This course generally introduces Autonomics, Communications & Networks, Nano Sensors & Systems, Biotechnology and other related studies and focuses on possible creative research areas so that students can choose their research themes.
IApplications of IT Convergence
n this course, students will learn how to perform research to support their projects which were defined and specified in ITCE500 Introduction to IT Convergence Engineering. The project will culminate in a submission of a conference or journal paper submission.
This course is intended for the students who are interested in understanding autonomic systems. First, the need and motivation for autonomic systems will be described. Next, we will review different autonomic architectures from the US, Europe and Asia, emphasizing core mechanisms such as control loops, management abstractions, and how sensors and effectors interface the autonomic manager to the entity being managed. We will then examine the salient features of representative autonomic systems, and augment this with practical examples based on our WCU ITCE program, and discuss potential research topics for autonomics graduate students. The course will conclude with examples that explain how to manage different types of systems, how to enable business needs to drive the management of systems and services, and how to orchestrate behavior.
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.
This course provides a detailed understanding of object-oriented information and data modeling, and how to use models to represent, analyze, and act on knowledge. This course gives a deeper insight into the foundations of modeling, and emphasizes the use of modern software engineering practices, such as patterns, to represent and process information for common modeling problems. A detailed review of object-oriented information modeling fundamentals will be conducted, followed by hands-on experience in building different types of models for various applications ranging from well structured use cases to ad hoc design. Elements from our WCU ITCE program will be used as examples for students to build, analyze, and optimize models throughout the course to reinforce the theory learned.
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 semisupervised learning.
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
This course will study the fundamental aspects of modern distributed systems. Issues concerned with distributed systems such as transparency, communication, resource sharing, fault tolerance, scalability, consistency, and security as well as those concerned with designing, developing, and managing distributed applications and services will be covered in this course. Special emphasis will lie on emerging Peer-to-Peer computing.
Students will learn security principle and types of security adaption of wireless networks such as WWAN, WLAN, WPAN, MANET. The security issues are handled in the respect of prevention and protection. The aim of the subject is to focus on fundamental issues regarding wireless network security and to make the students’ own researches possible.
The course deals with the principles and methods of self protection system to the unknown security intrusion from inner/outer system. The course studies detection of attack and intrusion, automatic detection of weakness, complementation of weakness, automatic learning about intrusion, and automatic backup etc. and the methods for reducing weaknesses
The course will start with the fundamental concepts in network and service management, illustrated through a number of prominent frameworks. It will discuss key challenges in network and service management today and show how these problems are tackled with example techniques from both theoretical and system design perspectives. This course will also show autonomic networking as a principle design objective in dealing with the current network and service management complexity.
This course focuses on approaches relating to representing different data in a common way, which is crucial for reasoning and planning for solving problems in autonomic systems. The course illustrates the importance of (1) defining a common form for relating different information from different sources to derive a combined understanding of a managed entity, (2) transforming the common representation of knowledge to a form amenable to efficient reasoning, and (3) adding constraints for performing intelligent search and planning.
This course explains how to apply semantic reasoning provided by autonomic systems to build systems for current and Future Internet applications. This course starts by reviewing finite state machines, and then using finite state machines to model formal as well as natural languages.
This course covers the new theory and topics of the Autonomics area.
Probability theory and random variables are discussed, which includes the relationship and transformation of random variables. Stochastic or random process is discussed, including stationary and nonstationary random processes, dynamics and filtering problems.
Prerequisite : Undergraduate level Probability theory, Signal and systems, Linear algebra
- Review the basic principles of linear analysis, probability, statistics, and random processes
- Learn the analysis of linear and nonlinear systems with random inputs.
- Learn the design of systems that satisfy some statistical conditions for signal detection and waveform estimation
- Learn about how the information theory is applied to communication systems
- Learn the properties of noise in the communication systems
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.
The main goal of this course is to study advanced topics on network technologies. The course begins with the basic concepts and techniques on computer networks, and then covers technical details in advanced topics on computer networks. This course also covers the state of the art protocols in networking technology.
Recently diverse wireless mobile networks are deployed. This course provides an in-depth understanding of the fundamental problems in the area of mobile networks and studies the state of the art solutions to solve the problems. This course also covers many important issues in the area of wireless mobile networks.
This course deals with the basic concepts that multimedia data can be effectively transferred through wire and wireless network. The course specifies media control technology considering networks and network control technology regarding media, introducing the best suitable technology which can connect those technologies.
This course introduces the conventional linear estimators in frequency and time domains. In the algorithm point of view, two issues associated with the number of computations and the numerical stability are addressed and the modified estimators are provided. Furthermore, modern estimators, mainly designed with linear programming, are tackled under mixed criteria.
Prerequisite: EECE 564(Linear System Theory) This course summarizes modern techniques, based on linear system theories, for analyzing and synthesizing linear and even nonlinear systems. Especially, so-called LMIs (linear matrix inequality), belonging to convex conditions, are used to design robust controllers against nonlinearities or uncertainties under various criteria.
This course covers the new theory and topics of the Communications & Networks area.
This course provides in depth understanding of nanotechnologies including nanoelectronics, functionalized carbon nanotubes or nanowires, and MEMS. The biomedical application like Biological field effects transisters (BioFETs) is covered in the course as well.
Prerequisites : EECE 554(Physics of Semiconductor and Display) This course covers analysis of semiconductor surface, quantum state, conduction mechanism at surface, optical properties and elastic properties, surface processing technique and device application.
This course covers the basic concept of microwave active circuit designs such as s-parameter, two-port network, matching circuit and gain/stability of transistor based amplifier. Then, the circuit design methodology for the important functional blocks of microwave transceivers such as broadband amplifiers, LNA, power amplifier, microwave mixer and power oscillator is studied.
The important RFIC chip design methods for the transceiver of the wireless communication system are studied. First, the transceiver architecture of the system is described. Then, the important functional blocks of the transceiver are covered. They include passive component design, LNA, mixer, oscillator and phase noise, and frequency synthesizer.
Covers CMOS analog integrated circuit design techniques using hand analysis and SPICE simulation, reviews the operation of single transistor amplifiers such as CS CG CD amplifiers, frequency response and stability, noise analysis, bandgap voltage source and current source bias circuits, single-ended and fully-differential CMOS OP amp circuits, switched capacitor filter, phase locked loop and delay locked loop.
Covers CMOS digital integrated circuit design techniques using hand analysis and SPICE simulation. Operation of CMOS inverter circuit, static logic circuit, dynamic logic circuits such as domino NORA and TSPC, pass transistor and differential logic circuits, VLSI building block circuits such as adder multiplier and data path, low power circuit technique, memory circuit such as ROM Fash memory SRAm and DRAM
Advanced semidoncutor device will be covered, one step higher level of Semiconductor device I
It includes basicsemiconductor physics, P/N junction, Bipolar and CMOS
The low power design of CMOS Integrated circuits is essential to implement the low power sensor networks. The class starts with the review of the CMOS device physics with the emphasis on the subthreshold operation. It covers the low power analog circuits such as OP amps, switched capacitor circuits, continuous time filters, analog-to-digital converters and RF circuits. It also covers the low power design technique of digital circuits including low power logic circuits and SRAMs.
This course provides a fundamental and in-depth knowledge of the theory of operation, modeling, parameter extraction, scaling issues, and higher order effects of active semiconductor devices that are used in mainstream semiconductor technology and emerging devices of practical interest. There will be a comprehensive review of the theories and latest models for the devices that are valid out to very high frequencies and the use of physical device modeling. A review of the latest device technologies and architectures will be presented. The course will be a prerequisite to the other applied courses in nanotechnology, nanoelectronics and photonics.
This course covers recent developments of nanodevices. Lectures focus on basic device fundamentals, second order effects, fabrication processes, characteristics, and reliability of novel devices. Through term project assignments, students are expected to gain an understanding of advanced electron devices.
The operation principles of the sensors for monitoring the human body or the environment will be introduced. The low power circuit techniques will be studed by using the CMOS technology. The front-end analog amplifier, filter, analog-to-digital converter, microprocessor, memory and RF circuits will be covered.
The operation principles of the nano semiconductor devices and the bio-medical sensors are covered. The application examples of the nano devices to bio-medical applications will be studied.
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
This course covers the new theory and topics of the Nano Sensors and systems area.
This course addresses ways of searching for and analyzing DNA and protein information, as well as providing insight into biological literature and the latest trends in and the future of bioinformatics.
This course explores the structures and regulation of receptors and ionic channels, and the molecular regulatory mechanisms of factors in signal pathways that emanate from them. In addition, the principles of enzyme chemical structures, functions, and application and related metabolic pathways and their significance as well as contemporary research techniques are addressed. In particular, emphasis is placed on enzyme kinetics, reaction mechanisms, and active sites, labeling and determination techniques, structural relationships among active inhibitors and active sites, and the modification of enzymes using genetic engineering and gene expression.
The focus of this course is on current understanding of aging process at an organismic level. Emphasis is placed on genetic control mechanisms that regulate aging and age-related diseases. Moreover, students will discuss key molecular signaling pathways that regulate aging processes, which are conserved across phyla.
The course helps students choose research topics.
The course analyses the emergingbiotech industry, its prospects and research directions. In addition, the course introduces basic and novel technologies in biotech industry.
This course explores the mechanisms through which the fertilized egg develops into an entity composed of various cells, tissues, and organs.
This course is designed to help students learn recent exiting advances in the molecular genetics. The topics include functional genetics, model organisms, molecular genomics. In addition, students will discuss breakthrough findings in the molecular genetics field.
This course covers the new theory and topics of the Biotechnology area.
This course consists of seminars on recent developments in various topics.
A research course for Master's thesis.
A research course for Ph. D. thesis