Incredibly many problems encountered in the field of computer science, electrical engineering, operations research and others become graph problems when formulated mathematically. To be able to handle these problems, it is essential to understand the definitions and concepts, fundamental results and basic algorithms in graph theory.
In this course, basic concepts are introduced, critical results and their derivations/proofs are presented and algorithms to solve basic graph problems are designed and analysed.
This is a second course on theory of computation, which comes after a first theory course such as automata and formal languages. It introduces a model of computation, Turing machines, and Church-Turing thesis. Then it examines the computability issues by considering the halting problem, problem reductions and undecidability. Next the computational complexity is investigated. The time complexity classes P and NP are introduced. NP-completeness and NP-complete problems are examined. Also space complexity and a few results are introduced. Other topics to be discussed are approximations, probabilistic algorithms, interactive proof system and cryptography
This course is a study of the evolution of computer architecture and the factors influencing the design of hardware and software elements of high-performance computer systems. The emphasis is on the major component subsystems of high performance computers: pipelining, instruction level parallelism, memory hierarchies, input/output, and network-oriented interconnections. Topics may include: instruction set design; processor micro-architecture and pipelining; cache and virtual memory organizations; protection and sharing; I/O and interrupts; in-order and out-of-order superscalar architectures; VLIW machines; vector supercomputers; multithreaded architectures; low-power designs; symmetric multiprocessors; memory models and synchronization; embedded systems; and parallel computers.
Through this course, students will gain in-depth knowledge on how modern operating system works through Linux.
Topics about resource management algorithms and data structures used in Linux will be discussed in detail. In addition, evaluation of microkernel and module-based monolithic kernel structures will help students understand full spectrum of operating system structure alternatives.
A comprehensive study of modeling computer systems, distributed systems and computer networks, and evaluating their performance. Topics include stochastic processes, queueing theory, operational analysis, mean value analysis, and so on.
As the circuit density increases the probability of a manufacturing defect increases. The higher expectation of reliability can only be met by more through and comprehensive testing of ICs. IC testing can be performed at different levels of abstraction. The objective is to find manufacturing defects, which cause a fault and hence failure, or a potential fault or failure.
The topics covered in this course include faul modelling, test generation, fault simulation, testable design, and fault-tolerance circuits.
This is an introductory course covering a broad range of software engineering topics including software development lifecycle models, processes, software development methods, testing, project management, and metrics. Software engineering principles such as abstraction, information hiding, modularity, etc. are also introduced. A number of papers that are considered seminal in software engineering will be discussed in the class. The purpose of this course is to introduce various software engineering concepts, techniques, and issues to students. A small team project will be assigned.
In this course, we consider the basic principles of 2D and 3D graphics. The hardware and software of a graphics system are introduced and then geometric transformations and interactive techniques are covered. We also consider 3D object representations, projections, viewing transformation, hidden surfaceremoval, and rendering of 3D objects. Programming assignments will be required to implement the basic concepts.
In this course, we consider various techniques for producing an animation. We cover construction and representation of 3D objects and motion control techniques for 3D object movements in an animation.
Animation packages for high quality rendering are introduced. Students are required to make a short animation to experience the animation production pipeline
Constructing and implementing a virtual environment takes understanding of many different disciplines. This course covers basics of such knowledge such as modeling of virtual objects and their interactive behavior, managing and using various VR devices and sensors, stereoscopic display and immersive effects, basic physical simulation such as collision detection, and most importantly, various theories for creating presence, this year's (2000) main theme. Important concepts are covered by paper lectures and student paper presentations, practiced through small scale homeworks, and put together in a larger final project
we learn various concepts and techniques for computer based simulation and how to apply them to real problems. The course coverse topics such as system modeling techniques, discrete system simulation, continuous system simulation, simulation langauges, and real world applications.
This course deals with pattern classification theory and practice. Among several pattern classification area, our emphasis is given to statistical pattern recognition. Students are strongly required to study probability and random process before taking this course. We deal with basic pattern classification technique, Bayes theory, parameter estimation, supervised learning, un-supervised learning, clustering and other advanced topics. Programming assingments will be given to students to strengthen their knowledge about the pattern classification theory.
Prerequisites : MATH 230 (Probability and Statistics)
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.
(Linguistics Basis for Natural Language Processing)
The course provides a basis of linguistic concepts and issues from the viewpoint of language engineering, focusing on various grammar models which are powerful enough to capture a lot of linguistic reality, and where implementations are readily available. We also cover their applications to machine translation and information retrieval.
This course teaches the fundamental concepts and techniques in human computer interaction. The students first study the human factors that affect the usability of computer systems and learn various forms of interfaces ranging from the traditional menus and forms to more innovative ones like 3D multimodal interfaces. Programming techniques and tools for HCI are introduced as well. The final phase of the course looks at various cases of HCI, those that were successful and not so, and the concepts learned are put to practice thruogh class projects.
Prerequisites: CSED 233 (Data Structure and Algorithms)
This course briefly introduces basic techniques of artificial intelligence: such as problem solving, heuristic search, knowledge representation, logic system and inference, as well as covers widely used application techniques such as planning, probabilistic reasoning, machine learning, speech and language processing and intelligent agent systems. Students should survey and design some of the practical and feasible artificial intelligence applications in any information system domain.
This course introduces two folds. One is to understand its operational principle of soft computing techniques such as fuzzy systems, neural networks, and evolutionary systems and to learn how to implement them. Another is to learn how to integrate these constituent techniques into an hybrid intelligent system that provides a more powerful and robust system performance and how to apply it for a variety of optimization problems such as time series prediction, protein structure prediction, optimal trajectory determination, optimal classifier design, location based services, human robot interaction, and ubiquitous and pervasive computing.
Algorithms are essential to any area of computer science and engineering and related fields. For practical solutions of problems, efficient algorithms must be designed and their matching data structures created. In this course we investigate general methods for designing and analysing algorithms. These methods are demonstrated by designing algorithms for various fundamental problems and by analysing the correctness and performance of the algorithms.
This course studies efficient methods for solving the network problems which is a special case of linear programming. Theoretical parts for developing algorithms and methodology for solving using computers and its problems are also included.
Covers unit processes of silicon IC fabrication, basic theory of semiconductor material and devices, the chemical engineering principles used in IC fabrication, crystal growth, chemical deposition, chemical etching, diffusion, metallization, lithography
This course will deal with the basic principles of plasma physics and plasma chemistry along with their applications to materials processing. Special focuses are to the semiconductor and materials processing by using plasma processes, which include the plasma chemistry reactions, plasma surface interactions, and the plasma reactor design.
Advanced computer design techniques are taught with design implementation practice using the VHDL language nd simulation. High-performance fixed and floating-point multiplier and divider (Wallace tree, Booth, etc.) design, RISC methods (register file, TLB, etc.), cache, pipeline, superpipeline, superscalar and other concepts are taught.
The purpose of this course is to introduce some basic image processing theory and techniques such as image enhancement, restoration, segmentation, edge detection, compression, transformation and filtering methods. In addition, modeling and computer programming will be conducted for validating properties of human visual system along with their recent application areas.
The course topics include some basic computer vision theories and techniques such as image formation, edge detection, stereo vision, photometric stereo, and 3D reconstruction from multiple views. The course will introduce 3-dimensional geometry of imaging systems and high level computer vision algorithm such as motion segmentation, boundary detection, symbolic image matching, motion segmentation, 3-dimensional scene reconstruction and object recognition through inference. In addition H/W and S/W techniques relating the biological visual perception model will be introduced as well as the hand-eye-coordination theory for the robot control.
This course and EECE 651, Applications of Soft Computing comprise the series of the Computational Intelligence courses. It covers the neural network architecture, its learning algorithms, and its applications to pattern recognition, signal processing, robotics and control. The architecture consists of a great variety of paradigms including the Multilayer Perceptron along with Back Propagation learning, Support Vector Machines, the Kohonen Clustering Network, and the Associative Memory.
Advanced nanodevices, semiconductors, quantum devices, statistics, and analyses are covered. Displays like LED, OLED, LCD, PQR are treated.
Electronic energy band structure, perturbation theory, effective mass theory, k·p theory, strain effects, optical gain and absorption in bulk and nanostructures, semiconductor lasers, high speed modulation
Graduate level course for advanced bipolar transistor physics course. It covers basics operation principle, p/n junction, heterojunction, emitter-base junction, base-collector junction, high current level behavior and equivalent circuit model for circuit design.
Basic properties of compound semiconductor, Interface analysis and application for compound semiconductor, Advanced process technology, High speed device(ex, HEMT, MISFET, MESFET), Integrated circuit using compound semiconductor.
Prerequisites : EECE 412(Electronic Materials Engineering)
Crystal growth theory, Bulk crystal growth, Liguid phase epitaxy(LPE), Vapor phase epitaxy(VPE), Metal Organic vapor phase epitaxy(MOVPE), Molecular beam epitaxy(MBE), Simulation and evaluation of crystal growth.
Review of the electrical theory, electronic circuits, semiconductor devices physics and digital electronics for the non-electronic engineers
Analysis of semiconductor surface, quantum state, conduction mechanism at surface, optical properties and elastic properties, Surface processing technique and device application.
Review of the semiconductor principles , junctions and MOSFET; the two-terminal, three-terminal and the four-terminal MOS structure; topics on implanted channels and small dimension effects; large signal and small signal modeling
Applied quantum mechanics for semiconductor devices, quantum electronics, and solid-state physics: state equation, state function, energy band, quatum statistics.
Application of quantum mechanics for operation principles of semiconductor and quatum-electronic devices
Review of Linear Algebra, Modeling of Physical System in the State space, Solution of State equations, controllability and observability, Kalman canonical forms, Phase plane portraits, PBH test, Discrete-time system, observer and pole placement, some nonlinear system examples
To provide an understanding of all the basic principles and techniques of robotic manipulator, also a comprehensive and up-to-date account of fundamentals of design, analysis and synthesis of robotic systems.
Prerequisite: Intro. to Engineering Electro-magnetics
Magnet system and transformer equivalent circuit are studied. Based on the dynamic modeling of DC motor torque-speed control methods are covered. Dynamic modeling of AC machinesis described in the rotating(synchronous) coordinate frame. Issues of permanent magnet synchronous motor design and control are studied.
The fundamental theory of power electronic systems and power converters such as phase-controlled rectifier, dc-to-dc converter, PWM inverter, power supply and resonant converter are covered. Also those waveform is analyzed.
This course covers an introductory account of the theory of optimal control and its applications which will provide the students with the background necessary for sound understanding of the optimal control systems.
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
The design techniques of VLSI systems are discussed with emphasis on the low design levels such as gate-level/circuit-level and physical-level layout. The top-down and bottom-up design methodology and layout design rules are also discussed. The design styles such as gate array and cell-based design, and various CAD software are discussed. In addition, the cocking schemes for synchronous systems are discussed. The design trends in the UDSM and SoC era are discussed. Then, the impacts of UDSM and low power design techniques are discussed. The class design project will provide chances to get the hands-on design experiences with extensive use of CAD software.
The algorithms and computational techniques for the computer-aided analysis of electronic circuits are discussed. After device modeling is discussed, the formulation of network equations using the Sparse Tableau Analysis and Modified Node Analysis methods are discussed. Methods to solve a set of linear equations, including Gaussian elimination and LU decomposition, are discussed, and the Newton-Raphson method to solve a set of nonlinear equations is discussed. Numerical integration methods to handle the nonlinear ordinary differential equations are discussed. Finally, various circuit analysis schemes of standard approach used for SPICE, nonlinear relaxation-based methods, waveform-relaxation method, waveform newton method, and waveform relaxation newton method are discussed.
In recent years, a trend has been to use general-purpose personal computers and workstations interconnected by a fast computer network in order to realize a low-cost supercomputer (this type of system is referred to as a cluster). In addition, larger configurations involving computers interconnected through the Internet (via wide area networks) have also been used to realize supercomputing on a massive scale by utilizing idle personal computers and workstations (idle processing resources) - this is referred to as grid computing. However, in order to effectively utilize cluster and grid computers, new parallel programming methods and tools have to be learned and used. Thus, this course will not only teach general parallel programming concepts, butt also concepts and tools necessary for the effective utilization of cluster and grid computers.
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.
The objective of this course is to learn the modulation/demodulation theory using amplitude, frequency, phase, pulse and digital communication methods such as ASK, FSK, PSK, etc. In addition, this course deals with random process theory, mathematical model for noise and effect of the noise in the communication system and also compares/analyzes various communication method.
This course introduces to the students the Information Theory that serves as the foundation for efficient data storage, compression, transmission, etc. It deals with the mathematical definition and properties of information, entropy, coding theorems, channel capacity, and rate-distortion, etc.
Digital communication is discussed and compared with analog communication. PCM, DPCM, and DM are discussed for speech coding. Segment companding, multiplexing- framing, synchronization, and digital switching are also discussed.
This course covers Cryptographic algorithm and protocol, and also explores the adaptation for these privacy protection, message authentication, identity vertification, digital signature.
This course covers chirp Z-transform, design of FIR/IIR digital filter and application to the speech processing or the image processing of new signal processing VLSI after the review about relation between continuous and discrete signal, Z-transform, and DFT(Discrete Fourier Transform).
Error-correcting codes are a key part of digital communication systems for reliable communication. Examples of error-correcting codes are BCH codes, Reed-Solomon codes and convolutional codes. Topics include their encoding and decoding methods, their performance evaluation, and their applications.
Linear algebra is a basic tool for analysis of linear systems in the areas of communications, control and signal processing. Topics include matrices, determinant, linear equations, vector spaces, eigenvalues and eigenvectors, orthogonal matrices, positive definite matrices, Jordan canonical form, least square approximation, matrix decomposition, and linear programming, etc.
Advanced theories on electromagnetic fields and waves including electrical properties of matter, wave equation and its solutions, wave propagation and polarization, reflection and transmission of plane waves, auxiliary vector potentials, electromagnetic theorems and principles, electromagnetic scattering and Green's functions.
Introduction to radar systems engineering. Many forms of radar equation, RCS (radar cross section), various clutter and ground effects, detection range, and radar antennas will be treated. Various radar techniques like MTI (Moving Target Indicator), AMTI, MTD, pulse doppler radar, tracking radar, CW and FM radars will be studied.
Theories on numerical calculations of electromagnetic scattering, coupling and antenna radiation including GO/GTD (Geometrical Optics / Geometrical Theory of Diffraction), PO/PTD (Physical Optics / Physical Theory of Diffraction), MOM (Methods of Moment), FEM (Finite Element Method), FDM (Finite Difference Method), FDTD (Finite Difference in Time Domain) and TLM (Transmission Line Method).
This course covers transmission lines,waveguides, scattering parameter, impedance matching, microwave resonators, power dividers, directional couplers, and plane wave propagation in a ferrite medium.
This course covers antenna fundamentals, dipoles, loops, arrays, line sources, helical antennas, biconical antennas, spiral antennas, horns, antenna synthesis, and antenna measurements.
The topics cover experiments such as grounding system and components character- istics, diode and transistor circuits, function generator, TTL/CMOS characteristics and applications, A/D converters and its applications, OP amplifier and its applications, finally PLL and its applications.
Device processes for integrated circuits: cleaning, diffusion, photolithography, deposition, and fabrication and measurement of diodes and transistors.
Experimental course for the Basic Semiconductor Electronics(EECE 559)
This course covers the basic concept of microwave active circuit designs such as s-parameter, two-port network, matching circuit and gain/stability of an transistor amplifier. Followed by the real circuit design methods for the functional block of microwave transceivers such as broadband amplifiers, LNA, power amplifier, mixer and oscillator.
This course consists of seminars on recent developments in various topics.
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.
Prerequisites : CSED 311(Computer Architecture), CSED 312(Operating Systems)
In this course, students will study basic concepts on dependable computing such as faults - either natural or man-made such as hacking, error, and failures, learn how to design dependable systems using redundant components such as hardware, software, time, and information, and study how to analyze dependable systems quantitatively and qualitatively. Cases that dependable computing concepts already applied are analyzed and learn recent research trends. Relation between dependable computing and security is also discussed in depth.
In this course, we study advanced concepts and techniques in database systems, including distributed/parallel databases and advanced indexing. We also study some of state-of-the-art database applications such as data warehouse, OLAP, data mining, and XML.
In this course, we study how to design efficient parallel algorithms for parallel computation and hot to analyze them. For basic algorithmic problems such as sorting, matrix multiplication, and graph ordering, we study how to design that have the minumum execution time, that require the least number of processors, or both and how to analyze them to know how much execution time the parallel algorithm has or how many processors it requires.
A new era in computer architecture is drawning. It is becoming increasingly difficult to obtain more performance from the time-honored von Neumann model, and many of the technological constraints that influenced its design over fourty years ago have changed drastically. Many of the arguments for processing a single instruction at a time no longer apply, and both commercial and research parallel processors are now available, allowing many processors to cooperate on a single problem. This course covers various topics on parallel processing, which include computational model, interconction networks, SIMD/MIMD architectures, and parallel algorithms.
This course teaches the fundamental aspects of real-time operating systems such as scheduling, concurrency, and distributed real-time communication. In addition to class lectures on theoretical results, each student of this course will be required to give presentations on the related papers and conduct a term project in order to understand how the practical real-time system works.
Network management entails monitoring and controlling various network devices on today's networks in order to provide a more reliable, secure and efficient network environment. This course covers the basic concepts and techniques used in network management. Also, international standards such as Internet network management framework and OSI network management framework will be studied. The students will get a chance to develop a prototype network management system.
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.
The objective of the course is to introduce students to the theoretical underpinnings of information retrieval (IR). This course will examine the design, usage, and evaluation of retrieval systems. We will focus on the underlying retrieval models, databases and system implementations. Retrieval technology both on and off the WWW will be examined.
This course covers Machine Translation (MT), i.e. the use of computers to translate (or help humans to translate) between natural languages. It provides a theoretical overview, and considers the essential linguistic and practical problems of MT in general. And then we look in detail at a number of paradigm systems and the work of various research centers. We also touch on evaluation issues.
This course introduces various recent statistical methods in natural language processing. We will cover basic statistical tools for computational linguistics and their application to part-of-speech tagging, statistical parsing, word sense disambiguation, machine translation, information retrieval and statistical discourse processing. If time permits, we will briefly cover some topics of statistical language models for speech recognition and text-to-speech systems.
Most of software engineering techniques are informal or semi-formal. Specifications made with these techniques are very difficult to analyze due to their informality. In this course, we study various formal specification and analysis techniques with mathematical foundation. Representative techniques from the state, process, and data based paradigms will be studied. Each team of two to three students will carry out a team project throughout the course making presentations to the class periodically to stimulate discussions.
This course will cover some advanced topics in VR. There will be three major topics addressed: Presence and Immersion, Image-based Modeling/Rendering, Time Critical Rendering Techniques and Distributed VR. Basic concepts are introduced through the textbooks and lectures, and more in-depth topics will be addressed by reading, presenting and discussing selected papers. Instead of a big final project, this course will ask of students to implement several smaller scaled projects
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.
Prerequisite: Introduction to Operations Research
This course deals with advanced topics on linear programming and the subjects includes simplex and revised simplex method, dual simplex method, sensitivity analysis, the concept of decomposition, transportation problems and their solutions.
This course deals with discrete optimization problems such as bin-packing, set covering, knapsack, assignment problem, TSP, vehicle routing problem and facility location, and their solution techniques such as exact methods, heuristics and meta heuristics. Computation complexity and real world's application problems are also discussed.
Approaches to scaling; current trends in MOS process integration; hot carrier effects, hot carrier resistant structures and mechanisms of the MOSFET degradation
The purpose of this course is to help graduate student learn the visual perception of robot for the 3-dimensional object recognition and introduce state-of-art 3-dimensional object recognition techniques. Course topics include vision sensors, such as photometric stereo and laser range scanner, extraction of 2.5-dimensional information, representation and modelling of 3-dimensional objects, information database construction via semantic network, and object recognition based on feature database. The coruse will introduce the object recognition with CAD data and its applications, such as bin-picking problem and road following.
This course teaches the general discipline of Computational Intelligence encompassing Evolutionary Computation, Fuzzy Logic, and their hybrid systems.
Soft Computing attempts to computationally model the process of the human's amazing capability of inferencing and learning amidst all kinds of uncertainties and imprecision of the environment. Then, its applications to robotics and automation will be shown while comparing their results against those from the conventional approaches. The students will program soft computing algorithms and then apply them to robotics and control via computer simulation.
The operation principle of microwave transistors such as MESFET, HEMT, MOSFET, Bipolar Transistor(BJT, SiGe HBT, III-V HBT) are covered. The detailed physical concepts are gain and speed, 1/f and high frequency noise, power characteristics and equivalent circuit models.
This course covers the unit processes for semiconductor device fabrication. After an overview of process requirements for a state-of-art device, the principle and process details of wafer fabrication, wafer cleaning, epitaxial film growth, thermal oxidation, ion implantation, chemical vapor deposition, wet and dry etching, metalization, and lithography are introduced and discussed.
The principles and the applications of plasma etching and deposition technology used in integrated circuits and display manufacturing will be the main subject to be covered. The plasma basics and up-to-dated plasma processing and display equipments will be included.
Lasers and other quantum electronic devices [PQR incl.], field quantization and density matrix, laser theory and applications are covered.
Quantum optical issues will be investigated including squeezed and coherent states, quantum distributions, coherence and HBT effects, atom-field interactions, laser photon statistics, and atom optics. Associated photonic quantum ring phenomena will be reviewed.
Quantum effect devices; physics of the quantum wire and quantum dot devices; electrical characteristics and processing techniques for the quantum devices; circuit design methodology for the quantum effect devices
It induces recursive parameter identification algorithm about MRAC and ARMA model by gradient, least square method. Furthermore, self tuning control, VSS and Adaptive Control System's system passivity, stability is studied. It covers results of robustness about unmodeled dynamics.
Describing function, Popov criterion, Lyapunov stability are studied. Existence and uniqueness of the solution of nonlinear differential equation are covered. Utilizing the methodology based on differential geometry, system equivalence and feedback linearizability are studied.
DC motor control theory is studied. Induction motor dynamics are described in the synchronous reference frame. Field orientation control methods are treated. Implementation issues utilizing the DSP processor are covered. Control methods of brushless DC motor and brushless AC motors are treated. Bandwidth of closed loop transfer function is studied.
This course covers in-depth Robert manipulator's dynamics and subject to research which is now under way. A controller's theme about servo-mechanism design, man-machine interface, teleoperation, force control, stereo vision are studied..
Brownian motion process is studied. Mean and variance of the state of the linear dynamic model driven by Gaussian white noise are derived.Stratonovich integral and Ito integral are studied. Linear quadratic Gaussian problem is treated for continuous-time and discrete-time cases.Principle of optimality and Bellman-Jacobi equation are studied. Ergodic theory and team theory are treated.
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.
In this course, students get the basic concepts and major results of system identification theory. Then students learn how to obtain plant model from real data and study real time identification algorithms. Students will improve their ability in solving real identification problems via term projects.
This course includes the following topics:
1. Divisor, coprimeness, definition of rack, unitary operation, Smith-MacMillan form and dynamical analysis of pole-zero of polynomial matrix.
2. Stability of interconnected systems, tracking problem, compensator design, decoupling methods.
3. norm min-max problem
This class presents various intelligent controller design techniques and stability analysis methods. The control methods that we cover in this class incude linear systems, PID control methods, adaptive control methods, learning control methods, fuzzy control methods, and neural network control methods.
Various computer algorithms, graph theory, and numerical analysis methods that are associated with the computer-aided software for the analysis and design of VLSI systems are discussed. Major application areas include circuit-level simulation, logic simulation, placement and routing, high-level synthesis, logic synthesis, physical synthesis. timing verification, testing, and layout. Both theory and applications are discussed, and class projects provide chances to have hands-on experiences for software development.
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.
High-speed data communication is discussed for broadband multimedia services. Theoretical and practical design methods of high-speed communication system are presented with an introductory discussion of broadband network. ISDN, HDSL/ ADSL/ VDSL, CATV, and wireless broadband services are discussed. ATM-LAN, Ethernet, and wireless LAN communication systems are also discussed.
One major goal of signal design is to design sequences with good (or optimal) correlation properties for spread spectrum communication systems, code-division multiple-access (CDMA) systems, and cryptosystems. Topics include maximal length sequences (or m-sequences), Walsh sequences, Kasami sequences, Gold sequences, quaternary sequences with low correlation and Hadamard matrices.
Advanced mathematical methods and tools in electromagnetics and microwave engineering including asymptotic methods, variational methods, perturbation techniques, Wiener-Hopf factorization methods.
Radar signal detection and estimation techniques, and the related ambiguity problems will be studied. Special purpose radars used for remote sensing will be emphasized, including SLAR (Side-Looking Airborne Radar), SAR (Synthetic Aperture Radar), altimeters and scatterometers. Various problems associated with the design of these systems will be treated.
This course covers asymptotic ray theory about diffusion and radiation of a high frequency in priority. The important thing in this process is GO(Geometrical Options), PO(Physical Options), GTD(Geometrical Theory of Diffraction), Uniform GTD(UTD), UAT(Uniform Asymptotic Theory), ECM(Equivalent Current Method), PTD(Physical Theory of Diffractio
In complex systems like ship, spacecraft, and airplane, it is very important that each subsystem does not interfere with others and also should not be affected electromagnetically by other subsystems. Various empirical, experimental and computer aided techniques to satisfy the specifications for conducted/radiated emission and susceptibility will be studied. Many computer techniques for the prediction of EMI in a given circuit board (PCB) will also be treated.
Wave propagation in dielectric waveguide and optical fiber, coupled mode theory, directional coupler, filter, resonator, phase shifter, modulator, photonic srystal devices.
This course covers the topics that are not taught in the regular courses and/that are related to the current interests and trends. This course can be taught by visiting professors.
A research course for Master's thesis.
This course covers the new theory and topics of the computer science area.
This course covers the new theory and topics of the computer theory area.
This course covers the new theory and topics of the computer systems area.
This course covers advanced topics in artificial intelligence research.
The research on the solution for non-linear objective functions with/without constraints are done. Also, Kuhn-Tucker condition, convergence theory, line search, steepest descent, Newton's conjugate gradient, quasi-Newton solution, primal, penalty, Lagrangian algorithms are studied.
Surface chemistry and analysis related with electronic materials processing are introduced. Especially basic surface atomic structure and surface reaction phenomena are dealt in this lecture. Also ultra-high-vacuum surface analysis techniques are introduced in the points of basic principle and applications in semicomductor material processing.
Intermolecular interactions between polymer and polymer, polymer and metal and polymer and ceramic are investigated in the points of Physics, Chemistry and Mechanics. The origin of intermolecular forces, i.e. Vander Waals force is studied in depth. The methods for measuring and improving surface energy and intermolecular forces are introduced.
This course covers the new theory and topics of the computer engineering area.
This course is related with the aspects of Biophysiology, Digital Signal Processing, Natural Language Processing, Linguistics for human language abilities. Extracted from each area, fundamental theories are summarized and taught under the umbrella of speech recognition and synthesis. At first, from the Biophysiogical point of view, the mechanisms of the auditory pathways and the speech generation are taught in detail. Next, speech recognition and synthesis aretaught in terms with speech signal processing and various pattern matching techniques. Phonetics, morphology, grammars, semantics, pragmatics are further explored together with some of the core theories of Chomskian grammars.
Topics in fundamental and applied science in solids and quantum theories of emerging areas in electronics
New area and recent topics in control engineering are studied.
In this course, a student learns about current interests and trends in communications and signal processing.
Study on recent topics of electromagnetics and microwave engineering published on various journals.
In this course, students are required to research on individual topics under the guidance of their advisors.
We will review new theories about schedule planning and for solving real scheduling problem. Furthermore, we will learn the information technology that is necessary to build a useful and efficient scheduling system.
A research course for Ph. D. thesis