AY 2018 Graduate School Course Catalog

Field of Study IT: Applied Information Technologies

2019/01/30

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開講学期
／Semester		 2018年度／Academic Year 　4学期 ／Fourth Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Pierre-Alain Fayolle
担当教員名
／Instructor		 Pierre-Alain Fayolle, Yohei Nishidate
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2017/12/12
授業の概要
／Course outline		 This course provides a description of the Java 2D API and the Java 3D API for the development of graphics applications with the Java programming language. While studying the main functionalities of these APIs, we will study how they are implemented and as a consequence look at the same time at some of the main techniques in computer graphics.
授業の目的と到達目標
／Objectives and attainment
goals		 The main objectives of the course are:
* The development of graphics program in Java using the Java 2D and 3D API
* The study of their implementation
* The study of the most common techniques in graphics programming
授業スケジュール
／Class schedule		 * Course introduction, Projects description, Concurrency (programming of multi-threaded applications) in Java
* Java 2D introduction, geometry
* Java 2D painting and stroking
* Java 2D rendering, Porter and Duff work on compositing, Text manipulation with Java 2D
* Java 2D image and image processing
* Interlude: ray-tracing
* Project 1 presentations
* Prelude: Polygon mesh processing and Polygon rendering pipeline
* Java 3D introduction, Scenegraph
* Java 3D shapes
* Java 3D appearance, lights, illumination model and shading
* Java 3D texture mapping
* Java 3D behaviors, and special behaviors
* Project 2 presentations
教科書
／Textbook(s)		 None.
Slides and notes for the course are provided.
成績評価の方法・基準
／Grading method/criteria		 Two projects: each of them has a weight of 50%.
履修上の留意点
／Note for course registration		 Knowledge of Java programming; Some basic knowledge of graphics programming (though we will review the most common techniques)
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Course web-site (internal)



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開講学期
／Semester		 2018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 2018/01/17
授業の概要
／Course outline		 We survey the physics and nature of sound waves (compression &
rarefaction, propagation, transmission, diffusion, diffraction, refraction,
spreading loss, absorption, boundary effects, non-point sources,
reflection, reverberation, superposition, beats & standing waves),
description and representation of sound (analog/digital, complex
analysis, waveforms, pulse code modulation, Fourier analysis),
measurements of sound and audio (sampling, aliasing, decibels,
pressure, power, intensity, level),
synthesis (additive, AM, FM, envelopes, filtering, equalizers,
spatialization, distortion),
psychophysics (loudness, masking, critical bands),
coding and compression (SNR, A-law, u-law, MP3, AAC, parametric stereo),
display and multichannel ("discrete") systems (transducers, 5.1, speaker arrays, WFS),
tuning,
and user interfaces (conferencing, virtual concerts, mixed and virtual reality).
授業の目的と到達目標
／Objectives and attainment
goals		 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, based on workstations and tablets, provide realtime
"hands-on" multimedia
educational opportunities that are stimulating and creative, as
students enjoy intuitive, experiential learning.
Utilized resources include
audio synthesis and multimedia data-flow visual programming (Pure Data),
audio editing and analysis software (Audacity),
interactive physics visualization and auralization physics applets (illustrating wave behavior, DSP, filtering, etc.),
advanced computational and plotting utilities (Mathematica),
effects processing (GarageBand),
and 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		 1. Overview: Course organization, assessment, tablets & courseware, hearing anatomy and physiology, analog vs. digital
2. Hearing: auditory pathway, pinna, psychoacoustics & perception
3. Waves: waveforms, phase, complex numbers, logarithms, FFT & Spectrogram
4. Waves: pressure (compression & rarefaction), propagation, transmission, diffusion, diffraction, refraction, spreading loss
5. Waves: absorption, boundary effects, non-point sources, reflection, reverberation, superposition, beats & standing waves
6. Frequency: tone, register, harmony
7. Harmonic content: harmonics, overtones, timbre, Fourier analysis, sampling theorem, aliasing, AM & FM
8. Musical Frequency: intervals, tone, semi-tone, pitch, octaves, scales
9. Intensity: volume, loudness, PCM
10. Intensity: pressure, power, envelope, RMS, decibels, level, masking
11. Multichannel: stereo, speaker arrays, spatialization
12. Applications; coding & compression, digital recording and audio editing, filtering, equalizers, speech synthesis
13. Time: duration, tempo, repetition, reversal, duty cycle, rhythm & cadence
14. Music: DTM composition, audio effects
教科書
／Textbook(s)		 William M. Hartmann: "Principles of Musical Acoustics" ("PMA"), Kindle edition.
Eric Heller: "Why You Hear what You Hear" ("WYHWYH"), Kindle edition.
Various materials prepared by the instructors.

Besides normal lectures and exercises, we'll also use iPads (one lent to each student for the term) extensively for courseware and interactive projects. A full list of relevant apps is listed on the course home page.
成績評価の方法・基準
／Grading method/criteria		 This course is concerned not only aesthetic issues, but also with technical issues, and as such would be useful to audio engineers and researchers, as well as musicians. Most of the coursework involves reading, homework exercises, and lab projects. There are mid-term and final exams.

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

http://puredata.info

http://puredatajapan.info

http://www.wolfram.com/mathematica/

http://www.apple.com/ios/garageband/

http://www.apple.com/mac/garageband/

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


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開講学期
／Semester		 2018年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Keitaro Naruse
担当教員名
／Instructor		 Keitaro Naruse, Yutaka Watanobe
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2018/02/06
授業の概要
／Course outline		 If we define a robot as a computer interacting with the real world physically, we use so many robots everyday such as elevators, cleaning robots, and so on. For designing, synthesizing, and analyzing robots, knowledge on how to represent robot structure and motion in computers are required, as well as one on sensors, actuators, modeling methods, and planning algorithms.
This course offers the introduction to robotics for graduate students in computer science and engineering major.
授業の目的と到達目標
／Objectives and attainment
goals		 The students will be able to
(A) make a path plan of a mobile robot and robot arm with kinematics
(B) make simulation of them with dynamics
(C) make recognition system of them
授業スケジュール
／Class schedule		 #1 Introduction and overview
#2 Mobile robot: frame transformation, homogeneous transformation, kinematics
#3 Exercise
#4 Robot arms: forward kinematics such as robot representation, frames and coordinate systems, homogeneous transformation, Denavit-Hartenberg method
#5 Exercise
#6 Robot arms: inverse kinematics numerical solution, Jacobian
#7 Exercise
#8 Mobile robots: dynamics, simulation
#9 Excercise
#10 Robot arm: Dynamics, simulation
#11 Excersice
#12 Recognition
#13 Exercise
#14 Exercise/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(100%) on numerical experiments on forward and inverse kinematics, forward dynamics, learning, and so on. We will use Matlab or other mathematical software for them.
履修上の留意点
／Note for course registration		 Introduction to robotics in the undergraduate course
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 http://iplab.u-aizu.ac.jp/moodle/


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開講学期
／Semester		 2018年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Keitaro Naruse
担当教員名
／Instructor		 Keitaro Naruse, Yuichi Yaguchi
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2018/02/06
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 students will be able to
(A) make a state space model of a given system
(B) determine stability, controllability, and observability
(C) design a regulator (controller)
(D) design an observer, including Kalman filter and particle filter
(E) design an optimal controller by linear quadratic regulator
(F) make simulation with matlab
授業スケジュール
／Class schedule		 #1 Introduction and overview
#2 Differential equation and state space model
#3 Excercise
#4 Stability, controllability, and regulator, Lyapnov method
#5 Excercise
#6 Observanility and observer
#7 Excercise
#8 Observer-regualator system, optimal control
#9 Excercise
#10 Discrete time Kalman filter
#11 Excercise
#12 Discrete time Monte Carlo filter
#13 Excercise
#14 Excercise/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(100%) on numerical experiments on control theory, which includes analyzing stability of a dynamical system, designing regulators, and so on.
履修上の留意点
／Note for course registration		 Related courses:
Undergraduate: "Introduction to robotics"
Graduate: "Advanced robotics"
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 http://iplab.u-aizu.ac.jp/moodle/


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開講学期
／Semester		 2018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		 Qiangfu Zhao
担当教員名
／Instructor		 Qiangfu Zhao, 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 2017/12/18
授業の概要
／Course outline		 Learning ability is one of the most fundamental abilities for realizing
“intelligence”. A system with the learning ability can become more and more
efficient and/or effective for solving given problems. Briefly speaking,
machine learning is a research field for studying theories, methodologies,
and algorithms that enable computing machines to learn and to become
intelligent. So far, many approaches have been proposed in the literature
for machine learning; and multilayer perceptron, convolutional neural
network, Bayesian network, and decision tree are just a few examples. In
this course, we categorize many existing approaches into a few groups,
namely, learning based on distance, learning based on probability, learning
based on layered structures, and learning based on tree structures. We do
not intend to cover all aspects of machine learning in this single course.
Instead, we will focus on several most well-known and well-applied
approaches. We suppose that, before taking this course, the students have
already studied some fundamental courses related to machine learning, say,
“Artificial intelligence” for undergraduate school, “Introduction to neural
networks” for graduate school, and so on. To know more about machine
learning or AI in general, we recommend the students to take other related
courses. For example, in the graduate school, the students may also take
courses related to big-data analysis; ontology and semantic web;
information retrieval; meta-heuristics; and so on.
授業の目的と到達目標
／Objectives and attainment
goals		 The main goal of this course is to study and understand the basic
concepts and mechanisms of several well-known and well-applied machine
learning approaches, including for example, k-means, self-organization;
Naïve Bayes classification; support vector machine; convolutional neural
network; deep auto-encoder; deep Boltzmann machine; Bayesian network;
decision tree, AdaBoost, random forest, etc. To reinforce the learned
knowledge, students will do some team projects in groups. Through these
projects, students will solve some real-life or synthesized problems using
some of the learned methods.
授業スケジュール
／Class schedule		 1 History of machine learning and artificial intelligence

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

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

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

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

5.     Statistical learning methods-2
- Bayesian network

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

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

8.     Learning based on layered structures-1
- Multilayer perceptron
- Support vector machine

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

10.  Learning based on layered structures-3
- Deep auto-encoder
- Restricted Boltzmann machine

11.  Learning based on tree structures-1
- Decision trees
- Multi-variate decision trees

12.  Learning based on tree structures-1
- Random forest
- AdaBoost

13.  Project II:
- Try one of the methods studied in lectures 9-13 for finding a “pattern
classifier”.
- Using at least two public datasets to verify the performance of the
method.
- Try to evaluate the performance using k-fold cross validation (k=3 or 5).

14.  Presentation of Project II
- Each team makes a 10 minutes presentation
- Evaluation will be based on the correctness, novelty, and understandability.
教科書
／Textbook(s)		 There is no textbook. We will distribute reading materials in the classes.
成績評価の方法・基準
／Grading method/criteria		 - Quiz: 20 points
- Project presentations and reports: 80 points
履修上の留意点
／Note for course registration		 The following courses are useful for this course:

- Probability and statistics (undergraduate course)
- Algorithms and data structures (undergraduate course)
- Artificial intelligence (undergraduate course)
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 1.     Machine learning, Tom M. Mitchell, McGraw-Hill, 1997.

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

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

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


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開講学期
／Semester		 2018年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Xin Zhu
担当教員名
／Instructor		 Xin Zhu, Wenxi Chen
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2017/12/12
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 familar 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		 Homework 60%
Project 40%
履修上の留意点
／Note for course registration		 Probability and statistics
Physics and chemistry
Database and network
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 東京大学　バイオインフォマティクス集中講義　監修： 高木　利久
バイオインフォマティクス事典　日本バイオインフォマティクス学会編集
日本バイオインフォマティクス学会　（http://www.jsbi.org/）
バイオインフォマティクス技術者認定試験（http://www.jsbi.org/nintei/）


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開講学期
／Semester		 2018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 2018/02/07
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, 100％
履修上の留意点
／Note for course registration		 Physics and chemistry
Electricity and electronics
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 http://i-health.u-aizu.ac.jp/IBSD/


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開講学期
／Semester		 2018年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Naru Hirata
担当教員名
／Instructor		 Hirohide Demura, Naru Hirata
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2018/02/28
授業の概要
／Course outline		 Generally, remote sensing refers to the activities of measurement the state of an object at far away. In many cases, electromagnetic waves including light are used as a means of sensing. In the narrower sense, remote sensing is observation of the Earth and other bodies with sensors on various platforms includes artifical satellites and airplanes.
This course outlines the wide aspects of remote sensing technology at first. Then, we will forcus on remote sensing by spacecraft. Detailed processes of data acquisition, reduction, analysis and interpretation of remote sensing data will be described.
Physical and mathematical knowledge is another topic of this course, because it is a important background to achieve scientifically practical measurement.
授業の目的と到達目標
／Objectives and attainment
goals		 By the end of the course, Student will
- Understand the concepts, features and usefulness of remote sensing
- Acquire knowledge and skills of computer science and engineering related to acquisition, analysis and interpretation of remote sensing data
- Obtain relevant mathematics / physics knowledge.
授業スケジュール
／Class schedule		 1 Course guidance
2 Introduction to Remote Sensing
3-4 Physical backgroud on Remote Sensing
5-6 Platform and Sensor for Remote Sensing
7-8 Characteristics of Remote Sensing Data
9 Radiometric Calibration of Remote Sensing Data
10 Geometric Correction of Remote Sensing Data
11 Multiband image data analysis
12 Geographic Information System (GIS)
13 Synthetic Aperture Radar (SAR)
14 Global Positioning System (GPS)
教科書
／Textbook(s)		 N/A
成績評価の方法・基準
／Grading method/criteria		 Homework, exercises and Class activites
Homework and exercises: 80%
Class activites: 20%
履修上の留意点
／Note for course registration		 Physics, Calculus, Linear Algebra, Image Processing, and Computer Graphics are recommended as prerequisites.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Image Processing and GIS for Remote Sensing: Techniques and Applications, Liu and Mason, 2016
https://www.amazon.co.jp/dp/1118724208/


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開講学期
／Semester		 2018年度／Academic Year 　2学期 ／Second Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Naru Hirata
担当教員名
／Instructor		 Naru Hirata, Hirohide Demura, JAXA/NAOJ Lecturers
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2018/02/28
授業の概要
／Course outline		 This course introduces fundamental knowledge on data analysis in lunar and planetary explorations. Ancillary information including spacecraft location and attitude is essential to handle data obtained by science instruments on board a spacecraft. We will study and exercise on handling and utilization of spacecraft ancillary data. Some parts of the lecture are given by guest lecturers from Japanese Space Exploration Agency (JAXA) and National Astronomical Observatory of Japan (NAOJ) via videoconference system.
授業の目的と到達目標
／Objectives and attainment
goals		 By the end of the course, students will have learned basic technologies to analyze lunar and planetary exploration data and be able to develop tools or software for exploration data analysis.
Student will also gain knowledge of handling of ancillary information with SPICE toolkit developed by NASA.
授業スケジュール
／Class schedule		 - Week 1
  - Introduction
- Week 2
  - Ancillary data and SPICE toolkit
  - Epoch information
- Week 3
  - Reference frame
  - Trajectory and Position of spacecraft
- Week 4
  - Conversion of refernce frame
- Week 5
  - Attitude of spacecraft
- Week 6
  - Shape model
- Week 7
  - Ephemeris and SPICE toolkit
教科書
／Textbook(s)		 N/A
成績評価の方法・基準
／Grading method/criteria		 Homework, exercises and Class activites
Homework and exercises: 80%
Class activites: 20%
履修上の留意点
／Note for course registration		 ITC08 Remote Sensing are recommended as prerequisites.
ITC10 Practical Data Analysis with Lunar and Planetary Database is closely connected with this course. ITC10 will introduce more practical topics on planetary data analyses. Students are recommended to finish ITC09 before taking ITC10.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 SPICE toolkit: http://naif.jpl.nasa.gov/naif/
Planetary Data System: http://pds.jpl.nasa.gov/
SELENE (Kaguya) Data archive: http://l2db.selene.darts.isas.jaxa.jp/
Hayabusa project science data archive: http://darts.isas.jaxa.jp/planet/project/hayabusa/


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開講学期
／Semester		 2018年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Hirohide Demura
担当教員名
／Instructor		 Hirohide Demura, Naru Hirata, Yoshiko Ogawa, Chikatoshi Honda, Kohei Kitazato, JAXA/NAOJ Lecturers
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2018/02/16
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		 #1 Demura (UoA) Photoclinometry, Hapke Photometric Function
#2 Honda (UoA) Performance Test of imaging sensors
#3 Kitazato (UoA) Spectroscopic Analysis for asteroids
#4 Ogawa (UoA) Spectroscopic Analysis for lunar and planets
#5 Matsumoto (NAOJ) Gravity field of the Moon
#6 Morota (Nagoya Univ.) Crater Chronology
#7 Ohtake (JAXA) Kaguya Data Analysis of the moon for multi band images
教科書
／Textbook(s)		 N/A
成績評価の方法・基準
／Grading method/criteria		 Comprehensive evaluation based on class activities (presentations, Q&A) and reports for each professor
履修上の留意点
／Note for course registration		 Related courses:
ITC08A "Remote Sensing"
ITC09A "Fundamental Data Analysis in Lunar and Planetary Explorations"
ITA19 "Reliable System for Lunar and Planetary Explorations"
SEA11 "Software Engineering for Space Programs"
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 N/A


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開講学期
／Semester		 2018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		 Satoshi Nishimura
担当教員名
／Instructor		 Satoshi Nishimura, Shigeo Takahashi
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2018/02/06
授業の概要
／Course outline		 This course provides fundamentals of 3D Computer Graphics (CG) and its hardware implementation, which is followed by the recent advancement of CG rendering techniques with GPUs.
授業の目的と到達目標
／Objectives and attainment
goals		 Through this course, students are expected to acquire fundamental knowledge about rendering algorithms and their parallelization techniques.  Students will also be able to obtain basic skills of GPU programming with the OpenGL Shading Language.
授業スケジュール
／Class schedule		 1. Introduction
2. Shape Modeling
3. Geometry Calculation
4. Rasterization
5. Lighting and Shading
6. Texture Mapping and Shadowing
7. Advanced Rendering Techniques
8. Volume Rendering
9. Fundamentals of Shader Programming
10. GPU-based Texture Mapping
11. GPU-based Lighting and Shading
12. GPU-based Normal Mapping
13. GPU-based Shadowing
14. Assignment Presentation
教科書
／Textbook(s)		 * J. D. Foley, A. van Dam, Computer Graphics, 2nd edition, 1995.
* T. Sagishima, T. Nishizawa, and S. Asahara, Parallel Processing for Computer Graphics (in Japanese), Corona Publishing, 1991.
* OpenGL Tutorial (http://www.opengl-tutorial.org/)
* Handouts
* Selected journal/conference papers
成績評価の方法・基準
／Grading method/criteria		 Presentation (75%), Reports (25%)
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 http://web-int.u-aizu.ac.jp/~nisim/cg_gpu/


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開講学期
／Semester		 2018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 2018/02/07
授業の概要
／Course outline		 Recently, there have been very large and complex data sets from nature, sensors, social networks, enterprises increasingly based on high speed computers and networks together.
Big data is the term for a collection of the data sets that it becomes difficult to process using on-hand database management tools or traditional data processing applications.
Data science is a novel term that is often used interchangeably with competitive intelligence or business analytics,
and it seeks to use all available and relevant data to effectively tell a story that can be easily understood by non-practitioners.
Data science based on the big data is expected to provide very potent prediction and analysis for information and knowledge of various fields of researches and businesses from the new data set.
Main objective of this course is to build up business viewpoints and target to use the big data, to learn technologies and skills to accomplish the business target.
Business targeting and modeling, decision making, data science process, database for big data, statistical analysis, data mining,
and how to use the technologies to achieve the business goal will be studied in detail.
授業の目的と到達目標
／Objectives and attainment
goals		 In this course, introductory knowledge and skill for big data analysis process and technology will be covered.
In detail, CRISP-DM for data analysis process, Hadoop and Spark platform for big data infrastructure, statistical analysis and several machine learning techniques for data analysis, and deep learning with Tensorflow will be studied by lecture and exercise.
Students can have broad and necessary knowledge and technique for data analysis on big data infrastructure.

1. Business Intelligence
授業スケジュール
／Class schedule		 1. Business Intelligence
2. Data Science Process
3. A Scenario of Business Analysis With Data Science Process (Ex: Market Analysis By Twitter)
4. Distributed File System, SQL and NoSQL, Hadoop Architecture, MapReduce Programming
5. Hadoop Exercise: Map-Reduce Programming for Word Count or TF-IDF Calculation
6. Hadoop Eco System (Hive and Mahout) and Motivation of Statistical Analysis and Data Mining
7. Statistical Analysis I: Summarization and Correlation, Multivariate Analysis I
8. Statistical Analysis II: Multi-Variant Analysis II and Regression Analysis Model
9. Case Study: Statistical Analysis By R
10. Data Mining I (Shallow Learning)
11. Data Mining II (Deep Learning)
12. Deep Learning Architecutres: MPN, CNN, RNN, Reinforcement Learning
13. Deep Learning Exercise by Tensorflow
14. Term Project and/or Examination
教科書
／Textbook(s)		 Lecture Slide: Will be provided on the lecture Web site
成績評価の方法・基準
／Grading method/criteria		 Examination  -----       50 %
Exercise LAB (Including Term Project, Attendance)  -----------------    50 %

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


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開講学期
／Semester		 2018年度／Academic Year 　2学期 ／Second Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Julian Villegas
担当教員名
／Instructor		 Julian Villegas, Michael Cohen
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2017/12/15
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 such techniques, 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.
授業スケジュール
／Class schedule		 Session 1. Introductions
Session 2. FFT workshop
Session 3. Consonance origin
Session 4. Consonance workshop
Session 5. Shepard Tones
Session 6. Shepard Tone workshop
Session 7. PSOLA and DTW
Session 8. PSOLA workshop
Session 9. Intro to Pd audio object programming
Session 10. Pd object programming workshop
Session 11. Intro to Faust
Session 12. Faust Workshop
Session 13. Intro to sound and microcontrollers
Session 14. Microcontroller Workshop
教科書
／Textbook(s)		 • U. Z ̈olzer, 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%
Workshops 60%
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 • Course website: http://onkyo.u-aizu.ac.jp/index.php/classes/music-tech/
• Theory and Techniques of Electronic Music (M. Puckette): http://msp.ucsd.edu/techniques.
htm
• Matlab documentation: www.mathworks.com/help/matlab/


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開講学期
／Semester		 2018年度／Academic Year 　4学期 ／Fourth Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Xin Zhu
担当教員名
／Instructor		 Xin Zhu
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2017/12/12
授業の概要
／Course outline		 Biomedical modeling and visualization is an important technology to extract useful information and discover the biomedical mechanisms buried in the huge amount of data produced in the basic biomedical researches and clinical medical practice. This course will introduce how to implement computer information technology in biomedical modeling and visualization. Main lecture contents include computer modeling and simulation of biological cells, organs, and systems, mathematical basis for biomedical modeling and simulation, physiological modeling and simulation, and biomedical visualization. Homework and projects will be assigned based on measured data in Biomedical Information Technology lab and medical database available in the Internet.
授業の目的と到達目標
／Objectives and attainment
goals		 This course will help students to obtain the skills and experiences in implementing computer information technology to biomedicine. Through this course, it will strengthen students' R&D ability in future biomedical research and work.
授業スケジュール
／Class schedule		 1. Biomedical modeling and visualization: its application in clinical and basic medicine
2. Mathematical basis for biomedical modeling and simulation
3. Cellular level modeling and simulation: Hodgkin-Huxley model
4. Tissue level modeling and simulation: rule-based model and reaction-diffusion model
5. Construction and visualization of biological models with realistic shapes
6. Organic modeling and simulation: whole-heart model
7. Computer simulation of arrhythmias: atrial fibrillation, supraventericular tachycardias, and ventricular fibrillation
8. Physiological modeling and simulation: heart rate variability, and its linear and nonlinear dynamics
9. Topics on other biomedical modeling and simulation: cerebral networks, bioheat transfer, biomechanics, biofluid mechanics, and etc.
10. General-purpose GPU in biomedical modeling and visualization
教科書
／Textbook(s)		 Handout will be distributed in class.
成績評価の方法・基準
／Grading method/criteria		 Homework              60%
Project an presentation   40%
履修上の留意点
／Note for course registration		 Digital signal processing
Computer graphics
Biomedical information technology
Image processing
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 http://www.physiome.jp/
http://www.physiome.org.nz/
http://www.nlm.nih.gov/
http://ecg.mit.edu/
http://www.u-aizu.ac.jp/~zhuxin/course


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開講学期
／Semester		 2018年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Yohei Nishidate
担当教員名
／Instructor		 Yohei Nishidate
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2017/12/13
授業の概要
／Course outline		 This course is a practical introduction to the finite element method. It focuses on algorithms of the finite element method for solid mechanics modeling. Mesh generation and visualization issues are considered.
授業の目的と到達目標
／Objectives and attainment
goals		 The course helps students to understand main algorithms of the finite element method and to gain practical skills in finite element programming.
授業スケジュール
／Class schedule		 1. Introduction. Formulation of finite element equations.  
2. Exercise 1.
3. Finite element method for solid mechanics problems 1.
4. Finite element method for solid mechanics problems 2.
5. Exercise 2.
6. Two dimensional isoparametric elements.
7. Three dimensional isoparametric elements.
8. Exercise 3.
9. Data format for finite element analysis.
10. Regular mesh generation.
11. Exercise 4.
12. Assembly and solution of finite element equations.
13. Exercise 5.
14. Visualization of finite element models and results.
教科書
／Textbook(s)		 1. Lecture Notes
2. Gennadiy Nikishkov, Programming Finite Elements in Java. Springer, 2010, 402 pp.
成績評価の方法・基準
／Grading method/criteria		 Exercises - 40%
Project - 40%
Attendance - 20%
履修上の留意点
／Note for course registration		 Calculus, Linear Algebra, Numerical Analysis, and some programming courses are recommended as prerequisites.


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開講学期
／Semester		 2018年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Yuichi Yaguchi
担当教員名
／Instructor		 Yuichi Yaguchi
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2017/12/12
授業の概要
／Course outline		 In order to determine your research theme related computer vision and image processing, you need to know the latest status of these fields. Actually, image processing needs many technical and conceptual background from computational algorithms such as Monte-Carlo, forests, dynamic programming, belief propagation, statistical analysis and so on.
In the lecture of image processing in the undergraduate course, we learned the concept of digital images and some basic techniques for analyzing image patterns, and this course provides fundamental algorithms how to understand images or patterns and the status which is necessary technically and conceptually to conduct your master/doctor thesis.
授業の目的と到達目標
／Objectives and attainment
goals		 We aim to present the fundamental knowledge for reading and writing academic papers related computer vision and image processing.
授業スケジュール
／Class schedule		 (Plan of Topic)
- Clustering
- Pattern Matching
- Segmentation
- Image Feature
- Understanding & Recognition
- Photo Bundle & Stereo
教科書
／Textbook(s)		 Main Coursebook - Richard Szeliski, Computer Vision: Algorithms and Applications.
(Not need to buy this book, but very helphul for understanding.)

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

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


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開講学期
／Semester		 2018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 2018/02/06
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		 Discussion (20), Exercises(60) and reports(20)
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 http://web-int.u-aizu.ac.jp/~j-huang/Lecture/ASP/asp.html


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開講学期
／Semester		 2018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 2018/01/11
授業の概要
／Course outline		 This course is primarily concerned with the methods for pattern processing involved in human action. We discuss advanced technology and paradigms for human action pattern processing and create new ideas pertaining to the topics covered. Especially, we focus on the current technologies related to on-line/off-line pattern recognition, pattern analysis, and applications of pattern processing using pen-tablets, touch panels, Kinects, IOS/Android smart devices, and game devices. We study on the related topics on human action pattern processing from the 3 points of view; recognition, authentication, and synthesis.
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 an overview of the pattern processing area related to human action.
- know how various techniques for human action pattern processing can be implemented in software.
授業スケジュール
／Class schedule		 Introduction to human action pattern processing
Fundamentals of on-line/off-line pattern recognition
Pattern recognition involved in human action (HA)
Current problems and solving methods associated with the following topics:
- Pen-based interactive systems
- Handwritten font generation
- Signature verification and writer identification system
- Human computer interaction (HCI) using laser pointer
- Brush painting systems
- HCI using calligraphy systems
- Gesture recognition using Kinect, Leap motion, Myo, and Wii remote controller
- Design of experiments associated with human action pattern processing
- HCI for smart and mobile devices
- Applications of image recognition and computer vision
The presentation of some application programs
Students' work:
- Investigation, presentation, and discussion of current techniques and producing new ideas.
- Programming related to pattern processing.
教科書
／Textbook(s)		 Materials collected from books and papers published in journals and proceedings 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 instructor.
Interest in the area of pattern processing.

参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Useful Links:
Course Web Site: http://web-int.u-aizu.ac.jp/~jpshin/GS/APP.html
References:
[1] Scott MacKenzie, Human-Computer Interaction: An Empirical Research Perspective (2013) ISBN-10: 0124058655
[2] Jonathan Lazar, Jinjuan Heidi Feng, Harry Hochheiser, Research Methods in Human-computer Interaction, Wiley; ISBN-10: 0470723378 (2010)
[3] Alan Dix, Janet E. Finlay, Gregory D. Abowd, Russell Beale, Human-Computer Interaction (2003)  ISBN-10: 0130461091
[4] C. Downton, S. Impedovo, Progress in Handwriting Recognition, World Scientific; ISBN-10: 981-02-3084-2 (Sep. 1996)
[5] S.-W. Lee, Advances in Handwriting Recognition, World Scientific; ISBN-10: 981-02-3715-4 (1999)


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開講学期
／Semester		 2018年度／Academic Year 　2学期 ／Second Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Julian Villegas
担当教員名
／Instructor		 Julian Villegas, Michael Cohen, Jie Huang
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2017/12/15
授業の概要
／Course outline		 The purpose of this course is to study the fundamentals of spatial hearing and its application to virtual environments. By using two ears, human among other species, are able to determine the direction from where a sound is being emitted in a real environment. For virtual environments (e.g., movies, games, recorded or live concerts) is desirable to provide the spatial cues found in nature to increase the realism of the scene. Besides reviewing the underlying theories of spatial hearing, this course focuses in practical implementations of binaural hearing techniques, so the course is intense in hands-on exercises, assignments, and projects mainly based on Pure-data programming language (http://puredata.info).
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 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 (HRTF) and multi-speaker systems.
授業スケジュール
／Class schedule		 Session 1. Introductions and motivation
Session 2. Spatial hearing and psychoacoustics
Session 3. Binaural difference cues
Session 4. (continuation)
Session 5. Elevation cues
Session 6. Distance cues
Session 7. Head-related impulse response and transfer function
Session 8. (continuation)
Session 9. Motion cues
Session 10. Room cues
Session 11. Loudspeaker techniques Session 12. (continuation)
Session 13. Applications
Session 14. Presentation of final projects
教科書
／Textbook(s)		 • Durand R. Begault, 3-D Sound for Virtual Reality and Multimedia, Academic Press, 2000.
• Jens Blauert, The Technology of Binaural Listening (Modern Acoustics and Signal Processing) • Various materials prepared by the instructors
成績評価の方法・基準
／Grading method/criteria		 Quizzes 30%
Assignments 15%
Mid-term project 25%
Final project 30%
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 • Course website: http://onkyo.u-aizu.ac.jp/index.php/3d-sound/
• J. Blauert, Spatial Hearing: The Psychophysics of Human Sound Localization. MIT Press, 1997.
• Bregman, Albert S., Auditory Scene Analysis: The Perceptual Organization of sound. Cambridge, Massachusetts: The MIT Press, 1990 (hardcover)/1994 (paperback).


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開講学期
／Semester		 2018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 2018/02/07
授業の概要
／Course outline		 This course is focused on the use of educational technology for language teaching. Each student will create a final project that demonstrates the lessons and work involved in this class. Final projects will exemplify authentic language products, such as lesson modules, employing audio and video recording.
授業の目的と到達目標
／Objectives and attainment
goals		 Students will:
- Create a customized CMS lesson that incorporates audio and video technology.
- Enhance the lesson by making use of Moodle activities and resources, such as quizzes and other supporting materials.
- Use customized modules, themes, activities, blocks and/or other augmentations to Moodle.
- Outline 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 and systematic manner.
- Describe the decision making process that informed the creation of the project.
授業スケジュール
／Class schedule		 Meeting 1 & 2: Introduction; Storytelling in video.  Scripts and storyboards. Framing and shot composition
Meeting 3 & 4: Planning; Video recording basics: imaging, visual composition, color, light, tripod
Meeting 5 & 6: Audio control. External microphones; Interviews
Meeting 7 & 8: Shot selection; Digital editing: Basics, titling and subtitling, transitions, layering, motion & speed control
Meeting 9 & 10: Field lighting techniques; Time and space in video. Continuity
Meeting 11 & 12: Uploading to YouTube; Presentations
Meeting 13 & 14: Presentations; Presentations
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 lesson that incorporates audio and video technology. 20%
- Enhance the lesson by making use of Moodle activities and resources, such as quizzes and other supporting materials. 15%
- Use customized modules, themes, activities, blocks and/or other augmentations to Moodle. 15%
- Outline 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 and 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		 2018年度／Academic Year 　4学期 ／Fourth Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Ian L. Wilson
担当教員名
／Instructor		 Ian L. Wilson
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2018/01/12
授業の概要
／Course outline		 This course introduces the mechanisms of speech articulation and how to measure them. It also investigates the mapping between articulation and acoustics. Articulation is investigated using tools such as ultrasound and video. Speech acoustics is investigated using Praat – open-source acoustic analysis software.
授業の目的と到達目標
／Objectives and attainment
goals		 After completing this course, students will be able to:

(1) describe how human speech is produced and how changes in articulation affect the acoustics of speech

(2) use an ultrasound machine to collect speech data

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

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

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

Week 3: Ultrasound speech data collection and analysis

Week 4: Mapping of articulation to acoustics I

Week 5: Mapping of articulation to acoustics II

Week 6: Audio-visual speech perception

Week 7: Phonetic variability I - within and across speakers

Week 8: Phonetic variability II - within and across languages; final project (in class)
教科書
／Textbook(s)		 Handouts and other materials will be made available on the course website in Schoology.
成績評価の方法・基準
／Grading method/criteria		 Participation:  35%
Assignments (Praat script writing, etc.):  35%
Final project:  30%
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 CLR Phonetics Lab website: CLR Phonetics Lab


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開講学期
／Semester		 2018年度／Academic Year 　4学期 ／Fourth Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Vitaly V. Klyuev
担当教員名
／Instructor		 Vitaly V. Klyuev
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2018/02/06
授業の概要
／Course outline		 When the end user needs information, he/she looks on the Internet. The Internet is the source of information of any kind: inquiries, entertainment, science, etc. In this course, we will study the key ideas of text mining, which are available to efficiently organize, classify, label and extract relevant information for today’s information-centric users.
授業の目的と到達目標
／Objectives and attainment
goals		 Text mining can be characterized as a group of techniques to extract useful information from texts. Intelligent information retrieval takes into account the meaning of the words in the texts, order of the words in the user queries, the authority of the document source, and the user feedback. We will present the most advanced models, methods and techniques to provide our students with the state of the art technologies in the area of the intelligent information retrieval and text mining.
授業スケジュール
／Class schedule		 The course covers the basic topics:
1. Exploring Text
2. Information and Knowledge Extraction
3. Semantic Retrieval of Text Documents
4. Opinion Mining
5. First Story Detection
6. Summarization
7. Questions & Answers
8. Trends in Modern Information Retrieval
Programming assignments: There will be several programming assignments. Their aim is to investigate various IR and web search tasks.
教科書
／Textbook(s)		 Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More by Matthew A. Russell, O'Reilly Media; 2 edition, 2013.

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

Christopher D. Manning, Prabhakar Raghavan and Hinrich Schutze, Introduction to Information Retrieval, Cambridge University Press. 2008
On-line version:
https://nlp.stanford.edu/IR-book/
成績評価の方法・基準
／Grading method/criteria		 The final grade will be calculated based on the following weights:
Assignments  -  50%
Quizzes during lectures  -  15%
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/





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開講学期
／Semester		 2018年度／Academic Year 　2学期 ／Second Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Yoichi Tomioka
担当教員名
／Instructor		 Yoichi Tomioka, Noriaki Asada
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2017/12/07
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. Introduction
2. Instrumentation and Unit system/ Instrumentation amount
3. Error and Accuracy in measurement
4. Least square method/ Interpolation
5. Instrumental measurement/ Sensor/ Sensing
6. Signal instrumentation
7. Processing and analysis of signals
8. Modeling and control of dynamic system
9. Feedback control/ Modeling and response analysis by frequency response function
10. Exercise: "System modeling and Analysis"
11. Feedback control/ Stability and system design
12. Exercise of controller design simulation 1
13. Advanced control theory/ Fuzzy control
14. Exercise of controller design simulation 2
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: 100%
履修上の留意点
／Note for course registration		 No prerequisite.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 http://web-int.u-aizu.ac.jp/~ytomioka/graduate/SC.html


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開講学期
／Semester		 2018年度／Academic Year 　後期集中 ／2nd Semester Intensi
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Naru Hirata
担当教員名
／Instructor		 Naru Hirata, Hirohide Demura, JAXA/NAOJ Lecturers
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2017/12/14
授業の概要
／Course outline		 This course focuses on developments of hardware instruments and control system for lunar and planetary explorations. Envisioned main target is the moon. This course follows an omnibus form given by invited lecturers (teleclasses) from JAXA, NAOJ, etc.
授業の目的と到達目標
／Objectives and attainment
goals		 To learn developments of hardware instruments and control system for landing missions.
To learn basic knowledge in space developments as topics of computer science and engineering.
授業スケジュール
／Class schedule		 Example: Schedule in AY2015
#1-4 Prof. Hanada (NAOJ) Science and Technology of Lunar Observatory
#5-7 Prof. Yamada (NAOJ) Lunar and Planetary Seismology
#8-10 Prof. Namiki (NAOJ) Modeling of Performance of a Laser Range Finder
#11-14 Prof. Araki (NAOJ) Lunar Laser Range Finder
#15-16 Prof. Kikuchi (NAOJ) Orbit Determination of Spacecraft by VLBI
教科書
／Textbook(s)		 N/A
成績評価の方法・基準
／Grading method/criteria		 Attendances (Presentations), Homeworks, or Reports every professors.
履修上の留意点
／Note for course registration		 preriquisite：
ITC09 Fundamental Data Analysis with Lunar and Planetary Database
related course：
ITC10 Practical Data Analysis with Lunar and Planetary Databases
SEA11 Software Engineering for Space Programs
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 https://arashima.u-aizu.ac.jp/groups/alps_openwiki/wiki/4af40/ITA19.html
References in UoA Library (in Japanese)


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開講学期
／Semester		 2018年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Xin Zhu
担当教員名
／Instructor		 Xin Zhu
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2017/12/12
授業の概要
／Course outline		 Biomedical imaging has been an essential diagnostic and therapeutic tool in clinical and basic medicine since the invention of X-ray photographer. Current imaging technology include X-ray photographer, X-ray CT, MRI, ultrasonic imaging, nuclear medicine imaging, endoscopic and laparoscopic imaging technology, and etc. Nowadays, the advancement of medicine requires the scientists and engineers to invent novel imaging modalities, improve the imaging quality and speed of current technology, and the software for accurate and quick analysis of medical images. We expect to train our students to obtain the physical and mathematical knowledge of biomedical imaging, understand the characteristics of different imaging technologies, and have the ability to do further research in biomedical image processing and analysis.
授業の目的と到達目標
／Objectives and attainment
goals		 We will train our students to master the theoretical basis of biomedical imaging, understand the characteristics and utilities of different imaging technologies, and have some basic abilities to conduct biomedical image processing and analysis.
授業スケジュール
／Class schedule		 1. X-ray CT: Basis of physics and mathematics, system and reconstruction algorithms
2. MRI: Physics and chemistry, system and reconstruction algorithms
3. Ultrasonic imaging: Physics, transducer, and A/B/C/D/F/M modes
4. Nuclear medicine and other imaging modalities: PET, SPECT, OCT, EIT, molecular imaging, and etc.
5. Endoscope and laparoscope: Basis of optics, CCD, CMOS, applications in diagnosis and therapies, and recent development
6. Image processing: Artifacts removal, enhancement, transformation, and etc.
7. Image segmentation: Laplacian filter, snake deformation, and region growing
8. Characteristic extraction from medical images: Preprocessing, region of interest, texture analysis, and characteristic extraction
9. Image information retrieval and registration: Retrieval and analysis of shape and texture, and image registration
10. Computer-aided diagnosis: Reviews on statistics, Bayes’ theorem, classification algorithms, cluster analysis, mammography and angiography
11. Special lecture by outside specialist
12. 3D visualization: Automatic and semi-automatic 3D image reconstruction from 2D slices
13. Surgical navigation system: Imaging and image processing technology for surgical navigation system
教科書
／Textbook(s)		   Mathematics and Physics of Emerging Biomedical Imaging by National Research council (free download from http://www.e-booksdirectory.com/details.php?ebook=3692),
Handout will be distributed in class.
成績評価の方法・基準
／Grading method/criteria		 Homework 60%
Project 40%
履修上の留意点
／Note for course registration		 Physics and chemistry
Electricity and electronics
Probability and statistics
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 はじめての核医学画像処理　
http://www.ne.jp/asahi/ma-ku/104216/
C言語で学ぶ医用画像処理　著者：広島国際大学保健医療学部　石田 隆行　編　オーム
社


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開講学期
／Semester		 2018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 2018/02/07
授業の概要
／Course outline		 Biosignal enhancement, feature extraction and physiological interpretation are
important aspects in biomedical engineering field. Various biosignals can be manipulated
through proper representation, transformation, visualization and optimization.
This course will introduce fundamental concepts and approaches, such as filtering,
detection, estimation, and classification for various biosignal processing and data mining.
It will provide students a brief picture of biosignal from detection to clinical application
by following the course “Introduction to Biosignal Detection”.
授業の目的と到達目標
／Objectives and attainment
goals		 1. To understand how to apply statistical mathematics and digital signal processing
methods to deal with various biosignals.
2. To understand how to utilize fundamental approaches of signal processing and data
mining in biomedical information technology field.
授業スケジュール
／Class schedule		 1. Introduction
2. Signal Separation and Decomposition
3. Signature Detection
4. Data Preprocessing
5. Time Domain Analysis
6. Frequency Domain Analysis
7. Nonlinear Domain Analysis
8. Biorhythm Estimation
9. Anomaly and Change Detection
10. Classification and Clustering
教科書
／Textbook(s)		 1. Biomedical Signal Processing and Signal Modeling, Eugene N. Bruce, ISBN:
978-0-471-34540-4, December 2000, Wiley
2. Practical Biomedical Signal Analysis Using MATLAB (Series in Medical Physics and
Biomedical Engineering), Katarzyn J. Blinowska and Jaroslaw Zygierewicz, CRC
Press; 1 edition (September 12, 2011), ISBN-10: 1439812020, ISBN-13:
978-1439812020
成績評価の方法・基準
／Grading method/criteria		 Research report and presentation, 100％
履修上の留意点
／Note for course registration		 Introduction to Biosignal Detection
Probability and Statistics
Discrete Mathematics and Linear Algebra
Digital Signal Processing
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 http://i-health.u-aizu.ac.jp/BPDM/


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開講学期
／Semester		 2018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
担当教員名
／Instructor		 Wenxi Chen, KENZAKI, Hiroo (RIKEN), NODA, Shigeho (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 2018/02/20
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.  This kind of simulation has widely applied to medical fields through drug discovery, surgical operations planning and 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. Software used in exercises will be prepared as teaching materials. Anyone can attend the course without prerequisites.
授業の目的と到達目標
／Objectives and attainment
goals		 We will learn basic theory and mathematical algorithms to solve basic governing equations in simulation of living matters from molecular scale to whole body as well as their wide applications in real world, especially in medical field.  More specifically,  
1) Molecular scale: basic theory and mathematical algorithm of molecular dynamics simulation and its wide applications,
2) Organ scale: basic theory and mathematical algorithm of structure analysis and non linear structure analysis for hard tissue simulation of human body and its practical applications,  
3) Fluid dynamics simulation in human body: basic theory and mathematical algorithm of fluid dynamics simulation in the human body and its practical medical applications.
In 1) and 2), trough exercises using PC, you will execute simulation by yourself and learn how to simulate problems and how to visualize those results.
授業スケジュール
／Class schedule		 1. Introduction of this course by Ryutaro Himeno　（1 lecture）
2. Molecular Simulation of Living Matter by Hiroo Kenzaki (2 lectures)
・ Basic Theory
・ Application
・ Exercise
3. Basic Theory of Hard Tissue Simulation and Computational Fluid Dynamics of Living Matter by Ryutaro Himeno (2 lectures)
・ Basic Theory of Hard Tissue Simulation
・ Basic Theory of Computational Fluid Dynamics
4. Computational Fluid Dynamics of Living Matter  (2 lectures)
・ Medical Application: Shigeho Noda
・ Exercise: Shigeho Noda
Total 7
教科書
／Textbook(s)		 No textbook but teaching materials will be provided in the course
成績評価の方法・基準
／Grading method/criteria		 Quizzes and Exercises at each class: 100%
履修上の留意点
／Note for course registration		 Bring your own PC for the exercise.  OS should be Windows 7 or later or Linux because of executing application software used in exercise.  If you can not prepare PC with Windows 7 or lator, or Linux, please contact the instructors.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Simulation software for exercise of molecular simulation of living matter.
Coarse-grained biomolecular simulation software CafeMol: http://www.cafemol.org/

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


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開講学期
／Semester		 2018年度／Academic Year 　4学期 ／Fourth Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Incheon Paik
担当教員名
／Instructor		 Incheon Paik
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2018/02/07
授業の概要
／Course outline		 The semantic Web is the second wave of Web technology, and its environment evolves from human-readable to machine-readable. The key technology of the semantic Web is knowledge representation technique–ontology, and its management.
Main issue of this course is to learn the semantic Web service technology: ontology,its learning and engineering, and its application to Web service. Background of web evolution, ontology for knowledge representation, Web service, and application to service composition will be covered.
If you have interests on the areas in the semantic Web service (SWS) technology, please e-mail to me (paikic@u-aizu.ac.jp) or visit my office (307-C).
授業の目的と到達目標
／Objectives and attainment
goals		 Main objective of this course is to give students ability of application of semantic technology based on some theoretic background. Historical motivation in Internet and Web technology, ontology basics and application, and how to apply ontology to other domains will be explained.
授業スケジュール
／Class schedule		 1. Introduction to Web Technologies and Semantic Web
2. Resource Description Framework (RDF) and DAML-OIL
3. Ontology Language - OWL (I)
4. Ontology Language (OWL) (II)
5. Semantic Web Rule Language
6. Ontology Design Exercise in OWL (Using Protege)
7. Rule Design Exercise in SWRL (Using Protege)
8. Rule Design Exercise in SWRL (Using Protege)
9. Ontology Learning by Text Mining
10. Ontology Matching and Merging
11. Ontology Engineering
12. Semantic Web Service Frameworks (OWL-S and BPEL)
13. Semantic Web Service Frameworks (WSMO)
14. Final Examination
教科書
／Textbook(s)		 Lecture Slides will be provided on lecture Web site.
成績評価の方法・基準
／Grading method/criteria		 1. Examination    --- 50%
2. Paper Presentation & Term Project --- 50%
履修上の留意点
／Note for course registration		 * Prerequisites:
- JAVA Programming I & II
- Web Programming
- Artificial Intelligence
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 * Reference
1) J. Davies, R. Studer, P. Warren, Semantic Web Technologies, Wiley, 2007.
2) A. Gomez-Perez, M. Fernandex-Lopez, O. Corcho, Ontological Engineering, Springer, 2004.
3) J. Davies, D. Fensel, F.V. Harmelen, Towards The Semantic Web, Ontology-Driven        Knowledge Management, Wiely, 2003.
4) M.C. Daconta, L.J. Obrst, K.T. Smith, The Semantic Web, Wiley, 2003.


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開講学期
／Semester		 2018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 2018/01/30
授業の概要
／Course outline		 The course puts emphasis on modeling and 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		 Modeling Web documents and databases;
Management of Large volume of data;
Information Retrieval models,
Knowledge extraction methods.
Web Data Mining.
授業スケジュール
／Class schedule		 weekly schedule is described as follows
1. Scientific data set archives, Big data Analytics
2. Conceptual modelling, Star Schema Warehouse,
   Modeling for IR, Object-oriented models, Semi-structured data
3. Data Mining: Tasks and Levels  of Mining process,
   Mining Techniques, Knowledge Extraction.

4. Data Warehouse: Materialized Views,
5. Aggregation and analysis models
6. Data Visualization. Information Retrieval systems.

7. Web Mining: Relevance Ranking, Similarity based
   retrieval, Relevance using Hyperlinks, Ontologies,
   Indexing of Documents,
教科書
／Textbook(s)		 Web programming, Sebesta (Pearson); Web Data Mining, by Bing Liu, (Springer), Data Mining and Analysis, M.J. Zaki and W. Meira, Jr.
成績評価の方法・基準
／Grading method/criteria		 Mid-term Quiz (30 points), two minor quiz ( 20 points each ), Class assignments on programming 30 points
履修上の留意点
／Note for course registration		 prior study of Database Systems, Computer Networks, Distributed Computing, Discrete mathematics is recommended
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Readings and lecture materials will be recommended by the instructor.


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開講学期
／Semester		 2018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		 Julian Villegas
担当教員名
／Instructor		 Julian Villegas, Michael Cohen
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2017/12/18
授業の概要
／Course outline		 This course explores the human-computer interface as used in interactive multimedia, namely the design of real-time computer games. We feature a project-based, “hands-on” approach, emphasizing creation of self-designed virtual worlds for CGM: consumer-generated media and UGC: user-generated content. The main vehicles of expression are “Alice,” and “Unity,” object-oriented, rapid-prototyping 3D scenario IDEs (integrated development environments), to combine segments on “desktop virtual reality,” motion graphics, color (and color gradients), graphical and visual design, texture mapping, sound, music, speech and dialog, as well as software engineering and parallel computing. We also use Sumo Paint, Audacity, and GarageBand as support tools for multimedia content creation. The power of experiential education is leveraged by lessons with an emphasis on practical experimentation, learning by doing.
授業の目的と到達目標
／Objectives and attainment
goals		 We survey the basics of game design, including human interfaces, including demonstrations and “hands-on” exercises with basics of multimedia: color models, image capture and compositing, graphic composition and 3D drawing, texture mapping, stereography, and audio (including dialog) & musical editing. Students use self-designed multimodal interfaces authored with object-oriented techniques to tell stories with virtual characters and cinematography (camera motion and gestures, “camerabatics”) for deterministic “machinima” (machine cinema = computer-generated movies) and to engage users in dynamic environments such as games and digital interactive story-telling.
授業スケジュール
／Class schedule		 Introduction, Scene Composition

Scripting

Numbers, Resolution, Scale; Event Handling

Photographic Capture and Texture Mapping

Drawing, Painting, Texture Mapping

Individual Project Presentations

3D Modeling

Color Models

Scripting

TTS (text-to-speech)

Audio Editing

DTM (desk-top music), BGM (background music)

Collision Detection & Rigid Body Physics

Group Project Presentations
教科書
／Textbook(s)		 Lecture notes prepared by instructors

Students are required to purchase chromastereoptic and anaglyphic eyewear, available from the instructor.
成績評価の方法・基準
／Grading method/criteria		 Exercises and quizzes: 35%
Exams: 25%
Individual Project: 20%
Group Project: 20%
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 http://web-int.u-aizu.ac.jp/~mcohen/welcome/courses/AizuDai/undergraduate/HI&VR/


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開講学期
／Semester		 2018年度／Academic Year 　2学期 ／Second Quarter
対象学年
／Course for;		 1st year , 2nd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Konstantin Markov
担当教員名
／Instructor		 Konstantin Markov
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2017/12/15
授業の概要
／Course outline		 Machine learning is one of the fastest-growing and most exciting fields of AI, and deep learning represents its true bleeding edge. Deep Learning is one of the most highly sought after skills in IT industry. In this course, students will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to complete successful machine learning projects. It will teach students how to train and optimize basic neural networks (NN), Convolutional neural networks (CNN), Recurrent neural networks (RNN, LSTM), autoencoders (AE), etc. Complete learning systems will be introduced via projects and assignments.
授業の目的と到達目標
／Objectives and attainment
goals		 Students will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as they solve these same problems effortlessly using deep learning methods. Students will master not only the theory, but also see how it is applied in practical case studies from various fields such as image recognition, music generation, natural language processing, etc.
授業スケジュール
／Class schedule		 1. Introduction and Background.
- Course introduction.
- Basic probability theory and statistics.
2. Machine Learning and Neural Networks
- Machine Learning fundamentals.
- Neural Networks fundamentals.
3. Deep Neural Networks basics I.
- Training – Back Propagation.
- Regularization and Normalization.
4. Deep Neural Networks basics II.
- Loss functions, Optimizations.
5. Feed-Forward DNN Applications.
- DNN classification and regression.
6. Convolutional Neural Networks (CNN).
- Translation invariance.
- Templates and filters.
7. CNN Applications.
- CNN for vision  – VGG, Inception.
- CNN for signal and text processing.
8. Recurrent Neural Networks (RNN).
- LSTM, GRU variants.
- Sequence and time series data modeling with RNN.
9. RNN Applications.
- RNN in Natural Language Processing.
- RNN for sequence generation.

10. Sequence-to-Sequence models (Seq2Seq)
Attention mechanism.
Word embeddings.
Seq2Seq for Language Translation.
11. Autoencoders (AE)
Denoising AE.
Variational AE.
AE for Dimensionality Reduction.
12. Tools for DNN application development I.
TensorFlow basics.
DNN variants with TensorFlow.
13. Tools for DNN application development II.
PyTorch.
Caffe2, Chainer, etc.
14. Project discussion.
教科書
／Textbook(s)		 I. Goodfellow,Y. Bengio and A. Courville, Deep Learning, MIT Press, 2016. Online version: http://www.deeplearningbook.org

T. Hope, Y. Resheff and I. Lieder, Learning Tensorflow: A Guide to Building Deep Learning Systems, Oreilly, 2017.

F. Chollet, Deep Learning With Python, Manning Pubs, 2017.
成績評価の方法・基準
／Grading method/criteria		 Laboratory exercises: 60 points
Project: 40 points
履修上の留意点
／Note for course registration		 As this is an intermediate to advanced level course, the following experience and skills are disirable:
- Programming experience (preferably in Python)
- Basic machine learning knowledge (especially supervised learning)
- Basic statistics knowledge (mean, variance, etc.)
- Linear algebra (vectors, matrices, etc.)
- Calculus (differentiation, integration, partial derivatives, etc.)

Prior to enrolling to this course, it is recommended (but not required) to take the following related courses:
- ITC12F Machine Learning
- CSA01 Neural Networks I: Fundamental Theory and Applications
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 http://hi-srv2.u-aizu.ac.jp/


Responsibility for the wording of this article lies with Student Affairs Division (Academic Affairs Section).

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