AY 2014 Graduate School Course Catalog

Field of Study IT: Applied Information Technologies

2015/02/01

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開講学期
／Semester		 2014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		 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 2014/09/27
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 - 40%
Home task on Java 3D - 50%
Attendance - 10%
履修上の留意点
／Note for course registration		 Java Programming. Recommended: Computer Graphics; Human Interface and Virtual Reality.


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開講学期
／Semester		 2014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 2014/09/27
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)
Mediacoustic CD-ROM
Various materials prepared by the instructors.
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.
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

dataflow-based DTM & audio effects DAW (digital audio workstation) Audiotool:
http://www.audiotool.com


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開講学期
／Semester		 2014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 , 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 2014/09/27
授業の概要
／Course outline		 If we define a robot at 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, 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.
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		 2014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 , Shigeru Kanemoto
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 2014/09/27
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
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		 2014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		 Takafumi Hayashi
担当教員名
／Instructor		 Takafumi Hayashi , Yong Liu
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 2014/09/27
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		 1. Overview of topics of pattern classification and its applications
Overview of topics of pattern classification and its applications will be presented. The overview of this course will be illustrated.

2. 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.

3. 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.

4. 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.

5. Kernel Methods
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.

6. Bayesian Decision Theory
Bayesian decision making with both discrete probabilities
and continuous probabilities will be studied.

7. 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.

8. Feature extraction from signals and images
Various kinds of feature extraction from signals and images will be discussed. Several advantages and disadvantages of digital signal processing and digital image processing will be discussed related with feature extraction and pattern recognition.

9-10. Sequence and Signal design and their applications
Sequences and signals which have special properties are used for pattern recognition and its related applications. Various kinds of sequences and signals will be introduced. Furthermore, several sequence constructions and applications of the sequences to communications, instrumentations　will be illustrated.

11-12. Signal Processing and Image Processing for Instrumentation
Signal processing, image processing, and various kinds of pattern recognition techniques are user for instrumentation. In these lecture, various kinds of signal processing and image processing for instrumentation are discussed.

13. Signal Processing for Communications
Signal processing is widely used for communications. Various kinds of signal processing schemes, which are used for communications, will be discussed.

14. Complex Event Processing and its Applications
Complex Event Processing is a powerful technique for various kinds of applications. As an application of pattern recognition, complex event processing and its applications will be discussed.

15. Final Review
Final Review will be provided.
教科書
／Textbook(s)		 The references will be informed later.
成績評価の方法・基準
／Grading method/criteria		 Assignments.
履修上の留意点
／Note for course registration		 None.


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開講学期
／Semester		 2014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 2014/09/27
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		 2014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 2014/09/27
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		 2014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		 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 2014/09/27
授業の概要
／Course outline		 This course is a introduction to the theory and practice of musical composition and performance using computers.
We survey the ways that composers and performers have used computers,
review basic music theory (tuning, intervals, chords, scales, harmony, melody, rhythm, notation),
composition and performance (live recording, digital editing, digital processing),
experiment with DTM (desk-top music)
using MIDI interfaces (general MIDI, patches, daisy chains, controllers, sequencers, and synthesizers),
and explore tangents such as voice synthesis, visual music, and ringtone generation.
Music theory lectures interleaved with lab sessions guide students
through composition/editing/audition of individual songs
in our studio
(featuring workstations with keyboard controllers and sequencing
software as well as various MIDI controllers and audio effects processors).
The course culminates in a informal recital of student-composed songs.
授業の目的と到達目標
／Objectives and attainment
goals		 Most of the courses that students take as computer science majors emphasize scientific discipline and the accumulation of "truth."
This computer music course includes such technically objective factors, but also encourages original, creative expression,
subjectively motivated by aesthetics rather than "correctness."
Unlike most other courses that try to converge on a "right answer" shared by everyone else,
artistic disciplines like computer music encourage originality, in which the best answer is one that is like no one else's!
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.
By taking this course, students will gain a basic understanding of the theory and practice of DTM (desk-top music), by having learned basic musical theory and by having personally composed a song using techniques explained in lectures.
授業スケジュール
／Class schedule		 Fundamentals; 基本
Overview of ways that composers and performers have used computers; コンピューターを使って、作曲者と演奏者による概観
Music theory; 音楽学
tuning; チューニング
intervals; 音程, 間隔
chords; 和音
scales; 音階
harmony; 調和
melody; 旋律
rhythm; 調子
notation; 記譜法
Music and sound: auditory display and control; 音楽と音： オーディトリー ディスプレーとコントロール
Performance and appreciation of synthesized music; シンセサイザー音楽の演奏
MIDI interfacing; ミディ
daisy chains; デイジー チェーン
controllers; 制御装置
sequencers; 連続楽譜記憶装置
synthesizers and samplers; シンセサイザー、サンプル
Use of computer music software; コンピューターミュージックソフト使用
DTM: desk-top music; デスクトップミュージック
Composition, sequencing, performance; 作曲、配列、演奏
Exploration of internet for source material on sound and music; 音と音楽を素材のインターネットによる調査
Use of software packages for music production; 音楽ソフトウェアーの使用
Sequencing; 作曲と表音法、連鎖
Patches; パッチス
DSP synthesis; 総合
Voice, speech, and singing synthesis; 音声合成
Visual music; ビシュアル音楽
Ringtone generation; 着信メロディ
Use of computer music hardware; コンピューターミュージック ハードの使用
教科書
／Textbook(s)		 Edly's Music Theory for Practical People, Musical EdVentures, 1999. (ISBN 0-9661616-0-2)
成績評価の方法・基準
／Grading method/criteria		 Most of the coursework involves
reading assignments, music theory drills, and lab exercises emphasizing creative applications of music technology.
homework
lab exercises (progressive song composition)
exams
term song composition
履修上の留意点
／Note for course registration		 (No particular musical ability, training, or experience is required or expected.)
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 course home page:
http://www.u-aizu.ac.jp/~mcohen/welcome/courses/AizuDai/graduate/Computer_Music/syllabus.html

GarageBand loop-based DTM (desk-top music) system:
http://www.apple.com/ilife/garageband

Band-in-a-Box chord-based DTM system:
http://www.pgmusic.com/bbmac.htm

Pure Data audio synthesis and multimedia data-flow visual programming system:
http://puredata.info

dataflow-based DAW (digital audio workstation) Audiotool:
http://www.audiotool.com


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開講学期
／Semester		 2014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
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 2014/09/27
授業の概要
／Course outline		 This course deals with advanced architectures for synthetic worlds from a unified view of software and hardware. This course first treats with the fundamentals of computer graphics and computer architectures, and then, presents application-specific computer architectures and parallel algorithms for 3D real-time image synthesis. In particular, parallel architectures/algorithms for polygon rendering and ray tracing are discussed in detail.
授業スケジュール
／Class schedule		 1. Introduction
2. Fundamentals of 3D Computer Graphics
3. Advanced Rendering Techniques
4. Hardware Elements for Graphics Systems
5. Parallel Polygon Rendering
6. Parallel Ray Tracing
7. Parallel Volume Rendering
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.
* Handouts
* Selected journal/conference papers
成績評価の方法・基準
／Grading method/criteria		 Attendance, Presentation, Reports
履修上の留意点
／Note for course registration		 Computer Graphics, Computer Architecture
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/vr_arch/


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開講学期
／Semester		 2014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 2014/09/27
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		 2014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 2014/09/27
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.
2. Formulation of finite element equations.
3. Finite element equations for heat transfer.
4. Finite element method for solid mechanics problems.
5. Finite element equations.
6. Assembly of the global equation system.
7. Finite elements. Two-dimensional triangular element.
8. Two-dimensional isoparametric elements.
9. Three-dimensional isoparametric elements.
10. Discretization.
11. Mesh generation.
12. Assembly algorithms. Displacement boundary conditions.
13. Solution of finite element equations.
14. Nonlinear Problems.
15. Visualization of finite element models and results.
16. Contouring on higher-order surfaces.
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%
Home task - 40%
Attendance - 20%
履修上の留意点
／Note for course registration		 Numerical Analysis;
Programming in any language.
Computer Graphics course is recommended.


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開講学期
／Semester		 2014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 2014/09/27
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 - https://sites.google.com/site/uaizuipu2013/

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.
AY2013, two reports (Bayesian Net, Clustering) and each report has 50 points.


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開講学期
／Semester		 2014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 2014/09/27
授業の概要
／Course outline		 Not offered in AY 2014


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開講学期
／Semester		 2014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		 Noriaki Asada
担当教員名
／Instructor		 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 2014/09/27
授業の概要
／Course outline		 "Remote sensing technology" is taught as an application of image processing.
Wave optics, radiation transfer theory and image processing for remote sensing are taught with some examples.
Object detection, image regeneration and pattern recognition are taught, too.
授業の目的と到達目標
／Objectives and attainment
goals		 Explain what is "Remote Sensing" and what remote sensing can do. Understand basics of optics, stereography and related physics. Think about real time analysis of 3-D images and total system for remote sensing.
授業スケジュール
／Class schedule		 ･ Basics of wave optics
･ Basics of image processing
･ Image geometry---Projection. Transformation of axes.
･ Structural pattern recognition
･ Detection of objects
･ Neural networks
･ Image processing expert systems
･ Basics of radiation transfer
･ Image regeneration
･ Image restoration
･Compensation---Geometric compensation. Radiation compensation. Density compensation.
･ Data compression
･ Density transformation
･ Spatial transformation
･ Three-dimensional measurements
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, Discussion and Presentation, Some examinations and questions.
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/~asada/graduate/RS.html


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開講学期
／Semester		 2014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 2014/09/27
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, 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 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,

- segmentation free recognition, latin and non-latin character recognition,
- cursive handwritten character and symbols, layout analysis,
- 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		 2014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 2014/09/27
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 (Official site, Japanese version).
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		 1. Introductions and Pd

2. Quantification of sound

3. Spatial hearing and psychoacoustics

4. Binaural difference cues I

5. Binaural difference cues II

6. Head related impulse responses

7. Room impulse responses

8. Motion and distance perception I

9. Motion and distance perception II

10. Special topics in sound spatialization

11. Headphone techniques

12. Loudspeaker techniques I

13. Loudspeaker techniques II

14. Workshop on final project

15. Applications I

16. Applications II
教科書
／Textbook(s)		 ・ Durand R. Begault, 3-D Sound for Virtual Reality and Multimedia, Academic Press, 2000. (online)
・ Various materials prepared by the instructors
成績評価の方法・基準
／Grading method/criteria		 Exercises and quizzes 10% Assignments 30% Mid-term project 30% Final project 30%
NOTE: Students are expected to read their emails frequenty.
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 (currently taught in ITC02: Introduction to Sound and Audio), and general audio signal processing techniques. These are some classes that students are encouraged to take concurrently or before this class:
・ ITC02 Introduction to Sound and Audio
・ 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://arts.u-aizu.ac.jp/spatialHearing/
・ 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).
・ http://puredata.info/docs/manuals/pd/
・ http://hyperphysics.phy-astr.gsu.edu/hbase/sound/soucon.html



University of Aizu http://www.u-aizu.ac.jp/


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開講学期
／Semester		 2014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		 John Brine
担当教員名
／Instructor		 John Brine
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 2014/09/27
授業の概要
／Course outline		 This course is focused on the use of technology for language teaching. Each student will create a final project that demonstrates the lessons and work involved in this class. Final projects should exemplify authentic language
courses.
授業の目的と到達目標
／Objectives and attainment
goals		 Students will:
- Create a customized CMS course that clearly displays its structure.
- Enhance the course with html and/or javascript code.
- Use customized modules, themes, activities, blocks and/or other augmentations to Moodle.
- Indicate in a syllabus the CALL materials that are created by the student
and borrowed from the Internet. Describe why each item is included.
- Organize the materials in an appropriate systematic manner.
- Describe the decision making process that informed the creation of the project.
授業スケジュール
／Class schedule		 Weekly project work based on the course objectives.
教科書
／Textbook(s)		 Reading material will be provided by the instructor.
成績評価の方法・基準
／Grading method/criteria		 - Create a customized CMS course that clearly displays its structure. 20%
- Enhance the course with html and/or javascript code. 15%
- Use customized modules, themes, activities, blocks and/or other augmentations to Moodle. 15%
- Indicate in a syllabus the CALL materials that are created by the student
and borrowed from the Internet. Describe why each item is included. 15%
- Organize the materials in an appropriate systematic manner. 15%
- Describe the decision making process that informed the creation of the project. 20%
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 http://moodle.u-aizu.ac.jp/moodle


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開講学期
／Semester		 2014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		 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 2014/09/27
授業の概要
／Course outline		 This course introduces students to the field of automatic speech recognition (ASR). It gives basic knowledge about speech science, i.e. speech production and perception by humans, digital speech processing techniques and speech models. Some fundamental aspects of the pattern recognition theory are given in order to make clear the specifics of classifier design and main parameter estimation methods. The Hidden Markov model is explained in detail since it is the main tool of current speech recognition technology. The design and training of the main parts of an ASR system, i.e. acoustic and language models is given, and their usage for decoding is explained. The whole process of ASR system design is presented and some more advanced methods such as noise robustness, discriminative training, and Bayesian networks are briefly described
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 pattern recognition theory and its application to speech recognition in particular, as well as to teach them how to build classifiers starting with feature extraction methods and ending with system evaluation techniques.
授業スケジュール
／Class schedule		 1.Course overview. Sounds and human speech systems. Phonetics.
Sound levels, Speech production, Speech perception.
Phonemes, Co-articulation, Syllables and words.
2.Speech signal processing.
Short-time Fourier Analysis, Linear Predictive Coding.
Cepstral processing, Pitch extraction.
3.Pattern classification.
Bayes' decision theory, Classifiers design.
Maximum likelihood estimation, MAP estimation.
Vector quantization, EM algorithm.
4.Hidden Markov model.
Dynamic programming and DTW, Forward algorithm, Viterbi algorithm, Baum-Welch algorithm.
Types of HMM, HMM limitations.
5.Acoustic modeling.
Context independent model units, Context dependent model units.
Training.
6.Language modeling.
Context free grammars, N-grams.
Perplexity.
7.Search algorithms.
Decoder basics.
Viterbi search, Stack search.
8.Environmental robustness.
Channel distortions, Additive noises.
Spectral subtraction, Viener filter.
9.ASR system design.
Data collection and labeling.
Model training, Evaluation.
10.Advanced algorithms.
Discriminative training, HMM/NN.
Bayesian networks, HMM/BN.
教科書
／Textbook(s)		 Handouts
成績評価の方法・基準
／Grading method/criteria		 Attendance: 30 points
Laboratory exercises: 30 points
Project: 40 points
履修上の留意点
／Note for course registration		 Basic knowledge about probability theory, distributions and random variables is required. Familiarity with some signal processing techniques, like filters, digital Fourier transform is a plus.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Books:
L. Rabiner, B. Juang, Fundamentals Of Speech Recognition, Prentice-Hall, 1993.
X. Huang, A. Acero, H. Hon, Spoken Language Processing: A guide to theory, Algorithm, and System Development, Prentice-Hall, 2001.
S. Theodoridis, K. Koutroumbas, Pattern Recognition, 4th edition, Elsevier, 2009.


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開講学期
／Semester		 2014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		 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 2014/09/27
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

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
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		 Assignments and projects to be announced
履修上の留意点
／Note for course registration		 There are no prerequisites to this course.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Course website can be found at:
http://aizuben.u-aizu.ac.jp/moodle/


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開講学期
／Semester		 2014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 2014/09/27
授業の概要
／Course outline		 Not offered AY2014.
授業の目的と到達目標
／Objectives and attainment
goals		 The course is about DBMS architectures for decision support systems. It is based on practical exercises and examples. Lectures depend on recent research developments from research papers -  from conference proceedings, journals and advanced      text books.
授業スケジュール
／Class schedule		 Entity-Relationship Model, Relational Model, Query Languages Web Data and XML, Database and Data-Warehouse System Architectures, Parallel Databases.

#       LECTURE TOPIC
-------------------------------------
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		 Courses on : Algorithms and Data Structures; Database Systems, Web Programming, Distributed Computing.
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

References
    Study material and notes recommended by the instructor(s).


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開講学期
／Semester		 2014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		 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 2014/09/27
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: enquiries, 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. Searching the Web
4. Clustering Documents
5. Text Categorization
6. Summarization
7. Questions & Answers
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

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		 2014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		 Noriaki Asada
担当教員名
／Instructor		 Noriaki Asada , Shigeru Kanemoto
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 2014/09/27
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.)		 URL: http://web-int.u-aizu.ac.jp/~asada/graduate/SC.html


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開講学期
／Semester		 2014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
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 2014/09/27
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		 Cf. Schedule in AY2013
#1-5 Hanada (NAOJ) Lunar Observatory
#6-10 Araki (NAOJ) Development of Lunar Laser Range Finder
#11-12 Oshigami (NAOJ) Ground Penetration Radar for Lunar orbiter, Kaguya
#13-16 Yamada (NAOJ) Development of Lunar Seismometer
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：
ITA22 Fundamental Data Analysis with Lunar and Planetary Database
related course：
ITA22 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		 2014年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 1.0
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 2014/09/27
授業の概要
／Course outline		 The course puts emphasis on management of large volume of data. It covers practical aspects in - information extraction, data visualization for decision support systems.
授業の目的と到達目標
／Objectives and attainment
goals		 Management of Large volume of data; Information Retrieval Techniques, Knowledge extraction methods. Web Data Mining.
授業スケジュール
／Class schedule		 1. Data Mining: Tasks and Levels  of Mining process,
   Mining Techniques, Knowledge Extraction.

2. Data Warehouse: Materialized Views,
3. Aggregation and analysis tools, Extended SQL
   (SQL 1999 specifications, SQL 2003 specifications)
4. Data Visualization. Information Retrieval systems.

5. Web Mining: Relevance Ranking, Similarity based
   retrieval, Relevance using Hyperlinks, Ontologies,
   Indexing of Documents,

6. Web Search Engines (Architecture of Google systems).

7. Information Retrieval and structured data.
教科書
／Textbook(s)		 1.  J.Han, M.Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2000
2.  Database Systems Concepts, Silberschatz, 5th Edition, 2006, McGraw Hill.
3.  Sam Kash Kachigan, Statistical Analysis: An Interdisciplinary Introduction to
    Univariate and Multivariate Methods,Radius Press, 1986
4.  Recommended reading list.
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		 Database Systems, Discrete Mathematics, Statistical Methods.
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.

- Further References: Study material and notes recommended by the instructor(s)


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開講学期
／Semester		 2014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		 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 2014/09/27
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)
4. Ontology Design Exercise (Using Protege)
5. Theories in Ontology Learning by Text Mining
6. Ontology Engineering
7. Semantic Web Service Frameworks (OWL-S, WSMO, BPEL)
8. Services Composition on Semantic Web
教科書
／Textbook(s)		 Lecture Slides
成績評価の方法・基準
／Grading method/criteria		 1. Examination
2. Paper Presentation & Term Project
3. Attendance
履修上の留意点
／Note for course registration		 - JAVA Programming I & II
- Web Programming
- Artificial Intelligence

Other related courses:
- Advanced Internet Technology
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		 2014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		 Naru Hirata , Hirohide Demura
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 2014/09/27
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．その後，探査データの中で最も一般的な画像データについて，その特性，データフォーマット，解析前の処理と科学的知見を引き出すための解析方法を習得する．なお，一部の講義は宇宙航空研究開発機構，国立天文台より招聘した外部講師による遠隔講義で実施する．

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		 - 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
  - 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
  - 探査画像データの特性とデータ処理，解析方法を理解する
- 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		 - 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?第四週
  - 位置情報
  - 姿勢情報
  - 形状モデル
- 第五週?第六週
  - 探査画像データの特性
  - フォーマット，ビューア
  - 輝度較正
- 第七週?第八週
  - 幾何補正，測光補正，地図投影
  - 科学解析（バンド間演算，分類）

- Week 1
  - Introduction
- Week 2
  - Ancillary data and SPICE toolkit
  - Epoch information
- Week 3 and 4
  - Position
  - Attitude
  - Shape model
- Week 5 and 6
  - Characteristics of exploration image data
  - Data format, Viewer
  - Radiometric Calibration
- Week 7 and 8
  - Geometric Correction, Photometric Correction, Map Projection
  - Scientific image analyses (Band math, Classification)
教科書
／Textbook(s)		 N/A
成績評価の方法・基準
／Grading method/criteria		 出席，課題およびレポート

Attendance, Homeworks and Reports
履修上の留意点
／Note for course registration		 ITA23 Practical Data Analysis with Lunar and Planetary Database は本コースの内容と強い関連を持つ．ITA23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ITA22を履修したのち，ITA23を履修することが望ましい．

ITA23 Practical Data Analysis with Lunar and Planetary Database is closely connected with this course. ITA23 will introduce more practical topics on planetary data analyses. Students are recommended to finish ITA22 before taking ITA23.
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		 2014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 , Kohei Kitazato , Chikatoshi Honda
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 2014/09/27
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		 Cf. Schedule in AY2013
#1-2 Demura (UoA) Photoclinometry, Hapke Photometric Function
#3-4 Honda (UoA) Performance Test of imaging sensors
#5-6 Kitazato (UoA) Spectroscopic Analysis
#7-8 Matsumoto (NAOJ) Gravity field of the Moon
#9-10 Morota (Nagoya Univ.) Crater Chronology
#11-16 Haruyama and Ohtake (JAXA)
Kaguya Data Analysis of the moon for multi band images and digital terrain model.
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		 Prerequisites: ITA22 Fundamental Data Analysis with Lunar and Planetary Database
Other Related courses:
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.)		 (in Japanese)
https://arashima.u-aizu.ac.jp/groups/alps_openwiki/wiki/2f54d/ITA23.html


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開講学期
／Semester		 2014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 2014/09/27
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
社


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開講学期
／Semester		 2014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 2014/09/27
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
3. Event detection
4. Data preprocessing
5. Time domain analysis
6. Frequency domain analysis
7. Chaotic analysis-1
8. Chaotic analysis-2
9. Envelope detection
10. Model estimation and predication
11. Trend and cycle
12. Detection of change
13. Classification
14. 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		 2014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		 Yen Neil Yuwen
担当教員名
／Instructor		 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 2014/09/27
授業の概要
／Course outline		 Human-centered computing (HCC) is the science of decoding human behavior. It discusses a computational approaches to understand human behavior all aspects of human beings. However, the complexity of this new domain necessitates alterations to common data collection and modeling techniques. This course covers the techniques that underlie the state-of-the-art systems in this emerging field. Students will develop a critical understanding of human-centered computing including fundamentals, approaches and applications/services.
授業の目的と到達目標
／Objectives and attainment
goals		 This course aims at instructing our students (especially master students) the fundamentals of human-centered computing. Through this course, students are expected to:

1) cultivate interdisplinary thinking skills;
2) be able to build systems that combine technologies with organizational designs;
3) understand the human, and translate the human needs to real-world systems.
授業スケジュール
／Class schedule		 This course will give an introduction that covers a wide range of theories, techniques, applications to the well-designed human-computer systems. Tentative syllabus is designed:

Lesson 1. Human-centered Computing: At a glance
A brief summary of Human-centered Computing will be given in the first class. Students are expected to have a comprehensive view of this area and know what are going to be taught in the following lectures.

Lesson 2. Sociology of Science and Technology - 1
Essentials of sociology are taught at the beginning, and their derive issues in the area of computer science and technology will be gone through. At this lesson, we concentrate on the computational sociology such as sociological theory and method, applied sociology, social construction of technology, and human ecology.

Lesson 3. Sociology of Science and Technology - 2
With the basics, this lesson goes further to introduce present situations and related researches, providing a chance for students to think and build their own understanding of this topic.

Lesson 4. Epistemology
This lesson introduces the basics of epistemology and its usage in implementing the human-centered scenarios. Sub-topics including fundamentals of knowledge, knowledge discovery and management, and tendency of epistemology, will be concentrated.

Lesson 5. Activity/Category Theory
Activity theory and category theory are approaches to understanding human behavior by examining social contexts (e.g., behaviors, motivations to specific purposes). This lesson introduces the basics of activity/category theory, and their usage and differences in system development are then discussed.

Lesson 6. Discussions & Presentations
New information and results of survey will be presented. After the presentation, students will be divided into groups (depending on number of students) for preparing the projects due at end of this semester.

Lesson 7. Distributed Cognition ? 1
In the following two weeks, an essential to human-centered system design is introduced. The theoretical part, such as cognitive theory of learning, cognition science, socially cognition, etc., and the methods to them will be given. Students are requested to bring his/her previous works (or projects) to the class for discussions.

Lesson 8. Distributed Cognition ? 2
Following the previous lesson, this lesson then gives real-world practices on the introduced theories and methods. Students are expected to cultivate the ability of system design.

Lesson 9. Action Research
This lesson pays more attention on the methods to implement the theory of action research than the theory itself. The comparison is also given between action research and software engineering. Students are expected to understand the similarity and difference among them.

Lesson 10. Participatory Design
An important factor, i.e., user, in software (or system) development is introduced. Approaches to help ensure the software design meets the needs and is usable are discussed. Some terms in software design such as sustainability, flexibility, extensibility, and etc. will be emphasized.

Lesson 11. User-centered Design
This lesson begins by developing a strong theoretical foundation in user-centered design, and various fields and testing methods including usability testing, contextual inquiry, and design-focused ethnographic methods are then explored.

Lesson 12. Assistive Technology
History and current state of assistive technology and the challenges that remain today are introduced. The specific concepts explored in this lesson include contextual inquiry, user analysis, and evaluation metrics.

Lesson 13. HCC in Emerging Computing Paradigms
This lesson talks about the HCC in pervasive computing, social computing and cloud computing.

Lesson 14. Discussions & Presentations
Assigned research papers will be presented and discussed. Following the presentation, few emerging topics will be given for free discussions.

Lesson 15. Project Demonstration
Each group will demonstrate the projects with details on motivation (and background), introduction to system design, and evaluation on HCC factors in the design.
教科書
／Textbook(s)		 No specific textbook will be used for this course. Slides and handouts will be prepared by instructors, according to the references (see below), and available on the course website for download. Papers, news, and videos related to the theme will be selected from top-rank academic publications (e.g., IEEE/ACM/IEICE Transactions and SCI-indexed peer-reviewed journals), and the Internet (e.g., with copyright permission) will be taught during the classes.
成績評価の方法・基準
／Grading method/criteria		 Presentation: 50%
Project: 30%
Attendance: 20%
履修上の留意点
／Note for course registration		 N/A
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Witold Pedrycz, Fernando Gomide (2007). Fuzzy Systems Engineering: Toward Human-Centric Computing
Wiebe Bijker, Of Bicycles, Bakelites, and Bulbs: Toward a Theory of Sociotechnical Change (Inside Technology)
Clifford Geertz, The Interpretation Of Cultures (Basic Books Classics) The Presentation of Self in Everyday Life
Victor Kaptelinin and Bonnie Nardi, Acting with Technology: Activity Theory and Interaction Design
Guy A. Boy (2011). The Handbook of Human-machine Interaction: A Human-centered Design Approach


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開講学期
／Semester		 2014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 2014/09/27
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		 -Business Intelligence: Business Data Analysis, Market Analysis, Decision Making
-DataScience Process: Understanding/Extraction/Manipulation of Data, Modeling, Evaluation,
and Implementation
-Big Data Infrastructure: Distributed File System, Hadoop, DB Connection, Data Acquisition
-DFS Case Study: Hadoop andMapReduce Programming, Hive, Mahout
-Statistical Analysis: Summarization, Correlation, Multivariate Analysis, Regression Analysis Model
ーDataMining: Classification, Clustering, Association, Cluster Analysis”Application of Big Data Technologies to a Business Target
授業スケジュール
／Class schedule		 An Overview of Big Data
-Course Introduction
-What is Big Data?
-Why Big Data?
-Potential Applications
-Examples
-Data Science Process
2.Data Science Process
-CRISP-DM
-Business Understanding
-Data Understanding
-Data Extraction
-Modeling
-Evaluation
-Deployment
3.A Scenario of Business Analysis with Data Science Process
-A Scenario of Business Analysis With Data Science Process (Ex: Market Analysis By
Twitter)
-Provide a Scenario of Business Analysis
-Twitter Data and Analysis
-Apply an Entire Data Science Process
4. HDFS, NoSQL, Map-Reduce Programming
-What is Big Data?
-Big Data Source
-Distributed File System
-Hadoop Distributed File System (HDFS)
-SQL and NoSQL
-Whatis Map-Reduce Operation?
5. Map-Reduce Programming Exercise (Word Count, TF-IDF Calculation)
-Hadoop Architecture and Basic Commands
-Basic JavaAPis for Hadoop Map-Reduce Programming
-Word Count by Map-Reduce
-What is TF-IDF
-Calculating TF-IDF by Map-Reduce
6. Hadoop Eco System
Short Introduction to Statistic Analysis and Data Mining
-Hadoop Eco System Outline
-Hive System
-Statistical Analysis
-Data Mining
-Data Mining Example Using Mahout
7. Statistical Analysis: Multivariate Regression -Basics
-回帰分析でわかることUseof regression analysis
-データのグラフ化Graphingand plotting data
-線形回帰モデル（モデ、ノレ化→推定→評価→予測w／信頼区間） Linear regression
models (from modeling to prediction)
8. Statistical Analysis: Multivariate Regression -Exercise
-演習：回帰モデル（モデル化→推定→評価→予測w／信頼区間） Exercise:
Regression models (from modeling to prediction)
-問題：気温とビールの売り上げ，気温と熱中症患者の搬送数，等Examples:Beer
sales and temperature, Heatstroke and temperature，…）
3
回帰分析でわからないことLimitationof regression analysis
9. Case study: Project using Multivariate regression
Project task： 2〜3間から選択制Students choose from two -three project task
candidates
-授業中に解析まで。レポートを課題→成績へ。Calculation during class.
Report submitted a丘erclass.
-教員側準備instructor’s prep紅ation
-問題，データexerciseand data
-学生の計算環境＝R か。studentscomputer exercise environment = R ? (Excel I
Eviews)
10. Data Mining -Classification
-Algorithms to Data Mining -Classification
-Data Training/Learning
-Inferring rudimentary rules
-Decision trees
-Covering rules
-Introduction to Weka
11. Data Mining -Association & Prediction
Algorithms to Data Mining -Association
-Data topology
-Correlation analysis
-Algorithms to Data Mining -Prediction
-Statistical classification
-Bayesian networks
-KNN algorithms, Linear models
12. Data Mining -Exercise
-Case Study
-Experiments (Homeworks) with Weka
-Classification
-Association
-Prediction
13. Project:Situation Awareness and Statistical Analysis On Big Data
-What is Situation Awareness (SA)?
-3 Levels for SA
-Role of Data Mining and Reasoning in SA
-Extracting Information from Big Data
-Entire Scenario of SA on Facebook Data
14. Retrieving, Storing, and Querying Big Data
-Retrieving Data from SNS
-Introduction to Face book APis and Data Format
-K-V Data Scheme on Hadoop
-Storing and Querying Data on Hive
-Using Map-Reduce Programming for SA
15. Mining and Analysis of Big Data for Situation Awareness
-Getting Data by Hive Query on Hadoop
-Perception Process by Data Mining: Classification & Clustering
-Comprehension by Ontology Reasoning
-Getting Data by Map-Reduce on Hadoop
-Perception Process by Statistical Analysis
16. Case Implementation by Accenture
-Introduction to Case Implementations and Consulting
*Lecturers and Tentative Assignment Plan
- Prof. Ryoji Sawa: 1-3
- Prof. Kenta Ofuji: 7-9
- Prof. Neil Yen: 10-12
- Prof. Incheon Paik: 4-6, 13-15
- Mr. Shinichiro Murashige (Accenture):16 or 1


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開講学期
／Semester		 2014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		 Wenxi Chen
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 2014/09/27
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.
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.生体流体シミュレーション
Computational Fluid Dynamics of Living Matter
・Basic Theory: Kazuyasu Sugiyama
・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		 Small exam at the end of each class: 50%
Report after the course: 50%
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.)		 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 7 あるいは８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


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開講学期
／Semester		 2014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		 Pham Tuan Duc
担当教員名
／Instructor		 Pham Tuan Duc
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 2014/09/27
授業の概要
／Course outline		 Mathematical, engineering, and computer-science techniques of representing the real world by computer programs have been playing an increasing role in medical and biological research during the last decades, and their impact is certainly going to increase further in the future. There are many aspects that make computer models very important in medicine and biology because medical diagnoses and biological systems are very complex, requiring the processing of multiple and large volumes of data
being inherently subject to uncertainty. This course is designed for postgraduate students in engineering, computer science, and informatics to obtain skills and knowledge in the applications of computer methods for solving problems in medicine and biology. This course particularly focuses on techniques
and methodologies for biomedical image analysis and pattern recognition based on the interplay of several approaches in the theories of probability, statistics, fuzzy sets, and information.
Keywords: Computerized models, pattern recognition, image analysis, medicine, biology.
授業の目的と到達目標
／Objectives and attainment
goals		 Upon completion of this subject, students will able to:
・Obtainthe advanced knowledge of computerized pattern analysis and image understanding.
・Acquire the basic skills to solve pattern and　image understanding problems of more specialized application-dependent domains in medicine add biology.
・Get insights into the latest developments and applications of computerized pattern recognition and image analysis in several areas of medicine and biology.
・Acquire the understanding of key concepts and appreciate useful applications of pattern and　image analysis of biomedical problems.
・Bemotivated for pursuing a higher research degree or research-oriented industrial career.
授業スケジュール
／Class schedule		 1. Overview of Computers in Medicine and　Biology
2. Probability and Statistics
3. Bayesian Decision Theory
4. Fuzzy sets and Fuzzy Measures
5. Uncertainty and Information
6. Best Linear Unbiased Prediction with Geostatistics
7. Image Processing Operators
8. Feature Extraction
9. Cluster Analysis
10. Classification Algorithms
11. Practical Applications to Medical Diagnoses I
12.PracticalApplications to Medical Diagnose sI
13. Practical Applications to Molecular Biology I
14. Practical Applications to Molecular Biolog IyI
15. Challenging Computational Problems in Medicine and Biology
教科書
／Textbook(s)		 There are no prescribed textbooks for this course.
・Printed handouts will be distributed to students only during class.
・Lecture notes and published papers are sufficient enough for understanding the covered
subjects
成績評価の方法・基準
／Grading method/criteria		 ・Students are required to complete two major assignments.
・Each assignment is worth 50% of the total marks.
・Late　submission of assignments will be subject to deduction of marks.
Important Points for Course Registration by Students:
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 [1] S. Theodoris, K. Koutroumbas, Pattern Recognition. Academic Press, 2009, 4th edition.
[2] R.C. Gonzalez & R.E. Woods, Digital Image Processing. Prentice-Hall, 2008, 3rd edition.
[3] A. Meyer-Baese, Pattern Recognition for Medical Imaging. Academic Press, 2004.
[4] F. Sepulveda, R. Poli, Intelligent Biomedical Pattern Recognition. Springer, 2014