AY 2019 Undergraduate School Course Catalog

Applications

2019/07/22

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開講学期
/Semester
2019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
Qiangfu Zhao
担当教員名
/Instructor
Qiangfu Zhao, Jung-pil Shin, Yen Neil Yuwen, Incheon Paik
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/01/28
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. 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
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
Exercises (40 points), and examinations (60 points)
履修上の留意点
/Note for course registration
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


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開講学期
/Semester
2019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
Pierre-Alain Fayolle
担当教員名
/Instructor
Pierre-Alain Fayolle, Yohei Nishidate, Shigeo Takahashi, Naru Hirata
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/15
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
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)

Professor Takahashi:
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: 50%
* Written examination: 50%
履修上の留意点
/Note for course registration
Some familiarity with the following domains 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.)
Course web-pages from last year:
Professor Takahashi:
* http://web-int.u-aizu.ac.jp/~shigeo/course/cglec/index.html
* http://web-int.u-aizu.ac.jp/~shigeo/course/cgex/index.html

Professor Fayolle:
* https://web-int.u-aizu.ac.jp/~fayolle/teaching/cg/index.html


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開講学期
/Semester
2019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
Xin Zhu
担当教員名
/Instructor
Yuichi Yaguchi, Xin Zhu
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/15
授業の概要
/Course outline
As shown in a proverb "Seeing is believing," images have important roles to communicate each other and to accumulate knowledge for humans. Especially, people can use image processing applications easily because camera device is installed into almost all mobile devices or image processing coprocessor is installed into every computer. Thus, the program of image recognition and understanding is a very important target for business. Image processing is a set of techniques which process or convert image given by cameras and extract novel information such as information recognition and knowledge understanding from these processes.
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
This class introduces low-level vision mainly which is bases of digital image processing. Low-level vision is processing digital images directly such as image acquisition, noise reduction, image compression, filtering etc. Lecture parts include learning how to process and discussing technical know-how of actual problems. Exercise parts include implementing leaned image processing technique and creating application.
The goal of this class is to create image processing application which is able to solve actual problems of image recognition.
授業スケジュール
/Class schedule
Total 14 Topics.
1. Introduction and Image Acquisition
2 Statistical analysis of image
3 Image Enhancement
4 Spatial Domain Filtering
5 Low-level Features of image
6 Spatial Features of image
7 Complex Features of images
8 Introduction to Color Images
9 Imaging Acquisition Devices
10 Frequency Domain Filtering
11 Discrete Cosine Transform and Coding
12 Image Compression and Image Restoration
13 Wavelet Transform
14 Pattern Matching
教科書
/Textbook(s)
We use web handouts:
http://hartman.u-aizu.ac.jp/
Image Processing 2019
In this year, students should learn movies and handout before class.

Reference Books:
- Rafael C. Gonzalez, Richard E. Woods: "Digital Image Processing: third edition" (Pearson Education, 2008)
- CG-ARTS: "Digital Image Processing (in Japanese)" (CG-ARTS, 2004)
成績評価の方法・基準
/Grading method/criteria
In AY2019, We plans 7 exercises and each has 10~15 points.
If grading point becomes over than 100, then it will be 100. Late submission of exercise will cause a reduction of 4 points in the final score.
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.)
Moodle - Image Processing Lab.
http://hartman.u-aizu.ac.jp/


Practical working experiences:

The course instructor Xin Zhu has practical working experiences. He had performed biomedical image processing at Tianjin University for 5 years, and has performed biomedical image processing at the University of Aizu for 15 years with the financial support from universities and JSPS. Based on his experiences, he can teach the basics of image processing.

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.


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開講学期
/Semester
2019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
Keitaro Naruse
担当教員名
/Instructor
Keitaro Naruse
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/15
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
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: 30%
Excercise: 40%
Final exam: 30%
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.)
http://iplab.u-aizu.ac.jp/moodle


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開講学期
/Semester
2019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
Michael Cohen
担当教員名
/Instructor
Julian Villegas, Michael Cohen
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/28
授業の概要
/Course outline
This course explores the human-computer interface as used in interactive multimedia,
namely the design of realtime computer games.
We feature a project-based, "hands-on" approach, emphasizing creation of self-designed
virtual worlds for CGM: consumer-generated media and UGC: user-generated content.
The main vehicle of expression is "Unity,"
a 3D game engine,
combining segments on "desktop virtual reality,"
motion graphics,
color (and color gradients), graphical & visual design, texture mapping, sound, music, speech & dialog, as well as software engineering and parallel computing.
We also use
Photopea,
Audacity, and GarageBand as
support tools for multimedia content creation.
The power of experiential education is leveraged by lessons with an emphasis on practical experimentation,
learning by doing.
授業の目的と到達目標
/Objectives and attainment
goals
We survey the basics of game design, including human interfaces, including demonstrations and "hands-on" exercises with basics
of multimedia: color models, image capture and compositing, graphic composition and 3D drawing, texture mapping,
stereography, and audio (including dialog) & musical editing. Students use self-designed
multimodal interfaces authored with object-oriented techniques to tell
stories with virtual characters and cinematography (camera motion and
gestures, "camerabatics") for deterministic "machinima" (machine cinema =
computer-generated movies) and to engage users in dynamic environments
such as games and digital interactive story-telling.
授業スケジュール
/Class schedule
1 Introduction, Scene Composition
2 Scripting Unity
3 Numbers, Resolution, Scale; Event Handling
4 Photographic Capture and Texture Mapping
5 Drawing, Painting, Texture Mapping
6 Individual Project Presentations
7 3D Modeling
8 Color Models
9 Scripting
10 TTS (text-to-speech)
11 Audio Editing
12 DTM (desk-top music), BGM (background music)
13 Collision Detection & Rigid Body Physics
14 Group Project Presentations
教科書
/Textbook(s)
Lecture notes prepared by instructors.

Students are required to purchase chromastereoptic and anaglyphic eyewear (cost: ¥1,000 total), available from the instructor.
They are also required to supply a 4 GB USB memory stick, available from the instructor (¥500).
成績評価の方法・基準
/Grading method/criteria
Most of the coursework involves lab exercises emphasizing creative applications of digital contents creation tools, highlighting design and invention as much as discovery. Weekly "checkpoint" exercises verify specific skill sets--- including scenario authoring and storyboarding, drawing and painting, color models and specification, digital compositing (layers, overlays, texture mapping), stereography (autostereograms, anaglyphics, chromastereoscopy, including 3D drawing), audio editing SFX (sound effects), dialog generated with TTS (text-to-speech) synthesis tools, and DTM for BGM (desk-top music composition for background music)--- progressively accumulating into fully realized virtual worlds, stories, or games. There are also creative studio exercises, occasional quizzes, and exams. The course's annual chromastereoptic art contest is juried, and winning entries are exhibited in a special display in the University library. Student scenarios (plays, movies, & games), highlighting originally created worlds and spaces, composed individually (mid-term) and as teams (end-of-term), are presented to the entire class in special review sessions.

Exercises and quizzes: 35%, Exams: 25%, Individual Project: 20%, Group Project: 20%.
履修上の留意点
/Note for course registration
This course is in conjunction with graduate school course ITA33: Multimedia Machinima; マルチメディア マシニマ.
Students who pass this course and go on to graduate school at the U. of Aizu may not register for ITA33.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
Related web pages:
course home page: http://web-int.u-aizu.ac.jp/~mcohen/welcome/courses/AizuDai/undergraduate/IT06 HI&VR
PhotoBooth photo capture: https://support.apple.com/en-jo/guide/photo-booth/welcome/mac
Photos photo manipulation: https://www.apple.com/macos/photos/
OS X "say" TTS (text-to-speech) utility: http://developer.apple.com/library/mac/#documentation/Darwin/Reference/ManPages/man1/say.1.html
Audacity audio editor: http://audacity.sourceforge.net
Photopea image editor: https://www.photopea.com/
GarageBand DTM (desk-top music) composition application: http://www.apple.com/mac/garageband/
University of Aizu virtual tour: http://www.u-aizu.ac.jp/~mcohen/welcome/courses/AizuDai/undergraduate/HI&VR/VirtualTour
Chromastereoptic stereo system: http://www.chromatek.com/
Unity: https://unity3d.com/
Unity tutorials: https://unity3d.com/learn/tutorials
Blender: https://www.blender.org

Prof. Cohen instructor has several years of industrial experience related to the course contents.
Besides appointments to academic positions at the University of Washington (Industrial Engineering Dept.) and the African University of Technology (Computer Science Dept.) teaching these topics,
he has worked in industry on virtual environments, especially audio interfaces, at Bellcore (Bell Communications Research),
doing R&D of synchronous and asynchronous network services, including hypermedia collaborative tools and TTS,
and at the NTT Human Interface Laboratories,
researching stereotelephony, digital typography, hypermedia, and groupware, and visual languages.
Prof. Villegas has practical working experience. He worked for the Productivity National Center in Colombia and as a private consultant for five years. He was involved in the development of web-based industry productivity solutions.


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開講学期
/Semester
2019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
Wenxi Chen
担当教員名
/Instructor
Wenxi Chen, Cong-Thang Truong, Xin Zhu
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/12
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 TV, PC, and smartphone; electrocardiograms 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 biomedical signal processing, speech processing, image processing, audio and video data compressing, pattern recognition, communication systems and so forth.
授業の目的と到達目標
/Objectives and attainment
goals
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
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
教科書
/Textbook(s)
Textbooks:
1. Schaum's Outline of Signals and Systems, 3rd Edition (Schaum's Outlines) 2013/12/9 Hwei P Hsu, 2664 Yen
2. Schaum’s Outline of Digital Signal Processing, 2nd Edition (Schaum's Outlines) 2011/9/28 Monson H. Hayes, 2350 Yen

Reference books:
1. Digital Signal Processing (Int'l Ed) 2011/6/1, Sanjit K. Mitra, 10769Yen
2. ディジタル信号処理(第2版)、萩原将文、森北出版、2376円
3. MATLAB対応ディジタル信号処理、樋口龍雄、川又政征、森北出版、3564円
成績評価の方法・基準
/Grading method/criteria
1. Mid-term exam: 20%
2. Final exam: 30%
3. Exercises: 40%
4. Quiz: 10%
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 Xin Zhu has practical working experiences. He had performed biomedical signal processing at Tianjin University for 5 years, and has performed ECG, EEG, and pulse signal processing at the University of Aizu for 15 years. He had also performed collaboration research with Shinken Corp. and Asahi Denshi Corp. for the development of biomedical signal processing algorithms for about 5 years. Based on his experiences, he can teach the basics of signal processing and linear systems.
2. Lecture and Exercise
http://web-int.u-aizu.ac.jp/spls/
3. MIT OpenCourseWare
https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/index.htm


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開講学期
/Semester
2019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
Julian Villegas
担当教員名
/Instructor
Julian Villegas, Michael Cohen
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/15
授業の概要
/Course outline
The purpose of this course is two-fold: To learn some techniques used for extracting information from acoustic signals and to use acoustic signals to display information. Hearing is the second most important sensory modality and it is sometimes preferable than vision to display and acquire information. For example, a car navigation system delivers guidance using speech, or you verbally ask your mobile phone to dial some number. In this course, we briefly review the main characteristics of sound, audio, and their processing for human-computer interaction.
授業の目的と到達目標
/Objectives and attainment
goals
• Students who approve this course are expected to be able to extract information from acoustic signals that can be used as input for other techniques.
• These students are also expected to be able to use acoustic signals to explore big data.
• Given some application constraints (real-time, computing power, etc.) students at the end of the term should be able to decide which of the presented techniques is best for extracting/displaying data using acoustic signals.
授業スケジュール
/Class schedule
1 Introductions
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 Filters
9 Time-frequency processing
10 Advanced processing
11 Wavelet and Cepstrum
12 Speech technologies
13 Sonification
14 Concepts of intelligent and learning systems
教科書
/Textbook(s)
• W. M. Hartmann, Signals, Sound, and Sensation. Modern acoustics and signal processing, Wood- bury, NY; USA: American Institute of Physics, 1997.
• 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.
• Various materials prepared by the instructor
成績評価の方法・基準
/Grading method/criteria
Exercises 30%
Quizzes 30%
Final exam 40%
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 a Ikerbasque researcher for about three years at the laboratory of phonetics in the Basque Country University.

Course website: http://onkyo.u-aizu.ac.jp/index.php/classes/SAP/


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開講学期
/Semester
2019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
Chikatoshi Honda
担当教員名
/Instructor
Chikatoshi Honda, Shigeo Takahashi
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/15
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 (Chap.1-7), and learn about information visualization on the premise of various concepts / algorithms in the latter part (Chap.8-14).
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
1. Computational geometry -Introduction-
2. Line segment intersections
3. Convex hulls
4. Voronoi diagrams
5. Delaunay triangulations
6. Polygon triangulation
7. Binary space partitions
8. Scatterplot matrices
9. Parallel coordinate plots
10. Tree diagrams
11. Treemaps
12. Force-directed layout
13. Adjacency matrices
14. 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 50%
Final examination 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.)
Computational Geometry: Algorithms and Applications, 3rd edition
M. de Berg, others,
Springer, 2008

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


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開講学期
/Semester
2019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
Yuichi Yaguchi
担当教員名
/Instructor
Yuichi Yaguchi, Incheon Paik
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/15
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
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.
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.
履修上の留意点
/Note for course registration
If you attend 1 week (2 lecture), we grant 1 point for your score. We score F if the lecture is absent more than 5 times or if there are not more than three exercises to submit.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
Moodle: http://hartman.u-aizu.ac.jp/
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


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