AY 2025 Undergraduate School Course Catalog

Applications

2025/02/23

Open Competency Codes Table Back

開講学期
／Semester		 2025年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 3rd year
単位数
／Credits		 4.0
責任者
／Coordinator		 ZHAO Qiangfu
担当教員名
／Instructor		 ZHAO Qiangfu, SHIN Jungpil, PAIK Incheon, THENUWARA HANNADIGE Akila Sanjaya.S
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 Courses preferred to be learned prior to this course (This course assumes understanding of entire or partial content of the following courses)
1) In LI14 Computer Science and Engineering Seminar II
2) FU01 Algorithms and Data Structures (useful to understand graph-based search)
3) MA09 Mathematical Logic (useful to understand processes for formal proof)
4) FU03 Discrete Mathematics  (useful to understand set, concept, fuzzy set, etc.).
更新日／Last updated on 2025/01/21
授業の概要
／Course outline		 Artificial intelligence (AI) is a research field that studies how to realize intelligent thinking and behavior using a computing machine. The ultimate goal of AI is to make a machine that can learn, plan, and solve problems autonomously. Although AI has been studied for more than half a century, we still cannot make a machine that is as intelligent as a human in all aspects. However, we do have many successful applications. In some cases, the machine equipped with AI technology can be even more intelligent than us. The Alpha-Go system which defeated the world GO champion is a well-known example. In medical diagnosis and machinery design, different kinds of "expert systems" have been widely used to support the human users. Recently, generative AIs are becoming very intelligent and can solve many problems better than human, although they still cannot find and determine what problems to solve. In fact, we human beings are becoming more intelligent (may not be wiser) with the help of these kinds of machines.

The main research topics in AI include: problem solving, reasoning, planning, natural language understanding, computer vision, automatic programming, machine learning, and so on. Of course, these topics are closely related with each other. For example, the knowledge acquired through learning can be used both for problem solving and for reasoning. In fact, the skill for problem solving itself should be acquired through learning. Also, methods for problem solving are useful both for reasoning and planning. Further, both natural language understanding and computer vision can be solved using methods developed in the field of pattern recognition.
授業の目的と到達目標
／Objectives and attainment
goals		 [Corresponding Learning Outcomes]
(A)Graduates are aware of their professional and ethical responsibilities as an engineer, and are able to analyze societal requirements, and set, solve, and evaluate technical problems using information science technologies in society.

[Competency Codes]
C-IS-001, C-IS-002-2, C-IS-003, C-IS-004-1, C-IS-005-2

In this course, we will study the most fundamental knowledge for understanding AI. Specifically, we will study

(1) Search: Problem formulation and search;
(2) Knowledge representation: Expert system, semantic network, and frame;
(3) Reasoning: Propositional logic, predicate logic, and fuzzy logic;
(4) Learning: Pattern recognition, multilayer neural networks, self-organizing neural networks, and decision tree.

After this course, we should be able to

(1) Know how to use the basic search methods;
(2) Understand the basic methods for problem formulation and knowledge representation;
(3) Understand the basic idea of automatic reasoning;
(4) Know some basic concepts related to pattern recognition and neural networks.


Due to limited time, theoretic proofs and formal notations will be eliminated as far as possible, so that we can get the full picture of AI easily. Students who become interested in AI may study further in the graduate school.
授業スケジュール
／Class schedule		 (1)  Introduction to AI
- What is AI?
- Related research fields
- A brief review of AI history
- Some key persons

(2)  Problem formulation
- State space representation
- Review of tree and graph
- Search graph
- Search tree

(3) Search - I
- Random search
- Search with closed list
- Search with open list
- Depth-first search
- Breadth-first search
- Uniform cost search

(4)  Search -II
- What is heuristic search?
- Hill climbing method
- Best first search
- A* algorithm


(5)  Production systems
- Production system
- Inference engine, working memory, and knowledge base
- Pattern matching
- Conflict resolution
- Forward inference
- Backward inference

(6)  Ontology
- What is ontology?
- Semantic network
- Frame
- Structural knowledge
- Declarative knowledge
- Procedural knowledge
- Inheritance

(7) Propositional logic
- Propositional logic
- Definition of logic formula
- Meaning of logic formula
- Classification of logic formula
- Proof based on truth table
- Basic laws
- Clausal form/Conjunctive canonical form
- Formal proof

(8)  First order predicate logic
- Predicate logic
- Term and logic formula
- Clausal form/Conjunctive canonical form
- Standardization of logic formula
- Unification and resolution
- Horn clause and selective negative linear resolution
- A brief introduction to Prolog

(9)  Fuzzy logic
- Definition of fuzzy set
- Membership function
- Notation of fuzzy set
- Operations of fuzzy set
- Fuzzy number and operations
- Extension principle
- Fuzzy rules
- De-fuzzification
- Fuzzy control

(10) Pattern recognition
- What is pattern recognition?
- Feature vector
- Nearest neighbor classifier
- Linear discriminant function
- Multi-class pattern recognition
- The k-means algorithm

(11) Distance-based neural networks
- Self-organizing neural network
- Winner-take-all learning
- Learning vector quantization
- R4-rule
- Evaluation of learning results

(12) Multilayer neural network
- What is a neural network?
- Modeling of one neuron
- Learning rules for one neuron
- Layered neural network
- Learning of multilayer neuron network

(13) Decision trees
- What is a decision tree?
- Inference with decision trees
- Induction of decision trees
- Multi-variate decision trees
- Induction of multi-variate decision trees

(14) Intelligent search algorithms
- Genetic algorithm
- Individual, population, genotype, phenotype, and genetic operations
- Particle swarm optimization
- Particles, personal factor, and social factor
教科書
／Textbook(s)		 Qiangfu ZHAO and Tatsuo HIGUCHI, Artificial Intelligence: from fundamentals to intelligent searches, Kyoritsu, ISBN: 978-4-320-12419-6, 2017 (In Japanese)
成績評価の方法・基準
／Grading method/criteria		 Final Exam: 60
Quiz: 10
Report: 20
Program: 20
履修上の留意点
／Note for course registration		 1) The students are encouraged to take Linear algebra and Discrete mathematics first before studying this course.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 [1] Introduction to Artificial Intelligence, Shinji Araya, KYORITSU SHUPPAN, ISBN4-320-12116-3 (in Japanese)
[2] New Artificial Intelligence (Fundamental), Takashi Maeda and Fumio Aoki, Ohmsha, ISBN4-274-13179 (in Japanese)
[3] Series for New Generation Engineering, Artificial Intelligence, Riichiro Mizoguchi and Toru Ishida, Ohmsha, ISBN4-274-13200-5 (in Japanese)
[4] Artificial Intelligence: a modern approach, S. Russell and P. Norvig, Prentice Hall, ISBN0-13-080302-2
[5] URL of this course: http://web-ext.u-aizu.ac.jp/~qf-zhao/TEACHING/AI/AI.html


Open Competency Codes Table Back

開講学期
／Semester		 2025年度／Academic Year 　2学期 ／Second Quarter
対象学年
／Course for;		 3rd year
単位数
／Credits		 3.0
責任者
／Coordinator		 FAYOLLE Pierre-Alain
担当教員名
／Instructor		 FAYOLLE Pierre-Alain, NISHIDATE Yohei, TAKAHASHI Shigeo, HIRATA Naru
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2025/01/09
授業の概要
／Course outline		 The computer graphics (CG) course teaches techniques used for creating, manipulating and animating images of three-dimensional objects by computers.
CG techniques and algorithms are used in fields such as:
* CAD (Computer-aided design): mechanical design, architectural design and circuit design, rapid prototyping.
* Entertainment: film production, animation, and games.
* Virtual Reality: flight simulation, operation, and support.
* Visualization: results of simulation, information visualization.
授業の目的と到達目標
／Objectives and attainment
goals		 [Corresponding Learning Outcomes]
(A)Graduates are aware of their professional and ethical responsibilities as an engineer, and are able to analyze societal requirements, and set, solve, and evaluate technical problems using information science technologies in society.

[Competency Codes]
C-GV-004, C-GV-005, C-GV-006, C-GV-007

This course provides an introduction to computer graphics.
It provides algorithmic and mathematical foundations for the main components of computer graphics: modeling, rendering and animation.
It provides a practical introduction to implementing these algorithms including programming using the OpenGL library.
授業スケジュール
／Class schedule		 Professor Fayolle:
1) Introduction to CG and OpenGL
2) Description of the 3D viewing pipeline
3) Geometric transformations, projective transformations
4) Illumination model, lighting and shading
5) Parameterization, texture mapping
6) Animation (skeleton based, kinematics)
7) Animation (physics-based)
8) Ray-casting and ray-tracing
9) Rasterization, hidden surface removal, compositing
10) Geometric modeling (surface/solid modeling)
11) Geometric modeling (polygon mesh processing)
12) Geometric modeling (parametric curves and surfaces)
13) GPU programming (GLSL, vertex and fragment shaders, ...)
14) GPU programming (Advanced shaders)


1) Guidance
2) Solid modeling
3) Boundary representations
4) 2D geometric transformations
5) 3D geometric transformations
6) Viewing transformations
7) Hidden surface removal
8) Shading
9) Mapping
10) Animation
11) Ray tracing
12) Free-form curves and surfaces
13) Non-photorealistic rendering
14) Fundamentals of GPU programming
教科書
／Textbook(s)		 Computer Graphics (for CG engineers) by Issei Fujishiro et al.
ISBN 978-4-903474-49-6
(optional)
成績評価の方法・基準
／Grading method/criteria		 * Exercises, homework, and quizzes: 40%
* Final examination: 60%
履修上の留意点
／Note for course registration		 Some familiarity with the following subjects is expected:
* Calculus, linear algebra
* Programming
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 * https://web-int.u-aizu.ac.jp/~fayolle/teaching/cg/index.html
* http://web-int.u-aizu.ac.jp/~shigeo/course/cglec/index.html
* http://web-int.u-aizu.ac.jp/~shigeo/course/cgex/index.html


Open Competency Codes Table Back

開講学期
／Semester		 2025年度／Academic Year 　2学期 ／Second Quarter
対象学年
／Course for;		 4th year
単位数
／Credits		 3.0
責任者
／Coordinator		 YAGUCHI Yuichi
担当教員名
／Instructor		 YAGUCHI Yuichi, TBD-3
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2025/01/21
授業の概要
／Course outline		 “A picture is worth a thousand words”
Images play an extremely important role in our daily lives in terms of communication and knowledge accumulation. Especially in recent years, nearly 100% of mobile devices are equipped with cameras, making it possible for all people to easily capture and handle images. In addition, with the increasing availability of a wide variety of images via the Web and other media, the automatic recognition and understanding of images by computers has become an important business objective.
Image processing is a technology to process images taken by cameras and other devices to obtain the desired recognition and understanding based on the information.
This class include how to input images to computer, data compression, image process technique such as noise reduction or image condition adjustment, filtering for extracting image feature and recognition and understanding these image features. This class aims to understand image processing by discussing "How to extract novel information from images?" as well as introducing basic image processing techniques.
授業の目的と到達目標
／Objectives and attainment
goals		 [Corresponding Learning Outcomes]
(A) Graduates are aware of their professional and ethical responsibilities as an engineer, and are able to analyze societal requirements, and set, solve, and evaluate technical problems using information science technologies in society.
(B) Graduates are able to respond to changes in social environment and technology, and are able to learn spontaneously throughout life
(C) Graduates are able to apply their professional knowledge of mathematics, natural science, and information technology, as well as the scientific thinking skills such as logical thinking and objective judgment developed through the acquisition of said knowledge, towards problem solving

In this class, we will focus on the basic aspects of digital image processing, namely Low-Level Vision. Low-Level Vision includes the direct processing of images, such as image input and output, processing methods for changing the appearance of images and extracting features, and image compression. In the lectures, we will learn about these basic techniques, and by discussing case studies of propositions, we will learn the know-how for applying them.
- Learn techniques for processing digital images
- Learn basic techniques for digital image processing that can be applied to a wide range of applications
- Learn how to use Python (or C++) and OpenCV
- Understand the important processes involved in establishing an image processing pipeline, from image acquisition and representation
- Understand the basic procedures and methods for extracting features and segments from images
In the exercises, you will apply the basic techniques you have learned in the lectures and practice applying them through problem-solving exercises. The ultimate goal is to be able to recognize a series of image processing issues and create image processing applications by solving the applied problems given in the Term Project.
授業スケジュール
／Class schedule		 Lecture Schedule:
1. Introduction to Image Processing, Fundamentals (Chapter 1)
- Definition, Origins, Fields, Steps, Components, Introduction to OpenCV by Python
2. Intensity Transformation (Chapter 2, Chapter 3)
- Histogram, Binarization, Gamma Trans., Smoothing, Sharpeing
3. Convolution and Spatial Filter I (Chapter 3)
- Mean Filter, Median Filter, Smoothing, Edge Filter, Laplacian Filter
4. Convolution and Spatial Filter II (Chapter 3)
- Sharpeing spatial filter, Pass Filter, Image Convolution, Spatial Enhancement
5. Morphological Filter (Chapter 9)
- Pixel Relation, Erosion/Dilation, Opening/Closing, Hit-or-Miss, Grayscale
6. Filtering in the Frequency Domain I (Chapter 4)
- FFT, DFT, 2D-DFT/IDFT, Low-pass, High-pass, Band-pass
7. Filtering in the Frequency Domain II (Chapter 5)
- Image Restoration and Reconstruction, Ringing Effect, Wiener Filter
8. Color Image Processing (Chapter 6)
- Color Model, Pseudo Color, Full-Color Image Processing, Noise
9. Wavelet and Other Image Transforms (Chapter 7)
- Matrix-based, Correlation, Fourier-related, Haar, Wavelet
10. Image Segmentation I (Chapter 10)
- Point, Line, Edge, Region, Texture
11. Image Segmentation II (Chapter 10)
- Clustering, Subpixels, Graphcuts, Watershed
12. Feature Extraction I (Chapter 11)
- Extractor, Corner, Interest Point, Line, Region
13. Feature Extraction II (Chapter 11, 12)
- Descriptor, SIFT, Object Recognition
14. Image Compression (Chapter 8)
- Lossless compression, Lossy Compression, Coding, JPEG, PNG

Assignment Schedule
1. Image Enhancement (1W – 4W)
2. Spatial Filtering (3W – 6W)
3. Morphological Filtering (5W – 8W)
4. Frequency Domain Filtering (6W – 9W)
5. Color Image Processing and (8W – 11W)
6. Image Segmentation (10W – 13W)
7. Object Recognition with Feature Extraction (12W – 14W end)
教科書
／Textbook(s)		 Refer to the handout from ELMS
Recommended reading: Not compulsory to buy, but recommended to buy
- R. C. Gonzalez and R. E. Woods, “Digital Image Processing, 4th Edition” (Pearson Education 2017)
- CG-ARTS Association: “Digital Image Processing, 2nd Edition” (CG-ARTS Association, 2024)
成績評価の方法・基準
／Grading method/criteria		 Exercise submissions: 10 points per submission x 7 = 70 points
Final exam: 30 points
Total: 100 points

If the first exercise submission is not submitted by the deadline, each submission will be penalized by -4 points.
(If the score is below 0, the score for that submission will be 0 points.)
There will be no mid-term or final exams.
Attendance will be considered as long as you hand in the assignments given in class. (The points for the assignments will not be included in your final grade, but they will be a good indicator of your level of understanding.)
However, this does not apply if you inform the teacher in advance that you will be absent. (You will not be penalized for absences due to illness, job hunting, or attendance at events, etc.)
履修上の留意点
／Note for course registration		 Students absent of lectures should contact the lecturers in advance. Final scores will not be reduced in case of job hunting, illness, and event participation.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Practical working experiences：

The course instructor Yuichi Yaguchi is studied a set of image processing techniques from 2008, and he has teaching experience of this course from 2011 to 2017. He also has practical working experience of image processing research such as the finding irregular control by drive recorder, remote survey system of sewage plant by web camera, visual SLAM system for robot system and so on. He also teach the advanced
course of image processing such as image recognition and understanding in graduate school.


Open Competency Codes Table Back

開講学期
／Semester		 2025年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 4th year
単位数
／Credits		 3.0
責任者
／Coordinator		 NARUSE Keitaro
担当教員名
／Instructor		 NARUSE Keitaro
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2025/01/23
授業の概要
／Course outline		 In the modern society, computer engineers should understand basic theory on robots and control theory, because computers are introduced in many robots and control devices. This course gives fundamental knowledge on them to computer science and engineering major students. In the control theory, the students study about the concept of feedback control and related theory and method. On the other hand, because robots work in the real world, the students study how we should model and represent the world in computers and how a robot should make a plan with them. The students will learn them with a series of exercises for understanding the topics deeper.
授業の目的と到達目標
／Objectives and attainment
goals		 [Corresponding Learning Outcomes]
(A)Graduates are aware of their professional and ethical responsibilities as an engineer, and are able to analyze societal requirements, and set, solve, and evaluate technical problems using information science technologies in society.

[Competency Codes]

For robotics part, the students will learn basic theory and methods for representing robot motion mathematically as well as a planning method for robots. The students will learn
(A1) Configuration space method: we can represent robots and objects in computer.
(A2) Planning method such as artificial potential method, road map method, cell decomposition method: we can be make a plan for robots

On the other hand, for the automatic control theory, the students will learn basic fundamental knowledge on feedback control, which includes
(B1) Transfer functions and block diagrams: we can model a dynamical system.
(B2) Stability and steady state error: we can understand how control system works.
(B3) PID control system: we can design a controller for a target system.
授業スケジュール
／Class schedule		 #1: Overview and introduction
#2: Configuration space for circular robot
#3: Configuration space for rectangular robot
#4: Artificial potential method
#5: Road map method
#6: Cell decomposition method
#7: Sampling based planning
#8: Robot equations of motion
#9: Principle of feedback control
#10: Steady state error
#11: Stability of control system
#12: PID control
#13: Advantage of feedback control
#14: Summary
教科書
／Textbook(s)		 None.
Related materials are distributed in a course ware.
成績評価の方法・基準
／Grading method/criteria		 Quiz: 20%
Excercise: 40%
Final exam: 40%
履修上の留意点
／Note for course registration		 As related courses, the students are expected to understand programming languages, linear system, and electrical circuits.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 LMS


Open Competency Codes Table Back

開講学期
／Semester		 2025年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 4th year
単位数
／Credits		 3.0
責任者
／Coordinator		 NASSANI Alaeddin
担当教員名
／Instructor		 NASSANI Alaeddin
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 No prerequisites beyond basic programming courses.
However, these courses are recommended:
  LI10: Intro. to Multimedia Systems
  IT02: Computer Graphics
更新日／Last updated on 2025/01/24
授業の概要
／Course outline		 This course provides a comprehensive introduction to the exciting world of interactive technologies, with a focus on Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)
Students will explore fundamental principles of Human-Computer Interaction (HCI) and 3D User Interface (UI) design while gaining practical skills in industry-standard software, focusing particularly on the creation of real-time VR games and simulations.

Emphasizing a project-based, "hands-on" approach, students will learn to craft their own virtual worlds using Unity, a versatile 3D game engine. The curriculum covers a range of topics including human perception, virtual and augmented reality, game design principles, motion graphics, color theory, music composition, speech synthesis and more.

Supporting tools such as Unity, Blender, Audacity will be introduced to aid in the multimedia content creation. Through practical experimentation and experiential learning, students will engage in hands-on activities to reinforce their understanding of the course concepts.
授業の目的と到達目標
／Objectives and attainment
goals		 [Corresponding Learning Outcomes]
(A) Graduates are aware of their professional and ethical responsibilities as an engineer, and are able to analyze societal requirements and set, solve, and evaluate technical problems using information science technologies in society.

(C) Graduates are able to apply their professional knowledge of mathematics, natural science, and information technology, as well as the scientific thinking skills such as logical thinking and objective judgment developed through the acquisition of said knowledge, towards problem solving.

[Competency Codes]
C-GV-001, C-HI-005

By the end of the course, students should be able to: design, develop, and implement their own interactive Virtual Reality (VR), Augmented Reality (AR), or Mixed Reality (MR) experiences, demonstrating proficiency in using industry-standard tools like Unity, Blender, and Audacity, while applying principles of Human-Computer Interaction (HCI) and 3D User Interface (UI) design to create engaging and immersive virtual worlds, games, or simulations
授業スケジュール
／Class schedule		 Meeting 1: Overview & 2D with Photopea
Meeting 2: 3D with Blender
Meeting 3: Audio Editing with Audacity
Meeting 4: Handheld AR
Meeting 5: Unity Intro
Meeting 6: Unity Interaction
Meeting 7: Particle & Terrain
Meeting 8: Sound in Games & Background Music
Meeting 9: Virtual Reality (VR)
Meeting 10: Avatar Embedding in Unity
Meeting 11: Node-based Scripting
Meeting 12: Tracking and Gesture Recognition
Meeting 13: Physiological Sensing
Meeting 14: Collaborative Mixed Reality (MR)
Meeting 15: Final Project
教科書
／Textbook(s)		 Lecture notes prepared by instructors, TAs, & SAs.
成績評価の方法・基準
／Grading method/criteria		 The majority of coursework in this course revolves around hands-on lab exercises, prioritizing the creative utilization of digital content creation tools to foster design innovation and originality. Weekly "checkpoint" exercises serve to validate specific skills to create engaging and interactive VR/AR experiences. Regular feedback will be provided throughout the course to guide students and help them refine their work.

The course assessment breakdown is as follows:
Quizes: 25%
Assignments: 35%
Group Project: 40%

These assessments ensure comprehensive evaluation of student learning and achievement throughout the course.
履修上の留意点
／Note for course registration		 このコースは大学院コース ITA33: マルチメディア マシニマと連動しています。
このコースに合格して会津大学大学院に進学した場合は、ITA33 への登録資格はありません。
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Photopea image editor: https://www.photopea.com
Blender: https://www.blender.org
Unity: https://unity.com
Unity tutorials: https://learn.unity.com/tutorials
Google Cardboard: https://arvr.google.com/cardboard/apps/, https://developers.google.com/cardboard
Audacity audio editor: https://www.audacityteam.org/
macOS "say" TTS (text-to-speech) utility: https://support.apple.com/en-is/guide/mac-help/mh27448/mac
GarageBand: https://www.apple.com/mac/garageband/
Vuforia: https://developer.vuforia.com/
Snap AR & Lens Studio https://ar.snap.com/
ZIG SIM https://zig-project.com/
HCI Research https://www.sciencedirect.com/book/9780128053904/research-methods-in-human-computer-interaction

The course instructor has experience in VR and AR including development, research and teaching.


Open Competency Codes Table Back

開講学期
／Semester		 2025年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 3rd year
単位数
／Credits		 4.0
責任者
／Coordinator		 CHEN Wenxi
担当教員名
／Instructor		 CHEN Wenxi, TRUONG Cong Thang, TBD-3
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2025/01/24
授業の概要
／Course outline		 Signals and systems are present in any aspects of our world. The examples of signals are speech, audio, image and video signals in consumer electronics such as TV, PC, and smartphone; vital signs in medical systems; electronic radar waveforms in military equipment. Signal processing is concerned with the representation, transformation and manipulation of signals, and extraction of the significant information contained in signals. For example, we may wish to remove the noise in speech signals to make them clear, or to enhance an image to make it more natural. Signal processing is one of the fundamental theories and techniques to construct modern information systems. During the last century, lots of theories and methods have been proposed and widely studied in signal processing. This course includes the concept of continuous-time and discrete-time signals, representations of signals in time, frequency, and other transform domains, representations and analyses of systems, filter structures and designs.
The course is a prerequisite course for your further studying on other related courses, such as voice processing, image processing, audio and video signal compressing, pattern recognition and classification, biomedical signal processing, development of communication and security systems, and so forth.
授業の目的と到達目標
／Objectives and attainment
goals		 [Corresponding Learning Outcomes]
(A) Graduates are aware of their professional and ethical responsibilities as an engineer, and are able to analyze societal requirements, and set, solve, and evaluate technical problems using information science technologies in society.
(B) This course is to provide students with the foundations and tools of signal processing, particularly the time-invariant system in both continuous and discrete domains. We will mainly study the following topics: signal representation in time domain, Fourier transform, sampling theorem, linear time-invariant system, discrete convolution, z-transform, discrete Fourier transform, and discrete filter design.
After this course, the students should be able to understand how to analyze a given signal or system using various transforms; how to process signals to make them more useful; and how to design a signal processor (digital filter) for a given problem.
授業スケジュール
／Class schedule		 Prof. TRUONG’s Class
1. Introduction to Signals and Systems
2. Linear Time-Invariant System (continuous-time)
3. Linear Time-Invariant System (discrete-time)
4. Continuous Fourier Series and Fourier Transform
5. Discrete Fourier Series, Fourier Transform, and FFT
6. Fourier Transform Analysis of Signals and Systems
7. Midterm exam
8. Laplace Transform
9. Z-Transform
10. Structures for Digital Filters I: FIR Filter
11. Digital Filter Design I: FIR Filter
12. Structures for Digital Filters II: IIR Filter
13. Digital Filter Design II: IIR Filter
14. Applications of Signal Processing

Prof. CHEN and WANG’s Class
1. Introduction to Signals and Systems
2. Linear Time-Invariant System (continuous-time)
3. Linear Time-Invariant System (discrete-time)
4. Continuous Fourier Series and Fourier Transform
5. Discrete Fourier Series, Fourier Transform, and FFT
6. Fourier Transform Analysis of Signals and Systems
7. Laplace Transform
8. Z-Transform
9. Structures for Digital Filters I: FIR Filter
10. Digital Filter Design I: FIR Filter
11. Structures for Digital Filters II: IIR Filter
12. Digital Filter Design II: IIR Filter
13. Applications of Signal Processing
14. Review
教科書
／Textbook(s)		 Textbooks:
1. Schaum's Outline of Signals and Systems, (Schaum's Outlines) 2019/10/16 Hsu, Hwei P., 3835 Yen
2. Schaum’s Outline of Digital Signal Processing, (Schaum's Outlines) 2011/9/7 Hayes, Monson H., 3658 Yen

Reference books:
1. Digital Signal Processing: A Computer-Based Approach 2010/9/10, Sanjit K. Mitra, 20106 Yen
2. ディジタル信号処理(第2版・新装版)–2020/12/17, 萩原 将文  (著)、森北出版、2420 円
3. MATLAB対応 ディジタル信号処理(第2版)–2021/11/12, 川又政征 (著), 阿部正英 (著), 八巻俊輔 (著), 樋口龍雄 (監修)、森北出版、3630 円
成績評価の方法・基準
／Grading method/criteria		 Prof. TRUONG’s Class
* Quizzes: 10%
* Exercises: 40%
* Mid-term exam: 20%
* Final exam: 30%

Prof. CHEN and WANG’s Class
* Quizzes: 10%
* Exercises: 40%
* Final exam: 50%
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.)		 1. Instructors
The course instructor Wenxi Chen has practical working experience. He worked for Nihon Kohden Industrial Corp. for 5 years where he was involved in R&D of bioinstrumentation, signal processing and data analysis. Based on his experience, he can teach the basis of signal processing and linear systems.

The course instructor Cong Thang Truong has practical working experiences. He worked for Electronics and Telecommunications Research Institute (ETRI) of South Korea for 5 years where he was involved in R&D of multimedia signal processing and communications. He also actively contributed in ISO/IEC & ITU-T standards of signals and systems for more than 10 years. Based on his experiences, he can teach the basics of signal processing and linear systems.

The course instructor Zhishang Wang has practical working experience. He had performed computational techniques for analyzing and processing image data at University of Freiburg for 3 years and had performed data processing and analysis at the University of Aizu for 5 years. He had also conducted research on data analysis in collaboration with Rexev Corp. Based on his experiences, he can teach the basics of signal processing and linear systems.

2. MOODLE for Handouts, Quizzes and Exercises
https://elms.u-aizu.ac.jp/login/index.php

3. MIT OpenCourseWare "Signals and Systems"
https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/index.htm


Open Competency Codes Table Back

開講学期
／Semester		 2025年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 4th year
単位数
／Credits		 3.0
責任者
／Coordinator		 VILLEGAS OROZCO Julian Alberto
担当教員名
／Instructor		 VILLEGAS OROZCO Julian Alberto, NASSANI Alaeddin
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 LI10 Introduction to Multimedia Systems
更新日／Last updated on 2025/01/20
授業の概要
／Course outline		 Hearing is arguably the second most important sensory modality, and it is sometimes preferable to vision for displaying and acquiring information. For example, a car navigation system delivers guidance using speech, or you verbally ask your mobile phone to dial a number.

In this course, we briefly review the main characteristics of sound, audio, and their processing for human-computer interaction. The purpose of this course is twofold:

   1. To learn techniques for extracting information from acoustic signals.
   2. To use acoustic signals to display information.
授業の目的と到達目標
／Objectives and attainment
goals		 [Corresponding Learning Outcomes]
(A)Graduates are aware of their professional and ethical responsibilities as an engineer, and are able to analyze societal requirements, and set, solve, and evaluate technical problems using information science technologies in society.

[Competency Codes]
C-GV-001, C-GV-002

- Students will develop the ability to extract meaningful information from acoustic signals for use in various applications, including speech processing, music analysis, and environmental sound recognition.

- Students will learn to apply acoustic signal processing techniques to explore and analyze large datasets, leveraging audio as a tool for big data applications.

- By the end of the course, students will be able to evaluate and select the most appropriate audio processing techniques based on application constraints such as real-time performance, computational efficiency, and data accuracy.
授業スケジュール
／Class schedule		 1 Course overview, introduction to Pure-data
2 Physics of sound
3 Sound waves and rooms
4 Sound perception
5 Sound perception (continuation)
6 Basic audio processing
7 Basic audio processing (continuation)
8 Electroacoustics, human voice
9 Time-frequency processing of audio
10 Digital Audio Effects
11 Spatial hearing
12 Sonification
13 Speech technologies (synthesis)
14 Speech technologies (recognition)
教科書
／Textbook(s)		 • V. Pulkki and M. Karjalainen, Communication acoustics: an introduction to speech, audio and psychoacoustics. John Wiley & Sons, 2015.
• T. Hermann, A. Hunt, and J. G. Neuhoff, The sonification handbook. Logos Verlag Berlin, 2011.
• W. M. Hartmann, Signals, Sound, and Sensation. Modern acoustics and signal processing, Wood- bury, NY; USA: American Institute of Physics, 1997.
• Various materials prepared by the instructor
成績評価の方法・基準
／Grading method/criteria		 Exercises 40%
Quizzes 30%
Final exam 30%
履修上の留意点
／Note for course registration		 This course is offered in English exclusively.
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. He worked as an Ikerbasque researcher for about three years at the laboratory of phonetics in the Basque Country University.

Class: Lecture followed by Exercises


Open Competency Codes Table Back

開講学期
／Semester		 2025年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 3rd year
単位数
／Credits		 3.0
責任者
／Coordinator		 HONDA Chikatoshi
担当教員名
／Instructor		 HONDA Chikatoshi, TAKAHASHI Shigeo
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 MA01 Linear Algebra I, MA02 Linear Algebra II, FU01 Algorithms and Data Structures I, FU03 Discrete Systems
更新日／Last updated on 2025/01/23
授業の概要
／Course outline		 Computational geometry is one of important field of computer science to solve geometric problems. In recent, to solve geometric problem with large data and handle with high-speed processing is required for such as geographic information system (GIS), computational graphics (CG), computer-aided design (CAD), and pattern recognition, robotics, and others.
In the class, students learn about computational geometric concepts in the first half section and learn about information visualization on the premise of various concepts / algorithms in the latter part.
授業の目的と到達目標
／Objectives and attainment
goals		 To understand basic concepts and algorithms of computational geometry and students will be able to apply it for specific problems.
授業スケジュール
／Class schedule		 Introduction -Examples of applications-
Line segment intersections
Convex hulls
Voronoi diagrams
Delaunay triangulations
Polygon triangulation
Scatterplot matrices
Parallel coordinate plots
Tree diagrams
Treemaps
Node-link diagrams
Adjacency matrices
Text and document visualization
Advanced issues in information visualization
教科書
／Textbook(s)		 Prepared handouts
成績評価の方法・基準
／Grading method/criteria		 Exercise 40%
Final examination 60%
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Computational Geometry: Algorithms and Applications, 3rd edition
M. de Berg, others,
Springer, 2008

Information Visualization: An Introduction, 3rd edition
Robert Spence
Springer, 2014


Open Competency Codes Table Back

開講学期
／Semester		 2025年度／Academic Year 　4学期 ／Fourth Quarter
対象学年
／Course for;		 3rd year
単位数
／Credits		 3.0
責任者
／Coordinator		 YAGUCHI Yuichi
担当教員名
／Instructor		 YAGUCHI Yuichi, PAIK Incheon
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2025/01/21
授業の概要
／Course outline		 Understanding natural language by computer is a very important technology for communication between human and machine interaction. Also, our real world has so many documents described by natural language. Therefore, the techniques of natural language processing are bases of the information science for understanding it by computers with extracting meaning and reconstruction sentences.
In this lecture, we learn about the techniques of natural language processing using Python and Natural Language Toolkit (NLTK) to understand and extract the meanings of sentences and reconstruction or search it.
Additionally, we learn the basic techniques of process sentences to apply the other country languages.
授業の目的と到達目標
／Objectives and attainment
goals		 [Corresponding Learning Outcomes]
(A)Graduates are aware of their professional and ethical responsibilities as an engineer, and are able to analyze societal requirements, and set, solve, and evaluate technical problems using information science technologies in society.

[Competency Codes]
C-IS-001, C-IS-002-1, C-IS-003, C-IS-004-2, C-IS-005-1, C-PL-003, C-PL-005, C-PL-007


In this lesson, exercises are performed using the Natural Language Toolkit available in Python. Explanation of Python grammar, etc. will be done within the lecture.
In the lecture, we explain the basic idea and algorithm of natural language processing. In the exercise, we use actual data to perform parsing of natural language, document clustering, semantic analysis, etc.
Also, we try to construct an easy document search engine with the help of NLTK.
授業スケジュール
／Class schedule		 Lecture Contents:
1. Introduction to Natural Language Processing
    - Intro. to NLP, Difficulty, Python Programming
2. Corpus and Lexical Resources
    - NLTK on Python, Usage for Corpus, Raw Data Import
3. Tokenizing and Regular Expression
    - Tokenize, Regular Expression, Stemming, Lemmatizing, WordNet
4. Word Sense I
    - Word Sense Disambiguation, Naïve Bayes
5. Word Sense II
    - Part of Speech, Tagging, N-Gram
6. Document Model
    - Dictionary, Bag of Words, TF*IDF, Dimension Reduction
7. Document Classification & Clustering
    - Vector Space Representation, Feature, Fisher’s LDA, K-Means Clustering
8. Review 1
    - Python Code Review, Effective Code Writing
9. Information Extraction
    - Chunking & Chinking, Named Entity Extraction, Relation Extraction
10. Sentence Structure
    - Phase Structure, Context Free Grammar, Parsing, Dependency Grammar
11. NLTK on Japanese
    - Japanese Corpus, Japanese Raw Data Acquisition, Japanese Tools
12. Information Retrieval 1
    - IR System Design, Indexing, Retrieving
13. Information Retrieval 2
    - Invert Indexing, Bag of Words, Link Analysis
14. Review 2

Assignment Contents
1. Python Programming with NLTK
    - How to write the code?, How to use NLTK?
2. Document Acquisition
    - Raw data acquisition, Tokenize, Stemming, Lemmatizing
3. Vector Space Representation
    - Tagging, Normalize, Stop-word, Dictionary, Key-Value, TF*IDF
4. Document Classification
    - Bag of Features, Dimension Reduction, Classification, Clustering
5. Information Extraction
    - Chunking, Chinking, Triple Structure
6. Sentence Structure
    - Parsing, Dependency Extraction
7. Information Retrieval System
    - Integrate previous techniques for IR System, System Evaluation
教科書
／Textbook(s)		 Steven Bird, Ewan Klein, Edward Loper "Analyzing Text with the Natural Language Toolkit", O’Reilly
成績評価の方法・基準
／Grading method/criteria		 Exercises: 7 exercises with each 10 points, total 70 points.
Final examination: 30 points.
Total: 100 points.
If you are unable to submit an assignment during the submission period, you will lose 4 points (if this results in a score of 0 or less, the score will be 0).
履修上の留意点
／Note for course registration		 Attendance is required for both lectures and practical training.
If you inform the teacher in advance that you will be absent, the situation regarding the delay in submission, etc. will be taken into account. (Absences due to illness, job hunting, attendance at events, etc. will not be penalized and will be given a period of time without points deducted.)
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Natural Language Toolkit http://www.nltk.org/
NLTK Book http://www.nltk.org/book/
Introduction to Information Retrieval https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf


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

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