2016/02/01 |
Back |
開講学期 /Semester |
2015年度/Academic Year 3学期 /Third Quarter |
---|---|
対象学年 /Course for; |
1st year , 2nd year |
単位数 /Credits |
2.0 |
責任者 /Coordinator |
Pierre-Alain Fayolle |
担当教員名 /Instructor |
Pierre-Alain Fayolle , Yohei Nishidate |
推奨トラック /Recommended track |
- |
履修規程上の先修条件 /Prerequisites |
- |
更新日/Last updated on | 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. |
Back |
開講学期 /Semester |
2015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 | 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, loudness), synthesis (additive, AM, FM, envelopes, filtering, equalizers, spatialization, distortion), psychophysics (loudness, masking, critical bands), coding and compression (SNR, A-law, u-law, MP3, AAC, parametric stereo), display and multichannel ("discrete") systems (transducers, 5.1, speaker arrays, WFS), tuning, and user interfaces (conferencing, virtual concerts, mixed and virtual reality). |
教科書 /Textbook(s) |
Introduction to Sound, by Charles E. Speaks (ISBN 1-56593-979-4) Various materials prepared by the instructors. Besides normal lectures and exercises, we'll also use iPads (one lent to each student for the term) extensively for courseware and interactive projects. |
成績評価の方法・基準 /Grading method/criteria |
This course is concerned not only aesthetic issues, but also with technical issues, and as such would be useful to audio engineers and researchers, as well as musicians. Most of the coursework involves reading, homework exercises, and lab projects. There are mid-term and final exams. |
履修上の留意点 /Note for course registration |
Basic math and physics. (No prior knowledge of audio techniques is assumed.) |
参考(授業ホームページ、図書など) /Reference (course website, literature, etc.) |
Related web pages course home page: http://www.u-aizu.ac.jp/~mcohen/welcome/courses/AizuDai/graduate/Sound+Audio/syllabus.html invitation video: http://www.youtube.com/watch?v=rVGuqf6NqR8&feature=c4-overview&list=UU6juLwkSyA0xPibKhRbHbcQ courseware: http://sonic.u-aizu.ac.jp audio editing software Audacity: http://audacity.sourceforge.net audio effects processing software GarageBand: http://www.apple.com/mac/garageband/ technical computing software Mathematica: http://www.wolfram.com/mathematica audio synthesis and multimedia data-flow visual programming Pure Data ("Pd"): http://puredata.info TTS (text-to-speech) software Apple OS X say: http://developer.apple.com/library/mac/#documentation/Darwin/Reference/ManPages/man1/say.1.html dataflow-based DTM & audio effects DAW (digital audio workstation) Audiotool: http://www.audiotool.com |
Back |
開講学期 /Semester |
2015年度/Academic Year 1学期 /First Quarter |
---|---|
対象学年 /Course for; |
1st year , 2nd year |
単位数 /Credits |
2.0 |
責任者 /Coordinator |
Keitaro Naruse |
担当教員名 /Instructor |
Keitaro Naruse , Noriaki Asada |
推奨トラック /Recommended track |
- |
履修規程上の先修条件 /Prerequisites |
- |
更新日/Last updated on | 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/ |
Back |
開講学期 /Semester |
2015年度/Academic Year 3学期 /Third Quarter |
---|---|
対象学年 /Course for; |
1st year , 2nd year |
単位数 /Credits |
2.0 |
責任者 /Coordinator |
Keitaro Naruse |
担当教員名 /Instructor |
Keitaro Naruse , Shigeru Kanemoto |
推奨トラック /Recommended track |
- |
履修規程上の先修条件 /Prerequisites |
- |
更新日/Last updated on | 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/ |
Back |
開講学期 /Semester |
2015年度/Academic Year 4学期 /Fourth Quarter |
---|---|
対象学年 /Course for; |
1st year , 2nd year |
単位数 /Credits |
2.0 |
責任者 /Coordinator |
Takafumi Hayashi |
担当教員名 /Instructor |
Takafumi Hayashi , Yong Liu , Yen Neil Yuwen |
推奨トラック /Recommended track |
- |
履修規程上の先修条件 /Prerequisites |
- |
更新日/Last updated on | 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. |
Back |
開講学期 /Semester |
2015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 | 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/) |
Back |
開講学期 /Semester |
2015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 | 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/ |
Back |
開講学期 /Semester |
2015年度/Academic Year 1学期 /First Quarter |
---|---|
対象学年 /Course for; |
1st year , 2nd year |
単位数 /Credits |
2.0 |
責任者 /Coordinator |
Noriaki Asada |
担当教員名 /Instructor |
Noriaki Asada , 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 | 2015/03/16 |
---|---|
授業の概要 /Course outline |
"Remote sensing technology" is taught as an application of image processing. Wave optics, radiation transfer theory and image processing for remote sensing are taught with some examples. Object detection, image regeneration and pattern recognition are taught, too. |
授業の目的と到達目標 /Objectives and attainment goals |
Explain what is "Remote Sensing" and what remote sensing can do. Understand basics of optics, stereography and related physics. Think about real time analysis of 3-D images and total system for remote sensing. |
授業スケジュール /Class schedule |
1.Introduction 2.what is image 3.Remote sensing projects 4.Basics of optics 1 5.Basics of optics 2 6.Sensors 1 7.Sensors 2 8.Telecommunication to satellites 9.How to get image files 10.Geometric transformation of image 11.Pre-processing of image 1 - Compensations 12.Pre-processing of image 2 - Enhancement 13.Image analysis by eyes 14.Spectroscopy 15.Spectrum analysis of image 16.Mosaic & Stereo image 17.Texture analysis of image 18.Applications of remote sensing 19.Free Discussion |
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 |
Final Report, Discussion and Presentation ,Some examinations and questions |
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 |
Back |
開講学期 /Semester |
2015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 | 2015/03/16 |
---|---|
授業の概要 /Course outline |
This course introduces fundamental knowledge on data analysis in lunar and planetary explorations. Ancillary information including spacecraft location and attitude is essential to handle data obtained by science instruments on board a spacecraft. We will study and exercise on handling and utilization of spacecraft ancillary data at first. Then we will investigate handling of image data that is one of the major data type obtained by exploration missions. This topic includes characteristics, data format, pre-processing, and scientific analyses of image data. Some parts of the lecture are given by guest lecturers from Japanese Space Exploration Agency (JAXA) and National Astronomical Observatory of Japan (NAOJ) via videoconference system. |
授業の目的と到達目標 /Objectives and attainment goals |
- By the end of the course, students will have learned basic technologies to analyze lunar and planetary exploration data and be able to develop tools or software for exploration data analysis. Student will also gain knowledge of - Handling of ancillary information with SPICE toolkit developed by NASA - Characteristics, data format, pre-processing, and scientific analyses of exploration image data |
授業スケジュール /Class schedule |
- Week 1 - Introduction - Week 2 - Ancillary data and SPICE toolkit - Epoch information - Week 3 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 |
Attendance, Homeworks and Reports |
履修上の留意点 /Note for course registration |
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/ |
Back |
開講学期 /Semester |
2015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 | 2015/03/16 |
---|---|
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 |
Omnibus Style, order of AY2014 will be announced at the 1st lecture. Cf. Schedule in AY2014 #1-2 Demura (UoA) Introduction, Photoclinometry (Shape From Shading), and Hapke Photometric Function #3-4 Honda (UoA) Performance Test of imaging sensors #5-6 Ogawa (UoA) Kaguya Data Analysis of the Moon with Spectrometer #7-8 Ohtake (JAXA) Kaguya Data Analysis of the Moon with Multiband Images (Camera) #9-10 Oshigami (NAOJ) Kaguya Data Analysis of the Moon with Radar Sounder #11-12 Matsumoto (NAOJ) Gravity field of the Moon #13-14 Kitazato (UoA) Spectroscopic Analysis #15-16 Morota (Nagoya Univ.) Crater Chronology of the Moon |
教科書 /Textbook(s) |
N/A |
成績評価の方法・基準 /Grading method/criteria |
Attendances (Presentations), Homeworks, or Reports every professors. |
履修上の留意点 /Note for course registration |
Related Courses ITC08 Remote Sensing ITC09 Fundamental Data Analysis in Lunar and Planetary Explorations ITA19 Reliable System for Lunar and Planetary Explorations SEA11 Software Engineering for Space Programs |
参考(授業ホームページ、図書など) /Reference (course website, literature, etc.) |
N/A |
Back |
開講学期 /Semester |
2015年度/Academic Year 3学期 /Third Quarter |
---|---|
対象学年 /Course for; |
1st year , 2nd year |
単位数 /Credits |
2.0 |
責任者 /Coordinator |
Julian Villegas |
担当教員名 /Instructor |
Julian Villegas , Michael Cohen |
推奨トラック /Recommended track |
- |
履修規程上の先修条件 /Prerequisites |
- |
更新日/Last updated on | 2015/09/14 |
---|---|
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) Session 5. Modulation: Ring modulator, Amplitude modulator, Single-side-band modulator, Frequency and phase modulator, Detectors, Averagers, Amplitude scalers, Vibrato, Stereo phaser, Rotary loudspeaker effect, SSB effects, Simple morphing: amplitude following, Modulation vocoder. Session 6. (continuation) Session 7. Nonlinear FXs: Dynamic range control, Limiter, Compressor and expander, Noise gate, De-esser, Infinite limiters, Musical distortion and saturation effects, Valve simulation, Over- drive, distortion and fuzz, Harmonic and subharmonic generation, Tape saturation, Exciters, Enhancers Session 8. (continuation) Session 9. Time-segment processing: Variable speed replay, Time stretching, Pitch shifting, Time shuffling and granulation Session 10. Phase-vocoders: Basics, implementations, effects (time-frequency filters, dispersion, time stretching, pitch shifting, stable/transient components separation, mutating, robotization,whisperization, denoising) Session 11. (continuation) Session 12. Source-filter processing: ”Source-filter separation, Channel vocoder, Linear predictive coding (LPC) Cepstrum, Source-filter transformations, Vocoding or cross-synthesis, Formant changing, Spectral interpolation, Pitch shifting with formant preservation” Session 13. (continuation) Session 14. Time, Frequency warping : Algorithms for warping, Short-time warping and real-time implementation, Vocoder-based approximation of frequency warping, Time-varying frequency warping, Pitch-shifting inharmonic sounds, Inharmonizer, Vibrato, glissando, trill, etc. Session 15. (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/ |
Back |
開講学期 /Semester |
2015年度/Academic Year 4学期 /Fourth Quarter |
---|---|
対象学年 /Course for; |
1st year , 2nd year |
単位数 /Credits |
2.0 |
責任者 /Coordinator |
Satoshi Nishimura |
担当教員名 /Instructor |
Satoshi Nishimura |
推奨トラック /Recommended track |
- |
履修規程上の先修条件 /Prerequisites |
- |
更新日/Last updated on | 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/ |
Back |
開講学期 /Semester |
2015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 | 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 |
Back |
開講学期 /Semester |
2015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 | 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. |
Back |
開講学期 /Semester |
2015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 | 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. |
Back |
開講学期 /Semester |
2015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 | 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 |
Back |
開講学期 /Semester |
2015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 | 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) |
Back |
開講学期 /Semester |
2015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 | 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 |
Back |
開講学期 /Semester |
2015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 | 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 |
Back |
開講学期 /Semester |
2015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 | 2015/02/26 |
---|---|
授業の概要 /Course outline |
This course introduces the mechanisms of speech articulation and how to measure them. It also investigates the mapping between articulation and acoustics. Articulation is investigated using tools such as ultrasound and video. Speech acoustics is investigated using Praat – open-source acoustic analysis software. |
授業の目的と到達目標 /Objectives and attainment goals |
After completing this course, students will be able to: (1) describe how human speech is produced and how changes in articulation affect the acoustics of speech (2) use an ultrasound machine to collect speech data (3) use software to process images of ultrasound data (4) analyze speech acoustics and write short scripts to automatically analyze acoustic data (5) understand acoustic concepts such as speech waveforms, formants, FFT, and sine wave speech synthesis |
授業スケジュール /Class schedule |
Week 1: How speech is produced and how articulation is measured Week 2: Acoustic properties of speech sound classes Week 3: Ultrasound speech data collection and analysis Week 4: Mapping of articulation to acoustics I Week 5: Mapping of articulation to acoustics II Week 6: Audio-visual speech perception Week 7: Phonetic variability I - within and across speakers Week 8: Phonetic variability II - within and across languages |
教科書 /Textbook(s) |
Handouts and other materials will be made available on the course website. |
成績評価の方法・基準 /Grading method/criteria |
Assignments and projects to be announced. |
Back |
開講学期 /Semester |
2015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 | 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. |
Back |
開講学期 /Semester |
2015年度/Academic Year 3学期 /Third Quarter |
---|---|
対象学年 /Course for; |
1st year , 2nd year |
単位数 /Credits |
2.0 |
責任者 /Coordinator |
Vitaly V. Klyuev |
担当教員名 /Instructor |
Vitaly V. Klyuev |
推奨トラック /Recommended track |
- |
履修規程上の先修条件 /Prerequisites |
- |
更新日/Last updated on | 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 |
Back |
開講学期 /Semester |
2015年度/Academic Year 3学期 /Third Quarter |
---|---|
対象学年 /Course for; |
1st year , 2nd year |
単位数 /Credits |
2.0 |
責任者 /Coordinator |
Noriaki Asada |
担当教員名 /Instructor |
Noriaki Asada , Shigeru Kanemoto |
推奨トラック /Recommended track |
- |
履修規程上の先修条件 /Prerequisites |
- |
更新日/Last updated on | 2015/02/13 |
---|---|
授業の概要 /Course outline |
Automatic control is a key technology for utilizing electrical and mechanical machines in our daily life, such as automobiles, railways, airplanes or electrical appliances. To design safe, efficient and intelligent machines, the advanced and intelligent algorithms in instrumentation and control engineering theory are indispensably important. The purpose of this course is to learn the basic principles how to sense machine states and how to control the machine behaviors. In the sensing theory, we study the sensing principle and mechanism, sensing data processing and analyzing methods and measurement error and accuracy estimation methods. In the control theory, we study the modeling methods for controlled machines or systems and the classical and advanced design methods of controllers through practical examples and computer based simulation exercise. |
授業の目的と到達目標 /Objectives and attainment goals |
To learn the basic principles how to sense machine states and how to control the machine behaviors. |
授業スケジュール /Class schedule |
1. Instrumentation and Unit system/ Instrumentation amount 2. Error and Accuracy in measurement 3. Least square method/ Interpolation 4. Instrumental measurement/ Sensor/ Sensing 5. Signal instrumentation 6. Processing and analysis of signals 7. Modeling and control of dynamic system 8. Feedback control/ Modeling and response analysis by frequency response function 9. Feedback control/ Stability and system design 10. Control theory based on state space equation 11. Advanced control theory/ Fuzzy control and adaptive control 12. Exercise of controller design simulation |
教科書 /Textbook(s) |
No text is used. Class is hold by handouts in Homepage. |
成績評価の方法・基準 /Grading method/criteria |
Reports and exercises. |
履修上の留意点 /Note for course registration |
No prerequisite. |
参考(授業ホームページ、図書など) /Reference (course website, literature, etc.) |
http://web-int.u-aizu.ac.jp/~asada/graduate/SC.html |
Back |
開講学期 /Semester |
2015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 | 2015/02/27 |
---|---|
授業の概要 /Course outline |
This course focuses on developments of hardware instruments and control system for lunar and planetary explorations. Envisioned main target is the moon. This course follows an omnibus form given by invited lecturers (teleclasses) from JAXA, NAOJ, etc. |
授業の目的と到達目標 /Objectives and attainment goals |
To learn developments of hardware instruments and control system for landing missions. To learn basic knowledge in space developments as topics of computer science and engineering. |
授業スケジュール /Class schedule |
Example: Schedule in AY2014 #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) |
Back |
開講学期 /Semester |
2015年度/Academic Year 前期集中 /1st Semester Intensi |
---|---|
対象学年 /Course for; |
1st year , 2nd year |
単位数 /Credits |
1.0 |
責任者 /Coordinator |
Incheon Paik |
担当教員名 /Instructor |
Incheon Paik |
推奨トラック /Recommended track |
- |
履修規程上の先修条件 /Prerequisites |
- |
更新日/Last updated on | 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. |
Back |
開講学期 /Semester |
2015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 | 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 |
Back |
開講学期 /Semester |
2015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 | 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/ |
Back |
開講学期 /Semester |
2015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 | 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 |
Back |
開講学期 /Semester |
2015年度/Academic Year 前期集中 /1st Semester Intensi |
---|---|
対象学年 /Course for; |
1st year , 2nd year |
単位数 /Credits |
1.0 |
責任者 /Coordinator |
Ryutaro Himeno |
担当教員名 /Instructor |
Ryutaro Himeno , Wenxi Chen , Kenzaki H. (RIKEN) , Noda S. (RIKEN) |
推奨トラック /Recommended track |
- |
履修規程上の先修条件 /Prerequisites |
- |
更新日/Last updated on | 2015/03/02 |
---|---|
授業の概要 /Course outline |
From molecular scale to human body, computer simulation of living matter has become practical due to the development of computer performance, computation scheme and experimental measurement. These simulation has widely applied to medical fields through drug discovery, surgical operations etc.. In this course, we will learn those basic theories and current status: molecular simulation using molecular dynamics and continuum mechanics simulation including structure analysis and fluid dynamics. In addition, we will experience them through exercises using PCs. |
授業の目的と到達目標 /Objectives and attainment goals |
We will learn basic theory and mathematical algorithms to solve basic governing equations in simulation of living matters from molecular scale to whole body as well as their wide applications in real world, especially in medical field. More specifically, 1) Molecular scale: basic theory and mathematical algorithm of molecular dynamics simulation and its wide applications, 2) Organ scale: basic theory and mathematical algorithm of structure analysis and non linear structure analysis for hard tissue simulation of human body and its practical applications, 3) Fluid dynamics simulation in human body: basic theory and mathematical algorithm of fluid dynamics simulation in the human body and its practical medical applications. In 1) and 2), trough exercises using PC, you will execute simulation by yourself and learn how to simulate problems and how to visualize those results. |
授業スケジュール /Class schedule |
1.Introduction of this course by Ryutaro Himeno 2.Molecular Simulation of Living Matter by Hiroo Kenzaki ・Basic Theory ・Application ・Exercise 3.Hard Tissue Simulation of Living Matter by Ryutaro Himeno ・Basic Theory 4.生体流体シミュレーション Computational Fluid Dynamics of Living Matter ・Basic Theory: Kazuyasu Sugiyama ・Medical Application: Ryutaro Himeno ・Exercise: Shigeho Noda Total 8 |
教科書 /Textbook(s) |
No textbook but teaching materials will be provided in the course |
成績評価の方法・基準 /Grading method/criteria |
Small exam at the end of each class: 50% Report after the course: 50% |
履修上の留意点 /Note for course registration |
Bring your own PC for the exercise. OS should be Windows 7 or 8 or Linux because of executing application software used in exercise. If you can not prepare PC with Windows 7 or 8, or Linux, please contact the instructors. |
参考(授業ホームページ、図書など) /Reference (course website, literature, etc.) |
PCを使った実習のために各自PCを持参すること。OSは実行するソフトウェアの関係でWindows 7 あるいは8またはLinux。他のOSしか用意できない場合は事前に相談すること |
Back |
開講学期 /Semester |
2015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 |
Pham Tuan Duc |
担当教員名 /Instructor |
Pham Tuan Duc |
推奨トラック /Recommended track |
- |
履修規程上の先修条件 /Prerequisites |
- |
更新日/Last updated on | 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 |