AY 2018 Undergraduate School Course Catalog

Applications

2019/01/30

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開講学期
/Semester
2018年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
3rd year , 4th 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 2018/08/23
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 Deep Blue system which defeated the world chess 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: Production 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
2018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 2018/02/15
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 programming 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 / visualization
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.)
Web-pages for previous offerings of the course:
* Prof. Takahashi's page for the course
* Prof. Fayolle's page for the course



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開講学期
/Semester
2018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 2017/12/12
授業の概要
/Course outline
As shown in a proverb "Seeing is believing," image has important role 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 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.
In this class studies 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. Also, this class aims to understand image processing by discussion about "How to extract novel information from images?" with introducing basic image processing techniques.
授業の目的と到達目標
/Objectives and attainment
goals
This class studies 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 part is learning how to process and discussing technical know-how of actual problems.
Exercise part is 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 2018
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 AY2017, We plans 7 exercises and each has 10~15 points.
If grading point becomes over than 100, then it will be 100.
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/


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開講学期
/Semester
2018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
Wenxi Chen
担当教員名
/Instructor
Wenxi Chen, Xin Zhu
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2018/06/25
授業の概要
/Course outline
In this course, bioinformatics, biomedical instrumentation and measurement technology, and medical image processing technology will be taught. This course aims to provide interdisciplinary knowledge for undergraduate students. In addition, sequence analysis of bioinformatics, measurement of ECG and blood pressure are also included in this course as exercise. Finally, an external expert will be invited to give a special lecture to introduce the latest developments of Biomedical Information Technology field.
授業の目的と到達目標
/Objectives and attainment
goals
This is an introductory course about the application of information technology in biomedical engineering field.
授業スケジュール
/Class schedule
1 Introduction to bioinformatics (Zhu)
1.1 Brief introduction to bioinformatics
1.2 Nucleotide, amino-acid sequence and pairwise alignment
1.3 Hidden Markov model, motif search, and protein structural prediction
1.4 Open Reading Frame search, database search, multiple alignment and phylogenetic tree
1.5 Introduction to Medical Informatics
1.6 Big data in medicine and precision medicine
1.7 Mini Test
2 Introduction to Biomedical Information Technology (Chen)
2.1 Basis of Biomedical Information
2.2 Detection of Biomedical Information
2.2.1 Blood Pressure and Electrocardiogram
2.2.2 Body Temperature and SpO2
2.3 Medical Imaging
2.3.1 Endoscope, Fundus Camera, Ultrasound Imaging, Thermography
2.3.2 Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Single Photon Emission Computed Tomography (SPECT)
2.4 Therapy Technologies
2.4.1 Automated External Defibrillator (AED), Pacemaker, Artificial Organs
2.4.2 Extracorporeal Shockwave Lithotripsy (ESWL), MRI-guided Focused Ultrasound Surgery (MRIgFUS), Gamma Knife
3 Special lecture by an external export
3.1 The latest developments of Biomedical Information Technology
教科書
/Textbook(s)
・Hiroyuki Toh、Introduction to Bioinformatics
・Masahiko Okada、Introduction to Medical Equipments
成績評価の方法・基準
/Grading method/criteria
paper test and mini report 60%
exercises 40%
履修上の留意点
/Note for course registration
Algorithms and Data Structures
Signal Processing and Image Processing
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
http://web-ext.u-aizu.ac.jp/course/bmclass/


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開講学期
/Semester
2018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 2018/02/14
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
2018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 2018/01/17
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 vehicles of expression are "Alice" and "Unity,"
object-oriented, rapid-prototyping 3D scenario IDEs (integrated development environments), to combine 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
Sumo Paint,
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
Introduction to basic concepts related to physics; space (physical and otherwise) and topology; numbers and algorithmic complexity, including exponential processes; software engineering and programming (parameterization, randomization, recursion, data structures, event handling); interactive multimedia and sensory modalities; graphics and CG (computer graphics) rendering; CAD (computer-aided design); visual languages; stereopsis and binocular vision (autostereograms, SIRDs, anaglyphics, & chromastereoscopy, including 3D drawing); image-based rendering; sound, audio, TTS (text-to-speech synthesis), and SFX (sound effects) editing; DTM (desk-top music) composition for BGM (background music); interface paradigms, digital interactive story-telling, and machinima.

1 Introduction, Scene Composition
2 Scripting Alice
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

Related art-forms include
  animation: illustration, character design, modeling, combining drawing, images, & movement to convey meaning or action,
  dramatic writing: playwriting and screenwriting for storytelling,
  graphic design: 2-dimensional information presentation,
  interactive design and game development: entertainment computing and rich-media development,
  motion media: choreography of avatars and objects,
  sculpture: 3-dimensional modeling,
  sequential art: storyboards combining words and pictures for effective narratives,
  themed entertainment: virtual environment design, &
  visual effects: crafting illusions.
教科書
/Textbook(s)
Lecture notes prepared by instructors.

Students are required to purchase chromastereoptic and anaglyphic eyewear, available from the instructor.
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
Prerequisites and other related courses which include important concepts relevant to the course

None in particular beyond basic programming courses.
However, these courses are especially recommended:
  L10: Intro. to Multimedia Systems (http://www.u-aizu.ac.jp/~shigeo/course/mms/)
  ITC01: Java 2D/3D Graphics (http://web-int.u-aizu.ac.jp/~fayolle/teaching/java_2d_3d/index.html)
  ITC11: 3D Computer Graphics and GPU Programming; 3次元コンピュータグラフィックスとGPUプログラミング (http://web-int.u-aizu.ac.jp/~nisim/cg_gpu/)
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
course home page: http://web-int.u-aizu.ac.jp/~mcohen/welcome/courses/AizuDai/undergraduate/HI&VR
newsletter article about the course: http://sighci.org/uploads/SIGHCI%20Newsletters/AIS_SIGHCI_Newsletter_v12_n1.pdf#page=10 (pages 10-11 if not automatically scrolled)
Alice desktop virtual reality IDE: http://www.alice.org
"Last Lecture" of Randy Pausch, the original architect of Alice: http://www.cmu.edu/randyslecture/
PhotoBooth photo capture: http://www.apple.com/macosx/what-is-macosx/photo-booth.html
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
SumoPaint image editor: http://www.sumopaint.com/app/
GarageBand DTM (desk-top music) composition application: http://www.apple.com/ilife/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


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開講学期
/Semester
2018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 2017/11/28
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 about 2500 Yen
2. Schaum's Outline of Digital Signal Processing, 2nd Edition (Schaum's Outlines)  2011/9/28 Monson H. Hayes about 2500 Yen

Reference books:
1. Digital Signal Processing (Int'l Ed)  2011/6/1 about 6,000 Yen, Sanjit K. Mitra (著)
2. ディジタル信号処理(第2版)、萩原将文(著)、森北出版、約2300円
3. MATLAB対応ディジタル信号処理、樋口龍雄、川又政征(著)、森北出版、約3500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. Lecture and Exercise
    http://web-int.u-aizu.ac.jp/spls/
2. 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
2018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 2017/12/15
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 (contin- uation)
6 Basic audio processing
7 Basic audio processing (continuation)
8 Filters
9 Time-frequency process- ing
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 instructors
成績評価の方法・基準
/Grading method/criteria
Exercises 50%
Final exam 50%
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
Course website: http://onkyo.u-aizu.ac.jp/index.php/classes/SAP/


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開講学期
/Semester
2018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 2017/12/18
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. Multivariate Data Visualization
9. Tree Visualization
10. Network Visualization
11. Text Visualization
12. Geospatial Visualization
13. Interaction in Visualization
14. Perception in 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
2018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 2018/02/23
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
1. Introduction to Natural Language Processing and Information Retrieval:
Python Programming
Text & Speech, Language, Syntax overview, Python
2.Text corpus
3.Access raw data and parsing 1 – HTML
4.Access raw data and parsing 2 – Regular Expression
5.Word clustering
6.Text clustering 1 – Supervised Learning
7.Text clustering 2 – Unsupervised Learning
8.Text information extraction - Chunking
9.Sentence analysis – Syntax Analysis Algorithms
10.Syntax construction
11.Meaning – propositional logic
12.Japanese language processing – Separating Words and Syntax Analysis
13.Technology of document retrieval: TF-IDF, N-gram, Latent Semantic Analysis
14.Technology of web search – Page rank, HITS algorithm
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
Attendance is not scored, but 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