AY 2015 Undergraduate School Course Catalog

Applications

2016/02/01

Back
開講学期
／Semester		 2015年度／Academic Year 　後期 ／Second Semester
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 2015/02/03
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 (36 points), quizzes (12 points), and examinations (52 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


Back
開講学期
／Semester		 2015年度／Academic Year 　後期 ／Second Semester
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 , Julian Villegas , 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 2015/01/26
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.
* 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		 1) Introduction to CG and OpenGL
2) 3D viewing
3) Transformations
4) Lighting/Shading
5) Texturing
6) Blending, anti-aliasing and fog
7) Animation 1
8) Animation 2
9) Ray-casting and ray-tracing
10) Geometric modeling 1
11) Geometric modeling 2
12) Bezier curves and surface patches
13) B-Splines and NURBS
14) GPU programming
15) Project
教科書
／Textbook(s)		 [optional]
魏大名他、「情報処理学会編集　IT Text コンピュータグラフィックス」、株式会社オーム社
成績評価の方法・基準
／Grading method/criteria		 * Written tests (problem-sets and final exam) - 55 points
* Programming exercises and project - 45 points

[Note: this may slightly vary depending on each instructor]
履修上の留意点
／Note for course registration		 * Basic math skills (calculus and linear algebra)
* Basic data-structure and algorithms
* Programming (C or C++ or Java)

Formal prerequisites: None
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Websites from previous years:
* http://web-int.u-aizu.ac.jp/~fayolle/teaching/2014/CG/index.html
* /home/course/CGclass/2009/index.html

External:
* http://www.opengl.org/


Back
開講学期
／Semester		 2015年度／Academic Year 　前期 ／First Semester
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 2015/02/02
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://iplab.u-aizu.ac.jp/moodle/
Image Processing 2015
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		 10 Exercises * 7 points = 70.
Extra project has 50 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://iplab.u-aizu.ac.jp/moodle/


Back
開講学期
／Semester		 2015年度／Academic Year 　前期 ／First Semester
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 , Pham Tuan Duc
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 2015/01/27
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.
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		 Part 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 Mini Test

Part 2. Introduction to Biomedical Information Technology (Chen)
2-1 Basis of Biosignal
2-2 Biosignal Detection Technology
2-3 Medical Image Technology
2-4 Therapy Technologies
2-5 Mini Report

Part 3. Introduction to Biomedical Image Processing (Pham)
3-1 Binary extraction of regions of interest in molecular/biomedical images: Thresholding methods
3-2 Binary extraction of regions of interest in molecular/biomedical images: Cluster analysis methods
3-3 Texture characterization of molecular/biomedical images: The gray-level co-occurrence matrix
3-4 Texture characterization of molecular/biomedical images: Geostatistical methods
3-5 Mini Test
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
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/


Back
開講学期
／Semester		 2015年度／Academic Year 　前期 ／First Semester
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		 Noriaki Asada , 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 2015/02/03
授業の概要
／Course outline		 現代社会ではコンピュータは様々な機器の制御に広く用いられ、制御などの知識が必要不可欠となっている。本科目ではコンピュータ科学・工学専攻の学生のための、ロボット工学と自動制御理論の基礎を与えるものである。とくに実世界において制御理論の中心であるフィードバック制御の概念を学ぶことと、ロボットを制御するための基本的な理論と考え方を習得することに主眼を置く。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。
授業の目的と到達目標
／Objectives and attainment
goals		 ロボット工学に関しては、ロボットの運動を表現するための数学的な手法である運動学とプランニング手法を学ぶ。そして学生は
(1) コンフィグレーション空間と運動方程式を学ぶことにより、ロボットの運動をコンピュータ内で表現できるようになる。
(2) 人工ポテンシャル法、ロードマップ法、セル分解法などのプランニング手法を学ぶことにより、ロボットの動作計画ができるようになる。
一方、自動制御理論に関しては基本的な考え方であるフィードバック制御を学ぶ。そして学生は
(1) 伝達関数、ブロック線図などを学ぶことにより、簡単なフィードバック制御を実現できるようになる。
(2) 時間遅れ要素と定常誤差を学ぶことにより、制御系の安定性を解析できるようになる。
(3) 与えられたシステムのPID制御を構築できるようになる。
また最後にロボットシステムの構成について学ぶ。
授業スケジュール
／Class schedule		 第１週　講義の概要と序論
第２週　円形ロボットのコンフィギュレーション空間
第３週　矩形ロボットとロボットアームのコンフィギュレーション空間
第４週　人工ポテンシャル法
第５週　ロードマップ法
第６週　セル分解法
第７週　サンプリングに基づく経路生成
第８週　ロボットの運動方程式
第９週　フィードバック制御の原理
第１０週　時間遅れ要素と定常誤差
第１１週　制御システムの安定性
第１２週　PID制御
第１３週　フィードバック制御の利点
第１４週　センサ，アクチュエータ，システム構成
第１５週　まとめ
教科書
／Textbook(s)		 なし。
必要な資料は、授業中に配布する。
成績評価の方法・基準
／Grading method/criteria		 講義のクイズ：３０％
演習：３０％
期末試験：４０％
履修上の留意点
／Note for course registration		 関連科目として、コンピュータ理工学実験、プログラミングＣ，線形システム理論。

履修規程上の先修条件：L5 コンピュータ理工学実験
A1 人工知能
A7 線形システム論
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


Back
開講学期
／Semester		 2015年度／Academic Year 　前期 ／First Semester
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		 Michael Cohen , Julian Villegas
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 2015/02/03
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


Back
開講学期
／Semester		 2015年度／Academic Year 　後期 ／Second Semester
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 2015/02/03
授業の概要
／Course outline		 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.
履修上の留意点
／Note for course registration		 Formal prerequisites:None


Back
開講学期
／Semester		 2015年度／Academic Year 　前期 ／First Semester
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 2015/02/03
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: 13 points
Exercises: 27 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