AY 2024 Graduate School Course Catalog AY 2024 Graduate School Course Catalog AY 2024 Graduate School Course Catalog
| 2024/11/21 |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
VILLEGAS OROZCO Julian Alberto |
| 担当教員名 ／Instructor |
VILLEGAS OROZCO Julian Alberto |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
IT09 Sound and Audio Processing ITA10 Spatial Hearing and Virtual 3D Sound This course is offered exclusively in English |
| 更新日／Last updated on | 2024/01/26 |
|---|---|
| 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 Pure-data, C/C++, Matlab/Octave, and Faust. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
Students who approve this course are expected to: 1. Understand some basic techniques used in digital audio effects, computer music, and terminology on this topic. 2. Be able to create their own digital audio effect chain. |
| 授業スケジュール ／Class schedule |
1 Introductions 2 Visual programming for audio 3 DFT, causality and stability 4 Basic Filters 5 Time-varying effects I 6 Time-varying effects II 7 Modulation 8 Nonlinear FXs I 9 Nonlinear FXs II 10 Creating GUI for AFX chain in a smartphone 11 Pitch and rhythm 12 Programming audio objects in C 13 Deploying audio projects 14 Final presentations |
| 教科書 ／Textbook(s) |
• U. Zölzer, 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 40% Final project 60% Based on the techniques studied in class, student propose an audio chain to be deployed in a mobile platform. This chain is demonstrated in front of the class in the last session of the course. |
| 履修上の留意点 ／Note for course registration |
Experience on Pure-data is desirable. |
| 参考(授業ホームページ、図書など) ／Reference (course website, literature, etc.) |
• Theory and Techniques of Electronic Music (M. Puckette): http://msp.ucsd.edu/techniques.htm • Julius Orion Smith III website: https://ccrma.stanford.edu/~jos/ The course instructor has practical working experience. He worked as an Ikerbasque researcher for about three years at the laboratory of phonetics in the Basque Country University. |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
NISHIDATE Yohei |
| 担当教員名 ／Instructor |
NISHIDATE Yohei |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
Calculus, Linear Algebra, Numerical Analysis, and some programming courses are recommended as prerequisites. |
| 更新日／Last updated on | 2024/01/26 |
|---|---|
| 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) |
Lecture Notes |
| 成績評価の方法・基準 ／Grading method/criteria |
Exercises - 50% Project - 50% |
| 参考(授業ホームページ、図書など) ／Reference (course website, literature, etc.) |
Gennadiy Nikishkov, Programming Finite Elements in Java. Springer, 2010, 402 pp. |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
YAGUCHI Yuichi |
| 担当教員名 ／Instructor |
YAGUCHI Yuichi |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/01/26 |
|---|---|
| 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 |
1. Course Instruction, Introduction to Image Recognition and Understanding Image Formation and Representation Image Acquisition and Optics 2. Low-level Image Feature: Pixel, Voxel, Line, Block, Corner Image Feature and Algorithms: SIFT, SURF, HOG, Joint Image Feature and Sparse Representation 3. Image Segmentation – K-Means, Mean-shift Image Cutting - Sneaks, Watershed Object Clustering - K-Means, Fuzzy c-Means, Sequential Clustering, Hieralchical Clustering - First Report: Image Segmentation and Clustering 4. Pattern Recognition 1 – Sparse Representation with Linear Classification Pattern Recognition 2 – Naïve Bayes, Support Vector Machine Pattern Recognition 3 – Neural Network - Second Report: Image Recognition – Find Human Faces 5. Image Understanding 1 – Bayesian Net Image Understanding 2 – Principal Component Analysis, Latent Semantic Indexing Image Retrieval – Bag of Visual Worlds, Sparse Component Analysis - Third Report: Bayesian Net Calculation 6. Motion Feature – Optical Flow, Dense Optical Flow Pattern Matching – Dynamic Time Warping, Continuous DP Motion Analysis – Pixel Trajectory, Gesture Recognition 7. Neural Network and Deep Neural Network General Object Recognition by YoLo |
| 教科書 ／Textbook(s) |
Main Coursebook - Richard Szeliski, Computer Vision: Algorithms and Applications. (Not need to buy this book, but very helpful 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 have 25~40 points. |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
HUANG Jie |
| 担当教員名 ／Instructor |
HUANG Jie |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/01/26 |
|---|---|
| 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 the 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 |
Exercises (80%) 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 |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
SHIN Jungpil |
| 担当教員名 ／Instructor |
SHIN Jungpil |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/02/01 |
|---|---|
| 授業の概要 ／Course outline |
This course deals with the design, analysis, and development of methods for the classification or description of patterns, objects, signals, and processes. The main goal of this area is to develop advanced technology and paradigms for human activity pattern processing, and our ability to create new ideas related to the topics covered. There are many pattern recognition applications that exists today, including online /offline pattern recognition, the use of pen-tablets, pattern processing, touch panels, RGB-D cameras, iOS / Android smart devices, and virtual reality. We focus on related issues in human activity pattern processing from 3 perspectives: Recognition, Authentication, and Synthesis. This course will be delivered via onsite and online. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
At the end of this course, students will be able to: - Perceive an overview of the field of pattern processing related to human activity and pattern processing. - Learn how various techniques of human activity pattern processing can be applied to the software. |
| 授業スケジュール ／Class schedule |
Introduction to human activity pattern processing Fundamentals of online/offline pattern recognition Pattern recognition involves human activity (HA) Current problems and solving methods associated with the following topics: - Non-touch Interface for Character Input - Pen-based interactive systems - Handwritten font generation - Signature verification and writer identification system - Brush painting systems - HCI using calligraphy systems - Gesture recognition using RGB-D, Leap motion, Myo controller, and web camera - Disease diagnosis using pen-tablet - Daily activity recognition using smartwatch and camera sensor - Multichannel EEG signal analysis for brain computer interface (BCI) - Design of experiments associated with human activity 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, research report, and discussion of current techniques and producing new ideas. - Programming related to pattern processing. |
| 教科書 ／Textbook(s) |
There are a lot of textbooks available online. Instructors will provide selected topics from books and various journals and conference papers, moreover, our goal in this course is to give you a broad perspective on the field. |
| 成績評価の方法・基準 ／Grading method/criteria |
Investigation, presentation, and research report (40%) 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/HAPP.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 |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
VILLEGAS OROZCO Julian Alberto |
| 担当教員名 ／Instructor |
VILLEGAS OROZCO Julian Alberto, HUANG Jie |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
IT09 Sound and Audio Processing ITA01 Digital Audio Effects |
| 更新日／Last updated on | 2024/01/26 |
|---|---|
| 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 a 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. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
Be able to understand the basic mechanisms of spatial hearing, as well as the terminology on this topic. • Be able to decide which of the presented techniques is best for creating the 3D aural illusion. • Be able to implement virtual 3D sound environments based on headphones and multi-loudspeaker systems. |
| 授業スケジュール ／Class schedule |
Session 1. Introductions and motivation Session 2. Spatial hearing and psychoacoustics Session 3. Lateralization Session 4. Lateralization (continuation) Session 5. Elevation perception Session 6. Distance perception Session 7. Room effects Session 8. Motion perception Session 9. Head-related impulse responses and transfer functions Session 10. (continuation) Session 11. Loudspeaker techniques Session 12. (continuation) Session 13. Ambisonics Session 14. DiRAC and other recent developments |
| 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 40% Assignments 60% |
| 履修上の留意点 ／Note for course registration |
* This course uses Matlab and Pure-data for practical demonstrations. Some assignments must be completed in either of these languages as well. |
| 参考(授業ホームページ、図書など) ／Reference (course website, literature, etc.) |
Prof. Villegas has practical working experience. He worked as an Ikerbasque researcher for about three years at the laboratory of phonetics in the Basque Country University. • Bregman, Albert S., Auditory Scene Analysis: The Perceptual Organization of sound. Cambridge, Massachusetts: The MIT Press, 1990 (hardcover)/1994 (paperback). |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
BLAKE John |
| 担当教員名 ／Instructor |
BLAKE John |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
None |
| 更新日／Last updated on | 2024/01/26 |
|---|---|
| 授業の概要 ／Course outline |
The primary aim of this practical course is to enable participants to create a prototype technology-enhanced language learning tool. Participants are first introduced to the underlying education and language learning theories. Next, the current state-of-the-art tools and techniques are explored. Participants then design, develop and evaluate a prototype language learning tool. This may be completed individually or in teams. The course culminates in a software demonstration. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
[Competency codes] C-EC-008-1, C-EC-009-1 By the end of the course participants will: (a) be able to explain the relevant educational and language learning theories; (b) know the current state-of-the-art language learning tools; and (c) have designed, developed and evaluated a language learning tool. |
| 授業スケジュール ／Class schedule |
The content of the course will be tailored to the requirements of the participants by the class teacher. Block 1 Theory and Tools Session 1 Underlying theories of language and learning Session 2 Overview of education technology Session 3 Computer-assisted language learning (CALL) Session 4 Intelligent CALL Session 5 Online language learning Session 6 Natural language generation Session 7 Virtual reality and augmented reality Block 2 Prototype development Session 8 Remit and requirements Session 9 Problem breakdown Session 10 Prototype development I Session 11 Prototype development II Session 12 Prototype development III Session 13 Prototype development IV Session 14 Demonstrations |
| 教科書 ／Textbook(s) |
No textbook. Materials will be provided. |
| 成績評価の方法・基準 ／Grading method/criteria |
Coursework: 40% Report: 20% Demonstration: 40% |
| 履修上の留意点 ／Note for course registration |
None |
| 参考(授業ホームページ、図書など) ／Reference (course website, literature, etc.) |
https://u-aizu.ac.jp/~jblake/course_tell/tell_unit_01.html |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
WILSON Ian |
| 担当教員名 ／Instructor |
WILSON Ian |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/01/26 |
|---|---|
| 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) 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 |
Classes 1 and 2: How speech is produced and how articulation is measured Classes 3 and 4: Acoustic properties of speech sound classes; Praat script writing Classes 5 and 6: Using Praat to synthesize vowels and manipulate speech Classes 7 and 8: Ultrasound speech data collection and analysis Classes 9 and 10: Mapping of articulation to acoustics Classes 11 and 12: Spectrogram reading and lip reading Classes 13 and 14: Phonetic variability - within and across speakers/languages; final project |
| 教科書 ／Textbook(s) |
Handouts and other materials will be made available on the course website in Moodle. |
| 成績評価の方法・基準 ／Grading method/criteria |
Active participation in class: 40% Assignments (Praat script writing, etc.): 20% Final project: 40% |
| 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 |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
PAIK Incheon |
| 担当教員名 ／Instructor |
PAIK Incheon, YAGUCHI Yuichi |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
1. NLP-IR 2. Linear Algebra |
| 更新日／Last updated on | 2024/01/24 |
|---|---|
| 授業の概要 ／Course outline |
Natural Language Processing (NLP) is a rapidly developing field with broad applicability in computer science and other various applications. From linguistic and textual data, we can get very useful information, and the data can be used for creating artificial intelligence (AI) applications such as language translator, several kinds of text generation, chatting, etc. In this course, you will study some basic of theoretical and methodological introduction to NLP, and its application to information retrieval, text mining, several language processors using AI. Also, we will focus on strategies and toolkits for NLP and Deep Learning (DL) in the Python programming language. Throughout this course, the sources, architectures and tools we will focus on will be introduced for student's own term project. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
Students will obtain knowledge about foundational understanding in NLP methods and strategies. They will also learn principle of neural language processing and its architecture for several AI application. And they can know how to evaluate the characteristics of NLP technologies and frameworks as they carry out practical exercise and term project using NLP and DL toolkits available. |
| 授業スケジュール ／Class schedule |
1. Introduction to NLP and DL - Python, Google Colaboratory, NLTK Library, Word Tokenizing 2. Word Sense and Lexical Analysis - Tagging, Collocation, LSA 3. Primary Language Model - TF-IDF, BoW, N-Gram, Max Entropy, K-Skip, Classification 4. Information Retrieval System and NLP - Similarity, Retrieval, Organization 5-6. Exercise(I) 7. Deep Learning Architectures for Language Model(I) 8. Deep Learning Architectures for Language Model(II) 9. Deel Learning Application for Neural Language 10. Alignment of Large Language Model with SFT & RLHF (I) 11. Alignment of Large Language Model with SFT & RLHF (II) 12. Application of Aligned Large Language Model and Prompt Engineering 13-14. Project |
| 教科書 ／Textbook(s) |
- A lecturer will provide necessary materials. |
| 成績評価の方法・基準 ／Grading method/criteria |
- Term Project: 100% |
| 履修上の留意点 ／Note for course registration |
- There can be homework such as pre-reading or material preparation during lectures. |
| 参考(授業ホームページ、図書など) ／Reference (course website, literature, etc.) |
- It will be introduced on the Moodle lecture Web page. |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
OHTAKE Makiko |
| 担当教員名 ／Instructor |
OHTAKE Makiko, OGAWA Yoshiko, HONDA Chikatoshi, YAMADA Ryuhei |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/02/05 |
|---|---|
| 授業の概要 ／Course outline |
This course focuses on developments of hardware instruments including rover and control system for lunar and planetary explorations. Envisioned main target is the moon. This course follows an omnibus form and the course consists of a classroom lecture and practices. Practices consist of maneuvering a rover and obtaining/processing instrument data onboard the rover. |
| 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. To practices maneuvering a rover and obtaining/processing instrument data. |
| 授業スケジュール ／Class schedule |
Q3: Wed 1-2 & 5-6 periods (part of these periods) + one day experiment in RTF, Minami-Soma on 29 Nov. Tentative schedule in AY2024. <Time Table> Lecture@UoA 1-3 Prof. Ohtake Introduction of General Space Probe (12 Jun; 1, 2, and 5 periods) 4-5 Prof. Ogawa Data in Exploration Programs (19 Jun; 1 and 2 periods) 6-8 Prof. Honda Path-finding (26 Jun; 1, 2, and 5 periods) 9-14 Prof. Yoshimitsu(JAXA) Rover for Planetary Exploration (TBD) Practice@RTF on 29 Nov. 15-18 Prof. Yamada Preparations 19-24 Prof. Yamada Practice Presentation&Wrap-up@UoA 25-28 Prof. Ohtake (TBD) |
| 教科書 ／Textbook(s) |
N/A |
| 成績評価の方法・基準 ／Grading method/criteria |
Presentation and report on the practice. |
| 履修上の留意点 ／Note for course registration |
prerequisite：N/A related course：ITC09 Fundamental Data Analysis with Lunar and Planetary Database 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.) |
This course is supported by "FY2022-24 Coordination Funds for Promoting AeroSpace Utilizaiton MEXT, Japan". |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
CHEN Wenxi |
| 担当教員名 ／Instructor |
CHEN Wenxi |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
Some basic knowledge of biosignals, probability and statistics, discrete mathematics and linear algebra, and digital signal processing are required. |
| 更新日／Last updated on | 2024/01/15 |
|---|---|
| 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 decomposition, transformation, representation, classification, optimization and visualization. This course will introduce fundamental concepts and approaches, such as filtering, detection, estimation, and data mining for various biosignals in temporal domain, frequency domain and nonlinear domain. It will provide students a brief picture of biosignal from detection to analysis, from physiological significance to clinical application 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 engineering field. |
| 授業スケジュール ／Class schedule |
1. Introduction 2. Decomposition and Reconstruction of Biosignals 3. Detection of Biosignatures 4. Processing of Biosignals and Biosignatures 5. Analysis of HRV in Time Domain 6. Analysis of HRV in Frequency Domain 7. Analysis of HRV in Nonlinear Domain |
| 教科書 ／Textbook(s) |
Biomedical Signal Processing and Signal Modeling, Eugene N. Bruce, ISBN: 978-0-471-34540-4, December 2000, Wiley https://www.wiley.com/en-jp/Biomedical+Signal+Processing+and+Signal+Modeling-p-9780471345404 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 https://www.crcpress.com/Practical-Biomedical-Signal-Analysis-Using-MATLAB/Blinowska-Zygierewicz/p/book/9781439812020 Seamless Healthcare Monitoring - Advancements in Wearable, Attachable, and Invisible Devices, Editors: Tamura, Toshiyo, Chen, Wenxi, Springer International Publishing, 2018, DOI 10.1007/978-3-319-69362-0, eBook ISBN 978-3-319-69362-0, Hardcover ISBN 978-3-319-69361-3 https://www.springer.com/us/book/9783319693613 |
| 成績評価の方法・基準 ／Grading method/criteria |
A summary report by compiling a series of assignments, 100% |
| 参考(授業ホームページ、図書など) ／Reference (course website, literature, etc.) |
The course instructor has practical working experience and has worked for 5 years at Nihon Kohden Industrial Corp., a professional manufacturer of world famous medical equipment, and has been engaged in R & D for bioinstrumentation, signal processing and data analysis. Based on this experience, he will teach the fundamental knowledge and latest advancements in “Biosignal Processing and Data Mining”. Moodle for course handouts and other related information https://elms.u-aizu.ac.jp/login/index.php |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024年度／Academic Year 前期集中 ／1st Semester Intensive |
|---|---|
| 対象学年 ／Course for; |
1st year , 2nd year |
| 単位数 ／Credits |
1.0 |
| 責任者 ／Coordinator |
HIMENO Ryutaro(Juntendo Univ.) |
| 担当教員名 ／Instructor |
HIMENO Ryutaro(Juntendo Univ.), KENZAKI Hiroo (RIKEN), NODA Shigeho (RIKEN), CHEN Wenxi |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/01/16 |
|---|---|
| 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 or Linux because of executing application software used in exercise. If you can not prepare PC with Windows, 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.) |
Work experience (Himeno): I have practical work experiences about 15 years at NISSAN Motor Co. for using Fluid simulation handled in biofluid simulation. Based on this experience, I teach the basics of fluid simulation. 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 |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
PAIK Incheon |
| 担当教員名 ／Instructor |
PAIK Incheon |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/01/24 |
|---|---|
| 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. Presentation and 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. |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
MARKOV Konstantin |
| 担当教員名 ／Instructor |
MARKOV Konstantin |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
This course is given in English. |
| 更新日／Last updated on | 2024/01/29 |
|---|---|
| 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. Advanced DNN models. Transformer, BERT, GPT-2, LLMs 13. DNN training strategies. Tips and tricks. 14. Project discussion. |
| 教科書 ／Textbook(s) |
I. Goodfellow,Y. Bengio and A. Courville, Deep Learning, MIT Press. Online version: http://www.deeplearningbook.org T. Hope, Y. Resheff and I. Lieder, Learning Tensorflow: A Guide to Building Deep Learning Systems, Oreilly. F. Chollet, Deep Learning With Python, Manning Pubs. |
| 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.) |
https://elms.u-aizu.ac.jp/ |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
ILIC Peter |
| 担当教員名 ／Instructor |
ILIC Peter |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/01/26 |
|---|---|
| 授業の概要 ／Course outline |
Today a comprehensive understanding of the intersection between learning theory and Information and Communication Technology (ICT) is both critical and timely. This course focuses on the convergence of learning theory and ICT in education, particularly examining their interplay. It will delve into the challenges researchers and educators face as emerging technologies reshape the educational landscape. Emphasizing a blend of theoretical insights and practical applications, the course provides invaluable knowledge for navigating education in a technologically advanced era. Throughout the course, students will engage in rigorous readings and dynamic discussions, reflecting on learning theories, teaching practices, and pedagogical approaches in the context of ICT’s opportunities. The curriculum is particularly relevant for students aspiring to contribute to educational software development or to integrate technology into teaching. This emphasis on readings and discussions will deepen students' understanding of the complex relationship between educational theories and technological advancements. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
1. Develop knowledge of Online Educational Technologies. 2. Develop understanding of key Learning Theories influencing educational technology. 3. Develop a critical understanding of the limits of online technologies in education. 4. English discussion and argumentative skills will be further developed. |
| 授業スケジュール ／Class schedule |
Section One: 1. Introduction to Learning Theory and ICT i. Homework: Reading/Quiz 1 2. History of Learning Theory and ICT i. Homework: Reading/Quiz 2 Section Two: 1. Behaviorist Learning Theory I i. Homework: Reading/Quiz 3 2. Behaviorist Learning Theory II i. Homework: Reading/Quiz 4 Section Three: 1. Cognitivist Learning Theory I i. Homework: Reading/Quiz 5 2. Cognitivist Learning Theory II i. Homework: Reading/Quiz 6 Section Four: 1. Constructivist Learning Theory I i. Homework: Reading/Quiz 7 2. Constructivist Learning Theory II i. Homework: Reading/Quiz 8 Section Five: 1. Connectivism/Others i. Homework: Reading/Quiz 9 2. Connectivism II/Collaborativist II i. Homework: Reading/Quiz 10 |
| 教科書 ／Textbook(s) |
No textbook will be used. Course material will be made available on Moodle. |
| 成績評価の方法・基準 ／Grading method/criteria |
20% Discussion Participation 20% Online Quizzes: 20% 1st Response Paper: 20% 2nd Response Paper: 20% 3rd Response Paper: Late assignments will lose 10% per day. After 5 days, a late assignment will receive a mark of 0%. |
| 履修上の留意点 ／Note for course registration |
In-class discussion participation is included in grading. English language proficiency is required (TOEIC 600+, or with permission from instructor). Attendance will be recorded. |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
FAYOLLE Pierre-Alain |
| 担当教員名 ／Instructor |
FAYOLLE Pierre-Alain, NISHIDATE Yohei |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/01/17 |
|---|---|
| 授業の概要 ／Course outline |
This course provides an introduction to numerical geometry processing using Java as a programming language. We give a presentation of the Java 2D API (which is part of any Java SDK) for 2D processing and rendering, then give a presentation of the OpenGL API via its Java bindings for 2D and 3D rendering. Armed with this knowledge, we look at curve and surface modeling, first from a continuous point of view, then from a discrete point of view, with computer implementations in mind. Finally, we provide an overview of the 3D modeling pipeline (acquisition, alignment, reconstruction) and look at several methods for processing digital shapes. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
The main objectives of the course are: • The study of common techniques in graphics programming and their connections with the Java 2D API and the OpenGL API (via its Java bindings) • The study of common techniques in numerical geometry processing (curves and surfaces modeling, the 3D modeling pipeline, digital shape processing techniques) • The development of graphics and geometry processing programs in Java (using the Java 2D, and OpenGL API) |
| 授業スケジュール ／Class schedule |
1. Course introduction; Smooth curves 2. Discrete curves 3. Java 2D introduction and geometry 4. Java 2D (rendering) 5. OpenGL bindings (core mode; shaders) 6. OpenGL bindings continued 7. Project 1 presentations; 3D pipeline 8. 3D pipeline, alignment 9. Surface reconstruction 10. Triangle mesh representations, simplification 11. Simplification, subdivision 12. Shape parameterization 13. Spectral methods 14. Project 2 presentations; Spectral methods (continued) |
| 教科書 ／Textbook(s) |
None. Slides, notes, and code 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. |
| 参考(授業ホームページ、図書など) ／Reference (course website, literature, etc.) |
Course website (internal) https://web-int.u-aizu.ac.jp/~fayolle/teaching/java_2d_3d/index.html |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024年度／Academic Year 前期集中 ／1st Semester Intensive |
|---|---|
| 対象学年 ／Course for; |
1st year , 2nd year |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
VILLEGAS OROZCO Julian Alberto |
| 担当教員名 ／Instructor |
VILLEGAS OROZCO Julian Alberto |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/01/26 |
|---|---|
| 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, radiation, 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, display and multichannel ("discrete") systems (transducers, 5.1, speaker arrays), 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 |
Demonstration-rich formal lectures interleaved with laboratory sessions provide a rigorous, theoretical background as well as practical experience regarding basic audio operations. The university's exercise rooms feature multimedia workstations, at which students can work individually or in teams to explore concepts regarding sound and audio. 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 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, pinnae, 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, major & minor 9. Intensity: volume, loudness, PCM, electroacoustics 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. Charles E. Speaks: "Introduction to Sound" (ISBN 1-56593-979-4) (http://www.delmarlearning.com/browse_product_detail.aspx?catid=1258&isbn=1565939794, http://www.amazon.com/Introduction-Sound-Acoustics-Sciences-Singular/dp/1565939794/ref=sr_1_1?ie=UTF8&s=books&qid=1207619383&sr=1-1) Hyperphysics: Sound & Hearing (http://hyperphysics.phy-astr.gsu.edu/hbase/sound/soucon.html) 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 explores theoretical and practical approaches to sound, audio, and music, so is interesting to audio engineers and researchers as well as musicians. Most of the coursework involves reading, homework exercises, and "hands on" 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 |
Note: This course is prerequisite for "Spatial Hearing in Virtual Environment," http://web-ext.u-aizu.ac.jp/official/curriculum/syllabus/2023_2_E_005.html#SS744591 |
| 参考(授業ホームページ、図書など) ／Reference (course website, literature, etc.) |
course home page: https://u-aizu.ac.jp/~mcohen/welcome/courses/AizuDai/graduate/ITC02A%20Sound%20&%20Audio/syllabus.html iPad software: https://u-aizu.ac.jp/~mcohen/spatial-media/iPadOS Audacity audio editor: http://audacity.sourceforge.net/?lang=ja GarageBand DTM (desk-top music) composition application: https://www.apple.com/jp/mac/garageband/ technical computing software Mathematica: https://www.wolfram.com/mathematica/index.php.ja |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
NARUSE Keitaro |
| 担当教員名 ／Instructor |
NARUSE Keitaro, WATANOBE Yutaka |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/01/25 |
|---|---|
| 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/ |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
NARUSE Keitaro |
| 担当教員名 ／Instructor |
NARUSE Keitaro, YAGUCHI Yuichi |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/01/25 |
|---|---|
| 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/ |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
ZHAO Qiangfu |
| 担当教員名 ／Instructor |
ZHAO Qiangfu, LIU Yong, YAGUCHI Yuichi |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
- Probability and statistics (undergraduate course) - Algorithms and data structures (undergraduate course) - Artificial intelligence (undergraduate course) |
| 更新日／Last updated on | 2024/01/24 |
|---|---|
| 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; convolutional neural network; deep auto-encoder; deep Boltzmann machine; Bayesian network; decision tree, decision ensembles, etc. To reinforce the learned knowledge, students will do some projects. Through these projects, students will solve some real-life or synthesized problems using some of the learned methods. Students are encouraged to work in a team, to solve the problems together, and to learn how to communicate and collaborate with others. |
| 授業スケジュール ／Class schedule |
Some contents given below might be changed/improved year by year based on the newest trends in this field. 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. Learning based on tree structures - Decision trees - Multi-variate decision trees - Decision tree ensembles (forests) 7. Presentation and report for projects of the 1st half 8. Learning based on layered structures-1 - Multilayer neural networks - Deep auto-encoder 8. Learning based on layered structures-2 - Convolutional neural network - Transfer learning 10. Learning based on layered structures-3 - Methods for improving the performance of deep neural networks 11. Generative Neural Network - 1 - Restricted Boltzmann machine 12. Generative Neural Network -2 - Generative Adversarial Networks - Applications of GAN 13. Attentional machine learning models - Transformer - BERT - Vision Transformer 14. Presentation and report for projects of the 2nd half |
| 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/Short Test*: 20 points - Project reports: 80 points * Simple quiz or short tests (no so often) may be conducted in some classes to confirm some fundamental knowledge we have learned. |
| 履修上の留意点 ／Note for course registration |
This is a fundamental course related to machine learning. In this course, we focus on basic theories and methodologies so that students can, after taking this course, understand better about the basic ideas behind existing learning models and algorithms, and have better chance to propose their own models or algorithms. Students who are more interested in programming, or who want to learn how to use some open source programs, may take course like "ITA34 Practical Deep Learning". |
| 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. |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
ZHU Xin |
| 担当教員名 ／Instructor |
ZHU Xin, CHEN Wenxi |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/01/25 |
|---|---|
| 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 familiar 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. Some topics on the implementation of AI in bioinformatics will also be introduced. |
| 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. Recently, AI-enhanced algorithms have also been developed to confirm the 3D structures of proteins. This year, we will perform an implementation study on Covid-2019 or influenza virus, and their variants using the skills learned in this course. |
| 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. AI in bioinformatics 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/） The course instructor Xin Zhu has practical working experiences. He had performed research in biomedical engineering at Tianjin University for 7 years, and has performed related research at the University of Aizu for 15 years with the financial support from universities and JSPS. He has also received a certificate in online bioinformatics lectures. Based on his experiences, he can teach the basics of bioinformatics. |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
CHEN Wenxi |
| 担当教員名 ／Instructor |
CHEN Wenxi, ZHU Xin |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
Some basic knowledge of Physics and Chemistry, Electricity and Electronics is necessary. |
| 更新日／Last updated on | 2024/01/15 |
|---|---|
| 授業の概要 ／Course outline |
Biosignals refer to various physical, chemical, mechanical, thermal, electrical and magnetic quantities that contain information of health condition in physiology and psychophysiology. They are presented as different forms in physical quantity, chemical reaction, and electrical activity, and cover a wide spectrum of physiological information in temporal and frequency domains. Biosignal detection is a procedure by which we can determine or measure the quantity that characterizes the property or state of human biological condition. Various modalities using diversified engineering principles on the basis of physics and chemistry, electricity and electronics are applied in biosignal detection. This course will provide introductory knowledge on the methodologies for detecting various biosignals, mention some aspects in biomedical instrumentation that differ from industrial measurement, and introduce application of IoT, AI, big data analytics and the latest advancements in seamless healthcare monitoring briefly. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
The objectives of this course are to introduce briefly some fundamental concepts and approaches for detecting biosignals, and to provide introductory knowledge for the students to pursue their further study of other advanced courses in biomedical engineering field. The goals to be achieved are: 1. To understand fundamental features and behaviors of various biosignals. 2. To understand application of fundamental physical and chemical principles in detecting various biosignals. 3. To understand the requirements in biosignal detection that differ from industrial measurements in some aspects. 4. To understand application of IoT, AI, big data analytics and the latest advancements in seamless healthcare monitoring. |
| 授業スケジュール ／Class schedule |
1. Introduction 2. Motion & Force 3. Direct Pressure 4. Indirect Pressure 5. Direct Flow 6. Indirect Flow 7. Respiration 8. Body Temperature 9. Bioelectricity 10. Biomagnetism 11. Biochemistry-1 12. Biochemistry-2 13. Biochemistry-3 14. Seamless 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 https://www.crcpress.com/Biomedical-Sensors-and-Instruments/Tagawa-Tamura-Oberg/p/book/9781420090789 Seamless Healthcare Monitoring - Advancements in Wearable, Attachable, and Invisible Devices, Editors: Tamura, Toshiyo, Chen, Wenxi, Springer International Publishing, 2018, DOI 10.1007/978-3-319-69362-0, eBook ISBN 978-3-319-69362-0, Hardcover ISBN 978-3-319-69361-3 https://www.springer.com/us/book/9783319693613 |
| 成績評価の方法・基準 ／Grading method/criteria |
Paper survey and study report, 100％ |
| 参考(授業ホームページ、図書など) ／Reference (course website, literature, etc.) |
The course instructor has practical working experience and has worked for 5 years at Nihon Kohden Industrial Corp., a professional manufacturer of world famous medical equipment, and has been engaged in R&D for bioinstrumentation, signal processing and data analysis. Based on this experience, he will teach the basic knowledge and latest technology in “Introduction to Biosignal Detection”. Moodle for course handouts and other related information https://elms.u-aizu.ac.jp/login/index.php |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
HIRATA Naru |
| 担当教員名 ／Instructor |
DEMURA Hirohide, HIRATA Naru |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
N/A |
| 更新日／Last updated on | 2024/01/25 |
|---|---|
| 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 Practice of remote sensing data analysis 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 |
Grades will be based on assignments given in class. |
| 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/ |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
HIRATA Naru |
| 担当教員名 ／Instructor |
HIRATA Naru, OHTAKE Makiko, DEMURA Hirohide |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
N/A |
| 更新日／Last updated on | 2024/01/25 |
|---|---|
| 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. |
| 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 - SPICE toolkit in web and python |
| 教科書 ／Textbook(s) |
N/A |
| 成績評価の方法・基準 ／Grading method/criteria |
Grades will be based on assignments given in class. |
| 履修上の留意点 ／Note for course registration |
ITC08A Remote Sensing are recommended as prerequisites. ITC10A Practical Data Analysis with Lunar and Planetary Database is closely connected with this course. ITC10A will introduce more practical topics on planetary data analyses. Students are recommended to finish ITC09 before taking ITC10A. |
| 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/ |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
DEMURA Hirohide |
| 担当教員名 ／Instructor |
DEMURA Hirohide, HIRATA Naru, OGAWA Yoshiko, HONDA Chikatoshi, KITAZATO Kohei, JAXA/NAOJ Lecturers, OHTAKE Makiko, RAGE Uday Kiran |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/02/06 |
|---|---|
| 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 |
#0 Demura (UoA) Guidance #1-7 Omnibus Style by... RAGE (UoA) RasterMiner as a GIS HONDA (UoA) Performance Test of imaging sensors KITAZATO (UoA) Spectroscopic Analysis for asteroids OHTAKE (UoA) Kaguya Data Analysis of the moon for multi band images OGAWA (UoA) Spectroscopic Analysis for lunar and planets MATSUMOTO (NAOJ) Gravity field of the Moon MOROTA (Tokyo Univ.) Crater Chronology |
| 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.) |
The course instructors has working experiences: Instructors are familiar with JAXA Space Development Projects. |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
NISHIMURA Satoshi |
| 担当教員名 ／Instructor |
NISHIMURA Satoshi, TAKAHASHI Shigeo |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/01/24 |
|---|---|
| 授業の概要 ／Course outline |
This course provides fundamentals of 3D Computer Graphics (CG) and its hardware implementation, which is followed by the recent advancement of CG rendering techniques with GPUs. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
Through this course, students are expected to acquire fundamental knowledge about rendering algorithms and their parallelization techniques. Students will also be able to obtain basic skills of GPU programming with the OpenGL Shading Language. |
| 授業スケジュール ／Class schedule |
1. Introduction 2. Shape Modeling 3. Geometry Calculation 4. Rasterization 5. Lighting and Shading 6. Texture Mapping and Shadowing 7. Exercise: Fundamentals of Shader Programming 8. Exercise: GPU-based Texture Mapping 9. Advanced Rendering Techniques 10. Volume Rendering 11. Exercise: GPU-based Lighting and Shading 12. Exercise: GPU-based Normal Mapping 13. Exercise: GPU-based Shadowing 14. Assignment Presentation |
| 教科書 ／Textbook(s) |
* J. Hughes, et al., Computer Graphics: Principles and Practice, 3rd edition, 2012. * 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%) |
| 履修上の留意点 ／Note for course registration |
Prerequisites in the case when undergraduate students take this course: IT02: Computer Graphics |
| 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/ |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
PAIK Incheon |
| 担当教員名 ／Instructor |
PAIK Incheon, OFUJI Kenta, RAGE Uday Kiran |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/01/24 |
|---|---|
| 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 for data analysis will be studied by lecture and exercise. Students can have broad and necessary knowledge and technique for data analysis on big data infrastructure. |
| 授業スケジュール ／Class schedule |
1. Data (Analysis/Science/Engineering) Process 2. A Scenario of Business Analysis with Data Science Process 3. Big Data Infrastructure (Hadoop & Apache Spark) 4. Big Data Analysis and Deep Learning 5. Statistical Analysis 1 (Linear Regression) 6. Statistical Analysis 2 (Multivariate Analysis - PCA, FA) 7. Statistical Analysis 3 (Statistical Tests) 8. Statistical Analysis 4 (Wrap up ) 9. Data Mining 1 10. Data Mining 2 11. Data Mining 3 12. Data Mining 4 13. Exercise(Statics, Spark&DM) #1 14. Exercise(Statics, Spark&DM) #2 15. 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 & Python Programming - Machine Learning and Data Mining Basics |
| 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/ |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2024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 |
RAGE Uday Kiran |
| 担当教員名 ／Instructor |
RAGE Uday Kiran, SAXENA Deepika |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2024/01/29 |
|---|---|
| 授業の概要 ／Course outline |
This course is designed to equip students with the necessary skills and knowledge to leverage big data stored in global cloud databases for socio-economic development. The course is divided into two main parts, each focusing on distinct but interconnected aspects - data science and cloud computing. Part 1: Data Science Concepts: • Structured Query Language (SQL): Students will gain an understanding of SQL, a powerful language for managing and manipulating relational databases. • Extract, Transformation, and Load (ETL) Technique: This involves the processes of extracting data from various sources, transforming it into a usable format, and loading it into a target database. • Knowledge Discovery in Data: The course covers methods and techniques for discovering valuable insights from large datasets. • Data Preprocessing Techniques: Techniques to clean and prepare data for analysis. • Dimensionality Reduction Techniques: Methods to reduce the number of input variables in a dataset. • Supervised and Unsupervised Learning Techniques: Introduction to both types of machine learning approaches. Part 2: Cloud Computing Concepts: • Cloud Computing Fundamentals: Students will gain insights into the basics of cloud computing, including its deployment models and service models. • Cluster Computing, Grid Computing, and Utility Computing: Exploration of various computing paradigms within the cloud environment. • Cloud Data Management: Focus on storage solutions within the cloud and practical application of MapReduce for big data analytics. • Cloud Resource Management Strategies: Strategies for optimizing performance and considerations for security, including encryption, authentication, and best practices. • Data Lakes: A dedicated section covering the architecture of data lakes and their integration with cloud platforms. Overall Emphasis: The course aims to empower students with state-of-the-art knowledge discovery methods, tools, and techniques in both data science and cloud computing. The integration of these two fields is crucial for unlocking valuable insights from big data stored in globally-spanning cloud databases, with a goal of contributing to socio-economic development. We hope this course provides a comprehensive and interdisciplinary approach, covering both the analytical and technical aspects necessary for extracting meaningful information from large datasets in a cloud computing environment. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
This course aims to increase the competitiveness of the students by achieving the following aims: 1) To provide knowledge on how to extract, store, and process voluminous data needed for the analytical purposes 2) Empowering the students in choosing the right learning algorithm for this task by providing the knowledge on the strengths and weakness of various data mining and deep learning 3) The students will understand and differentiate cluster, grid, and utility computing and grasp essential cloud computing concepts, including deployment models. The student will be able to show deep knowledge of cloud paradigms and data science applications. 4) The students will be able to apply Advanced Data Science in the Cloud, implement cloud-based data management techniques like MapReduce and learn resource optimization, security measures, and data lakes integration. |
| 授業スケジュール ／Class schedule |
Fundamentals 1. The Universe of Discourse, mini-world, and Entity-Relational Schema 2. Structured Query Language 3. Database Management Systems Data Science topics 4. Date Warehousing Technology 5. Extraction, Transformation, and Load 6. Knowledge Discovery in Data Cloud Computing topics 7. Introduction to Cluster Computing, Grid Computing and Utility Computing 8. Introduction To Cloud Computing 9. Cloud Data Management and Map Reduce 10. Cloud Resource Management 11. Cloud Data Security 12. Data lakes |
| 教科書 ／Textbook(s) |
Data Mining: Concepts and Techniques Book by Han et al. (Springer) Cloud Computing: Concepts, Technology & Architecture (The Pearson Service Technology Series from Thomas Erl) 1st Edition, Kindle Edition |
| 成績評価の方法・基準 ／Grading method/criteria |
Students will be graded based on the project and the final exam. The project carries 50% weightage, 10% weightage to classroom exercises, 25% weightage to exercises, and the final exam has 15% weightage. |