AY 2017 Undergraduate School Course Catalog

Applications

2018/01/30

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開講学期
／Semester		 2017年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 3rd year
単位数
／Credits		 3.0
責任者
／Coordinator		 Qiangfu Zhao
担当教員名
／Instructor		 Qiangfu Zhao, Jung-pil Shin, Yen Neil Yuwen
推奨トラック
／Recommended track		 VH,RC
履修規程上の先修条件
／Prerequisites		 M7 & F8

更新日／Last updated on 2017/01/24
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, and self-organizing neural networks.

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
- A* algorithm
- Game tree search

(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)  Midterm Exam

(8) 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

(9)  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

(10)  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

(11) Other methods for reasoning
- Deductive and inductive inference
- Abduction
- Hypothetical reasoning
- Uncertain reasoning
- Probabilistic reasoning
- Bayesian Network
- Cased-based reasoning

(12) Pattern recognition
- What is pattern recognition?
- Feature vector
- Template matching
- Linear discriminant function
- Multiple template matching
- NNC and k-NN
- The k-means algorithm
- Evaluation of learning results

(13) 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

(14) Self-organizing neural network
- Nearest neighbor classifier
- Self-organizing neural network
- Winner-take-all learning strategy
- Learning vector quantization
- R4-rule

(15) Final review
教科書
／Textbook(s)		 Series for New Generation Engineering, Artificial Intelligence, Riichiro Mizoguchi and Toru Ishida, Ohmsha, ISBN4-274-13200-5 (in Japanese)
成績評価の方法・基準
／Grading method/criteria		 Exercises (39 points), quizzes (13 points), and examinations (48 points)
履修上の留意点
／Note for course registration		 * Probability and statistics
* Automata and languages

Formal prerequisites:M7 Probability and Statistic
F8 Automata and Languages
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] New Artificial Intelligence (Advanced), Takashi Maeda and Fumio Aoki, Ohmsha, ISBN4-274-13198-X (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		 2017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		 Pierre-Alain Fayolle
担当教員名
／Instructor		 Pierre-Alain Fayolle, Xin Zhu, Yohei Nishidate, Yuichi Yaguchi, Shigeo Takahashi
推奨トラック
／Recommended track		 CM,VH,BM,
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2017/01/23
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.
* Computer art: art and commercials.
* Entertainment: film production, animation, and games.
* Virtual Reality: flight simulation, operation and support.
* Visualization: Results of simulation, Human Genome and Human Body Model.
授業の目的と到達目標
／Objectives and attainment
goals		 The objectives of the course are:
* To learn the theory behind some of the fundamentals CG techniques and algorithms
* To acquire practical skills by implementing and experimenting with these techniques (using the OpenGL library)
授業スケジュール
／Class schedule		 Introduction to CG and OpenGL
3D viewing pipeline
Transformations
Lighting/Shading
Texturing
Blending, anti-aliasing and fog
Animation (kinematics, physics-based)
Ray-casting and ray-tracing
Rasterization
Geometric modeling (implicit surfaces, polygon mesh, parametric curves and surfaces)
GPU programming (GLSL, vertex and fragement shaders, ...)
教科書
／Textbook(s)		 Computer Graphics (for CG engineers) by Issei Fujishiro et al.
成績評価の方法・基準
／Grading method/criteria		 - exercises and project (including report): 45%
- written tests: 55%
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 2016 course web-site (internal)
OpenGL
Three.js


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開講学期
／Semester		 2017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		 Yuichi Yaguchi
担当教員名
／Instructor		 Keitaro Naruse, Yuichi Yaguchi
推奨トラック
／Recommended track		 VH,BM
履修規程上の先修条件
／Prerequisites		 A8

更新日／Last updated on 2017/01/27
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 15 Topics.
0 Course Instruction
1 Course Instruction
1 Image Acquisition and Sampling
1 Physical Definition
2 Principle of Camera
3 Definition of Digital Image
4 Resampling and Aliacing
2 Statistical Expression
1 Counting, Histogram and Accumulated Histogram
2 Mean, Mode, Median
3 Diviation, Skewness, Curtsis, Momemt
4 Resion of Interest, Windowing
3 Image Enhancement
1 Grobal Operation - Negation, Log Transform, Gamma Transform
2 Grobal Binalization - Basic Grobal Thresholding, Otsu Thresholding
3 Local Enhancement - Adaptive Local Enhancement
4 Local Binalization - Adaptive Local Thresholding
4 Spatial Filtering
1 Principle of Spatial Filtering - Mean Filter
2 Denoising Filter - Gaussian Filter, Median Filter
3 Enhansing Filter - Laplasian Filter, Unsharp Mask
4 Border Effect
5 Low-level Feature
1 Human Vision and Brain
2 Point, Line, Area
3 Gradient, Edge - Sobel, Prewitt
4 Region, and Connectivity
6 Spatial Feature
1 Hough Transform
2 Morphological Analysis
3 Opening/Closing
4 Canny Edge
7 Complex Feature
1 Histogram of Orientation Gradients
2 Pyramidal Image
3 Scale Invaliant Feature Tracker
4 Speeded Up Robust Features
8 Color Image
1 Physical Definition of Color
2 RGB/CMYK Color Model
3 HSV Color Model
4 CIE XYZ/L*a*b* Color Model
9 Imaging Devices
1 CCD/CMOS Camera Definition
2 YUV/YCrCb Color Model
3 Color Printer Definition
4 Disering
10 Frequency Filtering
1 2D Fourier Transform
2 Low-Pass Filter
3 High-Pass/Band Pass Filter
4
11 Image Composition
1 DCT - Hadamard Transform
2 Sparse Coding
3 Fractal Coding
4
12 Image Complession
1 Codebook
2 JPEG
3 GIF
4 PNG
13 Image Restortion
1 Median/Partial Mean Filter
2 Weiner Decmposition
3 Super Resolution
4 Basic Image Stitching
14 Wavelet Image
1 Definition of Wavelet
2 Haar Wavelet
3 Daubecies Wavelet
4 Continuous Wavelet - Mexican Hat
15 Pattern Matching
1 Block Matching
2 Correlation Matching
3 Phase Only Matching
4 Dynamic Programming Matching
教科書
／Textbook(s)		 We use web handouts:
http://hartman.u-aizu.ac.jp/
Image Processing 2017
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 8 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		 2017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		 BM
履修規程上の先修条件
／Prerequisites		 A8 & F11

更新日／Last updated on 2017/03/02
授業の概要
／Course outline		 [NOTE] This course is subject to be cancelled at the end of AY2017.  The
students will not be able to re-take this course in AY2018 if they fail
AY2017.

本コースでは、生命現象を解明するための情報技術（バイオインフォマティクス）、生体情報を計測するための技術と生体疾患を治療するための技術、医用画像処理技術などについて学ぶ。いずれも、学部レベルにおける幅広い学習を想定している。また、座学による理論の勉強とともに、情報処理とデータ解析、血圧と心電図の測定等の実践的な演習も行う。最後に、外部専門家による特別講義を企画し、バイオメディカル情報工学領域の最新動向を紹介する。
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		 情報技術をバイオメディカル領域に応用するための入門的コースである。
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 バイオインフォマティクス入門Introduction to bioinformatics (Zhu)
1.1 バイオインフォマティクス総論Brief introduction to bioinformatics
1.2 塩基・タンパク質配列・ペアアラインメントアルゴリズムNucleotide, amino-acid sequence and pairwise alignment
1.3 データベース検索・配列マルチラインメント・隠れマルコフモデルHidden Markov model, motif search, and protein structural prediction
1.4 Open Reading Frame検索・モチーフ検索・進化系統樹・タンパク質立体構造予測Open Reading Frame search, database search, multiple alignment and phylogenetic tree
1.5 医療情報学入門Introduction to Medical Informatics
1.6 医療ビッグデータ・プレシジョン・メディシンBig data in Medicine and Precision Medicine
1.7 小テストMini Test
2 生体情報技術入門 Introduction to Biomedical Information Technology (Chen)
2.1 生体情報の基礎 (Basis of Biomedical Information)
2.2 生体情報計測技術 (Detection of Biomedical Information)
2.2.1 血圧と心電 Blood Pressure and Electrocardiogram
2.2.2 体温と血中酸素飽和度 Body Temperature and SpO2
2.3 医用画像技術 (Medical Imaging)
2.3.1 内視鏡Endoscope, 眼底カメラFundus Camera, 超音波画像Ultrasound Imaging, サーモグラフィThermography
2.3.2 コンピュータ断層撮影法Computed Tomography (CT), 磁気共鳴映像法Magnetic Resonance Imaging (MRI), 陽電子放出断層撮影法Positron Emission Tomography (PET), 単光子放射断層撮影Single Photon Emission Computed Tomography (SPECT)
2.4 治療技術 (Therapy Technologies)
2.4.1 自動体外式除細動器Automated External Defibrillator (AED), ペースメーカーPacemaker, 人工臓器Artificial Organs
2.4.2 体外衝撃波結石破砕装置Extracorporeal Shockwave Lithotripsy (ESWL), MRIガイド集束超音波手術MRI-guided Focused Ultrasound Surgery (MRIgFUS), ガンマ―ナイフGamma Knife
3 外部講師による特別講義 Special lecture by an external export
3.1 バイオメディカル情報工学の最新動向（The latest developments of Biomedical Information Technology）
教科書
／Textbook(s)		 ・藤博幸、はじめてのバイオインフォマティクス
・岡田 正彦、医療機器が一番わかる
成績評価の方法・基準
／Grading method/criteria		 ペーパーテストとミニレポート　６０点満点
演習（週単位の演習、プロジェクト演習）　４０点満点
履修上の留意点
／Note for course registration		 アルゴリズムとデータ構造
信号処理と画像処理

[NOTE] This course is subject to be cancelled at the end of AY2017.  The
students will not be able to re-take this course in AY2018 if they fail
AY2017.
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		 2017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		 Keitaro Naruse
担当教員名
／Instructor		 Keitaro Naruse
推奨トラック
／Recommended track		 RC
履修規程上の先修条件
／Prerequisites		 L5 & A1 & A7

更新日／Last updated on 2017/01/31
授業の概要
／Course outline		 また講義だけではなく、演習を通じてより深い理解を目指す。
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: Introduction
#2: Configuration space: Circular robots, polygon robots, and robot arms
#3: Planning method:  artificial potential method, road map method, cell decomposition method, sampling based method
#4: Robot motion
#5: Feedback control: concept, transfer functions, block diagrams, steady state error, stability, PID control system
#6: 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: 30%
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.)		 http://iplab.u-aizu.ac.jp/moodle


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開講学期
／Semester		 2017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		 Michael Cohen
担当教員名
／Instructor		 Julian Villegas, Michael Cohen
推奨トラック
／Recommended track		 VH
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2017/01/23
授業の概要
／Course outline		 This course introduces advanced undergraduates and graduate students to the basics of human interface technology and the virtual reality paradigm, especially through "desktop VR" (a.k.a. "fishtank VR"). We use a project-based, "hands-on" approach, emphasizing creation of self-designed virtual worlds. The main vehicle of expression is "Alice," an object-oriented, graphical 3D scenario IDE (integrated development environment), to contextualize segments on "desktop virtual reality," color (and color gradients), graphical & visual design, texture mapping, sound, music, & dialog, as well as software engineering. Segments on stereoscopy and image-based rendering are also included. We also use Mathematica, SumoPaint, and Audacity & 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 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, IBR (image-based rendering), 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 "machinema" (machine cinema = computer-generated movies) and to engage users in nondeterministic, dynamic environments such as event-driven 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.

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.
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 mid-term and final exams. The course's annual chromastereoptic art contest is juried, and winning entries are exhibited in a special display in the University library. Students also contribute panoramic images to a virtual hyperlinked tour of the University. 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		 (none)
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/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/osx/apps/#photobooth
photo manipulation: http://www.apple.com/mac/iphoto/
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/
Mathematica computational mathematics application: http://www.wolfram.com/mathematica
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
StereoMerger stereo viewer: http://www.stereomerger.com


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開講学期
／Semester		 2017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		 Kazuyoshi Mori
担当教員名
／Instructor		 Kazuyoshi Mori, Cong-Thang Truong
推奨トラック
／Recommended track		 CN,RC
履修規程上の先修条件
／Prerequisites		 －

更新日／Last updated on 2017/03/02
授業の概要
／Course outline		 [NOTE] This course is subject to be cancelled at the end of AY2017.  The
students will not be able to re-take this course in AY2018 if they fail
AY2017.

The concept and theory of linear systems are needed in almost all electrical engineering fields and in many other engineering and scientific disciplines as well. This course focuses on the representations, analysis and effects of linear systems. We will address both continuous-time and discrete-time representations.
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 linear system theory, particularly the time-invariant case in both continuous-time and discrete-time.
授業スケジュール
／Class schedule		 (1) Introduction to linear signals and systems
(2) Linear Time-Invariant (LTI) Systems
(3) Continuous-Time LTI Systems　(including Laplace Transform Analysis)
(4) Discrete-Time LTI Systems　(including Z-Transform Analysis)
(5) Fourier Analysis
(6) State Space Analysis
教科書
／Textbook(s)		 Title: Schaum's Outline of Theory and Problems of Signals and Systems
3rd Ed.(Schaum's Outlines)
Author: Hwei P. Hsu
Publisher: Mcgraw-Hill
ISBN-10: 0071829466
ISBN-13: 978-0071829465
成績評価の方法・基準
／Grading method/criteria		 Student evaluation should be decided by Examination, Reports, Quiz,
Attendance, and so on.

Professor Truong's criteria can be found at
http://web-int.u-aizu.ac.jp/~thang/ls/ls.htm

Mori's one can be found at
http://web-int.u-aizu.ac.jp/~k-mori/ls/
履修上の留意点
／Note for course registration		 Formal prerequisites:None

[NOTE] This course is subject to be cancelled at the end of AY2017.  The
students will not be able to re-take this course in AY2018 if they fail
AY2017.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 -


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開講学期
／Semester		 2017年度／Academic Year 　2学期 ／Second Quarter
対象学年
／Course for;		 3rd year
単位数
／Credits		 2.0
責任者
／Coordinator		 Qiangfu Zhao
担当教員名
／Instructor		 Masahide Sugiyama, Qiangfu Zhao, Shuxue Ding
推奨トラック
／Recommended track		 CF,VH,RC,BM
履修規程上の先修条件
／Prerequisites		 M5

更新日／Last updated on 2017/01/24
授業の概要
／Course outline		 The signal for processing is mathematically modeled as a function or a sequence of numbers that represent the state or behavior of a physical system. The examples of the signals range from speech, audio, image and video in multimedia systems, electrocardiograms in medical systems (ECG/EKG), to electronic radar waveforms in military.  Signal processing is concerned with the representation, transformation, and manipulation of signals and the information they contain. For example, we may wish to remove the noise in speech to make it 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 half century, lots of theories and methods have been proposed and widely studied in digital signal processing. In this semester, we only study the fundamentals of discrete-time signals and systems. The course content includes the concept and the classification of discrete-time signal, representations of signals in time, frequency, z- and discrete frequency domains, representations and analyses of systems, and filter designs.

The course is a prerequisite course for your further studying of other multimedia related courses, such as 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		 In this course, 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 lecture, the students should be able to understand how to analyze a given signal or system using tools such as Fourier transform and z-transform; what kind of characteristics should we analyze to know the property of a signal or system; 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) An introduction to signals and systems, and representation of
     signals in time domain
(2) Linear, time-invariant systems, impulse response and convolution sum
(3) Fourier transform, frequency response and sampling theorem
(4) The z-transform and its properties
(5) The inverse z-transform
(6) System function and system stability
(7) Mid-term examination
(8) Discrete Fourier transform (DFT)
(9) Fast Fourier transform (FFT)
(10) Signal processing with MatLab/SciLab
(11) Fundamental structures of digital filters
(12) Design of FIR filters
(13) Design of IIR filters
(14) Applications of digital signal processing
(15) Review
教科書
／Textbook(s)		 Digital signal processing with MatLab, Tatsuo Higuchi and Masayuki Kawamata, Morikita, 2015 (in Japanese)
成績評価の方法・基準
／Grading method/criteria		 Quizzes: 14 points
Exercises: 26 points
Examinations: 60 points
履修上の留意点
／Note for course registration		 The class schedules, the textbooks, and the evaluation methods for the three classes might be different. Shown here is just an example. For detailed information, please refer to the web page of each class
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 [1] Richard G. Lyons, Understanding Digital Signal Processing, Prentice Hall, 1996. ISBN:0201634678.

[2] S. W. Smith, The Scientist and Engineer's and Guide to Digital Signal Processing, California Technical Publishing, 1997. ISBN: 0-9660176-3-3.  http://www.dspguide.com/pdfbook.htm (free on-line text
in pdf format)

[3] Shouji Shimada et al., Fundamentals of Digital Signal Processing, Koronasha, 2006 (in Japanese)

[4] http://web-ext.u-aizu.ac.jp/~qf-zhao/TEACHING/DSP/syllabus.html


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

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