Information Technology lab and medical database available in the Internet.
授業の目的と到達目標
/Objectives and attainment
goals
This course will help students to obtain the skills and experiences in implementing computer information technology to biomedicine. Through this course, it will strengthen students' R&D ability in future biomedical research and work.
授業スケジュール
/Class schedule
1. Biomedical modeling and visualization: its application in clinical and basic medicine
2. Mathematical basis for biomedical modeling and simulation
3. Cellular level modeling and simulation: Hodgkin-Huxley model
4. Tissue level modeling and simulation: rule-based model and reaction-diffusion model
5. Construction and visualization of biological models with realistic shapes
6. Organic modeling and simulation: whole-heart model
7. Computer simulation of arrhythmias: atrial fibrillation, supraventericular tachycardias, and ventricular fibrillation
8. Physiological modeling and simulation: heart rate variability, and its linear and nonlinear dynamics
9. Topics on other biomedical modeling and simulation: cerebral networks, bioheat transfer, biomechanics, biofluid mechanics, and etc.
10. General-purpose GPU in biomedical modeling and visualization
教科書
/Textbook(s)
Handout will be distributed in class.
成績評価の方法・基準
/Grading method/criteria
Homework              60%
Project an presentation   40%
履修上の留意点
/Note for course registration
Digital signal processing
Computer graphics
Biomedical information technology
Image processing
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
http://www.physiome.jp/
http://www.physiome.org.nz/
http://www.nlm.nih.gov/
http://ecg.mit.edu/
http://www.u-aizu.ac.jp/~zhuxin/course

Practical working experinces

The course instructor Xin Zhu has practical working experiences. He had performed biomedical image processing at Tianjin University for 5 years, and has performed biomedical image processing at the University of Aizu for 15 years with the financial support from universities and JSPS. Based on his experiences, he can teach this course.


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
西舘 陽平
担当教員名
/Instructor
西舘 陽平
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/02/02
授業の概要
/Course outline
This course is a practical introduction to the finite element method. It focuses on algorithms of the finite element method for solid mechanics modeling. Mesh generation and visualization issues are considered.
授業の目的と到達目標
/Objectives and attainment
goals
The course helps students to understand main algorithms of the finite element method and to gain practical skills in finite element programming.
授業スケジュール
/Class schedule
1. Introduction. Formulation of finite element equations.  
2. Exercise 1.
3. Finite element method for solid mechanics problems 1.
4. Finite element method for solid mechanics problems 2.
5. Exercise 2.
6. Two dimensional isoparametric elements.
7. Three dimensional isoparametric elements.
8. Exercise 3.
9. Data format for finite element analysis.
10. Regular mesh generation.
11. Exercise 4.
12. Assembly and solution of finite element equations.
13. Exercise 5.
14. Visualization of finite element models and results.
教科書
/Textbook(s)
Lecture Notes
成績評価の方法・基準
/Grading method/criteria
Exercises - 50%
Project - 50%
履修上の留意点
/Note for course registration
Calculus, Linear Algebra, Numerical Analysis, and some programming courses are recommended as prerequisites.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
Gennadiy Nikishkov, Programming Finite Elements in Java. Springer, 2010, 402 pp.


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
矢口 勇一
担当教員名
/Instructor
矢口 勇一
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/28
授業の概要
/Course outline
In order to determine your research theme related computer vision and image processing, you need to know the latest status of these fields. Actually, image processing needs many technical and conceptual backgrounds from computational algorithms such as Monte-Carlo, forests, dynamic programming, belief propagation, statistical analysis and so on.
In the lecture of image processing in the undergraduate course, we learned the concept of digital images and some basic techniques for analyzing image patterns, and this course provides fundamental algorithms how to understand images or patterns and the status which is necessary technically and conceptually to conduct your master/doctor thesis.
授業の目的と到達目標
/Objectives and attainment
goals
We aim to present the fundamental knowledge for reading and writing academic papers related computer vision and image processing.
授業スケジュール
/Class schedule
1.
  Course Instruction, Introduction to Image Recognition and Understanding
  Image Formation and Representation
  Image Acquisition and Optics
2.
  Low-level Image Feature: Pixel, Voxel, Line, Block, Corner
  Image Feature and Algorithms: SIFT, SURF, HOG,
  Joint Image Feature and Sparse Representation
3.
  Image Segmentation – K-Means, Mean-shift
  Image Cutting -  Sneaks, Watershed
  Object Clustering - K-Means, Fuzzy c-Means, Sequential Clustering,   Hieralchical Clustering
   - First Report: Image Segmentation and Clustering
4.
  Pattern Recognition 1 – Sparse Representation with Linear Classification
  Pattern Recognition 2 – Naïve Bayes, Support Vector Machine
  Pattern Recognition 3 – Neural Network
   - Second Report: Image Recognition – Find Human Faces
5.
  Image Understanding 1 – Bayesian Net
  Image Understanding 2 – Principal Component Analysis, Latent Semantic Indexing
  Image Retrieval – Bag of Visual Worlds, Sparse Component Analysis
   - Third Report: Bayesian Net Calculation
6.
  Motion Feature – Optical Flow, Dense Optical Flow
  Pattern Matching – Dynamic Time Warping, Continuous DP
  Motion Analysis – Pixel Trajectory, Gesture Recognition
7.
  Image Calibration – Calibration Technique, Image Stitching
  Stereo Image – Epipolar Geometry
  Wide-baseline Stereo – Factorization, Bundle Adjustment

教科書
/Textbook(s)
Main Coursebook - Richard Szeliski, Computer Vision: Algorithms and Applications.
(Not need to buy this book, but very helpful for understanding.)

Course website - http://hartman.u-aizu.ac.jp/course/view.php?id=5

Prerequisites and other related courses which include important concepts relevant to the course:
Image processing and signal processing in the undergraduate school.
成績評価の方法・基準
/Grading method/criteria
Several reports are given for exercise (Feature Detection, Face detection, Bayesian Net, Clustering) and each report have 25~40 points.


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
黄 捷
担当教員名
/Instructor
黄 捷
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/31
授業の概要
/Course outline
Multirate signal processing techniques are widely used in many areas of modern engineering such as communications, digital audio, measurements, image and signal processing, speech processing, and multimedia. A key characteristic of multirate algorithms is their high computational efficiency. The aim of this course is to give students an introduction of the fundamental theory of multirate signal processing and other related topics. Design techniques of FIR filters relevant to the multirate systems, digital filter banks and wavelet analysis will also be summarized.
授業の目的と到達目標
/Objectives and attainment
goals
Through the course, the student will understand the fundamental theory of multirate signal processing and be able to design multirate filter banks.
授業スケジュール
/Class schedule
1. Linear time-invariant system, Linear and circular convolution
2. Continuous and discrete Fourier transform, Allpass and Minimum Phase
3. Analytic signal, Time Frequency Analysis
4. Sampling Rate Conversion
5. Decimation and Interpolation
6. Two-channel filter banks
7. Filter banks with Polyphase Structure
8. Octave Filter Banks and wavelets
教科書
/Textbook(s)
N. J. Fliege, Multirate Digital Signal Processing, John Wiley & Sons 1994
Ljiljana Milić, Multirate Filtering for Digital Signal Processing: MATLAB Applications, Information Science Reference, 2009
J. H. McClellan, et al., Computer-Based Exercise for Signal processing using MATLAB, Penntice Hall, 1994.
成績評価の方法・基準
/Grading method/criteria
Reports and discussion (40) and exercises (60)
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
http://web-int.u-aizu.ac.jp/~j-huang/Lecture/ASP/asp.html


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
愼 重弼
担当教員名
/Instructor
愼 重弼
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/11
授業の概要
/Course outline
This course deals with the design, analysis and development of methods for the classification or description of patterns, objects, signals and processes. The main goal of this area is to develop advanced technology and paradigms for human action pattern processing, and our ability to create new ideas related to the topics covered. There are many pattern recognition applications exists today, including online / offline pattern recognition, the use of pen-tablets, pattern processing, touch panels, RGB-D cameras, iOS / Android smart devices and virtual reality. We focus on related issues in human action pattern processing from 3 perspectives; Recognition, authentication, and synthesis. This course will be delivered via onsite and online.
授業の目的と到達目標
/Objectives and attainment
goals
At the end of this course, students will be able to:
- perceive an overview of the field of pattern processing related to human action and pattern processing.
- learn how various techniques of human action pattern processing can be applied to the software.
授業スケジュール
/Class schedule
Introduction to human action pattern processing
Fundamentals of online/offline pattern recognition
Pattern recognition involves human action (HA)
Current problems and solving methods associated with the following topics:
- Non-touch Interface for Character Input
- Pen-based interactive systems
- Handwritten font generation
- Signature verification and writer identification system
- Brush painting systems
- HCI using calligraphy systems
- Gesture recognition using RGB-D, Leap motion, Myo controller, and web camera
- Disease diagnosis using pen-tablet
- Daily activity recognition using smartwatch and camera sensor
- Multichannel EEG signal analysis for brain computer interface (BCI)
- Design of experiments associated with human action pattern processing
- HCI for smart and mobile devices
- Applications of image recognition and computer vision
The presentation of some application programs
Students' work:
- Investigation, presentation, research report, and discussion of current techniques and producing new ideas.
- Programming related to pattern processing.
教科書
/Textbook(s)
There are a lot of textbooks available online. Instructors will provide selected topics from books and various journals and conference papers, moreover, our goal in this course is to give you a broad perspective on the field.
成績評価の方法・基準
/Grading method/criteria
Investigation, presentation, and research report (40%)
Positive class participation (20%)
Programming project (40%)
履修上の留意点
/Note for course registration
Permission of the instructor.
Interest in the area of pattern processing.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
Useful Links:
Course Web Site: http://web-int.u-aizu.ac.jp/~jpshin/GS/HAPP.html
References:
[1] Scott MacKenzie, Human-Computer Interaction: An Empirical Research Perspective (2013) ISBN-10: 0124058655
[2] Jonathan Lazar, Jinjuan Heidi Feng, Harry Hochheiser, Research Methods in Human-computer Interaction, Wiley; ISBN-10: 0470723378 (2010)
[3] Alan Dix, Janet E. Finlay, Gregory D. Abowd, Russell Beale, Human-Computer Interaction (2003)  ISBN-10: 0130461091


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
ヴィジェガス オロズコ ジュリアン アルベルト
担当教員名
/Instructor
ヴィジェガス オロズコ ジュリアン アルベルト, コーエン マイケル, 黄 捷
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/26
授業の概要
/Course outline
The purpose of this course is to study the fundamentals of spatial hearing and its application to virtual environments. By using two ears, human among other species, are able to determine the direction from where a sound is being emitted in a real environment. For virtual environments (e.g., movies, games, recorded or live concerts) is desirable to provide the spatial cues found in nature to increase the realism of a scene. Besides reviewing the underlying theories of spatial hearing, this course focuses in practical implementations of binaural hearing techniques, so the course is intense in hands-on exercises, assignments, and projects mainly based on Pure-data programming language.

Note: depending on the evolution of the COVID-19 pandemic, this course may be offered online.
授業の目的と到達目標
/Objectives and attainment
goals
Be able to understand the basic mechanisms of spatial hearing, as well as the terminology on this topic.
• Be able to decide which of the presented techniques is best for creating the 3D aural illusion.
• Be able to implement virtual 3D sound environments based on headphones and multi-speaker systems.
授業スケジュール
/Class schedule
Session 1. Introductions and motivation
Session 2. Spatial hearing and psychoacoustics
Session 3. Lateralization
Session 4. Lateralization (continuation)
Session 5. Elevation perception
Session 6. Distance perception
Session 7. Room effects
Session 8. Motion perception
Session 9. Head-related impulse responses and transfer functions
Session 10. (continuation)
Session 11. Loudspeaker techniques
Session 12. (continuation)
Session 13. Ambisonics
Session 14. DiRAC and other recent developments
教科書
/Textbook(s)
• Durand R. Begault, 3-D Sound for Virtual Reality and Multimedia, Academic Press, 2000.
• Jens Blauert, The Technology of Binaural Listening (Modern Acoustics and Signal Processing)
• Various materials prepared by the instructors
成績評価の方法・基準
/Grading method/criteria
Quizzes 40%
Assignments 60%
履修上の留意点
/Note for course registration
* This course uses Matlab and Pure-data for practical demonstrations. Some assignments must be completed in either of these languages as well.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
Prof. Villegas has practical working experience. He worked as an Ikerbasque researcher for about three years at the laboratory of phonetics in the Basque Country University.

• Bregman, Albert S., Auditory Scene Analysis: The Perceptual Organization of sound. Cambridge, Massachusetts: The MIT Press, 1990 (hardcover)/1994 (paperback).


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開講学期
/Semester
2022年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
ウィルソン イアン
担当教員名
/Instructor
ウィルソン イアン
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/28
授業の概要
/Course outline
This course introduces the mechanisms of speech articulation and how to measure them. It also investigates the mapping between articulation and acoustics. Articulation is investigated using tools such as ultrasound and video. Speech acoustics is investigated using Praat – open-source acoustic analysis software.
授業の目的と到達目標
/Objectives and attainment
goals
After completing this course, students will be able to:
(1) describe how human speech is produced and how changes in articulation affect the acoustics of speech
(2) use an ultrasound machine to collect speech data
(3) analyze speech acoustics and write short scripts to automatically analyze acoustic data
(4) understand acoustic concepts such as speech waveforms, formants, FFT, and sine wave speech synthesis
授業スケジュール
/Class schedule
Classes 1 and 2: How speech is produced and how articulation is measured
Classes 3 and 4: Acoustic properties of speech sound classes; Praat script writing
Classes 5 and 6: Using Praat to synthesize vowels and manipulate speech
Classes 7 and 8: Ultrasound speech data collection and analysis
Classes 9 and 10: Mapping of articulation to acoustics
Classes 11 and 12: Spectrogram reading and lip reading
Classes 13 and 14: Phonetic variability - within and across speakers/languages; final project
教科書
/Textbook(s)
Handouts and other materials will be made available on the course website in Moodle.
成績評価の方法・基準
/Grading method/criteria
Active participation in class:  40%
Assignments (Praat script writing, etc.):  30%
Final project:  30%
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
CLR Phonetics Lab website: CLR Phonetics Lab


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
白 寅天
担当教員名
/Instructor
白 寅天, 矢口 勇一
推奨トラック
/Recommended track
先修科目
/Essential courses
1. NLP-IR
2. Linear Algebra
更新日/Last updated on 2022/01/28
授業の概要
/Course outline
Natural Language Processing (NLP) is a rapidly developing field with broad applicability in computer science and other various applications. From linguistic and textual data, we can get very useful information, and the data can be used for creating artificial intelligence (AI) applications such as language translator, several kinds of text generation, chatting, etc. In this course, you will study some basic of theoretical and methodological introduction to NLP, and its application to information retrieval, text mining, several language processors using AI. Also, we will focus on strategies and toolkits for NLP and Deep Learning (DL) in the Python programming language. Throughout this course, the sources, architectures and tools we will focus on will be introduced for student's own term project.
授業の目的と到達目標
/Objectives and attainment
goals
Students will obtain knowledge about foundational understanding in NLP methods and strategies.  They will also learn principle of neural language processing and its architecture for several AI application. And they can know how to evaluate the characteristics of NLP technologies and frameworks as they carry out practical exercise and term project using NLP and DL toolkits available.
授業スケジュール
/Class schedule
1. Text Processing and Word Sense I
- Parsing, Tokenizing, Lemmatizing
   - Word sense
2. Word Sense II and Lexical Analysis
   - Part of Speech, Tagging
   - Lexical acquisition, Collocation
   - Latent Semantic Analysis
3. TF-IDF and Text Classification
4. Information Retrieval
5. Deep Learning Architectures for Language Model
6. Neural Language Translation
7. Neural Text Summarization and Generation
8. Neural Question and Answering System
教科書
/Textbook(s)
- A lecturer will provide necessary materials.
成績評価の方法・基準
/Grading method/criteria
- Term Project: 100%
履修上の留意点
/Note for course registration
- There can be homework such as pre-reading or material preparation during lectures.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
- It will be introduced on the Moodle lecture Web page.


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
富岡 洋一
担当教員名
/Instructor
富岡 洋一, 浅田 智朗
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/25
授業の概要
/Course outline
車や鉄道,航空機,家電設備,工場の生産設備など我々が日常利用している電気機器・機械装置では,その安全で効率的な運用に「自動制御」という考え方が欠かせない。計測と制御では,このような機器の状態を「計測」し,その結果に基づいて機械を「制御」するための基本的原理を学ぶ。計測の基本原理としては,センサの動作原理,計測データの処理・分析法,測定誤差の評価法などを学ぶ。また,制御の基本原理としては,周波数領域および時間領域での被制御対象のモデリングと,制御器の設計手法を学ぶ。さらに,先端的なデジタル技術をベースにしたアドバンスト制御についても,応用事例を通じて学んでゆく。
授業の目的と到達目標
/Objectives and attainment
goals
自動制御の基本として,計測の基本原理,実践法,応用,注意点,計測データの処理,分析法,測定誤差の評価を,制御の基本原理として,周波数領域および時間領域での被制御対象のモデリングと,制御器の設計手法を学ぶ。
授業スケジュール
/Class schedule
1. イントロダクション
2. 計測と単位系/計測量
3. 測定誤差と精度
4. 最小二乗法とデータの補間
5. 機械的測定/センサ/センシング
6. 信号計測法
7. 信号の処理と分析
8. ラプラス変換
9. 動的システムのモデリングと制御
10. 伝達関数によるモデル化と応答特性
11. フィードバック制御,システムの安定性と制御系設計
12. PID制御系の設計シミュレーション演習1
13. システム同定、シーケンス制御,ファジー制御,
14. PID制御系の設計シミュレーション演習2
教科書
/Textbook(s)
特に使用しない。LMS上のハンドアウトを使用する。
成績評価の方法・基準
/Grading method/criteria
レポート,課題 100%
履修上の留意点
/Note for course registration
特に無し


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  後期集中 /2nd Semester Intensi
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
大竹 真紀子
担当教員名
/Instructor
大竹 真紀子, 小川 佳子, 本田 親寿, 山田 竜平
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/02/21
授業の概要
/Course outline
This course focuses on developments of hardware instruments including rover and control system for lunar and planetary explorations. Envisioned main target is the moon. This course follows an omnibus form and the course consists of a classroom lecture and a practice of planning a lunar exploration.
授業の目的と到達目標
/Objectives and attainment
goals
To learn developments of hardware instruments and control system for landing missions.
To learn basic knowledge in space developments as topics of computer science and engineering.
To practice (test) the obtained knowledge by planning their own lunar exploration.
授業スケジュール
/Class schedule
Tentative schedule in AY2022.
#1-3 Prof. Ohtake: Science and Tchnology for the lunar and planetary explorations.
#4-5 Prof. Ogawa: Spectral Instruments
#6-7 Prof. Honda: Route Planning for Rover Obstacle Avoidance
#8-9 All prof.: Practice-Route Planning Using Real Field Data
#10-11 Prof. Yamada: 3D Mapping Using Rover System
#12-14 All prof. : Practice-3D Mapping Using Rover System
教科書
/Textbook(s)
N/A
成績評価の方法・基準
/Grading method/criteria
Report on the practice.
履修上の留意点
/Note for course registration
preriquisite:N/A
related course:ITC09 Fundamental Data Analysis with Lunar and Planetary Database
ITC10 Practical Data Analysis with Lunar and Planetary Databases SEA11 Software Engineering for Space Programs
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
N/A


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
朱 欣
担当教員名
/Instructor
朱 欣
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/19
授業の概要
/Course outline
Biomedical imaging has been an essential diagnostic and therapeutic tool in clinical and basic medicine since the invention of X-ray photographer. Current imaging technology include X-ray photographer, X-ray CT, MRI, ultrasonic imaging, nuclear medicine imaging, endoscopic and laparoscopic imaging technology, and etc. Nowadays, the advancement of medicine requires the scientists and engineers to invent novel imaging modalities, improve the imaging quality and speed of current technology, and the software for accurate and quick analysis of medical images. We expect to train our students to obtain the physical and mathematical knowledge of biomedical imaging, understand the characteristics of different imaging technologies, and have the ability to do further research in biomedical image processing and analysis.
授業の目的と到達目標
/Objectives and attainment
goals
We will train our students to master the theoretical basis of biomedical imaging, understand the characteristics and utilities of different imaging technologies, and have some basic abilities to conduct biomedical image processing and analysis.
授業スケジュール
/Class schedule
1. X-ray CT: Basis of physics and mathematics, system and reconstruction algorithms
2. MRI: Physics and chemistry, system and reconstruction algorithms
3. Ultrasonic imaging: Physics, transducer, and A/B/C/D/F/M modes
4. Nuclear medicine and other imaging modalities: PET, SPECT, OCT, EIT, molecular imaging, and etc.
5. Endoscope and laparoscope: Basis of optics, CCD, CMOS, applications in diagnosis and therapies, and recent development
6. Image processing: Artifacts removal, enhancement, transformation, and etc.
7. Image segmentation: Laplacian filter, snake deformation, and region growing
8. Characteristic extraction from medical images: Preprocessing, region of interest, texture analysis, and characteristic extraction
9. Image information retrieval and registration: Retrieval and analysis of shape and texture, and image registration
10. Computer-aided diagnosis: Reviews on statistics, Bayes’ theorem, classification algorithms, cluster analysis, mammography and angiography
11. Special lecture by outside specialist
12. 3D visualization: Automatic and semi-automatic 3D image reconstruction from 2D slices
13. Surgical navigation system: Imaging and image processing technology for surgical navigation system
教科書
/Textbook(s)
  Mathematics and Physics of Emerging Biomedical Imaging by National Research council (free download from http://www.e-booksdirectory.com/details.php?ebook=3692),
Handout will be distributed in class.
成績評価の方法・基準
/Grading method/criteria
Homework 60%
Project 40%
履修上の留意点
/Note for course registration
Physics and chemistry
Electricity and electronics
Probability and statistics
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
はじめての核医学画像処理 
http://www.ne.jp/asahi/ma-ku/104216/
C言語で学ぶ医用画像処理 著者:広島国際大学保健医療学部 石田 隆行 編 オーム


Practical working experiences

The course instructor Xin Zhu has practical working experiences. He had performed biomedical image processing at Tianjin University for 5 years, and has performed biomedical image processing at the University of Aizu for 15 years with the financial support from universities and JSPS. Based on his experiences, he can teach this course.


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
陳 文西
担当教員名
/Instructor
陳 文西
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/28
授業の概要
/Course outline
Biosignal enhancement, feature extraction and physiological interpretation are important aspects in biomedical engineering field. Various biosignals can be manipulated through proper representation, transformation, classification, optimization and visualization.
This course will introduce fundamental concepts and approaches, such as filtering, detection, estimation, and classification for various biosignal processing and data mining. It will provide students a brief picture of biosignal from detection to clinical application by following the course “Introduction to Biosignal Detection”.
授業の目的と到達目標
/Objectives and attainment
goals
1. To understand how to apply statistical mathematics and digital signal processing methods to deal with various biosignals.
2. To understand how to utilize fundamental approaches of signal processing and data mining in biomedical information engineering field.
授業スケジュール
/Class schedule
1. Introduction
2. Decomposition and Reconstruction of Biosignals
3. Detection of Biosignatures
4. Processing of Biosignals and Biosignatures
5. Analysis of HRV in Time Domain
6. Analysis of HRV in Frequency Domain
7. Analysis of HRV in Nonlinear Domain
教科書
/Textbook(s)
 Biomedical Signal Processing and Signal Modeling, Eugene N. Bruce, ISBN: 978-0-471-34540-4, December 2000, Wiley
https://www.wiley.com/en-jp/Biomedical+Signal+Processing+and+Signal+Modeling-p-9780471345404

 Practical Biomedical Signal Analysis Using MATLAB (Series in Medical Physics and Biomedical Engineering), Katarzyn J. Blinowska and Jaroslaw Zygierewicz, CRC Press; 1 edition (September 12, 2011), ISBN-10: 1439812020, ISBN-13: 978-1439812020
https://www.crcpress.com/Practical-Biomedical-Signal-Analysis-Using-MATLAB/Blinowska-Zygierewicz/p/book/9781439812020

 Seamless Healthcare Monitoring - Advancements in Wearable, Attachable, and Invisible Devices, Editors: Tamura, Toshiyo, Chen, Wenxi, Springer International Publishing, 2018, DOI 10.1007/978-3-319-69362-0, eBook ISBN 978-3-319-69362-0, Hardcover ISBN 978-3-319-69361-3
https://www.springer.com/us/book/9783319693613
成績評価の方法・基準
/Grading method/criteria
Research report, 100%
履修上の留意点
/Note for course registration
Introduction to Biosignal Detection
Probability and Statistics
Discrete Mathematics and Linear Algebra
Digital Signal Processing
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
The course instructor has practical working experience and has worked for 5 years at Nihon Kohden Industrial Corp., a professional manufacturer of world famous medical equipment, and has been engaged in R & D for bioinstrumentation, signal processing and data analysis. Based on this experience, he will teach the fundamental knowledge and latest advancements in “Biosignal Processing and Data Mining”.

http://i-health.u-aizu.ac.jp/BPDM/ 


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  前期集中 /1st Semester Intensi
対象学年
/Course for;
1年 , 2年
単位数
/Credits
1.0
責任者
/Coordinator
姫野 龍太郎(理研)
担当教員名
/Instructor
姫野 龍太郎(理研), 検崎 博生(理研), 野田 茂穂(理研), 陳 文西
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/03/08
授業の概要
/Course outline
コンピュータの性能向上と計算手法の進化、そして各種計測手法の発展により、これまで不可能だった生体のシミュレーションが広い範囲で可能になってきている。この生体シミュレーション技術の基礎と現状を、ミクロ(生体分子シミュレーション)とマクロ(生体硬組織・生体流体シミュレーション)両面から学ぶとともに、実習を通してその一部を体験する。生体分子シミュレーションでは、分子動力学のシミュレーションの基礎から実際の応用例までを学ぶとともに、実際に分子動力学シミュレーションを体験する。同様に生体硬組織と生物流体の基礎方程式から解法、実際の応用例を学び、医療画像からの血流シミュレーションを体験する。
授業の目的と到達目標
/Objectives and attainment
goals
ミクロからマクロまでの生体のシミュレーションの方法の基礎方程式と計算方法とその種々の応用の実際を学ぶ。具体的には、
1)生体分子:分子動力学シミュレーション
2)生体硬組織:構造力学の基礎と生体のシミュレーションに必要な非線形構造力学
3)生体流体:血流を主な対象とした流体シミュレーションの基礎方程式と解法

このうち1)と3)については実習を通して、実際に自分で問題を解けることを目指す。
授業スケジュール
/Class schedule
1. 概要紹介:姫野龍太郎  (1コマ)
2. 生体分子シミュレーション:検崎博生 (2コマ)
・ 基礎理論
・ 応用
・ 演習
3. 生体硬組織シミュレーション: 姫野龍太郎 (2コマ)
・ 基礎理論
4. 生体流体シミュレーション
・ 基礎理論: 姫野龍太郎
・ 医療応用: 姫野龍太郎
・ 演習: 野田茂穂 (2コマ)
合計7コマ

教科書
/Textbook(s)
教科書は使わず、必要な教材は資料として提供する。
成績評価の方法・基準
/Grading method/criteria
各授業内での小テストおよび課題、実習での評価:100%
履修上の留意点
/Note for course registration
PCを使った実習のために各自PCを持参すること。OSは実行するソフトウェアの関係でWindowsまたはLinux。他のOSしか用意できない場合は事前に相談すること
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
実務経験(姫野):生体流体シミュレーションで扱う流体シミュレーションは、日産自動車(株)に勤務していたときに業務で活用してきた約15年の実務経験がある。この経験を元に流体シミュレーションの基礎を教授する。

Simulation software for exercise of molecular simulation of living matter.
Coarse-grained biomolecular simulation software CafeMol: http://www.cafemol.org/

Simulation software for exercise of Blood flow simulation of living matter.
The system is based on VCAD System.
http://vcad-hpsv.riken.jp/en/release_software/block/04.php



科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
白 寅天
担当教員名
/Instructor
白 寅天
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/28
授業の概要
/Course outline
The semantic Web is the second wave of Web technology, and its environment evolves from human-readable to machine-readable. The key technology of the semantic Web is knowledge representation technique–ontology, and its management.
Main issue of this course is to learn the semantic Web service technology: ontology,its learning and engineering, and its application to Web service. Background of web evolution, ontology for knowledge representation, Web service, and application to service composition will be covered.
If you have interests on the areas in the semantic Web service (SWS) technology, please e-mail to me (paikic@u-aizu.ac.jp) or visit my office (307-C).
授業の目的と到達目標
/Objectives and attainment
goals
Main objective of this course is to give students ability of application of semantic technology based on some theoretic background. Historical motivation in Internet and Web technology, ontology basics and application, and how to apply ontology to other domains will be explained.
授業スケジュール
/Class schedule
1. Introduction to Web Technologies and Semantic Web
2. Resource Description Framework (RDF) and DAML-OIL
3. Ontology Language - OWL (I)
4. Ontology Language (OWL) (II)
5. Semantic Web Rule Language
6. Ontology Design Exercise in OWL (Using Protege)
7. Rule Design Exercise in SWRL (Using Protege)
8. Rule Design Exercise in SWRL (Using Protege)
9. Ontology Learning by Text Mining
10. Ontology Matching and Merging
11. Ontology Engineering
12. Semantic Web Service Frameworks (OWL-S and BPEL)
13. Semantic Web Service Frameworks (WSMO)
14. Presentation and Final Examination
教科書
/Textbook(s)
Lecture Slides will be provided on lecture Web site.
成績評価の方法・基準
/Grading method/criteria
1. Examination    --- 50%
2. Paper Presentation & Term Project --- 50%

履修上の留意点
/Note for course registration
* Prerequisites:
- JAVA Programming I & II
- Web Programming
- Artificial Intelligence
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
* Reference
1) J. Davies, R. Studer, P. Warren, Semantic Web Technologies, Wiley, 2007.
2) A. Gomez-Perez, M. Fernandex-Lopez, O. Corcho, Ontological Engineering, Springer, 2004.
3) J. Davies, D. Fensel, F.V. Harmelen, Towards The Semantic Web, Ontology-Driven        Knowledge Management, Wiely, 2003.
4) M.C. Daconta, L.J. Obrst, K.T. Smith, The Semantic Web, Wiley, 2003.



科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
3.0
責任者
/Coordinator
ヴィジェガス オロズコ ジュリアン アルベルト
担当教員名
/Instructor
ヴィジェガス オロズコ ジュリアン アルベルト, コーエン マイケル
推奨トラック
/Recommended track
先修科目
/Essential courses

通常のプログラミングの講座以外特に無し。
おすすめされた :
  ITC01A: Java 2D/3D Graphics (http://u-aizu.ac.jp/official/curriculum/syllabus/2021_2_J_005.html#ITC01A, http://web-int.u-aizu.ac.jp/~fayolle/teaching/java_2d_3d/)
  ITC11A: 3次元コンピュータグラフィックスとGPUプログラミング (http://u-aizu.ac.jp/official/curriculum/syllabus/2021_2_J_005.html#ITC11A, http://web-int.u-aizu.ac.jp/~nisim/cg_gpu/)
更新日/Last updated on 2022/01/27
授業の概要
/Course outline
このコースでは、インタラクティブマルチメディア、つまりリアルタイムゲーム、マンマシンインタフェースを学ぶクラスです。
この講座では学部生や大学院生を対象に、特にデスクトップVR(別名、fishtank VR)を通してヒューマンインターフェースの技術と仮想現実の範例を紹介します。
講座ではCGM(消費者生成メディア)とUGC(ユーザー生成コンテンツ)を活用して自身でデザインした仮想世界の作成を実際に行います。
主に、オブジェクト指向でラピッドプロトタイピング3Dシナリオ統合開発環境である「Unity」を用いて、ソフトウェア工学同様、デスクトップVR、
モーション グラフィックス、
色彩 (とカラー勾配)、図形・視覚のデザイン、テクスチャマッピング、音、音楽、スピーチ、
ソフトウェア工学、
イベント 駆動型 プログラミング、
並列計算、
対話の分野の表現を行います。
Photopea、Blender、 Audacity or WavePad、GarageBand or Reaperなどのソフトも用います。
実用的な実験、体験的なレッスンなどに力を入れています。

コロナウィルス感染の為、いくつかの授業はZoomを使ってオンラインとなります。また、実参加とオンライン参加同時の授業もあります。
授業の目的と到達目標
/Objectives and attainment
goals
This course spans the same topics as its undergraduate sibling
IT06: Human Interface & Virtual Reality; ヒューマン インターフェイスと仮想現実 (Game Design),
but is more demanding,
both quantitatively and qualitatively.
Quantitatively, for assignments in which a certain number of objects are assigned,
graduate students are expected to make ”half-again" (50% more) beyond the requirements for undergraduates.
Qualitatively, graduate students are required
to make stereographic displays (via anaglyphic rendering for Blender CAD, parallel channels for Unity scenarios),
explore more audio effects,
parameterize text-to-speech synthesis,
and enrich default spatial soundscapes.
授業スケジュール
/Class schedule
基本概念への導入は物理学に関係します。スペース(物理的なものやその他)とトポロジー、指数プロセスを含む数とアルゴリズムの複雑性、ソフトウェア工学とプログラミング(パラメーター表示・ランダム化・再帰・データ構造・イベント処理)、インタラクティブマルチメディアと知覚様式、グラフィックスとCGのレンダリング、CAD(コンピュータ設計)、視覚言語、立体映像と立体視(3D図面を含むオートステレオグラム・ランダムドットステレオグラム・アナグリフ・クロマステレオスコープ)、パノラマでターノラマなイメージとイメージベースドレンダリング、音・音響・TTS(text-to-speech synthesis)とSFX(sound effects)編集、BGM(background music)のためのDTM(desk-top music)作曲、インターフェースパラダイム、デジタルインタラクティブストリーテリングとメチネマ。

1 Introduction, Tutorials
2 Scene Composition
3 Scripting, Event Handling
4 Photographic Capture and Texture Mapping
5 Drawing, Painting, Texture Mapping
6 Individual Project Presentations
7 3D Modeling (Blender)
8 Panoramic & photospherical imagery, skybox
9 Color Models, Scripting
10 TTS (text-to-speech) (macOS say)
11 Audio Editing (Audacity)
12 DTM (desk-top music), BGM (background music) (GarageBand)
13 Collision Detection & Rigid Body Physics
14 Group Project Presentations

アートフォームに関して含むもの
  アニメーション:イラスト、キャラクターデザイン、モデリング、イメージや動きを組み合わせた図
  ドラマチックライティング; 戯曲やストーリーテリングの脚本
  グラフィクデザイン:2次元情報の提示
  インターラクティブデザインとゲーム開発:エンターテイメントコンピューティングとリッチメディアの開発
  モーションメディア:アバターやオブジェクトの振付け
  彫刻:3次元モデリング
  シーケンシャルアート:効果的な物語の絵コンテ、映像
  テーマの決められたエンターテイメント:仮想現実のデザイン、
  視覚効果:クラフト幻想
教科書
/Textbook(s)
教師による授業、TAとSA.

学生は授業中にアナグリフとクロマステレオスコピック用の立体視用めがねを購入する必要があります(¥1,000)。
Google Cardboard (¥500)。
また、4GB USBメモリースティック(Type A)を購入する必要もあります(¥500)。
成績評価の方法・基準
/Grading method/criteria
必要な技術が身に付いているかを毎週『チェックポイント』課題で、発見やデザインの確認をします。シナリオ製作と絵コンテ製作、絵描きと色塗り、カラーモデルと仕様、デジタル合成(レイヤー・オーバレイ・テクスチャマッピング)、立体視(アナグリフ・クロマステレオグラム)、SFXを用いた音声編集、TTSによる音声との対話、BGM用のデスクトップ音楽の合成を通じて、仮想世界とストーリーを徐々に製作していきます。クリエイティブスタジオでの課題と時折クイズ、試験があります。クロマステレオスコピックアートコンテストを開催し、優秀な作品は大学図書館に展示されます。また、「Unity」を用いてシナリオ、動画、ゲームなどの独創的な作品を製作します。中間試験は個人製作、期末試験ではチームで製作し、スペシャルレビューセッションで授業中に発表します。

演習、クイズ: 35
試験: 25%
個別プロジェクト: 20%
グループ プロジェクト: 20%
履修上の留意点
/Note for course registration
This course is in conjunction with undergraduate school course IT06: Human Interface & Virtual Reality; ヒューマン インターフェイスと仮想現実年.
Students who passed this course as an undergraduate are ineligible to register for ITA33.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
Related web pages:
course home page: http://web-int.u-aizu.ac.jp/~mcohen/welcome/courses/AizuDai/undergraduate/IT06 HI&VR
PhotoBooth photo capture: https://support.apple.com/ja-jp/guide/photo-booth/welcome/mac
Photos photo manipulation: https://www.apple.com/jp/macos/photos/
macOS "say" TTS (text-to-speech) utility: http://developer.apple.com/library/mac/#documentation/Darwin/Reference/ManPages/man1/say.1.html
Audacity audio editor: http://audacity.sourceforge.net/?lang=ja
Photopea image editor: https://www.photopea.com
Google Cardboard: https://arvr.google.com/cardboard/?hl=ja, https://developers.google.com/cardboard?hl=ja, https://ja.wikipedia.org/wiki/Google_Cardboard
GarageBand DTM (desk-top music) composition application: https://www.apple.com/jp/mac/garageband/
Chromastereoptic stereo system: https://www.chromatek.com
Unity: https://unity.com/ja
Unity チュートリアル: https://unity.com/jp/learn/tutorials
Blender: https://www.blender.org (https://blender.jp)

Prof. Cohen has several years of industrial experience related to the course contents.
Besides appointments to academic positions at the University of Washington (Industrial Engineering Dept.) and the African University of Technology (Computer Science Dept.) teaching these topics,
he has worked in industry on virtual environments, especially audio interfaces, at Bellcore (Bell Communications Research),
doing R&D of synchronous and asynchronous network services, including hypermedia collaborative tools and TTS,
and at the NTT Human Interface Laboratories,
researching stereotelephony, digital typography, hypermedia, and groupware, and visual languages.
Prof. Villegas has practical working experience. He worked for the Productivity National Center in Colombia and as a private consultant for five years. He was involved in the development of web-based industry productivity solutions.


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
マルコフ コンスタンティン
担当教員名
/Instructor
マルコフ コンスタンティン
推奨トラック
/Recommended track
先修科目
/Essential courses
This course is given in English.
更新日/Last updated on 2022/01/28
授業の概要
/Course outline
Machine learning is one of the fastest-growing and most exciting fields of AI, and deep learning represents its true bleeding edge. Deep Learning is one of the most highly sought after skills in IT industry. In this course, students will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to complete successful machine learning projects. It will teach students how to train and optimize basic neural networks (NN), Convolutional neural networks (CNN), Recurrent neural networks (RNN, LSTM), autoencoders (AE), etc. Complete learning systems will be introduced via projects and assignments.
授業の目的と到達目標
/Objectives and attainment
goals
Students will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as they solve these same problems effortlessly using deep learning methods. Students will master not only the theory, but also see how it is applied in practical case studies from various fields such as image recognition, music generation, natural language processing, etc.
授業スケジュール
/Class schedule
1. Introduction and Background.
- Course introduction.
- Basic probability theory and statistics.
2. Machine Learning and Neural Networks
- Machine Learning fundamentals.
- Neural Networks fundamentals.
3. Deep Neural Networks basics I.
- Training – Back Propagation.
- Regularization and Normalization.
4. Deep Neural Networks basics II.
- Loss functions, Optimizations.
5. Feed-Forward DNN Applications.
- DNN classification and regression.
6. Convolutional Neural Networks (CNN).
- Translation invariance.
- Templates and filters.
7. CNN Applications.
- CNN for vision  – VGG, Inception.
- CNN for signal and text processing.
8. Recurrent Neural Networks (RNN).
- LSTM, GRU variants.
- Sequence and time series data modeling with RNN.
9. RNN Applications.
- RNN in Natural Language Processing.
- RNN for sequence generation.
10. Sequence-to-Sequence models (Seq2Seq)
Attention mechanism.
Word embeddings.
Seq2Seq for Language Translation.
11. Autoencoders (AE)
Denoising AE.
Variational AE.
AE for Dimensionality Reduction.
12. Advanced DNN models.
Transformer, BERT, GPT-2.
13. DNN training strategies.
Tips and tricks.
14. Project discussion.
教科書
/Textbook(s)
I. Goodfellow,Y. Bengio and A. Courville, Deep Learning, MIT Press. Online version: http://www.deeplearningbook.org

T. Hope, Y. Resheff and I. Lieder, Learning Tensorflow: A Guide to Building Deep Learning Systems, Oreilly.

F. Chollet, Deep Learning With Python, Manning Pubs.
成績評価の方法・基準
/Grading method/criteria
Laboratory exercises: 60 points
Project: 40 points
履修上の留意点
/Note for course registration
As this is an intermediate to advanced level course, the following experience and skills are disirable:
- Programming experience (preferably in Python)
- Basic machine learning knowledge (especially supervised learning)
- Basic statistics knowledge (mean, variance, etc.)
- Linear algebra (vectors, matrices, etc.)
- Calculus (differentiation, integration, partial derivatives, etc.)

Prior to enrolling to this course, it is recommended (but not required) to take the following related courses:
- ITC12F Machine Learning
- CSA01 Neural Networks I: Fundamental Theory and Applications
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
https://elms.u-aizu.ac.jp/


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
イリチュ ピーター
担当教員名
/Instructor
イリチュ ピーター
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/28
授業の概要
/Course outline
In our technology-driven world, it is critical and timely to study the intersection of learning theory and ICT. This course will cover both learning theory and ICT in education particularly focusing on the intersection of the two. It will address the challenges faced by researchers and educators as new technologies transform the world of education. Students will gain the theoretical and practical knowledge invaluable for understanding education in our technology-driven world. We will discuss and reflect on the theory of learning and teaching practices and pedagogical approaches in relation to the opportunities afforded by ICT. It will be of particular interest to students who are interested in working on educational software development or teaching with technology.
授業の目的と到達目標
/Objectives and attainment
goals
1. Develop knowledge of Online Educational Technologies.
2. Develop understanding of key Learning Theories influencing educational technology.
3. Develop a critical understanding of the limits of online technologies in education.
授業スケジュール
/Class schedule
Section One:
1. Introduction to Learning Theory and ICT
        i. Homework: Reading/Quiz 1
2. History of Learning Theory and ICT
        i. Homework: Reading/Quiz 2

Section Two:
1. Behaviorist Learning Theory I
        i. Homework: Reading/Quiz 3
2. Behaviorist Learning Theory II
        i. Homework: Reading/Quiz 4

Section Three:
1. Cognitivist Learning Theory I
        i. Homework: Reading/Quiz 5
2. Cognitivist Learning Theory II
        i. Homework: Reading/Quiz 6

Section Four:
1. Constructivist Learning Theory I
        i. Homework: Reading/Quiz 7
2. Constructivist Learning Theory II
        i. Homework: Reading/Quiz 8

Section Five:
1. Connectivism/Collaborativist I
        i. Homework: Reading/Quiz 9
2. Connectivism II/Collaborativist II
        i. Homework: Reading/Quiz 10
教科書
/Textbook(s)
No textbook will be used. Course material will be made available on Moodle.
成績評価の方法・基準
/Grading method/criteria
1st Response Paper: 25%
2nd Response Paper: 25%
3rd Response Paper: 25%
Online Quizzes: 25%

Late assignments will lose 10% per day.
After 5 days, a late assignment will receive a mark of 0%.
履修上の留意点
/Note for course registration
In-class participation and attendance will be recorded.
Asking questions and responding to queries during classes is included in grading.
Good English language proficiency is expected.


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
ファヨール ピエール アラン
担当教員名
/Instructor
ファヨール ピエール アラン, 西舘 陽平
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/17
授業の概要
/Course outline
This course provides a description of the Java 2D API, the Java 3D API and the OpenGL API (via its Java bindings) for the development of graphics applications using the Java programming language. While going through the main functionalities of these APIs, we will study how they are implemented and as a consequence look at the same time at some of the main techniques in computer graphics (modeling and rendering techniques essentially).
授業の目的と到達目標
/Objectives and attainment
goals
The main objectives of the course are:
* The development of graphics programs in Java (using the Java 2D, Java 3D and OpenGL API)
* The study of the most common techniques in graphics programming (modeling and rendering techniques mostly)
* The study of some of the implementation details of these APIs
授業スケジュール
/Class schedule
1) Course introduction, Projects description, Java 2D introduction, and Java 2D geometry
2) Java 2D painting and stroking
3) Java 2D rendering, Porter and Duff work on compositing, Text manipulation with Java 2D
4) Java 2D image and image processing; Procedural modeling
5) Interlude: Ray-tracing, sphere-tracing (SDF rendering)
6) Prelude: Polygon mesh processing and the polygon rendering pipeline
7) OpenGL bindings (core mode; shaders)
8) Project 1 presentations
9) OpenGL bindings (continued)
10) Java 3D introduction, scene-graph
11) Java 3D shapes
12) Java 3D appearance, lights, illumination model and shading, texture mapping
13) Java 3D behaviors, and special behaviors
14) Project 2 presentations
教科書
/Textbook(s)
None.
Slides, notes and code are provided.
成績評価の方法・基準
/Grading method/criteria
Two projects: each of them is worth 50 points.
履修上の留意点
/Note for course registration
Knowledge of Java programming; Some basic knowledge of graphics programming (though we will go through and study some of the most common techniques in graphics programming during the course)
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
Course web-site (internal)
https://web-int.u-aizu.ac.jp/~fayolle/teaching/java_2d_3d/index.html


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
コーエン マイケル
担当教員名
/Instructor
コーエン マイケル, ヴィジェガス オロズコ ジュリアン アルベルト
推奨トラック
/Recommended track
先修科目
/Essential courses
(なし)
更新日/Last updated on 2022/01/31
授業の概要
/Course outline
We survey the physics and nature of sound waves (compression &
rarefaction, propagation, transmission, diffusion, diffraction, refraction,
spreading loss, absorption, boundary effects, non-point sources,
reflection, reverberation, superposition, beats & standing waves),
description and representation of sound (analog/digital, complex
analysis, waveforms, pulse code modulation, Fourier analysis),
measurements of sound and audio (sampling, aliasing, decibels,
pressure, power, intensity, level),
synthesis (additive, AM, FM, envelopes, filtering, equalizers,
spatialization, distortion),
human auditory system apprehension of sound (anatomy, physiology, psychology)
psychophysics (loudness, masking, critical bands),
coding and compression (SNR, A-law, u-law, MP3, AAC, parametric stereo),
display and multichannel ("discrete") systems (transducers, 5.1, speaker arrays, WFS),
tuning,
and user interfaces (conferencing, virtual concerts, mixed and virtual reality).
授業の目的と到達目標
/Objectives and attainment
goals
Demonstration-rich formal lectures interleaved with laboratory sessions
provide a rigorous, theoretical
background as well as practical experience regarding basic audio operations.
The university's exercise rooms feature multimedia workstations,
at which students can work individually or in teams
to explore concepts regarding sound and audio.
Interactive exercises, based on workstations and tablets, provide realtime
"hands-on" multimedia
educational opportunities that are stimulating and creative, as
students enjoy intuitive, experiential learning.
Utilized resources include
audio synthesis and multimedia data-flow visual programming (Pure Data),
audio editing and analysis software (Audacity),
interactive physics visualization and auralization physics applets (illustrating wave behavior, DSP, filtering, etc.),
advanced computational and plotting utilities (Mathematica),
effects processing (GarageBand),
and game design (Unity).
This course is intended to be useful to audio engineers and researchers, as well as musicians.
In other words, this course is about theory, simulation and practice:
playing with sound, learning by doing, and saying (instead of "see you") "hear you!"
Students who complete this course will be empowered with basic knowledge of sound and audio
and the confidence to apply those principals to generally encountered
situations in sound and audio engineering.
授業スケジュール
/Class schedule
1. Overview: Course organization, assessment, tablets & courseware, hearing anatomy and physiology, analog vs. digital
2. Hearing: auditory pathway, pinna, psychoacoustics & perception
3. Waves: waveforms, phase, complex numbers, logarithms, FFT & Spectrogram
4. Waves: pressure (compression & rarefaction), propagation, transmission, diffusion, diffraction, refraction, spreading loss
5. Waves: absorption, boundary effects, non-point sources, reflection, reverberation, superposition, beats & standing waves
6. Frequency: tone, register, harmony
7. Harmonic content: harmonics, overtones, timbre, Fourier analysis, sampling theorem, aliasing, AM & FM
8. Musical Frequency: intervals, tone, semi-tone, pitch, octaves, scales
9. Intensity: volume, loudness, PCM
10. Intensity: pressure, power, envelope, RMS, decibels, level, masking
11. Multichannel: stereo, speaker arrays, spatialization
12. Applications; coding & compression, digital recording and audio editing, filtering, equalizers, speech synthesis
13. Time: duration, tempo, repetition, reversal, duty cycle, rhythm & cadence
14. Music: DTM composition, audio effects
教科書
/Textbook(s)
William M. Hartmann: "Principles of Musical Acoustics" ("PMA"), Kindle edition.
Eric Heller: "Why You Hear what You Hear" ("WYHWYH"), Kindle edition.
Charles E. Speaks: "Introduction to Sound" (ISBN 1-56593-979-4) (http://www.delmarlearning.com/browse_product_detail.aspx?catid=1258&isbn=1565939794, http://www.amazon.com/Introduction-Sound-Acoustics-Sciences-Singular/dp/1565939794/ref=sr_1_1?ie=UTF8&s=books&qid=1207619383&sr=1-1)
Hyperphysics: Sound & Hearing (http://hyperphysics.phy-astr.gsu.edu/hbase/sound/soucon.html)

Various materials prepared by the instructors.

Besides normal lectures and exercises, we use Apple iPad tablets (one lent to each student for the duration of the term) extensively for courseware and interactive projects.
A list of relevant apps is on the course home page.
成績評価の方法・基準
/Grading method/criteria
This course explores theoretical and practical approaches to sound, audio, and music, so is interesting to audio engineers and researchers as well as musicians.
Most of the coursework involves reading, homework exercises, and "hands on" lab projects. There are mid-term and final exams.

Evaluation: Homework problem sets (25%), lab exercises (25%), midterm exam (25%) and final exam (25%).
履修上の留意点
/Note for course registration
This course is a prerequisite for ITA10, "Spatial Hearing and Virtual 3-D Sound."
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
course homepage:
http://u-aizu.ac.jp/~mcohen/welcome/courses/AizuDai/graduate/ITC02A%20Sound%20&%20Audio/syllabus.html

Audacity audio editor: http://audacity.sourceforge.net/?lang=ja
GarageBand DTM (desk-top music) composition application: https://www.apple.com/jp/mac/garageband/
technical computing software Mathematica: https://www.wolfram.com/mathematica/index.php.ja


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
成瀬 継太郎
担当教員名
/Instructor
成瀬 継太郎, 渡部 有隆
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/28
授業の概要
/Course outline
この講義ではロボット工学を情報技術の面から学ぶ.このとき課題となるのはロボットプランニングである.本講義では,移動ロボットとアーム型ロボットを例題として,経路計画と制御について学ぶ.具体的には座標変換,運動学,ヤコビ行列である.またmatlabを使った演習により理解を深める.
授業の目的と到達目標
/Objectives and attainment
goals
この科目を履修した学生は以下のことができるようになる.
(A) 移動ロボットとアーム型ロボットの運動学とプランニング
(B) 移動型ロボットのアーム型ロボットの動力学とシミュレーション
(C) ロボットの環境認識と学習
授業スケジュール
/Class schedule
#1 序論と概要
#2 移動ロボット,座標変換,運動学
#3 演習
#4 アーム型ロボットの順運動学,DH法,演習
#5 演習
#6 アーム型ロボットの逆運動学,ヤコビ行列,演習
#7 演習
#8 移動ロボット,動力学,ロボットシミュレーション
#9 演習
#10 アーム型ロボット,動力学,ロボットシミュレーション
#11 演習
#12 学習と環境認識
#13 演習
#14 演習・まとめ
教科書
/Textbook(s)
なし,授業中に配布する.
成績評価の方法・基準
/Grading method/criteria
レポート(100%)
履修上の留意点
/Note for course registration
学部のロボット工学と自動制御を履修していることが望ましい
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
http://iplab.u-aizu.ac.jp/moodle/


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
成瀬 継太郎
担当教員名
/Instructor
成瀬 継太郎, 矢口 勇一
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/28
授業の概要
/Course outline
本科目では現代制御理論を学ぶ.具体的には,状態空間モデル,安定性,可制御性,可観測性,レギュレータ,オブザーバ,線形二次レギュレータによる最適制御を学ぶ.また演習により理解を深める.
授業の目的と到達目標
/Objectives and attainment
goals
この科目を受講した学生は以下ができるようになる.
(A) システムの状態空間表現
(B) システムの安定性,可制御性,可観測性の判定,リャプノフ関数
(C) システムの制御器(レギュレータ)の設計
(D) システムの観測器(オブザーバ)の設計.カルマンフィルタとパーティクルフィルタを含む
(E) 最適制御系の設計
(F) matlabによる設計シミュレーション
授業スケジュール
/Class schedule
#1 序論と概要
#2 微分方程式と状態空間モデル
#3 演習
#4 安定性,可制御性,レギュレータの設計
#5 演習
#6 可観測性,オブザーバの設計
#7 演習
#8 オブザーバ・レギュレータシステムの設計,最適制御
#9 演習
#10 離散時間カルマンフィルタ
#11 演習
#12 離散時間モンテカルロマンフィルタ
#13 演習
#14 演習・まとめ
教科書
/Textbook(s)
なし,必要な資料は授業中に配布する.
成績評価の方法・基準
/Grading method/criteria
レポート(100%)による
履修上の留意点
/Note for course registration
関連科目(必須ではない)
学部:ロボット工学と自動制御
大学院:advanced robotics
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
http://iplab.u-aizu.ac.jp/moodle/


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
趙 強福
担当教員名
/Instructor
趙 強福, 劉 勇, 矢口 勇一
推奨トラック
/Recommended track
先修科目
/Essential courses
- Probability and statistics (undergraduate course)
- Algorithms and data structures (undergraduate course)
- Artificial intelligence (undergraduate course)
更新日/Last updated on 2022/01/25
授業の概要
/Course outline
Learning ability is one of the most fundamental abilities for realizing
“intelligence”. A system with the learning ability can become more and more
efficient and/or effective for solving given problems. Briefly speaking,
machine learning is a research field for studying theories, methodologies,
and algorithms that enable computing machines to learn and to become
intelligent. So far, many approaches have been proposed in the literature
for machine learning; and multilayer perceptron, convolutional neural
network, Bayesian network, and decision tree are just a few examples. In
this course, we categorize many existing approaches into a few groups,
namely, learning based on distance, learning based on probability, learning
based on layered structures, and learning based on tree structures. We do
not intend to cover all aspects of machine learning in this single course.
Instead, we will focus on several most well-known and well-applied
approaches. We suppose that, before taking this course, the students have
already studied some fundamental courses related to machine learning, say,
“Artificial intelligence” for undergraduate school, “Introduction to neural
networks” for graduate school, and so on. To know more about machine
learning or AI in general, we recommend the students to take other related
courses. For example, in the graduate school, the students may also take
courses related to big-data analysis; ontology and semantic web;
information retrieval; meta-heuristics; and so on.
授業の目的と到達目標
/Objectives and attainment
goals
The main goal of this course is to study and understand the basic
concepts and mechanisms of several well-known and well-applied machine
learning approaches, including for example, k-means, self-organization;
Naïve Bayes classification; support vector machine; convolutional neural
network; deep auto-encoder; deep Boltzmann machine; Bayesian network;
decision tree, AdaBoost, random forest, etc. To reinforce the learned
knowledge, students will do some team projects in groups. Through these
projects, students will solve some real-life or synthesized problems using
some of the learned methods.
授業スケジュール
/Class schedule
Some contents given below might be changed/improved year by year based on the newest trends in this field.  

1 History of machine learning and artificial intelligence

- Case studies
  - Learn how to classify patterns
  - Learn how to make a decision
  - Learn how to estimate/predict the future
  - Learn how to solve a problem efficiently/effectively

2.     Pattern recognition: a brief review
- Feature space representation of patterns
- Feature extraction and feature selection
- Distance-based classification
  - NNC and k-NNC; Voronoi diagram
  - Various distance measures
- Cluster analysis
  - k-means, self-organization, and vector quantization

3.     Fundamentals of machine learning
- Formulation of machine learning
- Ill-posed problem and regularization
- Classification and regression
- Taxonomy of learning algorithms
  - Supervised, semi-supervised, and unsupervised learning
  - Parametric and non-parametric learning
  - Deterministic and statistical learning
  - Online and off line learning
  - Evolutionary learning
  - Reinforcement learning

4.     Statistical learning methods-1
- Naïve Bayes classification
- Parzen widow

5.     Statistical learning methods-2
- Bayesian network

6.     Project I:
- Try one of the methods studied in the previous lectures.
- Using at least two public datasets to verify the performance of the
methods.
- Try to evaluate the performance using k-fold cross validation (k>5).

7.     Presentation of Project I
- Each team makes a 10 minutes presentation
- Evaluation will be based on the correctness, novelty, and understandability.

8.     Learning based on layered structures-1
- Multilayer perceptron

9.  Learning based on layered structures-2
- Convolutional neural network
- Deep auto-encoder

10.  Learning based on layered structures-3
- Methods for improving the performance of deep neural networks

11. Learning based on layered structure-4
- Restricted Boltzmann machine

12.  Learning based on tree structures-1
- Decision trees
- Multi-variate decision trees
- Decision tree ensembles (forests)

13.  Project II:
- Try one of the methods studied in lectures 8-12 for finding a “pattern
classifier”.
- Using at least two public datasets to verify the performance of the
method.
- Try to evaluate the performance using k-fold cross validation (k=3 or 5).

14.  Presentation of Project II
- Each team makes a 10 minutes presentation
- Evaluation will be based on the correctness, novelty, and understandability.
教科書
/Textbook(s)
There is no textbook. We will distribute reading materials in the classes.
成績評価の方法・基準
/Grading method/criteria
- Quiz: 20 points
- Project presentations and reports: 80 points
- Active Participation will also be considered in evaluation
履修上の留意点
/Note for course registration
This is a fundamental course related to machine learning. In this course, we focus on basic theories and methodologies so that students can, after taking this course, understand better about the basic ideas behind existing learning models and algorithms, and have better chance to propose their own models or algorithms. Students who are more interested in programming, or who want to learn how to use some open source programs, may take course like "ITA34 Practical Deep Learning".
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
1.     Machine learning, Tom M. Mitchell, McGraw-Hill, 1997.

2.     Machine learning: a probabilistic perspective, Kevin P. Murphy, The
MIT Press, 2012.

3.     Machine learning and deep learning, Tomohiro Odaka, Ohmsha, 2016.
(in Japanese)

4.     Introduction to Bayesian network, Kazuo Shigemasu, Maomi Ueno, and
Yoichi Motomura, Baifukan, 2007.


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
朱 欣
担当教員名
/Instructor
朱 欣, 陳 文西
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/19
授業の概要
/Course outline
Bioinformatics is to implement information technology to the research of molecular biology for the analysis of DNA, RNA, protein, and metabolism. Recent applications have been extended to system biology, drug design, and personalized medicine of cancer therapy. Due to the huge exponentially increasing number of DNA sequence data, it is urgent to train experts and engineers, who are familiar with the basic knowledge, analysis methods, and software tools of bioinformatics. In this course, students will learn the mathematical and biological basis of bioinformatics, genetic analysis and database search, gene discovery, and applications of informatics.
授業の目的と到達目標
/Objectives and attainment
goals
The goal is to train students to master the mathematical and biological basis of bioinformatics, the basic algorithms for nucleotide and protein sequence analysis, genetic database search and analysis, and the commonly used software and internet tools of bioinformatics. This year, we will perform an implementation study on Covid-2019 virus and its variants using the skills learned in this course.
授業スケジュール
/Class schedule
1. Biological basis: Cell structure and function, DNA, RNA, and protein
2. Basis of probability and statistics: Probability basis, Bayes’ theorem, probability distribution, histogram, regression, correlation coefficient, t test, and etc
3. Basis of Pattern recognition: Linear classification, Bayes classification, principal component analysis, Hidden Markov models and support vector machine
4. Basis of Data mining: Data preprocessing, mining frequent patterns, associations, and correlations, classification and prediction, and cluster analysis
5. Molecular biology database: DNA/Protein database, Genome database, motif-domain database, data retrieval, and data search
6. Sequence and genetic analysis: Pairwise alignment, multiple alignment, and BLAST/PSI-BLAST, FASTA
7. Gene discovery and data analysis: Microarray, cluster analysis
8. Genome analysis and genome medicine: Molecular phylogenetic tree: algorithm and application
9. Protein structure and prediction: 1st~4th Protein structure, PDB data, homologous protein
10. Computational chemistry: Molecular dynamics, force field, computer software, and etc
11. Special lecture by outside specialist
12. System biology and medicine: Application of genome research in genetic diseases: diagnosis and therapy
教科書
/Textbook(s)
はじめてのバイオインフォマティクス 編者: 藤博幸 講談社
Handout will be distributed in class.
成績評価の方法・基準
/Grading method/criteria
Homework 60%
Project 40%
履修上の留意点
/Note for course registration
Probability and statistics
Physics and chemistry
Database and network
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
東京大学 バイオインフォマティクス集中講義 監修: 高木 利久
バイオインフォマティクス事典 日本バイオインフォマティクス学会編集
日本バイオインフォマティクス学会 (http://www.jsbi.org/)
バイオインフォマティクス技術者認定試験(http://www.jsbi.org/nintei/)

The course instructor Xin Zhu has practical working experiences. He had performed research in biomedical engineering at Tianjin University for 7 years, and has performed related research at the University of Aizu for 15 years with the financial support from universities and JSPS. He has also received a certificate in online bioinformatics lectures. Based on his experiences, he can teach the basics of bioinformatics.


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
陳 文西
担当教員名
/Instructor
陳 文西, 朱 欣
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/26
授業の概要
/Course outline
Biosignals cover a wide spectrum of physiological information in time and frequency domains. Various modalities using diversified physical and chemical principles are applied in biosignal detection.
This course will provide introductory knowledge on the methodologies for detecting various physiological information, mention some aspects in biomedical instrumentation that differ from industrial measurement, and introduce application of IoT, AI, big data analytics and the latest advancements in seamless healthcare monitoring briefly.
授業の目的と到達目標
/Objectives and attainment
goals
1. To understand fundamental features and behaviors of various biosignals.
2. To understand application of fundamental physical and chemical principles in detecting various biosignals.
3. To understand the requirements in biosignal detection that differ from industrial measurements in some aspects.
4. To understand application of IoT, AI, big data analytics and the latest advancements in seamless healthcare monitoring.
授業スケジュール
/Class schedule
1. Introduction
2. Motion & Force
3. Direct Pressure
4. Indirect Pressure
5. Direct Flow
6. Indirect Flow
7. Respiration
8. Body Temperature
9. Bioelectricity
10. Biomagnetism
11. Biochemistry-1
12. Biochemistry-2
13. Biochemistry-3
14. Seamless Monitoring
教科書
/Textbook(s)
 Biomedical Sensors and Instruments, 2nd edition, Tatsuo Togawa et al., CRC Press, ISBN: 9781420090789, Publication Date: March 22, 2011
https://www.crcpress.com/Biomedical-Sensors-and-Instruments/Tagawa-Tamura-Oberg/p/book/9781420090789

 Seamless Healthcare Monitoring - Advancements in Wearable, Attachable, and Invisible Devices, Editors: Tamura, Toshiyo, Chen, Wenxi, Springer International Publishing, 2018, DOI 10.1007/978-3-319-69362-0, eBook ISBN 978-3-319-69362-0, Hardcover ISBN 978-3-319-69361-3
https://www.springer.com/us/book/9783319693613
成績評価の方法・基準
/Grading method/criteria
Paper survey and study report, 100%
履修上の留意点
/Note for course registration
Physics and chemistry
Electricity and electronics
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
The course instructor has practical working experience and has worked for 5 years at Nihon Kohden Industrial Corp., a professional manufacturer of world famous medical equipment, and has been engaged in R&D for bioinstrumentation, signal processing and data analysis. Based on this experience, he will teach the basic knowledge and latest technology in “Introduction to Biosignal Detection”.

Moodle for Handouts
https://elms.u-aizu.ac.jp/login/index.php

Course Website
http://i-health.u-aizu.ac.jp/IBSD/


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
平田 成
担当教員名
/Instructor
出村 裕英, 平田 成
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/28
授業の概要
/Course outline
リモートセンシングとは,広義には対象物の状態を遠隔から測定する手法のことを指す.多くの場合,光を含む電磁波が計測手段として用いられる.また,狭義には人工衛星などの宇宙機や航空機を,センサを搭載するプラットフォームとして,地球や他の天体を観測することを指す.
本科目では,まずリモートセンシング技術の多様な側面について概要を述べる.次いで,宇宙機によるリモートセンシングを題材として,データの取得から解析,解釈に至る過程を段階を追って詳説する.科学的に有用な測定のためには,その背景となる数学的知識や物理学的現象の理解も重要となるため,これらについても本科目で取り扱う.
授業の目的と到達目標
/Objectives and attainment
goals
リモートセンシングの概念,特徴,有用性を理解する.
リモートセンシングデータの取得,解析,解釈に関わるコンピュータ理工学の知識・技術を習得する.
また,関連する数学・物理学の知識を得る.
授業スケジュール
/Class schedule
1 ガイダンス
2 リモートセンシング概論
3-4 リモートセンシングに関わる光学,電磁気学的背景  
5-6 リモートセンシングプラットフォームとセンサ
7-8 リモートセンシングデータの特徴
9 リモートセンシングデータの放射量補正
10 リモートセンシングデータの幾何補正
11 マルチバンド画像解析
12 地理情報システム
13 合成開口レーダー
14 測位システム(GPS)
教科書
/Textbook(s)
N/A
成績評価の方法・基準
/Grading method/criteria
レポート,実習課題,授業中の質疑内容等により評価を行う
レポート・実習課題:80%
授業中の質疑内容等:20%
履修上の留意点
/Note for course registration
以下の内容を理解,習熟していることが望ましい.
基礎物理,微積分,線形代数,画像処理,
コンピュータグラフィックス.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
基礎からわかるリモートセンシング, 日本リモートセンシング学会(編), 2011
https://www.amazon.co.jp/dp/4844607790
Image Processing and GIS for Remote Sensing: Techniques and Applications, Liu and Mason, 2016
https://www.amazon.co.jp/dp/1118724208/


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
平田 成
担当教員名
/Instructor
平田 成, 大竹 真紀子, 出村 裕英
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/28
授業の概要
/Course outline
月惑星探査によって取得されたデータの解析にあたって基礎となる知識について学ぶ.探査機の科学観測データを取り扱う際には,探査機位置・姿勢情報等を含む補助データについての理解も必要不可欠となる.このため,まず補助データの利用方法について講義と実習を行う.
授業の目的と到達目標
/Objectives and attainment
goals
本講義の履修により,月惑星探査ミッションにおけるデータの解析手法を学び,それを実現するためのソフトウエア開発の基礎を習得する.また,NASAが開発したSPICE toolkitを用いた補助データの取り扱いを理解する
授業スケジュール
/Class schedule
- 第一週
  - イントロダクション
- 第二週
  - 補助データとSPICE toolkitの概要
  - 時刻情報
- 第三週
  - 座標系
  - 軌道・位置情報
- 第四週
  - 座標系の変換
- 第五週
  - 探査機の姿勢情報
- 第六週
  - 天体の形状モデル
- 第七週
  - 天文暦とSPICE toolkit
教科書
/Textbook(s)
N/A
成績評価の方法・基準
/Grading method/criteria
レポート,実習課題,授業中の質疑内容等により評価を行う
レポート・実習課題:80%
授業中の質疑内容等:20%
履修上の留意点
/Note for course registration
リモートセンシングの基礎知識(ITC08Aで取り扱う)を理解していることが望ましい.
ITC10A Practical Data Analysis with Lunar and Planetary Database は本コースの内容と強い関連を持つ.ITC10Aでは実践的な探査データの解析に関するトピックを取り上げるため,先にITC09Aを履修したのち,ITC10Aを履修することが望ましい.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
SPICE toolkit: http://naif.jpl.nasa.gov/naif/
Planetary Data System: http://pds.jpl.nasa.gov/
SELENE (Kaguya) Data archive: http://l2db.selene.darts.isas.jaxa.jp/
Hayabusa project science data archive: http://darts.isas.jaxa.jp/planet/project/hayabusa/


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
出村 裕英
担当教員名
/Instructor
出村 裕英, 平田 成, 小川 佳子, 本田 親寿, 北里 宏平, JAXA/NAOJ講師, 大竹 真紀子
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/31
授業の概要
/Course outline
This course is a combination of advanced lectures and exercises according to practical data analysis and tool-development in lunar and planetary explorations based on the antecedent course "Fundamental Data Analysis in Lunar and Planetary Explorations". This course follows an omnibus form given by ARC-Space professors and invited lecturers (teleclasses) from JAXA, NAOJ, etc.
授業の目的と到達目標
/Objectives and attainment
goals
To learn data analysis and making tools for the analysis from a viewpoint of remote sensing in lunar and planetary explorations
To learn basic knowledge in space developments as topics of computer science and engineering.
授業スケジュール
/Class schedule
#0 Demura (UoA) Guidance
#1-7 Omnibus Style by...
RAGE (UoA) RasterMiner as a GIS
HONDA (UoA) Performance Test of imaging sensors
KITAZATO (UoA) Spectroscopic Analysis for asteroids
OHTAKE (UoA) Kaguya Data Analysis of the moon for multi band images
OGAWA (UoA) Spectroscopic Analysis for lunar and planets
MATSUMOTO (NAOJ) Gravity field of the Moon
MOROTA (Tokyo Univ.) Crater Chronology
教科書
/Textbook(s)
N/A
成績評価の方法・基準
/Grading method/criteria
Comprehensive evaluation based on class activities (presentations, Q&A) and reports for each professor
履修上の留意点
/Note for course registration
Related courses:
ITC08A "Remote Sensing"
ITC09A "Fundamental Data Analysis in Lunar and Planetary Explorations"
ITA19 "Reliable System for Lunar and Planetary Explorations"
SEA11 "Software Engineering for Space Programs"
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
The course instructors has working experiences: Instructors are familiar with JAXA Space Development Projects.


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
西村 憲
担当教員名
/Instructor
西村 憲, 高橋 成雄
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/25
授業の概要
/Course outline
This course provides fundamentals of 3D Computer Graphics (CG) and its hardware implementation, which is followed by the recent advancement of CG rendering techniques with GPUs.
授業の目的と到達目標
/Objectives and attainment
goals
Through this course, students are expected to acquire fundamental knowledge about rendering algorithms and their parallelization techniques.  Students will also be able to obtain basic skills of GPU programming with the OpenGL Shading Language.
授業スケジュール
/Class schedule
1. Introduction
2. Shape Modeling
3. Geometry Calculation
4. Rasterization
5. Lighting and Shading
6. Texture Mapping and Shadowing
7. Exercise: Fundamentals of Shader Programming
8. Exercise: GPU-based Texture Mapping
9. Advanced Rendering Techniques
10. Volume Rendering
11. Exercise: GPU-based Lighting and Shading
12. Exercise: GPU-based Normal Mapping
13. Exercise: GPU-based Shadowing
14. Assignment Presentation
教科書
/Textbook(s)
* J. D. Foley, A. van Dam, Computer Graphics, 2nd edition, 1995.
* T. Sagishima, T. Nishizawa, and S. Asahara, Parallel Processing for Computer Graphics (in Japanese), Corona Publishing, 1991.
* OpenGL Tutorial (http://www.opengl-tutorial.org/)
* Handouts
* Selected journal/conference papers
成績評価の方法・基準
/Grading method/criteria
Presentation (75%), Reports (25%)
履修上の留意点
/Note for course registration
Prerequisites in the case when undergraduate students take this course:
   IT02: Computer Graphics  
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
http://web-int.u-aizu.ac.jp/~nisim/cg_gpu/


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
白 寅天
担当教員名
/Instructor
白 寅天, 大藤 建太, ラゲ ウダイ キラン
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/28
授業の概要
/Course outline
Recently, there have been very large and complex data sets from nature, sensors, social networks, enterprises increasingly based on high speed computers and networks together.
Big data is the term for a collection of the data sets that it becomes difficult to process using on-hand database management tools or traditional data processing applications.
Data science is a novel term that is often used interchangeably with competitive intelligence or business analytics, and it seeks to use all available and relevant data to effectively tell a story that can be easily understood by non-practitioners.
Data science based on the big data is expected to provide very potent prediction and analysis for information and knowledge of various fields of researches and businesses from the new data set.
Main objective of this course is to build up business viewpoints and target to use the big data, to learn technologies and skills to accomplish the business target.
Business targeting and modeling, decision making, data science process, database for big data, statistical analysis, data mining,
and how to use the technologies to achieve the business goal will be studied in detail.
授業の目的と到達目標
/Objectives and attainment
goals
In this course, introductory knowledge and skill for big data analysis process and technology will be covered.
In detail, CRISP-DM for data analysis process, Hadoop and Spark platform for big data infrastructure, statistical analysis and several machine learning techniques for data analysis, and deep learning for data analysis will be studied by lecture and exercise.
Students can have broad and necessary knowledge and technique for data analysis on big data infrastructure.
授業スケジュール
/Class schedule
1. Data (Analysis/Science/Engineering) Process
2. A Scenario of Business Analysis with Data Science Process
3. Big Data Infrastructure (Hadoop & Apache Spark)
4. Big Data Analysis and Deep Learning
5. Statistical Analysis 1 (Linear Regression)
6. Statistical Analysis 2 (Multivariate Analysis - PCA, FA)
7. Statistical Analysis 3 (Statistical Tests)
8. Statistical Analysis 4 (Wrap up )
9. Data Mining 1
10. Data Mining 2
11. Data Mining 3
12. Data Mining 4
13. Exercise(Statics, Spark&DM) #1
14. Exercise(Statics, Spark&DM) #2
15. Examination
教科書
/Textbook(s)
Lecture Slide: Will be provided on the lecture Web site

成績評価の方法・基準
/Grading method/criteria
Examination  -----       50 %
Exercise LAB (Including Term Project, Attendance)  -----------------    50 %

履修上の留意点
/Note for course registration
* Prerequisites:
For exercise, students should have skill and basic knowledge for the below:
- JAVA & Python Programming
- Machine Learning and Data Mining Basics
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
Reference:
1. Tom White, Hadoop, OREILLLY, 2011
2. Srinath Perera, Thilina Gunarathne, Hadoop Map-Reduce Programming, Packt Publishing, 2013
3. J.H Jeong, Biginning Hadoop Programming: Development and Operations, Wiki Books, 2012
4. Tan, Steinbach & Kumar,Introduction to data mining", Pearson Intrnational Edition, 2006
5. Tensorflow, https://www.tensorflow.org/





科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1年 , 2年
単位数
/Credits
2.0
責任者
/Coordinator
ラゲ ウダイ キラン
担当教員名
/Instructor
ラゲ ウダイ キラン
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/02/01
授業の概要
/Course outline
At its core, data science is a field of study that aims to use a scientific approach to extract meaning and insights from data. Machine learning, on the other hand, refers to a group of techniques used by data scientists that allow computers to learn from data. Data science and machine learning are both very popular buzzwords today. These two terms are often thrown around together but should not be mistaken for synonyms. Although data science includes machine learning, it is a vast field with many different tools.

Briefly, this course covers  the following topics: (1) Methods to store the raw data in database systems, (2) Processing (or ETL) techniques to handle voluminous data, (3) classic and recent data mining techniques to discover knowledge from databases, and (4) recent deep learning techniques (especially, fuzzy and graph-based deep learning techniques) published in top computer science conferences.
授業の目的と到達目標
/Objectives and attainment
goals
Data science is a very competitive and highly paid job. This course aims to increase the competitiveness of the students by achieving the following objectives:
1) To provide know-how on how to extract, store, and process voluminous data needed for the analytical purposes
2) Empowering the students in choosing the right learning algorithm for this task by providing the knowledge on the strengths and weakness of various data mining and deep learning
授業スケジュール
/Class schedule
Lecture topics:
1. Introduction to Data types
2. Introduction to ER-Schema and Relational Schema
3. PostGres and HBASE databases
4. Advance topics in ETL (Extraction, Transformation, and Load)
5. Data Science vs. Machine Learning (Common and distinct features)
6. Basic Data Science Techniques
7. Association Rule Mining, Fuzzy Rules, and Utility Rules
8. Clustering
9. Classification and Prediction
10. Dimensionality reduction using PCA and Tensors
11. Recent Data Science topics published in top Conferences (KDD, PKDD, PAKDD, IEEE BIGDATA)
12. Recent Deep-Learning topics covering publications in top conferences
13. Exercise class-1
14. Exercise class-2
教科書
/Textbook(s)
Data Mining: Concepts and Techniques  Book by Han  et al.  (Springer)
Periodic Pattern Mining by Uday et al. (Springer)
成績評価の方法・基準
/Grading method/criteria
Students will be graded based on the project and the final exam. The project carries 50% of weightage, 10% of weightage to classroom exercises, 25% of weightage of exercises, and the final exam has 15% of weightage.

In the project, a research article will be assigned to each student. Students have to write their code for an algorithm presented in the report.


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