postech wcu

TIMTABLE & SYLLABUS

2010 1nd Semester
Time/Date Mon. Tue. Wed.

Thu.

09:30 - 10:45

ITCE740A
  Special Topic in Nano Sensors & Systems A
(IT-BT Convergence)
Donhee Ham

ITCE542
  Microwave Active Circuits
Bumman Kim

ITCE502
Ontologies and Semantic Reasoning
John Strassner

ITCE740A
  Special Topic in Nano Sensors & Systems A
(IT-BT Convergence)
Donhee Ham

ITCE542
Microwave Active Circuits
Bumman Kim

ITCE502
Ontologies and Semantic Reasoning
John Strassner

11:00 -12:15

ITCE566
Advanced Molecular Genetics
Seung-Jae Lee

ITCE522
Personal Area Networks
Chansu Yu 

ITCE500
Introduction to IT Convergence Engineering
Various; lead is Prof. John Strassner

ITCE566
Advanced Molecular Genetics
Seung-Jae Lee

ITCE522
Personal Area Networks
Chansu Yu 

ITCE500
Introduction to IT Convergence Engineering
Various; lead is Prof. John Strassner

12:00-13:45

 

 

 

 
13:15- 14:30

ITCE601/EECE600
Distributed Processing
Raouf Boutaba

ITCE505
Probabilistic
Graphical Models
Seungjin Choi

ITCE546
Semiconductor Electronics
Bumman Kim

ITCE601/EECE600
Distributed Processing
Raouf Boutaba

ITCE505
Probabilistic
Graphical Models
Seungjin Choi

ITCE546
Semiconductor Electronics
Bumman Kim

14:45~ 16:00

ITCE544
Analog Integrated Circuits
Hongjune Park

ITCE504 MachineLearning
Seungjin Choi

ITCE521
  Statistical Communication Theory
Kyung Whoon Cheun

ITCE623
Estimation Theory
Poo Gyeon Park

ITCE544
Analog Integrated Circuits
Hongjune Park

ITCE504 MachineLearning
Seungjin Choi

ITCE521
  Statistical Communication Theory
Kyung Whoon Cheun

ITCE623
Estimation Theory
Poo Gyeon Park

16:15-17:30

ITCE645
Sensor Technology
Meyya Meyyapan, Jamal Deen

ITCE521
Statistical Communication Theory
Kyung Whoon Cheun

 

ITCE645
Sensor Technology
Meyya Meyyapan, Jamal Deen

ITCE521
Statistical Communication Theory
Kyung Whoon Cheun

 

 

SYLLABUS

◎ ITCE566 up to List

 

ITCE566
Course Title

Advanced Molecular Genetics

No. of Credits

3-0-3

Dept.

IT Convergence Engineering

Lecture Time

Mon. & Wed. (11:00 ~ 12:15)

Lecturer

Seung-Jae Lee

Lecture Room

Chem.
409

Type of Course Elective
Office
Location
  Tel.

 

e-mail

seungjaelee@postech.ac.kr

Objectives
Molecular genetics is one of the most important tools for modern biology.  The purpose of this course is training students to be equipped with comprehensive knowledge of up-to-date genetics at the graduate level.  Thus, the focus is learning current genetics concepts and techniques that have been used to answer fundamental biological questions.  In order to understand important concepts in genetics, students should solve problems of genetics on their own.  Therefore an essential element of this course is assignment composed of various problem sets.

Prerequisite and Compulsory
Undergraduate molecular biology; undergraduate genetics

Grading
30% assignments: 30% participation: 25% exam: 15% attendance

Class Materials
Hartwell et al., Genetics: From Genes to Genomes, 3rd Edition

References
Griffiths et al., Introduction to Genetic Analysis

Course Schedule
(Tentatively)

1. Introduction to molecular genetics (Chapter 1)

2. Mendelian genetics (Chapter 2 and 3)

3. Chromosome: linkage, recombination and mapping (Chapter 4 and 5)

4. Function of a gene: mutation (Chapter 7)

5. Chromosomal rearrangement (Chapter 14)

6. Non-Mendelian genetics: mitochondria and chloroplasts (Chapter 16)

7. Basic techniques for genetics and genomics (Chapter 9 and 10)

8. Functional genomics and proteomics (Chapter 11 and 12)

9. Developmental genetics: introduction to genetic model organisms (Chapter 20)

10. Model organisms: Yeast, Arabidopsis, C. elegans, Drosophila and mice (Reference A to E; papers)


ITCE544 up to List

ITCE544
Course Title

Analog Integrated Circuits

No. of Credits

3-0-3

Dept.

IT Convergence Engineering

Lecture Time

Mon. & Wed. (14:45 ~ 16:00)

Lecturer

Hongjune Park

Lecture Room

LG
102

Type of Course Elective
Office
Location
  Tel.

2234

e-mail

hjpark@postech.ac.kr

Objectives
- Learn and practice how to design CMOS analog integrated circuits including bias circuits, OP amp and PLL using hand analysis and SPICEsimulation(Focused learning of CMOS analog circuits rather than extensive coverage, Emphasis on home works and term project, Encourages active participation in class through term project)

Prerequisite and Compulsory
Electronic Circuits 1 & 2

Grading
Quiz style exams 50%(midterm 25%, Final 25%),  HW(3): 15%, Attendance5%,  Term project 30% (may be adjusted)

Class Materials
박홍준 ,  "CMOS 아날로그 집적회로 설계 (,)",  시그마 프레스 ,  1999, 2

References
B.Razavi,  "Design of Analog CMOS Integrated Circuits",  McGraw-Hill, 2001

Course Schedule

Week 1: Introduction, Quick review of MOS I-V characteristic, small         signal equivalent circuitWeek 2: Review of CS CG CD amp and cascode amp(Ch4),Week 3: Review of differential pair(Ch5),HW1: Differential pair with active loadWeek 4: Review of feedback and noise analysis (Ch6),Week 5: Review of frequency response and stability (Ch7)Week 6, 7: Ch 8 Current source and bias circuitsHW2: Current source designMidterm exam:Week 8, 9; Ch9 CMOS OP ampWeek 10; Quick review of Common mode feedback and fully differential OP amp (Ch10)HW3: OP amp designWeek 12, 14, 15, 16: Ch12 Phase-Locked LoopWeek 13: Term project presentation Final exam



◎ ITCE601/EECE600 up to List

ITCE601/EECE600
Course Title

Distributed Processing

No. of Credits

3-0-3

Dept.

IT Convergence Engineering

Lecture Time

T, T 09:30 ~ 10:45

Lecturer

Raouf Boutaba

Lecture Room

RIST
4406

Type of Course Elective
Office
Location
  Tel.

 

e-mail

rboutaba@postech.ac.kr

Objectives
- This course provides an introduction to the fundamentals of distributed computer systems, assuming the availability of facilities for data transmission. The structure of distributed systems using multiple levels of software is emphasized. Specific topics include:

  1. -       Distributed algorithms
  2. -       Distributed file systems
  3. -       Distributed databases,
  4. -       Security and protection,
  5. -       Distributed services such as the world-wide web, and
    Examples of research and commercial distributed systems

Prerequisite and Compulsory
Computer Networks course is not a prerequisite but provides information about the underlying facilities assumed in this course

Grading
Midterm (20%)

Date: Towards end of April. Exact date to be announced.

Final (30%)
Date: Towards mid June. Exact date to be announced.

Two take home assignments (tentative) (20%):
-One written assignment (10%)
-One programming assignment (10%)

Project (30%)
Due date: Towards mid June. Exact date to be announced.
Topics: To be announced

Class Materials
Lecture Notes covering the following modules will be distributed before class:

  1. -       Module 1: Fundamental Architectures and Models of Distributed Systems
  2. -       Module 2: Brief Overview of Computer Networks
  3. -       Module 3: Distributed Objects and Remote Invocation
  4. -       Module 4: Distributed Naming
  5. -       Module 5: Distributed File Systems
  6. -       Module 6: Synchronization
  7. -       Module 7: Data Replication
  8. -       Module 8: Fault Tolerance
  9. -       Module 8: Fault Tolerance

Module 9: Security

References
Textbook :

A.S. Tanenbaum and M. van Steen, Distributed Systems: Principles and Paradigms, 2nd edition, Prentice-Hall, 2007.
Optional :

G. Colouris, J. Dollimore, and T. Kindberg, Distributed Systems: Concepts and Design, 4th edition, Addison-Wesley, 2005

Course Schedule
Monday & Wednesday (13:15 ~ 14:30)
Others



◎ ITCE623 up to List

ITCE623
Course Title

Estimation Theory

No. of Credits

3-0-3

Dept.

IT Convergence Engineering

Lecture Time

Tue. & Thu. (14:45 ~ 16:00)

Lecturer

Poo Gyeon Park

Lecture Room

LG104

Type of Course Elective
Office
Location
  Tel.

2238

e-mail

ppg@postech.ac.kr

Objectives
- This course forces students to find a model with delay components and to implement
several control concepts related to delayed system theories with matlab, which
produces delayed system toolboxes.

Prerequisite and Compulsory
Linear Systems, Estimation Theory, Optimal Linear Control, and Robust Control

Grading

Class Materials

References

Course Schedule



ITCE500   up to List

ITCE500
Course Title

Introduction to IT Convergence Engineering

No. of Credits

3-0-3

Dept.

IT Convergence Engineering

Lecture Time

TBD,
Tue. & Thu. (11:00 ~12:15)

Lecturer

Various; lead is Prof. John Strassner

Lecture Room

TBD

Type of Course Elective
Office
Location
  Tel.

 

e-mail

johns@postech.ac.kr

Objectives
This course introduces the subject of convergence for information and communication technologies (ICT). Convergence is the creation of a new technology by merging distinct technologies, industries, and/or devices into a unified whole. This course will introduce key topics in four important ICT areas: nano-technology, bio-technology, communications and networking, and autonomic communications. Each of the four areas will be covered with emphasis on: (1) how different technologies within that area can be combined, and (2) how the four different technologies can be fused together to build Ubiquitous Health solutions. Each course area will be taught by experts in that field, including ITCE Distinguished Visiting Professors as well as POSTECH faculty. This foundational course will be used to aid the student in choosing their area of specialization in the Division of IT Convergence Engineering.

Prerequisite and Compulsory
NONE

Grading
Grading will be 60% homework, 40% student project

Class Materials
Lecture notes and readings will be assigned

References
John Strassner, “Realizing the Convergence Vision of Korea”, First International Conference on the Convergence Industry, keynote address, Seoul, Republic of Korea, November 23, 2009


Course Schedule
Meets 2 days per week, 75 minute lecture each meeting (prefer Mondays and Wednesdays). Starts March 1 and continues for 16 weeks.

Week 1: Introduction, Administration, and Definition of IT Convergence
Weeks 2-4: Nano-Technology
Weeks 5-7: Bio-Technology
Week 8: Student Project Week
Weeks 9-11: Communications and Networking
Weeks 12-14: Autonomics
Week 15: Student Project Week
Week 16: Student Project Presentations

 



◎ ITCE740A  up to List

ITCE740A
Course Title

Special Topic in Nano Sensors & Systems A
(IT-BT Convergence)

No. of Credits

3-0-3

Dept.

IT Convergence Engineering

Lecture Time

Mon. & Wed. (09:30 ~ 10:45)

Lecturer

Donhee Ham

Lecture Room

Rist
4406

Type of Course Elective
Office
Location
  Tel.

4441

e-mail

donhee@postech.ac.kr

Objectives
Tools from information technology and electrical engineering are increasingly utilized to provide methods that can complement traditional approaches in biotechnology and human healthcare. The present course seeks to cover this rather broad subject, with special attentions to examples of how electrical engineering tools have been and will be useful in biotechnology in direct interface with biological systems.

Prerequisite and Compulsory
Basic understanding of general physics;
basic understanding of general chemistry

Grading
Grading will be 100% based on a set of homework assignments: Homework problems will be on analysis, simulation, and design of various IT/EE systems aimed at applications in biotechnology.

Class Materials
There is no textbook. Copies of lecture notes will be handed out.

References
Alberts, Johson, Lewis, Raff, Roberts, Walter, “Molecular biology of the cell,” Garland Science

Course Schedule
Estimated course schedule

 

Part I: Bio-Imaging Techniques

  1. 1.      Magnetic resonance imaging (MRI) (2 weeks)
  2. 2.      Ultrasound imaging (1 week)

 

Part II: Fundamentals of Biomolecular Analytic Tools

  1. 3.      Molecular machinery of biological cells (1 week)
  2. 4.      Personalized, predictive, and preventive healthcare (0.5 week)
  3. 5.      Standard biomolecular analysis methods – electrophoresis, microarrays, ELISA sensors, DNA sequencing, PCR, various other techniques (3 weeks)

 

Part III: EE/IT Tools for Biomolecular Analysis

  1. 6.      Field-effect transistors and electronic DNA and protein microarray (1 week)
  2. 7.      CMOS image sensors and their application to biomolecular analysis (1 week)
  3. 8.      Nuclear magnetic resonance (NMR) and its application to biomolecular analysis (2 weeks)
  4. 9.      Single-molecule analysis techniques (1 week)

 

Part VI: Monitoring systems for ubiquitous healthcare

  1. 10.   EEG, ECG, blood pressure, sugar sensors, etc. (0.5 week)
  2. 11.   RF systems for healthcare (1 week)
  3. 12.   Body area network (1 week)
Energy harvesting and wireless power (1 week)



◎ ITCE504 up to List

ITCE504
Course Title

Machine Learning

No. of Credits

3-0-3

Dept.

IT Convergence Engineering

Lecture Time

Mon. & Wed. (14:45 ~ 16:00)

Lecturer

Seungjin Choi

Lecture Room

RIST 4406

Type of Course Elective
Office
Location
  Tel.

 

e-mail

Seungjin@postech.ac.kr

Objectives
Machine learning is the study of computer algorithms that allow computers to "learn". It is a method of creating computer algorithms such that computers are able to perform pattern recognition, prediction, decision, and so on. This introductory course on machine learning will address mathematical and statistical methods involving current statistical machine learning as well as various applications. Topics to be covered include density estimation, Bayes decision theory, latent variable models, mixture models, discriminant analysis, clustering, classification, dimensionality reduction, regression, kernel methods, VC-dimension, HMM, MLP, RBF, etc. Main focus will be given to statistical and probabilistic methods for machine learning, involving supervised, unsupervised, and semi supervised learning.

Prerequisite and Compulsory
No pre-requisite.However, the basic understanding of probability/statistics and linearalgebra  will be helpful.

Grading
Homework assignments: 50%
Midterm exam: 30%
Final term project: 20%

Class Materials
Pattern Recognition and Machine Learning (by C. Bishop, 2006, Springer)

References
1. Introduction to Machine Learning (by E. Alpaydin, 2004, MIT Press)2. Pattern Classification, 2nd edition (by Duda et al., 2000)

Course Schedule
[week 1]  Introduction and probability primer

[week 2] Density estimation (Kullback matching and maximum likelihood estimation)

[week 3] Expectation maximization

[week 4] Clustering: k-means and Mixture of Gaussians

[week 5] Spectral clustering

[week 6] Continuous latent variable models

[week 7] Linear models for regression

[week 8] midterm exam

[week 9] Bayesian linear regression and Bayesian decision theory

[week 10] Fisher linear discriminant analysis

[week 11]  Multilayer perceptron, LMS, Wiener filter

[week 12]  Logistic regression and GLM

[week 13] Mixture of experts

[week 14] Kernel methods:  Kernel PCA, Kernel FDA, SVM

[week 15] Hidden Markov models

[week 16] Final term project 

 



◎ ITCE542   up to List

ITCE542
Course Title

Microwave Active Circuits

No. of Credits

3-0-3

Dept.

IT Convergence Engineering

Lecture Time

Tue. & Thu. (09:30 ~ 10:45)

Lecturer

Bumman Kim

Lecture Room

 

Type of Course Elective
Office
Location
  Tel.

2231

e-mail

bmkim@postech.ac.kr

Objectives
This course covers the basic concept of Microwave active circuit design.

Specifically, it will cover two port network, matching network, microwave amplifier design, broadband amplifier, and Power amplifier.

 LNA, mixer, and oscillator designs will also covered.

Prerequisite and Compulsory
  Electronic circuit, Microwave engineering

Grading
-Exam             3 * 20%

homework          20%

Circuit design     20%

Class Materials
Microwave Transistor Amplifiers analysis and design by Guillermo Gonzalez Prentice Hall

References
Microwave circuit design using linear and nonlinear techniques by G. D. Vendelin, A. M. Pavio, U. L. Rohde Wiley InterscienceHigh Power GaAsFET Amplifier by John Walker Artech House

Course Schedule
1) representation of two port networks 2) matching network 3) microwave transistor amplifier design 4) noise matching and low noise amplifier design

5) broadband amplifier 6) power matching and power amplifier design  7) Mixer design  8) Oscillator design



◎ ITCE502 up to List

ITCE502
Course Title

Ontologies and Semantic Reasoning

No. of Credits

3-0-3

Dept.

IT Convergence Engineering

Lecture Time

Tue. & Thu. (09:30 ~10:45)

Lecturer

Prof. John Strassner

Lecture Room TBD Type of Course Elective
Office
Location
  Tel.

5605

e-mail

johns@postech.ac.kr

Objectives
This is an elective course for the Autonomics track of IT Convergence Engineering, though it is open to all. This course is intended for researchers and practitioners who are interested in designing ontologies to support knowledge engineering and management for use in semantic reasoning. This course emphasizes an understanding of the fundamentals required to build robust conceptual models using ontologies. The course starts by introducing different forms of knowledge representation, and then follows with an in-depth exploration of the main theoretical tools for building ontologies. This is then followed by describing in detail how to build a formal ontology. Tools that can be used to build ontologies, to merge different ontologies, and to reason using ontologies are then examined. The course concludes with a set of basic applications that illustrate fundamental concepts of ontologies, followed by more advanced applications that are especially applicable to autonomic systems. Students will gain first-hand experience in designing and building ontologies and reasoning systems. Students will also be exposed to key components of the Semantic Web and Expert Systems.

Prerequisite and Compulsory
NONE; ITCE 503 is recommended

Grading
Grading will be 10% class participation, 30% homework, 60% student project

Class Materials
Lecture notes and readings will be assigned

References
Jochen Schiller, Mobile Communications, 2nd Edition, Addison Wesley, 2003.

Course Schedule
Books
J. Strassner, “ Knowledge Engineering Using Ontologies ”, Handbook of Network and System Administration, edited by J. Bergstra and M. Burgess, chapter 3, section 4, pages 425-457, ISBN 9780444521989

R. Brachman, H. Levesque, “ Knowledge Representation and Reasoning ”, Morgan Kaufman, 2004, ISBN 1558609326

Papers

J. Strassner, J. Won-Ki Hong, K. Kang, “ A Framework for Modeling and Reasoning about Network Management Resources and Services to Support Information Reuse ”, Tenth International IEEE Conference on Information Reuse and Integration, Nevada, USA, August 10-12, 2009

J. Strassner, S. van der Meer, D. O’Sullivan, S. Dobson, “ The  Use of Context-Aware Policies and Ontologies to Facilitate Business-Aware Network Management ”, Journal of Network and System Management, Volume 17, Number 3, September, 2009, pages 255-284

O. Lassila, D. McGuinness, “ The Role of Frame-Based Representation on the Semantic Web ”, Technical Report KSL-01-02, Knowledge Systems Laboratory, Stanford University

 


◎ ITCE522 up to List

ITCE522
Course Title

Personal Area Networks

No. of Credits

3-0-3

Dept.

IT Convergence Engineering

Lecture Time

Mon. & Wed.(11:00~12:15)

Lecturer

Chansu Yu

Lecture Room

RIST 4406

Type of Course Elective
Office
Location
  Tel.

5624

e-mail

chansuyu@postech.ac.kr

Objectives
In this course, students will learn short-range wireless network solutions for personal and body area networks. Topics include network topologies, protocols, and industry standards for these networks such as Bluetooth, ZigBee, 802.15.3, and 802.15.4. They also include ultra low-power signal processing, RF communication near or in body networks, security provisions, and data fusion techniques. Personal and body area network scenarios and applications are also discussed. Students will have an opportunity to get first-hand experience with TinyOS-based sensornet and GNU Radio-based software radio platform.

Prerequisite and Compulsory
C, C++ and script languages for hands-on experiments, A basic knowledge of computer networks, wireless networks and operating systems

Grading
30% Midterm Exam (two)

30% Homework & Lab report

30% Two-phase project

10% Attendance & participation

Class Materials
(B) Body Sensor Networks (Guang-Zhong Yang, Ed.), Springer, 2006

References
I (P) WPANs to Personal Networks: Technologies and Applications (Ramjee Prasad, Luc Deneire), Artech

(S) Protocols and architecture for wireless sensor networks, H. Karl, A. Willig, Wiley, 2005.

(T) Mobile telemedicine: A computing and networking perspective, Y. Xiao, H. Chen, CRC Press, 2008.

IEEE 802.15 Working Group for WPAN (http://www.ieee802.org/15/) – 15.1 Bluetooth v1.1, 15.2 Coexistence, 15.3 MAC and PHY for High Rate WPAN, 15.4 Low Rate WPAN, 15.5 WPAN mesh

GNU Radio/USRP, http://gnuradio.org/

TinyOS, http://www.tinyos.net/

Principles of Wireless Networks, by K. Pahlavan and P. Krishnamurthy, Prentice Hall PTR, 2002.

Wireless Sensor Networks by Feng Zhao and Leonidas Guibas.

Biodesign: The process of innovating medical technologies, Zenios, Makower, Yock, Cambridge Press, 2010

Course Schedule
Introduction and overview (1 week)

WPAN and BSN scenarios and applications (1 week)

WLANs – 802.11 standards, QoS (802.11e), Security (802.11i, 802.11f) (1 week)

Bluetooth - Core specification, Profiles, Security, Future (1 week)

Biosensor design and interfacing – Electrochemical devices, instrumentation, biocompatibility, Handling sensor data (1 week)

802.15 – 802.15.3 (HR WPAN), Very HR WPAN (UWB, DS-UWB, MB-OFDM), 802.15.4 (LR WPAN), ZigBee (1.5 week)

Wireless communication - Inductive coupling, RF comm. in body, antenna, ultra-low power signal processing (1 week)

 


◎ ITCE505 up to List

ITCE505
Course Title

Probabilistic Graphical Models

No. of Credits

3-0-3

Dept.

IT Convergence Engineering

Lecture Time

Mon. & Wed. (13:15 ~ 14:30)

Lecturer

Seungjin Choi

Lecture Room   Type of Course Elective
Office
Location
  Tel.

2259

e-mail

Seungjin@postech.ac.kr

Objectives
Probabilistic graphical models are a happy marriage between probability theory and graph theory. Probabilistic graphical models are graphs in which nodes are random variables and on which conditional independence are encoded. They provide a natural and powerful tool for dealing with uncertainty and complexity which are playing an increasingly important role in the design and analysis of machine learning algorithms. Three topics are mainly covered, including representation (directed graphs, undirected graphs, factor graphs), probabilistic inference (sum product, belief propagation, junction tree, variational approximation, sampling methods) and learning (maximum likelihood, MAP, Bayesian estimation, expectation maximization). A term project is given, in which one can choose his/her own application such as computer vision, bioinformatics, natural language, data mining, networking, in order to see how probabilistic graphical models are applied

Prerequisite and Compulsory
No pre-requisite

Grading
homework assignments: 40%

a wrriten exam: 30%

a term project: 30%

Class Materials
An Introduction to Probabilistic Graphical Models
(unpublisehd book by M. I. Jordan, hardcopies will be given out in class)

References
1. Pattern Recognition and Machine Learning (by C. Bishop, 2006)
2. Probabilisti Graphical Models: Principles and Techniques (by D. Koller and N. Friedman, 2009)

Course Schedule
week 1: Conditional independence and factorization

week 2: Exact inference: sum-produc algorithm on undirected graphs

week 3: Belielf propagation on Bayesian networks and factor graphs

week 4: Tree distributions: Chow-Liu trees and mixture of trees

week 5: Density estimation

week 6: EM and variational Bayesian EM

week 7: Variational mixture of Gaussian

week 8: Variational PCA

week 9: Midterm exam

week 10: Regression and Bayesian linear regression

week 11: Bayeisan logistic regression

week 12:  Classification, Exponential families and GLM

week 14: Completely observable graphical models week 10 EM, FA, MFA

week 15: Linear dynamical systems and hidden Markov models

week 16: Sampling methods

 


◎ ITCE546  up to List

ITCE546
Course Title

Semiconductor Electronics

No. of Credits

3-0-3

Dept.

IT Convergence Engineering

Lecture Time

Tue. & Thu. (13:15 ~ 14:30)

Lecturer

Bumman Kim

Lecture Room

LG
102

Type of Course Elective
Office
Location
  Tel.

2231

e-mail

bmkim@postech.ac.kr

Objectives
Advanced semiconductor device will be covered, one step higher level of Semiconductor device I
It includes basic semiconductor physics, P/N junction, Bipolar, and CMOS
Nano CMOS wi ll be emphaiszied and will be teach by Prof. Jamal Deen

Prerequisite and Compulsory
semiconductor device 1

Grading
Mid-term Exam 2 times x 25%
Final Exam 30%
Report 20%

Class Materials
Electronic Devices for Integrated Circuits by Muller & Kamins, Wiley Publishing Co.

References

 

Course Schedule
1 - 2 week    Semiconductor Electronics

3   week        Metal - Semiconductor Contact

4 -5week      PN Junction

6 - 8week      Bipolar Junction Transistor

9 - 14 week   Nano CMOSFET

14- 16 week  Heterojunction Devices


◎ ITCE645 up to List

ITCE645
Course Title

Sensor Technology

No. of Credits

3-0-3

Dept.

IT Convergence Engineering

Lecture Time

 

Lecturer

Meyya Meyyapan, Jamal Deen

Lecture Room

RIST
4406

Type of Course Elective
Office
Location
  Tel.

 

e-mail

m.meyyappan@nasa.gov
jamal@mail.ece.mcmaster.ca

Objectives
Sensors are small devices, in a sense, designed to replace bulky analytical instruments to meet various needs in chemical, environmental, biomedical, agricultural, and several other industries. This course will discuss how micro and nanotechnologies have been shaping the sensor design and development. Development of sensors that are small, consume little power and inexpensive is key to realize the goals of U-Health and U-Environment initiatives which are becoming common across the world

Prerequisite and Compulsory

Undergraduate degrees in one of the following: Electrical Engineering, Mechanical Eng.,

Chemical Engineering, Material Science, Physics, Chemistry

Grading
One project with a final report and presentation 100%

Class Materials
Course viewgraphs will be posted on POVIS in advance

References

Course Schedule

Others


◎ ITCE521 up to List

ITCE521
Course Title

Statistical Communication Theory

No. of Credits

3-0-3

Dept.

IT Convergence Engineering

Lecture Time

Mon. & Wed. (14:45 ~ 16:00)

Lecturer

Kyung Whoon Cheun

Lecture Room

LG104

Type of Course Elective
Office
Location
  Tel.

2235

e-mail

cheun@postech.ac.kr

Objectives
1. Learn the basic working principles of digital communications 2. Learn a little about how the theory is applied to real systems 3. In most cases, I will leave the detailed derivations to you and  try to put more emphasis on the principles

Prerequisite and Compulsory

 

1. Undergraduate level communication theory  2. Working knowledge of basic probability theory  3. C programming and ability to perform simple simulations onS/W platform of choice (C, Matlab, SPW, COSSAP, etc.)
Grading
1. Quizes (announced and unannounced)  2. Homeworks  3. Class Participation (Answering to questions)  4. Exams

Class Materials
Class Notes - Kyungwhoon Cheun

References
Digital modulation and coding - Stephan G. Wilson  Digital Communications - Lee / Messerschmitt

Course Schedule
I. Review of linear systems theory, probability and random processes 1. Linear systems theory  2. Probability  3. Random processes  4. Computer techniques II. Introduction to digital Modems  1. Digital communications system structure  2. Pulse shaping and the eye diagram 3. Intersymbol interference  4. The discrete channel model  5. System performance   III. Detection and estimation theory  1. MAP / ML detection  2. MAP / ML estimation %  3. Detection with random process observation  4. Signal space concept  5. Geometric interpretation of the signal space  6. Performance evaluation under AWGN  IV. As time permits  -Spread-spectrum   -Adaptive equalization   -Synchronization -Optical communication  -Implementation  -Other topics
Others