2024年度 シラバス大学院 2024年度 シラバス大学院 2024年度 シラバス大学院
| 2024/11/24 現在 |
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| 開講学期 ／Semester |
2024年度／Academic Year 1学期 ／First Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
ヴィジェガス オロズコ ジュリアン アルベルト |
| 担当教員名 ／Instructor |
ヴィジェガス オロズコ ジュリアン アルベルト |
| 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. |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 1学期 ／First Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
西舘 陽平 |
| 担当教員名 ／Instructor |
西舘 陽平 |
| 推奨トラック ／Recommended track |
－ |
| 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. |
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| 開講学期 ／Semester |
2024年度／Academic Year 3学期 ／Third Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
矢口 勇一 |
| 担当教員名 ／Instructor |
矢口 勇一 |
| 推奨トラック ／Recommended track |
－ |
| 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 backgrounds 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 (Feature Detection, Face detection, Bayesian Net, Clustering) and each report have 25~40 points. |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 1学期 ／First Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
黄 捷 |
| 担当教員名 ／Instructor |
黄 捷 |
| 推奨トラック ／Recommended track |
－ |
| 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 |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 1学期 ／First Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
愼 重弼 |
| 担当教員名 ／Instructor |
愼 重弼 |
| 推奨トラック ／Recommended track |
－ |
| 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 |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 1学期 ／First Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
ヴィジェガス オロズコ ジュリアン アルベルト |
| 担当教員名 ／Instructor |
ヴィジェガス オロズコ ジュリアン アルベルト, 黄 捷 |
| 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). |
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| 開講学期 ／Semester |
2024年度／Academic Year 1学期 ／First Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
ブレイク ジョン |
| 担当教員名 ／Instructor |
ブレイク ジョン |
| 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 |
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| 開講学期 ／Semester |
2024年度／Academic Year 4学期 ／Fourth Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
ウィルソン イアン |
| 担当教員名 ／Instructor |
ウィルソン イアン |
| 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 |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 3学期 ／Third Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
白 寅天 |
| 担当教員名 ／Instructor |
白 寅天, 矢口 勇一 |
| 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. |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 3学期 ／Third Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
大竹 真紀子 |
| 担当教員名 ／Instructor |
大竹 真紀子, 小川 佳子, 本田 親寿, 山田 竜平 |
| 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". |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 3学期 ／Third Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
陳 文西 |
| 担当教員名 ／Instructor |
陳 文西 |
| 推奨トラック ／Recommended track |
－ |
| 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 |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 前期集中 ／1st Semester Intensive |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
1.0 |
| 責任者 ／Coordinator |
姫野 龍太郎（順天堂大） |
| 担当教員名 ／Instructor |
姫野 龍太郎（順天堂大）, 検崎 博生（理研）, 野田 茂穂（理研）, 陳 文西 |
| 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 |
コンピュータの性能向上と計算手法の進化、そして各種計測手法の発展により、これまで不可能だった生体のシミュレーションが広い範囲で可能になってきている。この生体シミュレーション技術の基礎と現状を、ミクロ（生体分子シミュレーション）とマクロ（生体硬組織・生体流体シミュレーション）両面から学ぶとともに、実習を通してその一部を体験する。生体分子シミュレーションでは、分子動力学のシミュレーションの基礎から実際の応用例までを学ぶとともに、実際に分子動力学シミュレーションを体験する。同様に生体硬組織と生物流体の基礎方程式から解法、実際の応用例を学び、医療画像からの血流シミュレーションを体験する。 |
| 授業の目的と到達目標 ／Objectives and attainment goals |
ミクロからマクロまでの生体のシミュレーションの方法の基礎方程式と計算方法とその種々の応用の実際を学ぶ。具体的には、 １）生体分子：分子動力学シミュレーション ２）生体硬組織：構造力学の基礎と生体のシミュレーションに必要な非線形構造力学 ３）生体流体：血流を主な対象とした流体シミュレーションの基礎方程式と解法 このうち１）と3）については実習を通して、実際に自分で問題を解けることを目指す。 |
| 授業スケジュール ／Class schedule |
1. 概要紹介：姫野龍太郎 (1コマ) 2. 生体分子シミュレーション：検崎博生 (2コマ) ・ 基礎理論 ・ 応用 ・ 演習 3. 生体硬組織シミュレーション: 姫野龍太郎 (2コマ) ・ 基礎理論 4. 生体流体シミュレーション ・ 基礎理論: 姫野龍太郎 ・ 医療応用: 姫野龍太郎 ・ 演習: 野田茂穂 (2コマ) 合計7コマ |
| 教科書 ／Textbook(s) |
教科書は使わず、必要な教材は資料として提供する。 |
| 成績評価の方法・基準 ／Grading method/criteria |
各授業内での小テストおよび課題、実習での評価：100% |
| 履修上の留意点 ／Note for course registration |
ＰＣを使った実習のために各自PCを持参すること。OSは実行するソフトウェアの関係でWindowsまたはLinux。他のOSしか用意できない場合は事前に相談すること |
| 参考(授業ホームページ、図書など) ／Reference (course website, literature, etc.) |
実務経験（姫野）：生体流体シミュレーションで扱う流体シミュレーションは、日産自動車（株）に勤務していたときに業務で活用してきた約１５年の実務経験がある。この経験を元に流体シミュレーションの基礎を教授する。 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 |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 4学期 ／Fourth Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
白 寅天 |
| 担当教員名 ／Instructor |
白 寅天 |
| 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. |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 2学期 ／Second Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
マルコフ コンスタンティン |
| 担当教員名 ／Instructor |
マルコフ コンスタンティン |
| 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/ |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 1学期 ／First Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
イリチュ ピーター |
| 担当教員名 ／Instructor |
イリチュ ピーター |
| 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. |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 4学期 ／Fourth Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
ファヨール ピエール アラン |
| 担当教員名 ／Instructor |
ファヨール ピエール アラン, 西舘 陽平 |
| 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 |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 前期集中 ／1st Semester Intensive |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
ヴィジェガス オロズコ ジュリアン アルベルト |
| 担当教員名 ／Instructor |
ヴィジェガス オロズコ ジュリアン アルベルト |
| 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 |
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. Various materials prepared by the instructors. Besides normal lectures and exercises, we'll also use iPads (one lent to each student for the term) extensively for courseware and interactive projects. A full list of relevant apps is listed on the course home page. |
| 成績評価の方法・基準 ／Grading method/criteria |
This course is concerned not only with aesthetic issues, but also with technical issues, and as such would be useful to audio engineers and researchers, as well as musicians. Most of the coursework involves reading, homework exercises, and lab projects. There are mid-term and final exams. Evaluation: Homework problem sets (25%), lab exercises (25%), midterm exam (25%) and final exam (25%). |
| 履修上の留意点 ／Note for course registration |
Note: This course is prerequisite for "Spatial Hearing and Virtual 3D Sound," http://u-aizu.ac.jp/official/curriculum/syllabus/2022_2_E_005.html#2445901 |
| 参考(授業ホームページ、図書など) ／Reference (course website, literature, etc.) |
course home page: http://u-aizu.ac.jp/~mcohen/welcome/courses/AizuDai/graduate/Sound+Audio/syllabus.html |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 1学期 ／First Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
成瀬 継太郎 |
| 担当教員名 ／Instructor |
成瀬 継太郎, 渡部 有隆 |
| 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 |
この講義ではロボット工学を情報技術の面から学ぶ．このとき課題となるのはロボットプランニングである．本講義では，移動ロボットとアーム型ロボットを例題として，経路計画と制御について学ぶ．具体的には座標変換，運動学，ヤコビ行列である．またmatlabを使った演習により理解を深める． |
| 授業の目的と到達目標 ／Objectives and attainment goals |
この科目を履修した学生は以下のことができるようになる． (A) 移動ロボットとアーム型ロボットの運動学とプランニング (B) 移動型ロボットのアーム型ロボットの動力学とシミュレーション (C) ロボットの環境認識と学習 |
| 授業スケジュール ／Class schedule |
#1 序論と概要 #2 移動ロボット，座標変換，運動学 #3 演習 #4 アーム型ロボットの順運動学，DH法，演習 #5 演習 #6 アーム型ロボットの逆運動学，ヤコビ行列，演習 #7 演習 #8 移動ロボット，動力学，ロボットシミュレーション #9 演習 #10 アーム型ロボット，動力学，ロボットシミュレーション #11 演習 #12 学習と環境認識 #13 演習 #14 演習・まとめ |
| 教科書 ／Textbook(s) |
なし，授業中に配布する． |
| 成績評価の方法・基準 ／Grading method/criteria |
レポート(100%) |
| 履修上の留意点 ／Note for course registration |
学部のロボット工学と自動制御を履修していることが望ましい |
| 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/ |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 4学期 ／Fourth Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
成瀬 継太郎 |
| 担当教員名 ／Instructor |
成瀬 継太郎, 矢口 勇一 |
| 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 |
本科目では現代制御理論を学ぶ．具体的には，状態空間モデル，安定性，可制御性，可観測性，レギュレータ，オブザーバ，線形二次レギュレータによる最適制御を学ぶ．また演習により理解を深める． |
| 授業の目的と到達目標 ／Objectives and attainment goals |
この科目を受講した学生は以下ができるようになる． (A) システムの状態空間表現 (B) システムの安定性，可制御性，可観測性の判定，リャプノフ関数 (C) システムの制御器（レギュレータ）の設計 (D) システムの観測器（オブザーバ）の設計．カルマンフィルタとパーティクルフィルタを含む (E) 最適制御系の設計 (F) matlabによる設計シミュレーション |
| 授業スケジュール ／Class schedule |
#1 序論と概要 #2 微分方程式と状態空間モデル #3 演習 #4 安定性，可制御性，レギュレータの設計 #5 演習 #6 可観測性，オブザーバの設計 #7 演習 #8 オブザーバ・レギュレータシステムの設計，最適制御 #9 演習 #10 離散時間カルマンフィルタ #11 演習 #12 離散時間モンテカルロマンフィルタ #13 演習 #14 演習・まとめ |
| 教科書 ／Textbook(s) |
なし，必要な資料は授業中に配布する． |
| 成績評価の方法・基準 ／Grading method/criteria |
レポート(100%)による |
| 履修上の留意点 ／Note for course registration |
関連科目（必須ではない） 学部：ロボット工学と自動制御 大学院：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/ |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 2学期 ／Second Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
趙 強福 |
| 担当教員名 ／Instructor |
趙 強福, 劉 勇, 矢口 勇一 |
| 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. |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 1学期 ／First Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
朱 欣 |
| 担当教員名 ／Instructor |
朱 欣, 陳 文西 |
| 推奨トラック ／Recommended track |
－ |
| 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. |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 1学期 ／First Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
陳 文西 |
| 担当教員名 ／Instructor |
陳 文西, 朱 欣 |
| 推奨トラック ／Recommended track |
－ |
| 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 |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 1学期 ／First Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
平田 成 |
| 担当教員名 ／Instructor |
出村 裕英, 平田 成 |
| 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 |
リモートセンシングとは，広義には対象物の状態を遠隔から測定する手法のことを指す．多くの場合，光を含む電磁波が計測手段として用いられる．また，狭義には人工衛星などの宇宙機や航空機を，センサを搭載するプラットフォームとして，地球や他の天体を観測することを指す． 本科目では，まずリモートセンシング技術の多様な側面について概要を述べる．次いで，宇宙機によるリモートセンシングを題材として，データの取得から解析，解釈に至る過程を段階を追って詳説する．科学的に有用な測定のためには，その背景となる数学的知識や物理学的現象の理解も重要となるため，これらについても本科目で取り扱う． |
| 授業の目的と到達目標 ／Objectives and attainment goals |
リモートセンシングの概念，特徴，有用性を理解する． リモートセンシングデータの取得，解析，解釈に関わるコンピュータ理工学の知識・技術を習得する． また，関連する数学・物理学の知識を得る． |
| 授業スケジュール ／Class schedule |
1 ガイダンス 2 リモートセンシング概論 3-4 リモートセンシングに関わる光学，電磁気学的背景 5-6 リモートセンシングプラットフォームとセンサ 7-8 リモートセンシングデータの特徴 9 リモートセンシングデータの放射量補正 10 リモートセンシングデータの幾何補正 11 マルチバンド画像解析 12 リモートセンシングデータ解析の実際 13 合成開口レーダー 14 測位システム(GPS) |
| 教科書 ／Textbook(s) |
N/A |
| 成績評価の方法・基準 ／Grading method/criteria |
授業中に出題する課題により成績を評価する。 |
| 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.) |
基礎からわかるリモートセンシング, 日本リモートセンシング学会(編), 2011 https://www.amazon.co.jp/dp/4844607790 Image Processing and GIS for Remote Sensing: Techniques and Applications, Liu and Mason, 2016 https://www.amazon.co.jp/dp/1118724208/ |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 2学期 ／Second Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
平田 成 |
| 担当教員名 ／Instructor |
平田 成, 大竹 真紀子, 出村 裕英 |
| 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 |
月惑星探査によって取得されたデータの解析にあたって基礎となる知識について学ぶ．探査機の科学観測データを取り扱う際には，探査機位置・姿勢情報等を含む補助データについての理解も必要不可欠となる．このため，まず補助データの利用方法について講義と実習を行う． |
| 授業の目的と到達目標 ／Objectives and attainment goals |
本講義の履修により，月惑星探査ミッションにおけるデータの解析手法を学び，それを実現するためのソフトウエア開発の基礎を習得する．また，NASAが開発したSPICE toolkitを用いた補助データの取り扱いを理解する |
| 授業スケジュール ／Class schedule |
- 第一週 - イントロダクション - 第二週 - 補助データとSPICE toolkitの概要 - 時刻情報 - 第三週 - 座標系 - 軌道・位置情報 - 第四週 - 座標系の変換 - 第五週 - 探査機の姿勢情報 - 第六週 - 天体の形状モデル - 第七週 - ブラウザとPythonによるSPICE toolkit |
| 教科書 ／Textbook(s) |
N/A |
| 成績評価の方法・基準 ／Grading method/criteria |
授業中に出題する課題により成績を評価する。 |
| 履修上の留意点 ／Note for course registration |
リモートセンシングの基礎知識（ITC08Aで取り扱う）を理解していることが望ましい． ITC10A Practical Data Analysis with Lunar and Planetary Database は本コースの内容と強い関連を持つ．ITC10Aでは実践的な探査データの解析に関するトピックを取り上げるため，先にITC09Aを履修したのち，ITC10Aを履修することが望ましい． |
| 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/ |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 3学期 ／Third Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
出村 裕英 |
| 担当教員名 ／Instructor |
出村 裕英, 平田 成, 小川 佳子, 本田 親寿, 北里 宏平, JAXA/NAOJ講師, 大竹 真紀子, ラゲ ウダイ キラン |
| 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. |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 2学期 ／Second Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
西村 憲 |
| 担当教員名 ／Instructor |
西村 憲, 高橋 成雄 |
| 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/ |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 1学期 ／First Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
白 寅天 |
| 担当教員名 ／Instructor |
白 寅天, 大藤 建太, ラゲ ウダイ キラン |
| 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/ |
| コンピテンシーコード表を開く 科目一覧へ戻る |
| 開講学期 ／Semester |
2024年度／Academic Year 2学期 ／Second Quarter |
|---|---|
| 対象学年 ／Course for; |
1年 , 2年 |
| 単位数 ／Credits |
2.0 |
| 責任者 ／Coordinator |
ラゲ ウダイ キラン |
| 担当教員名 ／Instructor |
ラゲ ウダイ キラン, サクセナ ディーピカー |
| 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. |