2015年度 シラバス大学院

IT教育研究領域　(応用情報工学)

2016/02/01  現在

科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 ピエール－アラン　ファヨール
担当教員名
／Instructor		 ピエール－アラン　ファヨール , 西舘　陽平
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 2015/02/27
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.


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 3.0
責任者
／Coordinator		 マイケル　コーエン
担当教員名
／Instructor		 マイケル　コーエン , ヴィジェガス　ジュリアン
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 2015/02/27
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), 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)		 音入門: 聴覚・音声科学のための音響学 (translation of Introduction to Sound, by Charles E. Speaks), by Arai Takayuki; 荒井 隆行 & Sugawara T.; 菅原勉 (ISBN 4-303-61020-8)

Besides normal lectures and exercises, we'll also use iPads (one lent to each student for the term) extensively for courseware and interactive projects.
成績評価の方法・基準
／Grading method/criteria		 Homework problem sets (25%), lab exercises (25%), midterm exam (25%) and final exam (25%). This course awards "half again" (1.5 x 2 = 3) credits compared to regular grad school classes because of its thrice weekly meetings and intensive laboratory component.
履修上の留意点
／Note for course registration		 Basic math and physics; 数学、物理. (No specialized knowledge is assumed.)
注: ITA10、空間聴覚とバーチャル３Ｄサウンドの講義を受ける為の必修クラスです。
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/?lang=ja

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

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

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


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 成瀬　継太郎
担当教員名
／Instructor		 成瀬　継太郎 , 浅田　智朗
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 2015/02/27
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/


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 成瀬　継太郎
担当教員名
／Instructor		 成瀬　継太郎 , 兼本　茂
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 2015/02/27
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/


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　4学期 ／Fourth Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 林　隆史
担当教員名
／Instructor		 林　隆史 , 劉　勇 , イエン　ニール　ユーウェン
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 2015/02/12
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.


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 朱　欣
担当教員名
／Instructor		 朱　欣 , 陳　文西
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2015/02/05
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/）


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 陳　文西
担当教員名
／Instructor		 陳　文西 , 朱　欣
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2015/03/02
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/


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 浅田　智朗
担当教員名
／Instructor		 浅田　智朗 , 出村　裕英 , 平田　成
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 2015/03/16
授業の概要
／Course outline		 本科目では，コンピュータによる画像処理技術の応用として代表的な“リモートセンシング技術”を教える。
画像取得の為に，波動光学，放射伝達理論を基礎とし，実地形と画像との相関を明らかにする為に，画像の幾何学，各種補正を教え，実践的な画像処理システムとして，ニューラルネットワーク，画像処理エキスパートシステムを説明する。
実例として，対象物の検出，画像再生，画像復元，各種変換による特徴抽出，構造的パターン認識を採り上げ，三次元計測の実際について説明する。
授業の目的と到達目標
／Objectives and attainment
goals		 「リモートセンシング」とは何か，何ができるかを説明できる。光学，立体視，関連の物理を理解する。３次元画像を理解し，リモートセンシングシステムを考えられる。
授業スケジュール
／Class schedule		 ・波動光学の基礎
・画像処理の基礎
・画像の幾何学---投影. 座標変換
・構造的パターン認識
・対象物検出
・ニューラルネットワーク
・画像処理エキスパートシステム
・放射伝達理論の基礎
・画像再生
・画像復元
・補正---幾何補正. 放射量補正. 濃度補正
・データ圧縮
・濃淡情報の変換
・空間的情報の変換
・三次元計測
教科書
／Textbook(s)		 特に使用しない。ＨＰ上のハンドアウトを使用する。
成績評価の方法・基準
／Grading method/criteria		 レポート，授業中の質疑，発表等による評価。
履修上の留意点
／Note for course registration		 以下の内容を理解，習熟していることが望ましい。
基礎物理. 微積分. 線形代数. 画像処理.
コンピュータグラフィックス.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 http://web-int.u-aizu.ac.jp/~asada/graduate/RS.html
・高木幹男，下田陽久 監修, 画像解析ハンドブック, 東京大学出版会.
・R. C. ゴンザレス, P. ウィンツ（R. C. Gonzalez and P. Wints）,
・ディジタル画像処理（Digital image processing）, Addison-Wesly.


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　2学期 ／Second Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 平田　成
担当教員名
／Instructor		 平田　成 , 出村　裕英 , JAXA/NAOJ講師
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 2015/03/16
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		 ITC10 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ITC09を履修したのち，ITC10を履修することが望ましい．

ITC10 Practical Data Analysis with Lunar and Planetary Database is closely connected with this course. ITC10 will introduce more practical topics on planetary data analyses. Students are recommended to finish ITC09 before taking ITC10.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 SPICE toolkit: http://naif.jpl.nasa.gov/naif/
Planetary Data System: http://pds.jpl.nasa.gov/
SELENE (Kaguya) Data archive: http://l2db.selene.darts.isas.jaxa.jp/
Hayabusa project science data archive: http://darts.isas.jaxa.jp/planet/project/hayabusa/


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 出村　裕英
担当教員名
／Instructor		 出村　裕英 , 平田　成 , 小川　佳子 , 本田　親寿 , 北里　宏平 , JAXA/NAOJ講師
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 2015/03/16
授業の概要
／Course outline		 月惑星データ解析の実際を通して、解析とそのためのツール開発を一体で学びます。

本学教員の他に、宇宙航空研究開発機構、国立天文台より講師を招き，遠隔授業を行います。
授業の目的と到達目標
／Objectives and attainment
goals		 主にリモートセンシングデータの解析を取り上げて、月惑星データ解析とそのためのツール開発の基礎を実習を通じて学ぶ。

コンピュータ理工学のトピックとして宇宙開発分野の基礎知識を学ぶ。
授業スケジュール
／Class schedule		 オムニバス形式で講義順は開講時に示します。

２０１４年度実績：
#1-2 出村（会津大） 開講説明、写真勾配法ならびにHapke式の導入
#3-4 本田（会津大）　画像センサの性能解析
#5-6 小川（会津大）　かぐや月分光計データの解析
#7-8 大竹（JAXA）　かぐや月分光カメラデータの解析
#9-10 押上（国立天文台）　かぐや月レーダーデータの解析
#11-12 松本（国立天文台）かぐや月重力場データの解析
#13-14 北里（会津大）　赤外分光計データの解析
#15-16 諸田（名古屋大学）月のクレーター年代学
教科書
／Textbook(s)		 N/A
成績評価の方法・基準
／Grading method/criteria		 出席（プレゼン等）、課題提出、レポート等、各講師指示による。
履修上の留意点
／Note for course registration		 関連科目
ITC08 Remote Sensing
ITC09 Fundamental Data Analysis in Lunar and Planetary Explorations
ITA19 Reliable System for Lunar and Planetary Explorations
SEA11 Software Engineering for Space Programs
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 N/A


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 ヴィジェガス　ジュリアン
担当教員名
／Instructor		 ヴィジェガス　ジュリアン , マイケル　コーエン
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 2015/09/15
授業の概要
／Course outline		 The purpose of this course is to study the fundamentals of audio signal processing and its application to music. Besides reviewing the underlying techniques, this course focuses in practical implementations of sound effects, so the course is intense in hands-on exercises, assignments, and projects mainly based on Matlab/Octave, C/C++, and Pure-data.
授業の目的と到達目標
／Objectives and attainment
goals		 • Students who approve this course are expected to understand the basic techniques employed in computer music, as well as the literature and terminology on this topic.

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

• Upon completion of this course, students should be able to create their own sound effect chain.
授業スケジュール
／Class schedule		 Session 1. Introductions: Course overview, materials, examination, introduction to computer music technologies.

Session 2. Basic Filters: Parametric filters, FIR filters, Convolution, Equalizers, Shelving filters, Peak filters, Wah-wah filter, Phaser, Time-varying equalizers

Session 3. Delay: Basic delay structure, FIR comb filter, IIR comb filter, Universal comb filter, Fractional delay lines, delay-based audio effects, Vibrato, Flanger, chorus, echo, Multi-band effects, Natural sounding comb filter.

Session 4. (continuation)
教科書
／Textbook(s)		 • U. Zolzer, editor. DAFX – Digital Audio Effects. John Wiley & Sons, New York, NY, USA, 2nd edition, 2011.

• Various materials prepared by the instructors
成績評価の方法・基準
／Grading method/criteria		 Exercises and quizzes 40%
Assignments 30%
Final project 30%
履修上の留意点
／Note for course registration		 This class does not have prerequisites, but it is recommended that students be familiarized with Pure-data programming paradigm, and general audio signal processing techniques. These are some classes that students are encouraged to take before this class:

• ITC02 Introduction to Sound and Audio
• ITA07 Advanced Signal Processing
• ITA10 Spatial Hearing and Virtual 3D Sound
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 • Course website: http://arts.u-aizu.ac.jp/courses/ita01/

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

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

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

• Octave documentation: www.gnu.org/software/octave/doc/interpreter/


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　4学期 ／Fourth Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 西村　憲
担当教員名
／Instructor		 西村　憲
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 2015/02/05
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.
授業の目的と到達目標
／Objectives and attainment
goals		 Through this course, students are expected to acquire knowledge about rendering algorithms and their parallelization techniques. Students will also be able to understand how rendering processes are performed in graphics-oriented processors.
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/


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　4学期 ／Fourth Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 朱　欣
担当教員名
／Instructor		 朱　欣
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2015/02/05
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


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 西舘　陽平
担当教員名
／Instructor		 西舘　陽平
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2015/02/10
授業の概要
／Course outline		 This course is a practical introduction to the finite element method. It focuses on algorithms of the finite element method for solid mechanics modeling. Mesh generation and visualization issues are considered.
授業の目的と到達目標
／Objectives and attainment
goals		 The course helps students to understand main algorithms of the finite element method and to gain practical skills in finite element programming.
授業スケジュール
／Class schedule		 1. Introduction. Formulation of finite element equations.  
2. Exercise 1.
3. Finite element method for solid mechanics problems 1.
4. Finite element method for solid mechanics problems 2.
5. Exercise 2.
6. Two dimensional isoparametric elements.
7. Three dimensional isoparametric elements.
8. Exercise 3.
9. Data format for finite element analysis.
10. Regular mesh generation.
11. Exercise 4.
12. Assembly algorithms. Displacement boundary conditions.
13. Solution of finite element equations.
14. Exercise 5.
15. Nonlinear Problems.
16. Visualization of finite element models and results.
教科書
／Textbook(s)		 1. Lecture Notes
2. Gennadiy Nikishkov, Programming Finite Elements in Java. Springer, 2010, 402 pp.
成績評価の方法・基準
／Grading method/criteria		 Exercises - 40%
Project - 40%
Attendance - 20%
履修上の留意点
／Note for course registration		 Calculus, Linear Algebra, Numerical Analysis, and some programming courses are recommended as prerequisites.


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 矢口　勇一
担当教員名
／Instructor		 矢口　勇一
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2015/02/27
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.


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 黄　捷
担当教員名
／Instructor		 黄　捷
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2015/03/12
授業の概要
／Course outline		 Multirate signal processing techniques are widely used in many areas of modern engineering such as communications, digital audio, measurements, image and signal processing, speech processing, and multimedia. A key characteristic of multirate algorithms is their high computational efficiency. The aim of this course is to give students an introduction of the fundamental theory of multirate signal processing and other related topics. Design techniques of FIR filters relevant to the multirate systems, digital filter banks and wavelet analysis will also be summarized.
授業の目的と到達目標
／Objectives and attainment
goals		 Through the course, the student will understand the fundamental theory of multirate signal processing and be able to design multirate filter banks.
授業スケジュール
／Class schedule		 1. Linear time-invariant system, Linear and circular convolution
2. Continuous and discrete Fourier transform, Allpass and Minimum Phase
3. Analytic signal, Time Frequency Analysis
4. Sampling Rate Conversion
5. Decimation and Interpolation
6. Two-channel filter banks
7. Filter banks with Polyphase Structure
8. Octave Filter Banks and wavelets
教科書
／Textbook(s)		 N. J. Fliege, Multirate Digital Signal Processing, John Wiley & Sons 1994
Ljiljana Milić, Multirate Filtering for Digital Signal Processing: MATLAB Applications, Information Science Reference, 2009
J. H. McClellan, et al., Computer-Based Exercise for Signal processing using MATLAB, Penntice Hall, 1994.
成績評価の方法・基準
／Grading method/criteria		 Attendance and discussion (40) and exercises (60)
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 http://web-int.u-aizu.ac.jp/~j-huang/Lecture/ASP/asp.html


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 愼 重弼
担当教員名
／Instructor		 愼 重弼
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2015/02/27
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, HCI(human computer interaction), and its application.

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

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

Pattern recognition on DAR.

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

- pen-based interactive system,

- oriental-pen writing/drawing simulation,

- handwritten font generation,

- signature verification, writer identification,

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

- designing of HCI experiment

- Modeling of HCI,

- smartphone system, other application system,

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

The presentation of some application program.

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

Course Web Site: http://web-int.u-aizu.ac.jp/~jpshin/GS/DAR.html

References:

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


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　2学期 ／Second Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 ヴィジェガス　ジュリアン
担当教員名
／Instructor		 ヴィジェガス　ジュリアン , マイケル　コーエン , 黄　捷
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 2015/02/27
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.
授業の目的と到達目標
／Objectives and attainment
goals		 - Students who pass this course will understand the basic underlying mechanisms of spatial hearing, as well as the literature and terminology on this topic.
- Given some application constraints (real-time, computing power, etc.) students at the end of the term should be able to decide which of the presented techniques is best for creating the 3D aural illusion.
- Upon completion of this course, students should be able to successfully implement virtual 3D sound environments based on head-related transfer functions (HRTFs) and multi-speaker systems.
授業スケジュール
／Class schedule		 Introductions and Pd
Quantification of sound
Spatial hearing and psychoacoustics
Binaural difference cues I
Binaural difference cues II
Head related impulse responses
Room impulse responses
Motion and distance perception I
Motion and distance perception II
Special topics in sound spatialization
Headphone techniques
Loudspeaker techniques I
Loudspeaker techniques II
Workshop on final project
Applications I
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 10%, Assignments 30%, Mid-term project 30%, and Final project 30%
履修上の留意点
／Note for course registration		 Students are expected to read their emails frequently.
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).
- www-crca.ucsd.edu/~msp/Pd_documentation/
- http://hyperphysics.phy-astr.gsu.edu/hbase/sound/soucon.html


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　2学期 ／Second Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 コンスタンティン　マルコフ
担当教員名
／Instructor		 コンスタンティン　マルコフ
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 2015/02/27
授業の概要
／Course outline		 This course introduces audio signal and information processing technologies with application to automatic speech recognition (ASR) and music information retrieval (MIR) tasks. It consists of three main parts: I) Fundamental methods and algorithms, II) Speech processing and recognition, and III) Music information processing and retrieval.  The first part gives a review of the fundamental methods and algorithms used for both speech and music processing such as digital signal processing for feature extraction and machine learning and pattern recognition for model training and information processing. The second part focuses on the specifics of the speech signal and describes the process of building a fully functional speech recognition system. Other speech related tasks, such as speaker and language recognition are also presented. Finally, the last part provides knowledge about music features and how to solve such music information retrieval tasks as music genre or emotion estimation, song similarity or song transcription, etc.
授業の目的と到達目標
／Objectives and attainment
goals		 The objective of this course is to make students familiar with the fundamentals of automatic speech recognition and music information retrieval technologies, as well as to teach them how to build simple ASR and MIR systems including feature extraction, model learning, testing and performance evaluation.
授業スケジュール
／Class schedule		 Part I.  Fundamental methods and algorithms.
          1. Digital signal processing.
                         - Short-time Fourier Analysis.
                         - Cepstral processing, Pitch extraction.
          2. Pattern classification.
                         - Bayes' decision theory,
                         - Classifiers design.
          3. Machine Learning.
                         - Vector quantization, Gaussian Mixture Models.
                         - Hidden Markov Models, Support Vector Machines.
                         - Neural Networks, Gaussian Processes.
Part II.  Speech processing and recognition.
          4. Speech feature extraction.
                         - Mell-Frequency Cepstral Coefficients (MFCC).
                         - Linear Predictive Coding Coefficients (LPCC).
                         - Deep Neural Network based features.
          5. Acoustic modeling.
                         - Context (in)dependent model units.
                         - Lexicon, Viterbi decoding.
          6. Language modeling.
                         - N-grams.
                         - Perplexity.
Part III.  Music information processing and retrieval.
          7. Music feature extraction.
                        - Timbre, Spectrum related featrues.
                        - Chromagram.
          8. Music Information retrieval.
                        - Genre classification.
                        - Mood estimation
                        - Similarity calculation.
教科書
／Textbook(s)		 1. L. Rabiner, B. Juang, Fundamentals Of Speech Recognition, Prentice-Hall, 1993.

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

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

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

Laboratory exercises: 40 points

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

http://web-ext.u-aizu.ac.jp/~markov/html/teaching.html


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開講学期
／Semester		 2015年度／Academic Year 　4学期 ／Fourth Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 イアン　ウイルソン
担当教員名
／Instructor		 イアン　ウイルソン
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 2015/02/27
授業の概要
／Course outline		 本講義では、調音のメカニズムとその計測方法について学習する。また、調音と音響の関連性についても焦点を当てていく。調音については超音波やビデオなどを用いて調べ、音響音声についてはPraat（オープンソースの音響分析ソフトウェア）を使用して調べる。
授業の目的と到達目標
／Objectives and attainment
goals		 本講義を通して、学生は以下のことが可能になる。

(1) 人の音声がどのように生成されるか、また、調音の変化が音響音声にどのような影響を与えるかを説明できる。

(2) 音声データ採集のために超音波機器を扱える。

(3) ソフトウェアを使用して超音波画像を処理できる。

(4) 音響音声を分析でき、また、音響データを自動分析する簡単なスクリプトを書ける。

(5) 波形やフォルマント、ＦＦＴ、サイン波による音声合成といった音響の概念を理解できる
授業スケジュール
／Class schedule		 第１週：音声の生成過程と調音の計測方法

第２週：音声分類における音響特性

第３週：超音波による音声データの採集と分析

第４週：調音と音響の関係I

第５週：調音と音響の関係II

第６週：視聴覚による言語知覚

第７週：音声の変動性I ― 話者間における違い

第８週：音声の変動性II ― 言語間における違い
教科書
／Textbook(s)		 ハンドアウトやその他の資料については、講義のウェブサイト上にアップロードする予定。
成績評価の方法・基準
／Grading method/criteria		 課題やプロジェクトについては後日アナウンスする。


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 サバシュ　バーラ
担当教員名
／Instructor		 サバシュ　バーラ
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 2015/03/02
授業の概要
／Course outline		 Implementation details for new applications will be discussed. The course considers the application side. The topics include Data Modeling, Advanced features of query: languages. Transactions and Recovery systems.
授業の目的と到達目標
／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		 Course Prerequisites
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.


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 ヴィタリー　クリュエフ
担当教員名
／Instructor		 ヴィタリー　クリュエフ
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 2015/02/27
授業の概要
／Course outline		 When the end user needs information, he/she looks on the Internet. The Internet is the source of information of any kind: inquiries, entertainment, science, etc. In this course, we will study the key ideas of text mining, which are available to efficiently organize, classify, label and extract relevant information for today’s information-centric users.
授業の目的と到達目標
／Objectives and attainment
goals		 Text mining can be characterized as a group of techniques to extract useful information from texts. Intelligent information retrieval takes into account the meaning of the words in the texts, order of the words in the user queries, the authority of the document source, and the user feedback. We will present the most advanced models, methods and techniques to provide our students with the state of the art technologies in the area of the intelligent information retrieval and text mining.
授業スケジュール
／Class schedule		 The course covers the basic topics:
1. Exploring Text
2. Information and Knowledge Extraction
3. 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


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 浅田　智朗
担当教員名
／Instructor		 浅田　智朗 , 兼本　茂
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 2015/02/13
授業の概要
／Course outline		 車や鉄道，航空機，家電設備，工場の生産設備など我々が日常利用している電気機器・機械装置では，その安全で効率的な運用に「自動制御」という考え方が欠かせない。計測と制御では，このような機器の状態を「計測」し，その結果に基づいて機械を「制御」するための基本的原理を学ぶ。計測の基本原理としては，センサの動作原理，計測データの処理・分析法，測定誤差の評価法などを学ぶ。また，制御の基本原理としては，周波数領域および時間領域での被制御対象のモデリングと，制御器の設計手法を学ぶ。さらに，先端的なデジタル技術をベースにしたアドバンスト制御についても，応用事例を通じて学んでゆく。
授業の目的と到達目標
／Objectives and attainment
goals		 自動制御の基本として，計測の基本原理，実践法，応用，注意点，計測データの処理，分析法，測定誤差の評価を，制御の基本原理として，周波数領域および時間領域での被制御対象のモデリングと，制御器の設計手法を学ぶ。
授業スケジュール
／Class schedule		 1. 計測と単位系／計測量
2. 測定誤差と精度
3. 最小二乗法とデータの補間
4. 機械的測定／センサ／センシング
5. 信号計測法
6. 信号の処理と分析
7. 動的システムのモデリングと制御
8. フィードバック制御／伝達関数によるモデル化と応答特性
9. フィードバック制御／システムの安定性と制御系設計
10. 状態方程式に基づく制御理論
11. アドバンスト制御理論／ファジー制御・適応制御
12. 制御系の設計シミュレーション演習
教科書
／Textbook(s)		 特に使用しない。ＨＰ上のハンドアウトを使用する。
成績評価の方法・基準
／Grading method/criteria		 レポート，課題
履修上の留意点
／Note for course registration		 特に無し。
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/SC.html


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　後期集中 ／2nd Semester Intensi
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 平田　成
担当教員名
／Instructor		 平田　成 , 出村　裕英 , JAXA/NAOJ講師
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 2015/02/27
授業の概要
／Course outline		 月惑星探査の機器および制御システムの開発を学ぶ。
想定される主ターゲットは月。
主に国立天文台より講師を招き，遠隔授業を行います。
授業の目的と到達目標
／Objectives and attainment
goals		 主に月着陸ミッションを取り上げて、月惑星探査の機器および制御システムの開発を実習を通じて学ぶ。
コンピュータ理工学のトピックとして宇宙開発分野の基礎知識を学ぶ。
授業スケジュール
／Class schedule		 参考として、2014年度の実績を挙げる。
#1-4 花田（国立天文台）　「月面天文台の科学と基礎技術」
#5-7 山田（国立天文台）　「惑星地震学の展開」
#8-10 並木（国立天文台）　「レーザ高度計のパフォーマンスモデル」
#11-14 荒木（国立天文台）　「レーザ測距による月探査」
#15-16 菊地（国立天文台）　「探査機の軌道決定に利用されるVLBIデータの解析手法について」
教科書
／Textbook(s)		 N/A
成績評価の方法・基準
／Grading method/criteria		 出席（プレゼン等）、課題提出、レポート等、各講師指示による。
履修上の留意点
／Note for course registration		 先修科目：
ITC09 Fundamental Data Analysis with Lunar and Planetary Database
関連科目：
ITC10 Practical Data Analysis with Lunar and Planetary Databases
SEA11 Software Engineering for Space Programs
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 https://arashima.u-aizu.ac.jp/groups/alps_openwiki/wiki/4af40/ITA19.html
すべて図書館にある書籍です。

NASAを築いた人と技術　巨大システム開発の技術文化
はやぶさ―不死身の探査機と宇宙研の物語
宇宙開発の５０年　スプートニクからはやぶさまで
衛星設計入門
宇宙工学入門　衛星とロケットの誘導・制御
宇宙工学入門II　宇宙ステーションと惑星間飛行のための誘導・制御
図説 宇宙工学
モデル予測制御
宇宙工学シリーズ
　 宇宙における電波計測と電波航法 （宇宙工学シリーズ 1 ）
　 ロケット工学 （宇宙工学シリーズ 2 ）
　 人工衛星と宇宙探査機 （宇宙工学シリーズ 3 ）
　 宇宙通信および衛星放送 （宇宙工学シリーズ 4 ）
　 宇宙環境利用の基礎と応用 （宇宙工学シリーズ 5 ）
　 気球工学 成層圏および惑星大気に浮かぶ科学気球の技術 （宇宙工学シリーズ 6 ）
　 宇宙ステーションと支援技術 （宇宙工学シリーズ 7 ）
　 イオンエンジンによる動力航行 （宇宙工学シリーズ 8 ）
　 宇宙からのリモートセンシング （宇宙工学シリーズ 9 ）


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　前期集中 ／1st Semester Intensi
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 1.0
責任者
／Coordinator		 白　寅天
担当教員名
／Instructor		 白　寅天
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 2015/03/05
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.
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 and materials will be provided on the Web
成績評価の方法・基準
／Grading method/criteria		 1. Examination    --- 45%
2. Paper Presentation --- 45%
3. Attendance --- 10%
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.


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 朱　欣
担当教員名
／Instructor		 朱　欣
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2015/02/05
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


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 陳　文西
担当教員名
／Instructor		 陳　文西
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2015/03/02
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/


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 白　寅天
担当教員名
／Instructor		 白　寅天
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 2015/03/05
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. This course aims at building up business viewpoints and target to use the big data, to learn technologies and skills to accomplish the business target.
授業の目的と到達目標
／Objectives and attainment
goals		 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. Topics focused are as follows:
- Business Analysis and Data Science
- Big Data Infrastructure and Programming
- Statistical Analysis
- Data Mining
- Big Data Application
- Case Implementation
授業スケジュール
／Class schedule		 - 4月7日 Business Intelligence
- 4月10日Data Science Process
- 4月14日A Scenario of Business Analysis With Data Science Process
- 4月17日Distributed File System, SQL and NoSQL, Hadoop Architecture, MapReduce Programming
- 4月21日Hadoop Exercise: Map-Reduce Programming for Word Count or TF-IDF Calculation
- 4月24日Hadoop Eco System (Hive and Mahout) and Motivation of Statistical Analysis and Data Mining
- 5月7日 Statistical Analysis I: Summarization and Correlation, Multivariate Analysis I
- 5月12日Statistical Analysis II: Multi-Variant Analysis II and Regression Analysis Model
- 5月15日Case Study: Statistical Analysis By R
- 5月19日Data Mining I: Classification and Clustering
- 5月22日Data Mining II: Association and Cluster Analysis
- 5月26日Case Study: Data Mining Exercise by Weka
- 5月29日Project: Situation Awareness and Statistical Analysis on Big Data
- 6月2日 Retrieving, Storing, Querying Big Data
- 6月5日 Mining and Analysis of Big Data for Situation Awareness
- 6月9日Case Implementation
- 6月12日Final Examination
教科書
／Textbook(s)		 The main textbook will be open on lecture Web site.
(Web) http://ebiz.u-aizu.ac.jp/lecture/2015-1/BigDataScience/
成績評価の方法・基準
／Grading method/criteria		 Detailed Grading Policy (Plan)
1)  Examination  -----       45 %
2)  Exercise & Term Project  -----------------    45 %
3)  Attendance (Including Quiz)  ---------------------  10 %
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Other References :
1. Tom White, Hadoop, OREILLLY, 2011
2. Srinath Perera, Thilina Gunarathne, Hadoop Map-Reduce Programming, Packt Publishing, 2013
3. J.H Jeong, Biginning Hadoop Programming: Development and Operations, Wiki Books, 2012


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　前期集中 ／1st Semester Intensi
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 1.0
責任者
／Coordinator		 姫野　龍太郎（理研）
担当教員名
／Instructor		 姫野　龍太郎（理研） , 陳　文西 , 検崎　博生（理研） , 野田 茂穂（理研）
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 2015/03/02
授業の概要
／Course outline		 コンピュータの性能向上と計算手法の進化、そして各種計測手法の発展により、これまで不可能だった生体のシミュレーションが広い範囲で可能になってきている。この生体シミュレーション技術の基礎と現状を、ミクロ（生体分子シミュレーション）とマクロ（生体硬組織・生体流体シミュレーション）両面から学ぶとともに、実習を通してその一部を体験する。生体分子シミュレーションでは、分子動力学のシミュレーションの基礎から実際の応用例までを学ぶとともに、実際に分子動力学シミュレーションを体験する。同様に生体硬組織と生物流体の基礎方程式から解法、実際の応用例を学び、医療画像からの血流シミュレーションを体験する。
授業の目的と到達目標
／Objectives and attainment
goals		 ミクロからマクロまでの生体のシミュレーションの方法の基礎方程式と計算方法とその種々の応用の実際を学ぶ。具体的には、
１） 生体分子：分子動力学シミュレーション
２） 生体硬組織：構造力学の基礎と生体のシミュレーションに必要な非線形構造力学
３） 生体流体：血流を主な対象とした流体シミュレーションの基礎方程式と解法
このうち１）と3）については実習を通して、実際に自分で問題を解けることを目指す。
授業スケジュール
／Class schedule		 1.概要紹介 x 1 姫野龍太郎

2.生体分子シミュレーション：剣崎博生
・基礎理論
・応用 x 1
・演習 x 1

3.生体硬組織シミュレーション: 姫野龍太郎
・基礎理論

4.生体流体シミュレーション
・基礎理論 x 1: 姫野龍太郎
・医療応用 x 1: 姫野龍太郎
・演習 x 1: 野田茂穂

合計８コマ
教科書
／Textbook(s)		 教科書は使わず、必要な教材は資料として提供する。
成績評価の方法・基準
／Grading method/criteria		 各授業内での小テスト：50%
レポート：50%
履修上の留意点
／Note for course registration		 ＰＣを使った実習のために各自PCを持参すること。OSは実行するソフトウェアの関係でWindows 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
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


科目一覧へ戻る
開講学期
／Semester		 2015年度／Academic Year 　4学期 ／Fourth Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 ファン　トウアン　ドゥク
担当教員名
／Instructor		 ファン　トウアン　ドゥク
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 2015/02/06
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.  Particularly this course 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.  
授業の目的と到達目標
／Objectives and attainment
goals		 • Obtain the 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 and 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.
• Be motivated toward pursuing a higher research degree or research-oriented career in industry.
授業スケジュール
／Class schedule		 1. Overview of Computers in Medicine and Biology
2. Probability and Statistics
3. Uncertainty and Information
4. Image Processing Operators
5. Feature Extraction
6. Cluster Analysis
7. Classification Algorithms
8. Practical Applications to Medical Diagnoses
9. Practical Applications to Molecular Biology
10. Challenging Computational Problems in Medicine and Biology
教科書
／Textbook(s)		 There are no prescribed textbooks for this course.
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.
履修上の留意点
／Note for course registration		 • Printed handouts will be distributed to students only during class.
• Lecture notes and published papers are sufficient enough for understanding the covered subjects.
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


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