AY 2017 Graduate School Course Catalog

Field of Study IT: Applied Information Technologies

2018/01/30

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開講学期
/Semester
2017年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Pierre-Alain Fayolle
担当教員名
/Instructor
Pierre-Alain Fayolle, Yohei Nishidate
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/23
授業の概要
/Course outline
This course focuses on practical issues of using Java 2D and Java 3D APIs for creating 2D and 3D graphics, virtual models and animations. Using Java 2D/3D for data visualization is discussed.
授業の目的と到達目標
/Objectives and attainment
goals
The course helps students to understand usage of Java 2D and Java 3D APIs and to gain practical skills in creating graphics applications in Java.
授業スケジュール
/Class schedule
1. Introduction. Review of Java 2D API.  
2. Graphic primitives.  
3. Painting and stroking.  
4. Transforming. Compositing. Clipping. Rendering Hints.  
5. Text and fonts.  
6. Images.  
7. Image filtering. Printing.  
8. Review of Java 3D API.  
9. Scene graph.  
10. Graphic primitives. Mathematical classes.  
11. Geometry classes.  
12. Appearance. Attributes. Material.  
13. Textures.  
14. Lights. Text3D.  
15. Interaction with the user. Behavior.  
16. Animations. Alpha and Interpolator classes. Morphing.
教科書
/Textbook(s)
1. Lecture notes
2. J.Knudsen, Java 2D Graphics. O'Reilly, 1999, 339 pp.
3. A.E. Walsh and D. Gehringer, Java 3D API Jump-Start. Prentice Hall, 2002, 245 pp.
4. D.Selman, Java 3D Programming, Manning, 2002, 376 pp
成績評価の方法・基準
/Grading method/criteria
Home task on Java 2D - 45%
Home task on Java 3D - 45%
Attendance - 10%
履修上の留意点
/Note for course registration
Prerequisites:
Java Programming; Computer Graphics.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
course web-site (internal)


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開講学期
/Semester
2017年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
3.0
責任者
/Coordinator
Michael Cohen
担当教員名
/Instructor
Michael Cohen, Julian Villegas
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/23
授業の概要
/Course outline
It is important to exploit sound as a full partner in computer-human interfaces,
and developing this potential motivates exploring analogs to visual
modes of expression and also developing expressive models unique to audio.
This course simultaneously explores issues in sound as well as tools available to manipulate audio.
授業の目的と到達目標
/Objectives and attainment
goals
The university's exercise rooms feature multimedia workstations,
at which students can work individually or in teams
to explore concepts regarding sound and audio.
Demonstration-rich formal lectures interleaved with laboratory sessions
provide a rigorous, theoretical
background as well as practical experience regarding basic audio operations.
Interactive exercises 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 our own web-based multimedia courseware.
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 about 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
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, loudness),
synthesis (additive, AM, FM, envelopes, filtering, equalizers, spatialization, distortion),
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).
教科書
/Textbook(s)
Introduction to Sound, by Charles E. Speaks (ISBN 1-56593-979-4)
Various materials prepared by the instructors.

Besides normal lectures and exercises, we'll also use iPads (one lent to each student for the term) extensively for courseware and interactive projects. A full list of relevant apps is listed on the course home page.
成績評価の方法・基準
/Grading method/criteria
This course is concerned not only aesthetic issues, but also with technical issues, and as such would be useful to audio engineers and researchers, as well as musicians. Most of the coursework involves reading, homework exercises, and lab projects. There are mid-term and final exams. Evaluation: Homework problem sets (25%), lab exercises (25%), midterm exam (25%) and final exam (25%). This course awards "half again" (1.5 x 2 = 3) credits compared to regular grad school classes because of its thrice weekly meetings and intensive laboratory component.
履修上の留意点
/Note for course registration
Basic math and physics. (No prior knowledge of audio techniques is assumed.)
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://www.u-aizu.ac.jp/~mcohen/welcome/courses/AizuDai/graduate/Sound+Audio/syllabus.html

invitation video:
http://www.youtube.com/watch?v=rVGuqf6NqR8&feature=c4-overview&list=UU6juLwkSyA0xPibKhRbHbcQ

courseware:
http://sonic.u-aizu.ac.jp

audio editing software Audacity:
http://audacity.sourceforge.net

audio effects processing software GarageBand:
http://www.apple.com/mac/garageband/

technical computing software Mathematica:
http://www.wolfram.com/mathematica

audio synthesis and multimedia data-flow visual programming Pure Data ("Pd"):
http://puredata.info

TTS (text-to-speech) software Apple OS X say:
http://developer.apple.com/library/mac/#documentation/Darwin/Reference/ManPages/man1/say.1.html


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開講学期
/Semester
2017年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Keitaro Naruse
担当教員名
/Instructor
Keitaro Naruse, Yutaka Watanobe
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/31
授業の概要
/Course outline
If we define a robot as a computer interacting with the real world physically, we use so many robots everyday such as elevators, cleaning robots, and so on. For designing, synthesizing, and analyzing robots, knowledge on how to represent robot structure and motion in computers are required, as well as one on sensors, actuators, modeling methods, and planning algorithms.
This course offers the introduction to robotics for graduate students in computer science and engineering major.
授業の目的と到達目標
/Objectives and attainment
goals
The course covers fundamentals on robotics such as mechanics, modeling and planning, and robotic intelligence, as well as discussing on current open issues remaining in information processing. After taking this course, the students are expected to be able to answer to the following questions. How are the robot motion and structure represented? What kind of system is needed for robot control and planning? What kind of intelligence are robots required? And so on.
授業スケジュール
/Class schedule
#1 introduction and overview
#2 robot arms: forward kinematics such as robot representation, frames and coordinate systems, homogeneous transformation, Denavit-Hartenberg method
#3 robot arms: inverse kinematics such as exact solution, numerical solution, Jacobian
#4 robot arms: dynamics such as deriving equations of motion, Lagrange method, Newton-Euler method, simulation methods
#5 mobile robots: kinematics, dynamics, non-holonomic constraints.
#6 robot and world representation such as workspaces, configuration spaces, cell decomposition, graph representation, artificial potential methods,
#7 planning of sequences: graph search methods, dynamic programming, reinforcement learning
#8 probabilistic planning: Markov decision process, partially observed Markov decision process, sensor based navigation such as sonar and laser range finder
#9 sensors and actuators
#10 robotics intelligence: behavior based planning
#11 robotic applications
#12 summary
教科書
/Textbook(s)
None. Related documents will be distributed in a class
成績評価の方法・基準
/Grading method/criteria
Reports on numerical experiments on forward and inverse kinematics, forward dynamics, learning, and so on. We will use Matlab or other mathematical software for them.
履修上の留意点
/Note for course registration
Introduction to robotics in the undergraduate course
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/


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開講学期
/Semester
2017年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Keitaro Naruse
担当教員名
/Instructor
Keitaro Naruse, Yuichi Yaguchi
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/31
授業の概要
/Course outline
This course is intended to introduce you to the mathematical foundations of the modern control theory. The aim of the course is to allow you to develop new skills and analytic tools required to analyze and design methods for the control of both linear and nonlinear dynamical systems.
授業の目的と到達目標
/Objectives and attainment
goals
The course covers fundamentals on the modern control theory using state vectors and system matrices. In the end of the course, the students will be able to use analytic tools to model and control a given physical system. Specifically, they can discuss how to determine if a given dynamical system is controllable and stabilizable. They can design state feedback controllers to change the evolution of a dynamical system. They can optimize the control system design to minimize the control energy spent or achieve control in minimum time.
授業スケジュール
/Class schedule
#1 Introduction and overview
#2 Motion representation method
- Equations of motions and differential equations
- Derivation of Equations of Motion: Lagrange method, Newton-Euler method
#3 Solution of equations of motion
- Mathematics: vectors, matrices, ranks, eigenvlaues and eigenvectors, eigenvalue decomposition, Jordan decomposition, norms
#4 System representations and solutions
- Solutions of continuous time systems, discrete time systems
- Solutions of time-variant systems, time-invariant systems
- Solutions of homogeneous systems and non-homogeneous systems
#5 Stability
- Linear system
- Lyapunov theorem
#6 Controllability and observability
#7 Observers and Kalman filters
#8 Regulators
#9 Optimal control
#11 Intelligent control
#12 Summary
教科書
/Textbook(s)
None. Related documets will be distributed in a class
成績評価の方法・基準
/Grading method/criteria
Reports on numerical experiments on control theory, which includes analyzing stability of a dynamical system, designing regulators, and so on.
履修上の留意点
/Note for course registration
Related courses:
Undergraduate: "Introduction to robotics"
Graduate: "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/


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開講学期
/Semester
2017年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Yuichi Yaguchi
担当教員名
/Instructor
Yuichi Yaguchi, Yen Neil Yuwen
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/27
授業の概要
/Course outline
This course provides a broad introduction to pattern recognition, machine learning, and related topics.
Topics include: Linear Models for Regression and Classification; Neural Network; Kernel Methods; Bayesian Decision Theory; Clustering and Classification; Feature extraction from signals and images; Sequence and Signal design and their applications;
Signal Processing and Image Processing for Instrumentation and Communications; Complex Event Processing and its Applications
Complex Event Processing. The course will also discuss some important applications of pattern recognition and machine
learning.
The methodology of mathematical modeilg will be discussed.
This course will provide the topics related with information geometry.
授業の目的と到達目標
/Objectives and attainment
goals
The objectives of this course is to provide fundamental concepts, theories, and algorithms for pattern recognition and machine learning, which are widely used in various kinds of fields.
授業スケジュール
/Class schedule
L1 COURSE OVERVIEW
Overview of topics of pattern classification and its applications will be presented. The overview of this course will be illustrated.

L2. Linear Models for Regression
This lecture introduces the models that fit a linear equation
to observed data between two variables. Least-squares estimation
and related techniques will be discussed.

L3. Linear Models for Classification
A linear classifier attempts to make a classification decision
on an object by the value of a liner combination of the object's
characteristics. Discriminative models, such as perceptron,
will be discussed.

L4. Neural Network
An artificial neural network is a mathematical model that consists
of an interconnected group of artificial neurons. Supervised
learning, such as back-propagation algorithm, will be
studied.

L5. Kernel Methods and Bayesian Decision Theory
Kernel methods refer to a class of pattern analysis algorithms
that map the data into a high dimensional feature space. Algorithms
including support vector machine and principal components analysis
will be introduced.
Bayesian decision making with both discrete probabilities
and continuous probabilities will be studied.


L6 CLUSTERING AND CLASSIFICATION
Classification is to assign objects to classes on the basis of measurements made on the objects, while clustering is to group a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Both centroid-based clustering and distribution-based clustering will be introduced.

L7 STRUCTURAL PATTERN RECOGNITION AND STATISTICAL ANALYSIS
Sub-topics include graph-theoretic methods, recognition with strings, and grammatical methods will be discussed. In addition to the fundamental theoretical framework of the topics, the statistical approaches for data analysis will be addressed.

L8 APPLICATIONS AND CASE STUDIES
State-of-the-art applications as well as cases related to the above-mentioned topics will be reviewed and discussed. Students are expected to learn why and how these applications are designed as well as for what purposes. This is especially for those students who work in the field of data mining, analysis, or management.
教科書
/Textbook(s)
Generally, we give slides on Moodle: http://hartman.u-aizu.ac.jp

Recommendation for reading: Christopher Bishop, "Pattern Recognition and Machine Learning", Springer
成績評価の方法・基準
/Grading method/criteria
We give four-research report for each 25 points until last class.
- Regression
- Classification
- Multi-layer Neural Network
- Support Vector Machine
履修上の留意点
/Note for course registration
None.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
Material for last year: http://hartman.u-aizu.ac.jp/course/view.php?id=6

Christopher Bishop, "Pattern Recognition and Machine Learning", Springer

2nd Edition (Amazon):
https://www.amazon.co.jp/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738
(Japanese):
https://www.amazon.co.jp/%E3%83%91%E3%82%BF%E3%83%BC%E3%83%B3%E8%AA%8D%E8%AD%98%E3%81%A8%E6%A9%9F%E6%A2%B0%E5%AD%A6%E7%BF%92-%E4%B8%8A-C-M-%E3%83%93%E3%82%B7%E3%83%A7%E3%83%83%E3%83%97/dp/4621061224/ref=pd_sim_14_7?_encoding=UTF8&psc=1&refRID=7CEBF665VGXN3NYYFNC1
https://www.amazon.co.jp/%E3%83%91%E3%82%BF%E3%83%BC%E3%83%B3%E8%AA%8D%E8%AD%98%E3%81%A8%E6%A9%9F%E6%A2%B0%E5%AD%A6%E7%BF%92-%E4%B8%8B-%E3%83%99%E3%82%A4%E3%82%BA%E7%90%86%E8%AB%96%E3%81%AB%E3%82%88%E3%82%8B%E7%B5%B1%E8%A8%88%E7%9A%84%E4%BA%88%E6%B8%AC-C-M-%E3%83%93%E3%82%B7%E3%83%A7%E3%83%83%E3%83%97/dp/4621061240/ref=pd_bxgy_14_img_2?_encoding=UTF8&psc=1&refRID=FYJJXRY056STM5DGNHE2

Old book (PDF):
http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf


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開講学期
/Semester
2017年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Xin Zhu
担当教員名
/Instructor
Xin Zhu, Wenxi Chen
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/30
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 family 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.
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
Attendance 20%
Homework 40%
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/)


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開講学期
/Semester
2017年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Wenxi Chen
担当教員名
/Instructor
Wenxi Chen, Xin Zhu
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/30
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, and especially highlight some aspects in
biomedical instrumentation that differ from industrial measurement.
授業の目的と到達目標
/Objectives and attainment
goals
1. To understand the fundamental knowledge on various physiological
information.
2. To understand the fundamental physical and chemical principles as well as
their application in detecting various physiological information.
3. To understand the reasons and requirements in biosignal detection that differ
from industrial measurements in some aspects.
授業スケジュール
/Class schedule
1. Introduction
2. Direct Pressure
3. Indirect Pressure
4. Direct Flow
5. Indirect Flow
6. Respiration
7. Motion & Force
8. Temperature
9. Bioelectricity
10. Biomagnetism
11. Biochemistry-1
12. Biochemistry-2
13. Biochemistry-3
14. Daily 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
成績評価の方法・基準
/Grading method/criteria
Research report and presentation
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.)
http://i-health.u-aizu.ac.jp/IBSD/


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開講学期
/Semester
2017年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Naru Hirata
担当教員名
/Instructor
Hirohide Demura, Naru Hirata
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/27
授業の概要
/Course outline
Generally, remote sensing refers to the activities of measurement the state of an object at far away. In many cases, electromagnetic waves including light are used as a means of sensing. In the narrower sense, remote sensing is observation of the Earth and other bodies with sensors on various platforms includes artifical satellites and airplanes.
This course outlines the wide aspects of remote sensing technology at first. Then, we will forcus on remote sensing by spacecraft. Detailed processes of data acquisition, reduction, analysis and interpretation of remote sensing data will be described.
Physical and mathematical knowledge is another topic of this course, because it is a important background to achieve scientifically practical measurement.
授業の目的と到達目標
/Objectives and attainment
goals
By the end of the course, Student will
- Understand the concepts, features and usefulness of remote sensing
- Acquire knowledge and skills of computer science and engineering related to acquisition, analysis and interpretation of remote sensing data
- Obtain relevant mathematics / physics knowledge.
授業スケジュール
/Class schedule
1 Introduction to Remote Sensing
2 Application examples of Remote Sensing
3 Platform and Sensor for Remote Sensing
4 Physical backgroud on Remote Sensing
5 Characteristics of Remote Sensing Data
6 Radiometric Calibration of Remote Sensing Data
7 Geometric Correction of Remote Sensing Data
8 Image Enhancement and Feature Extraction
9 Extraction of Spectral Information
10 Image Classification
11 Geographic Information System (GIS)
12 Synthetic Aperture Radar (SAR)
13 Global Positioning System (GPS)
教科書
/Textbook(s)
N/A
成績評価の方法・基準
/Grading method/criteria
Homework exercises and Discussion during lecture
履修上の留意点
/Note for course registration
Physics, Calculus, Linear Algebra, Image Processing, and Computer Graphics are recommended as prerequisites.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
Image Processing and GIS for Remote Sensing: Techniques and Applications, Liu and Mason, 2016
https://www.amazon.co.jp/dp/1118724208/


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開講学期
/Semester
2017年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Naru Hirata
担当教員名
/Instructor
Naru Hirata, Hirohide Demura, JAXA/NAOJ Lecturers
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/27
授業の概要
/Course outline
This course introduces fundamental knowledge on data analysis in lunar and planetary explorations. Ancillary information including spacecraft location and attitude is essential to handle data obtained by science instruments on board a spacecraft. We will study and exercise on handling and utilization of spacecraft ancillary data at first. Then we will investigate handling of image data that is one of the major data type obtained by exploration missions. This topic includes characteristics, data format, pre-processing, and scientific analyses of image data. Some parts of the lecture are given by guest lecturers from Japanese Space Exploration Agency (JAXA) and National Astronomical Observatory of Japan (NAOJ) via videoconference system.
授業の目的と到達目標
/Objectives and attainment
goals
By the end of the course, students will have learned basic technologies to analyze lunar and planetary exploration data and be able to develop tools or software for exploration data analysis.
Student will also gain knowledge of
  - Handling of ancillary information with SPICE toolkit developed by NASA
  - Characteristics, data format, pre-processing, and scientific analyses of exploration image data
授業スケジュール
/Class schedule
- Week 1
  - Introduction
- Week 2
  - Ancillary data and SPICE toolkit
  - Epoch information
- Week 3
  - Reference frame
  - Trajectory and Position of spacecraft
- Week 4
  - Conversion of refernce frame
  - Attitude of spacecraft
- Week 5
  - Finding astronomical events
- Week 6
  - Shape model
- Week 7
  - Characteristics of exploration image data
- Week 8
  - Scientific analyses of exploration image data
教科書
/Textbook(s)
N/A
成績評価の方法・基準
/Grading method/criteria
Homework exercises and Discussion during lecture
履修上の留意点
/Note for course registration
ITC08 Remote Sensing are recommended as prerequisites.
ITC10 Practical Data Analysis with Lunar and Planetary Database is closely connected with this course. ITC10 will introduce more practical topics on planetary data analyses. Students are recommended to finish ITC09 before taking ITC10.
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/


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開講学期
/Semester
2017年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Hirohide Demura
担当教員名
/Instructor
Hirohide Demura, Naru Hirata, Yoshiko Ogawa, Chikatoshi Honda, Kohei Kitazato, JAXA/NAOJ Lecturers
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/31
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
Omnibus Style

Classes in 2016
#1-2 Demura(UoA) Introduction, Phoclinometry and Hapke photometric function
#3-4 Kitazato (UoA) Spectroscopic Analysis
#5-6 Ogawa (UoA) Kaguya Data Analysis of the Moon with Spectrometer
#7-8 Honda (UoA) Verification of image sensors
#9-10 Morota (Nagoya Univ.) Crater Chronology of the Moon
#11-12 Matsumoto (NAOJ) Gravity field of the Moon
#13-14 Yamamoto  (NAOJ) Orbit analysis of satellites for space exploration
#15-16 Ohtake (JAXA) Kaguya Data Analysis of the Moon with Multiband Images (Camera)
教科書
/Textbook(s)
N/A
成績評価の方法・基準
/Grading method/criteria
Attendances (Presentations), Homeworks, or Reports every professors.
履修上の留意点
/Note for course registration
Related Courses
ITC08 Remote Sensing
ITC09 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.)
N/A


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開講学期
/Semester
2017年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Satoshi Nishimura
担当教員名
/Instructor
Satoshi Nishimura, Shigeo Takahashi
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/24
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. Advanced Rendering Techniques
8. Volume Rendering
9. GPU Architecture
10. Fundamentals of Shader Programming
11. GPU-Based Lighting and Shading
12. GPU-Based Texturing
13. GPU-Based Shadowing
14. GPU-Based Animation
15. 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
Attendance, Reports, Presentation
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/


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開講学期
/Semester
2017年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Julian Villegas
担当教員名
/Instructor
Julian Villegas, Michael Cohen
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/16
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 sound effects, so the course is intense in hands-on exercises, assignments, and projects mainly based on Matlab/Octave, C/C++, and Pure-data.
授業の目的と到達目標
/Objectives and attainment
goals
• Students who approve this course are expected to understand the basic techniques employed in computer music, as well as the literature and terminology on this topic.

• Students at the end of the term should be able to decide which of the presented techniques is best for creating a desired sound effect in music.

• Upon completion of this course, students should be able to create their own sound effect chain.
授業スケジュール
/Class schedule
1. Introductions: Course overview, materials, examination, introduction to computer music technologies,
2. FFT workshop: Spectrograms, long-term average spectrum, short-term average spectrum, etc.
3. Consonance origin: Tonotopic theory, Roughness, Roughness models, musical sounds
4. Consonance workshop Computing scales from spectra
5. Shepard Tones: Pitch as a complex sensation, chroma, pitch-height, circularity
6. Shepard Tone workshop: Create a parametric Shepard tone in Matlab
7. PSOLA and DTW: Changing pitch and speed without artifacts
8. PSOLA workshop: Time alignment of two audio signals
9. Beat Detection: Onset detection functions, linear programming, signal transformations
10. Beat Detection Workshop: Detecting the beat from different musical signals
11. Intro to Pd audio objects: Pure-data introductions, Pure-data structures, C compilation
12. Pd object workshop: Creating an audio processing object in Pd
13. Intro to Faust: Functional languages, Faust syntax, libraries overview
14. Faust Workshop: Creating an audio plugin in Faust
15. Intro. to sound and micro-controllers: Micro-controller vs. Micro-processors, audio hardware architecture, Arduino, etc.
16. Micro-controller: Workshop Creating a simple audio effect on a Arduino micro-controller.
教科書
/Textbook(s)
• U. Zolzer, 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
Quizzes and homework: 50%
Exercises (workshops): 50%
履修上の留意点
/Note for course registration
This class does not have prerequisites, but it is recommended that students be familiarized with Pure-data programming paradigm, and general audio signal processing techniques. These are some classes that students are encouraged to take before this class:

• ITC02 Introduction to Sound and Audio
• ITA07 Advanced Signal Processing
• ITA10 Spatial Hearing and Virtual 3D Sound
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
• Course website: http://onkyo.u-aizu.ac.jp/index.php/classes/music-tech/

• Theory and Techniques of Electronic Music (M. Puckette): http://msp.ucsd.edu/techniques.htm

• Julius Orion Smith III website: https://ccrma.stanford.edu/~jos/


• Matlab documentation: www.mathworks.com/help/matlab/


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開講学期
/Semester
2017年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Xin Zhu
担当教員名
/Instructor
Xin Zhu
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/30
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
Attendance              20%
Homework              40%
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


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開講学期
/Semester
2017年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Yohei Nishidate
担当教員名
/Instructor
Yohei Nishidate
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/30
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 algorithms. Displacement boundary conditions.
13. Solution of finite element equations.
14. Exercise 5.
15. Nonlinear Problems.
16. Visualization of finite element models and results.
教科書
/Textbook(s)
1. Lecture Notes
2. Gennadiy Nikishkov, Programming Finite Elements in Java. Springer, 2010, 402 pp.
成績評価の方法・基準
/Grading method/criteria
Exercises - 40%
Project - 40%
Attendance - 20%
履修上の留意点
/Note for course registration
Calculus, Linear Algebra, Numerical Analysis, and some programming courses are recommended as prerequisites.


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開講学期
/Semester
2017年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Yuichi Yaguchi
担当教員名
/Instructor
Yuichi Yaguchi
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/27
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 background 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
(Plan of Topic)
- Clustering
- Pattern Matching
- Segmentation
- Image Feature
- Understanding & Recognition
- Photo Bundle & Stereo
教科書
/Textbook(s)
Main Coursebook - Richard Szeliski, Computer Vision: Algorithms and Applications.
(Not need to buy this book, but very helphul 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 (Face detection, Bayesian Net, Clustering) and each report has 25~40 points.


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開講学期
/Semester
2017年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Jie Huang
担当教員名
/Instructor
Jie Huang
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/27
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
Attendance 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


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開講学期
/Semester
2017年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Jung-pil Shin
担当教員名
/Instructor
Jung-pil Shin
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/23
授業の概要
/Course outline
This course concerns the method for Document Analysis and Recognition. We will discuss on the advanced techniques of Document Analysis and Recognition and create the new idea based on this research theme. Especially, we focus on the current technologies related on the on-line/off-line recognition, analysis, HCI(human computer interaction), and its application.

授業の目的と到達目標
/Objectives and attainment
goals
At the completion of this course, students will be able to:
Have the overview of the Document Analysis and Recognition.
Be able to know how can be implemented for the programming of this area.
授業スケジュール
/Class schedule
Introduction to Document Analysis and Recognition(DAR).

Fundamentals on on-line recognition and off-line recognition.

Pattern recognition on DAR.

Current problems and solving methods of this area such as text recognition, handwriting recognition, HCI, and other applications:

- pen-based interactive system,

- oriental-pen writing/drawing simulation,

- handwritten font generation,

- signature verification, writer identification,

- handwritten gesture recognition using Wii remote controller, kinect, leap motion, oculus rift,

- designing of HCI experiment

- Modeling of HCI,

- smartphone system, other application system,

- the application and the relation to image recognition and computer vision,

The presentation of some application program.

Students' investigation work:
-Investigation, presentation and discussion about current techniques and producing the new idea.
-Programming related on Document Analysis and Recognition.
教科書
/Textbook(s)
Materials collected from books/papers of journal and proceeding which are selected and provided by the instructor.
成績評価の方法・基準
/Grading method/criteria
Investigation and presentation (40%)
Attendance and positive class participation (20%)
Programming project(40%)
履修上の留意点
/Note for course registration
Permission of the instructors.
Interest in the area of Document Analysis and Recognition.
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/DAR.html

References:

A. C. Downton, S. Impedovo, Progress in Handwriting Recognition, World Scientific; ISBN 981-02-3084-2 (Sep. 1996)
S.-W. Lee, Advances in Handwriting Recognition, World Scientific; ISBN 981-02-3715-4 (1999)
T. Pavlidis, Structural Pattern Recognition, Springer-Verlag; ISBN 3-540-08463-0 (1980)


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開講学期
/Semester
2017年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Julian Villegas
担当教員名
/Instructor
Julian Villegas, Michael Cohen, Jie Huang
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/16
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, humans 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) it 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 on practical implementations of binaural hearing techniques, so the course is rich in hands-on exercises, assignments, and projects, mainly based on the Pure-data programming language.
授業の目的と到達目標
/Objectives and attainment
goals
- Students who pass this course will understand the basic underlying mechanisms of spatial hearing, as well as the literature and terminology on this topic.
- Given some application constraints (real-time, computing power, etc.) students at the end of the term should be able to decide which of the presented techniques is best for creating the 3D aural illusion.
- Upon completion of this course, students should be able to successfully implement virtual 3D sound environments based on head-related transfer functions (HRTFs) and multi-speaker systems.
授業スケジュール
/Class schedule
Introductions and introduction to Pd
Quantification of sound
Spatial hearing and psychoacoustics
Binaural difference cues
(continuation)
Head-related impulse response and transfer function
Presentation of mid-term projects
Motion perception
(continuation)
Headphone techniques
(continuation)
Loudspeaker techniques
(continuation)
Applications
Presentation of final projects
教科書
/Textbook(s)
- Durand R. Begault, 3-D Sound for Virtual Reality and Multimedia, Academic Press, 2000. (online)
- J. Blauert, The Technology of Binaural Listening
- Various materials prepared by the instructors
成績評価の方法・基準
/Grading method/criteria
Assignments, Exercises, and Quizzes 50%
Mid-term project 25%
Final project 25%
履修上の留意点
/Note for course registration
Students are expected to read their emails frequently.

This class does not have prerequisites, but it is recommended that students be familiarized with Pure-data programming paradigm, and general audio signal processing techniques. These are some classes that students are encouraged to take, i.e.:

ITC02 Introduction to Sound and Audio
ITA01 Music Technology
ITA07 Advanced Signal Processing
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
- Course website: http://onkyo.u-aizu.ac.jp/index.php/classes/3d-sound/
- J. Blauert, Spatial Hearing: The Psychophysics of Human Sound Localization. MIT Press, 1997.
- Bregman, Albert S., Auditory Scene Analysis: The Perceptual Organization of sound. Cambridge, Massachusetts: The MIT Press, 1990 (hardcover)/1994 (paperback).
- www-crca.ucsd.edu/~msp/Pd_documentation/


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開講学期
/Semester
2017年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Konstantin Markov
担当教員名
/Instructor
Konstantin Markov
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/19
授業の概要
/Course outline
This course introduces audio signal and information processing technologies with application to automatic speech recognition (ASR) and music information retrieval (MIR) tasks. It consists of three main parts: I) Fundamental methods and algorithms, II) Speech processing and recognition, and III) Music information processing and retrieval.  The first part gives a review of the fundamental methods and algorithms used for both speech and music processing such as digital signal processing for feature extraction and machine learning and pattern recognition for model training and information processing. The second part focuses on the specifics of the speech signal and describes the process of building a fully functional speech recognition system. Other speech related tasks, such as speaker and language recognition are also presented. Finally, the last part provides knowledge about music features and how to solve such music information retrieval tasks as music genre or emotion estimation, song similarity or song transcription, etc.
授業の目的と到達目標
/Objectives and attainment
goals
The objective of this course is to make students familiar with the fundamentals of automatic speech recognition and music information retrieval technologies, as well as to teach them how to build simple ASR and MIR systems including feature extraction, model learning, testing and performance evaluation.
授業スケジュール
/Class schedule
Part I.  Fundamental methods and algorithms.
          1. Digital signal processing.
                         - Short-time Fourier Analysis.
                         - Cepstral processing, Pitch extraction.
          2. Pattern classification.
                         - Bayes' decision theory,
                         - Classifiers design.
          3. Machine Learning.
                         - Vector quantization, Gaussian Mixture Models.
                         - Hidden Markov Models, Support Vector Machines.
                         - Neural Networks, Gaussian Processes.
Part II.  Speech processing and recognition.
          4. Speech feature extraction.
                         - Mell-Frequency Cepstral Coefficients (MFCC).
                         - Linear Predictive Coding Coefficients (LPCC).
                         - Deep Neural Network based features.
          5. Acoustic modeling.
                         - Context (in)dependent model units.
                         - Lexicon, Viterbi decoding.
          6. Language modeling.
                         - N-grams.
                         - Perplexity.
Part III.  Music information processing and retrieval.
          7. Music feature extraction.
                        - Timbre, Spectrum related featrues.
                        - Chromagram.
          8. Music Information retrieval.
                        - Genre classification.
                        - Mood estimation
                        - Similarity calculation.
教科書
/Textbook(s)
1. L. Rabiner, B. Juang, Fundamentals Of Speech Recognition, Prentice-Hall, 1993.

2. X. Huang, A. Acero, H. Hon, Spoken Language Processing: A guide to theory, Algorithm, and System Development, Prentice-Hall, 2001.

3. S. Theodoridis, K. Koutroumbas, Pattern Recognition, 4th edition, Elsevier, 2009.

4. Y. Yang, H. Chen, Music Emotion Recognition, CRC Press, 2011.
成績評価の方法・基準
/Grading method/criteria
Attendance: 20 points

Laboratory exercises: 40 points

Project: 40 points
履修上の留意点
/Note for course registration
See the course website.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
This course is managed by Moodle system:

http://hi-mdle.u-aizu.ac.jp

(internal access only)


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開講学期
/Semester
2017年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Ian L. Wilson
担当教員名
/Instructor
Ian L. Wilson
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/30
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) use software to process images of ultrasound data

(4) analyze speech acoustics and write short scripts to automatically analyze acoustic data

(5) understand acoustic concepts such as speech waveforms, formants, FFT, and sine wave speech synthesis
授業スケジュール
/Class schedule
Week 1: How speech is produced and how articulation is measured

Week 2: Acoustic properties of speech sound classes; Praat script writing

Week 3: Ultrasound speech data collection and analysis

Week 4: Mapping of articulation to acoustics I

Week 5: Mapping of articulation to acoustics II

Week 6: Audio-visual speech perception

Week 7: Phonetic variability I - within and across speakers

Week 8: Phonetic variability II - within and across languages; final project (in class)
教科書
/Textbook(s)
Handouts and other materials will be made available on the course website.
成績評価の方法・基準
/Grading method/criteria
Attendance and Participation:  35%
Assignments (Praat script writing, etc.):  35%
Final projects:  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


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開講学期
/Semester
2017年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Subhash Bhalla
担当教員名
/Instructor
Subhash Bhalla
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/02/01
授業の概要
/Course outline
     Database Systems are very common. There are new
     Organic Databases in Healthcare and Geographic
     data.  This course considers DBMS architectures for
     all kind of decision support systems. It is based on practical
     exercises and examples.  Lectures depend on recent
     research developments from research papers -
     from conferences,  journals and advanced
     text books.
授業の目的と到達目標
/Objectives and attainment
goals
Implementation details foR new applications wilL
be discussed. The course considers the application side. The
topics include Data Modeling, Advanced features of query
:languages. Transactions and Recovery systems.
授業スケジュール
/Class schedule
1       Entity-Relationship Model     
2       Relational Model               
3       Advanced features in SQL       
4       Object-Oriented Databases        
5       Object-Relational Databases     
6       Web Data and XML,Architectures
7       Parallel Databases             
8       Decision Support Systems       
9       Distributed Databases        
教科書
/Textbook(s)
- Database Systems Concepts, by Korth, H.A., Silberschatz, A.,
      and Sudershan, S., 6th edition, McGrawHill Book Co., 2010
    - Various materials prepared by the instructor.
成績評価の方法・基準
/Grading method/criteria
Grade (100) : Quiz 1 - 3 (20,30,30 each), class assignments (10),
     Study project (10).
履修上の留意点
/Note for course registration
  Basic knowledge of Data management and computer networks is useful.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
Course directory for course handouts and exercise sheets.
Study material and notes will be recommended by the instructor(s).


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開講学期
/Semester
2017年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Vitaly V. Klyuev
担当教員名
/Instructor
Vitaly V. Klyuev
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/27
授業の概要
/Course outline
When the end user needs information, he/she looks on the Internet. The Internet is the source of information of any kind: inquiries, entertainment, science, etc. In this course, we will study the key ideas of text mining, which are available to efficiently organize, classify, label and extract relevant information for today’s information-centric users.
授業の目的と到達目標
/Objectives and attainment
goals
Text mining can be characterized as a group of techniques to extract useful information from texts. Intelligent information retrieval takes into account the meaning of the words in the texts, order of the words in the user queries, the authority of the document source, and the user feedback. We will present the most advanced models, methods and techniques to provide our students with the state of the art technologies in the area of the intelligent information retrieval and text mining.
授業スケジュール
/Class schedule
The course covers the basic topics:
1. Exploring Text
2. Information and Knowledge Extraction
3. Semantic Retrieval of Text Documents
4. Opinion Mining
5. First Story Detection
6. Summarization
7. Questions & Answers
8. Trends in Modern Information Retrieval
Programming assignments: There will be several programming assignments. Their aim is to investigate various IR and web search tasks.
教科書
/Textbook(s)
On-line documents will be used.
成績評価の方法・基準
/Grading method/criteria
The final grade will be calculated based on the following weights:
Assignments  -  40%
Quizzes during lectures  -  25%
Final examination  - 35 %
履修上の留意点
/Note for course registration
Knowledge of programming concepts and fundamental algorithms is necessary. Students should complete Java Programming 1 and 2, Algorithms and Data Structures, and Advanced Algorithms courses.

The Intelligent Information Retrieval and Text Mining course is a major course for students who would like to specialize in software engineering for Internet applications, and designing software applications.   
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/~vkluev/courses/IRTM/

Bibliography

Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More by Matthew A. Russell, O'Reilly Media; 2 edition, 2013.

Web Scraping with Python: Collecting Data from the Modern Web
by Ryan Mitchell, O'Reilly Media; 2015.

Christopher D. Manning, Prabhakar Raghavan and Hinrich Schutze, Introduction to Information Retrieval, Cambridge University Press. 2008
On-line version:
http://www-csli.stanford.edu/~hinrich/information-retrieval-book.html


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開講学期
/Semester
2017年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Yoichi Tomioka
担当教員名
/Instructor
Yoichi Tomioka, Noriaki Asada
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/30
授業の概要
/Course outline
Automatic control is a key technology for utilizing electrical and mechanical machines in our daily life, such as automobiles, railways, airplanes or electrical appliances.  To design safe, efficient and intelligent machines, the advanced and intelligent algorithms in instrumentation and control engineering theory are indispensably important.  The purpose of this course is to learn the basic principles how to sense machine states and how to control the machine behaviors.  In the sensing theory, we study the sensing principle and mechanism, sensing data processing and analyzing methods and measurement error and accuracy estimation methods.  In the control theory, we study the modeling methods for controlled machines or systems and the classical and advanced design methods of controllers through practical examples and computer based simulation exercise.
授業の目的と到達目標
/Objectives and attainment
goals
To learn the basic principles how to sense machine states and how to control the machine behaviors.
授業スケジュール
/Class schedule
1. Instrumentation and Unit system/ Instrumentation amount
2. Error and Accuracy in measurement
3. Least square method/ Interpolation
4. Instrumental measurement/ Sensor/ Sensing
5. Signal instrumentation
6. Processing and analysis of signals
7. Modeling and control of dynamic system
8. Feedback control/ Modeling and response analysis by frequency response function
9. Feedback control/ Stability and system design
10. Control theory based on state space equation
11. Advanced control theory/ Fuzzy control and adaptive control
12. Exercise of controller design simulation
教科書
/Textbook(s)
No text is used. Class is hold by handouts in Homepage.
成績評価の方法・基準
/Grading method/criteria
Reports and exercises.
履修上の留意点
/Note for course registration
No prerequisite.
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/~ytomioka/graduate/SC.html


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開講学期
/Semester
2017年度/Academic Year  後期集中 /2nd Semester Intensi
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Naru Hirata
担当教員名
/Instructor
Naru Hirata, Hirohide Demura, JAXA/NAOJ Lecturers
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/27
授業の概要
/Course outline
This course focuses on developments of hardware instruments and control system for lunar and planetary explorations. Envisioned main target is the moon. This course follows an omnibus form given by invited lecturers (teleclasses) from JAXA, NAOJ, etc.
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.
授業スケジュール
/Class schedule
Example: Schedule in AY2015
#1-4 Prof. Hanada (NAOJ) Science and Technology of Lunar Observatory
#5-7 Prof. Yamada (NAOJ) Lunar and Planetary Seismology
#8-10 Prof. Namiki (NAOJ) Modeling of Performance of a Laser Range Finder
#11-14 Prof. Araki (NAOJ) Lunar Laser Range Finder
#15-16 Prof. Kikuchi (NAOJ) Orbit Determination of Spacecraft by VLBI
教科書
/Textbook(s)
N/A
成績評価の方法・基準
/Grading method/criteria
Attendances (Presentations), Homeworks, or Reports every professors.
履修上の留意点
/Note for course registration
preriquisite:
ITC09 Fundamental Data Analysis with Lunar and Planetary Database
related course:
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.)
https://arashima.u-aizu.ac.jp/groups/alps_openwiki/wiki/4af40/ITA19.html
References in UoA Library (in Japanese)


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開講学期
/Semester
2017年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Xin Zhu
担当教員名
/Instructor
Xin Zhu
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/30
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
Attendance 20%
Homework 40%
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



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開講学期
/Semester
2017年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Wenxi Chen
担当教員名
/Instructor
Wenxi Chen
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/30
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, visualization and optimization.
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 technology field.
授業スケジュール
/Class schedule
1. Introduction
2. Signal Separation and Decomposition
3. Signature Detection
4. Data Preprocessing
5. Time Domain Analysis
6. Frequency Domain Analysis
7. Nonlinear Domain Analysis
8. Biorhythm Estimation
9. Anomaly and Change Detection
10. Classification and Clustering
教科書
/Textbook(s)
1. Biomedical Signal Processing and Signal Modeling, Eugene N. Bruce, ISBN:
978-0-471-34540-4, December 2000, Wiley
2. 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
成績評価の方法・基準
/Grading method/criteria
Research report and presentation
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.)
http://i-health.u-aizu.ac.jp/BPDM/


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開講学期
/Semester
2017年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Incheon Paik
担当教員名
/Instructor
Incheon Paik
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/30
授業の概要
/Course outline
Fundamental concept for business target and Big Data Analysis technology will be covered.
Course Outline:
- What is business target and how to model it through Big Data Science
- Big Data Infrastructure: Hadoop
- Data Mining Technology
- Statistical Analysis
- Application of Big Data Analysis to a Business Domain
授業の目的と到達目標
/Objectives and attainment
goals
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.
授業スケジュール
/Class schedule
1. Data Science Process
2. Data Science Process
3. A Scenario of Business Analysis With Data Science Process (Ex: Market Analysis By Twitter)
4. Distributed File System, SQL and NoSQL, Hadoop Architecture, MapReduce Programming
5. Hadoop Exercise: Map-Reduce Programming for Word Count or TF-IDF Calculation
6. Hadoop Eco System (Hive and Mahout) and Motivation of Statistical Analysis and Data Mining
7. Statistical Analysis I: Summarization and Correlation, Multivariate Analysis I
Statistical Analysis II: Multi-Variant Analysis II and Regression Analysis Model
8. Case Study: Statistical Analysis By R or ?
9. Data Mining I: Classification and Clustering
10. Data Mining II: Association and Cluster Analysis
11. Case Study: Data Mining Exercise
12. Term Project: Market Analysis on Twitter Data based on Hadoop, Scenario and Entire Development Process
13. How to Get Twitter Data Using Web API, Convert to Hadoop NoSQL DB, and Query the data
14. How to Apply Statistical Analysis and Data Mining
15. Case Implementation
教科書
/Textbook(s)
Lecture material will be provided.
成績評価の方法・基準
/Grading method/criteria
Examination  -----       45 %
Exercise LAB (Including Term Project, Attendance)  -----------------    45 %
Attendance(Including Quiz)  ---------------------  10 %
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
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


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開講学期
/Semester
2017年度/Academic Year  前期集中 /1st Semester Intensi
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
1.0
責任者
/Coordinator
Ryutaro Himeno
担当教員名
/Instructor
Ryutaro Himeno, Wenxi Chen, Kenzaki H. (RIKEN), Noda S. (RIKEN)
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/02/15
授業の概要
/Course outline
From molecular scale to human body, computer simulation of living matter has become practical due to the development of computer performance, computation scheme and experimental measurement.  These simulation has widely applied to medical fields through drug discovery, surgical operations etc… In this course, we will learn those basic theories and current status: molecular simulation using molecular dynamics and continuum mechanics simulation including structure analysis and fluid dynamics.  In addition, we will experience them through exercises using PCs. Softwares used in exercises will be prepared as teaching materials. Anyone can attend the course without prerequisites.
授業の目的と到達目標
/Objectives and attainment
goals
We will learn basic theory and mathematical algorithms to solve basic governing equations in simulation of living matters from molecular scale to whole body as well as their wide applications in real world, especially in medical field.  More specifically,  
1) Molecular scale: basic theory and mathematical algorithm of molecular dynamics simulation and its wide applications,
2) Organ scale: basic theory and mathematical algorithm of structure analysis and non linear structure analysis for hard tissue simulation of human body and its practical applications,  
3) Fluid dynamics simulation in human body: basic theory and mathematical algorithm of fluid dynamics simulation in the human body and its practical medical applications.
In 1) and 2), trough exercises using PC, you will execute simulation by yourself and learn how to simulate problems and how to visualize those results.
授業スケジュール
/Class schedule
1. Introduction of this course by Ryutaro Himeno
2. Molecular Simulation of Living Matter by Hiroo Kenzaki
・ Basic Theory
・ Application
・ Exercise
3. Hard Tissue Simulation of Living Matter by Ryutaro Himeno
・ Basic Theory
4. Computational Fluid Dynamics of Living Matter
・ Basic Theory: Ryutaro Himeno
・ Medical Application: Ryutaro Himeno
・ Exercise: Shigeho Noda
Total 8
教科書
/Textbook(s)
No textbook but teaching materials will be provided in the course
成績評価の方法・基準
/Grading method/criteria
Quizzes and Exercises at each class: 100%
履修上の留意点
/Note for course registration
Bring your own PC for the exercise.  OS should be Windows 7 or 8 or Linux because of executing application software used in exercise.  If you can not prepare PC with Windows 7 or 8, or Linux, please contact the instructors.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
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


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開講学期
/Semester
2017年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Incheon Paik
担当教員名
/Instructor
Incheon Paik
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/30
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. Ontology Design Exercise (Using Protege)
6. Ontology Learning by Text Mining(I)
7. Ontology Learning by Text Mining(II)
8. Ontology Matching and Merging
9. Ontology Engineering
10. Semantic Web Service Frameworks (OWL-S and BPEL)
11. Semantic Web Service Frameworks (WSMO)
12. Semantic Web Service Discovery
13. Service Composition on Semantic Web
14. Semantic Web Technology - Case Study (I)
15. Semantic Web Technology - Case Study (II)
16. Final Examination
教科書
/Textbook(s)
Lecture Slides
成績評価の方法・基準
/Grading method/criteria
1. Examination    --- 45%
2. Paper Presentation & Term Project --- 45%
3. Attendance --- 10%
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.)
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.


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開講学期
/Semester
2017年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
3.0
責任者
/Coordinator
Michael Cohen
担当教員名
/Instructor
Julian Villegas, Michael Cohen
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2017/01/16
授業の概要
/Course outline
This course introduces advanced undergraduates and graduate students to the basics of human interface technology and the virtual reality paradigm, especially through "desktop VR" (a.k.a. "fishtank VR"). We use a project-based, "hands-on" approach, emphasizing creation of self-designed virtual worlds. The main vehicle of expression is "Alice," an object-oriented, graphical 3D scenario IDE (integrated development environment), to contextualize segments on "desktop virtual reality," color (and color gradients), graphical & visual design, texture mapping, sound, music, & dialog, as well as software engineering. Segments on stereoscopy and image-based rendering are also included. We also use Mathematica, SumoPaint, and Audacity & GarageBand as support tools for multimedia content creation. The power of experiential education is leveraged by lessons with an emphasis on practical experimentation, learning by doing.
授業の目的と到達目標
/Objectives and attainment
goals
We survey human interfaces, including demonstrations and "hands-on" exercises with basics of multimedia: color models, image capture and compositing, graphic composition and 3D drawing, texture mapping, IBR (image-based rendering), stereography, and audio (including dialog) & musical editing. Students use self-designed multimodal interfaces authored with object-oriented techniques to tell stories with virtual characters and cinematography (camera motion and gestures, "camerabatics") for deterministic "machiniHma" (machine cinema = computer-generated movies) and to engage users in nondeterministic, dynamic environments such as event-driven games and digital interactive story-telling.
授業スケジュール
/Class schedule
1 introduction, creating machinima
2 story-boarding, functions & expressions, control structures, text & background, photographic imagery
3 lighting, camera movement, visibility, graphic composition, texture mapping
4 look & feel, parameters, randomization, functions, TTS (text-to-speech), narration, voice-over, dialog
5 ambient sound, SFX (sound effects)
6 interactivity, event handling, parameters, recursion, lists
7 space, conditionals, loops, debugging
8 individual presentations
9 color, chromastereopsis
10 binocular display
11 studio development
12 anaglyphics
13 music, composition, BGM (background music)
14 studio develpment
15 group presentations
教科書
/Textbook(s)
•  Lecture notes prepared by instructors; 教師による授業.
•  Reference (not required) textbook; 参考図書(購入の必要はない) Learning To Program with Alice (2nd Edition, Paperback), by Wanda P. Dann, Stephen Cooper, & Randy Pausch.
•  Students are required to purchase chromastereoptic and anaglyphic eyewear, available from the instructor.
成績評価の方法・基準
/Grading method/criteria
Most of the coursework involves lab exercises emphasizing creative applications of digital contents creation tools, highlighting design and invention as much as discovery. Weekly "checkpoint" exercises verify specific skill sets--- including scenario authoring and storyboarding, drawing and painting, color models and specification, digital compositing (layers, overlays, texture mapping), stereography (autostereograms, anaglyphics, chromastereoscopy), audio editing SFX (sound effects), dialog generated with TTS (text-to-speech) synthesis tools, and DTM for BGM (desk-top music composition for background music)--- progressively accumulating into fully realized virtual worlds or stories. There are also creative studio exercises, occasional quizzes, and mid-term and final exams. The course's annual chromastereoptic art contest is ThankStudents also contribute panoramic images to a virtual hyperlinked tour of the University. Student scenarios (plays, movies, & games), highlighting originally created worlds and spaces, composed individually (mid-term) and as teams (end-of-term), are presented to the entire class in special sessions.
履修上の留意点
/Note for course registration
(No prerequisities.)
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/~mcohen/welcome/courses/AizuDai/graduate/Multimedia_Machinima/

http://sighci.org/uploads/SIGHCI%20Newsletters/AIS_SIGHCI_Newsletter_v12_n1.pdf#page=10

http://www.alice.org


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E-mail Address: sad-aas@u-aizu.ac.jp