For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ECE4249 | Computer Vision | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | Korean | Yes |
| This course focuses in the study of theories for image analysis. The first part consists of Image formulation model, early processing, boundary detection, region growing and segmentation, motion detection, merging and introduction of morphology. The second part, we cover basic concepts of statistical model, dis- criminant function, decision boundary and rules and neural network for visual pattern recognition. | |||||||||
| ECE4270 | Image Processing | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | English | Yes |
| This class provides fundamental knowledge for acquisition, processing, display of digital image signals by studying such topics as mathematical modeling of image signal, sampling, spatial and temporal resolution, human visual system, quantization theory, basic 2D signal processing, 2D transform, frequency analysis, filtering, image enhancement, color space, color processing, and compression and reconstruction. Selected practical applications are analysed for better understanding of such techniques. | |||||||||
| ECE4280 | Wireless Networks Cornerstone | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | English | Yes |
| This course provides an overview on wireless networking principles and technologies from the view point of computer science majors. The major themes will focus on the fundamentals and principles covering the protocol stacks of wireless networks, and wireless data services. | |||||||||
| ECE4285 | Theory and coding of generative AI | 3 | 6 | Major | Bachelor/Master | Electrical and Computer Engineering | Korean | Yes | |
| Along with the rapid advancement of AI through deep learning in recent years, generative artificial intelligence (AI) models like Stable Diffusion and ChatGPT are rapidly infiltrating various industries, thus being expected to lead the Fourth Industrial Revolution. In this educational program, the goal is to understand the theory and principles behind these deep learning and generative AI models. Students will develop practical skills necessary for utilizing these powerful technologies through computer programming practical applications and projects. Specifically, this course covers: (1) Understanding and hand-on coding of deep learning models such as CNN, YOLO, Semantic Segmentation, and GANs. (2) Learning the mathematical explanation and practical applications of Diffusion models, including understanding the principles of image generation through the analysis of open-source code of Stable Diffusion. (3) Understanding language generation models like Seq2Seq and Transformer architecture used in ChatGPT, and practical applications in natural language processing and related fields. Through this course, students will gain the knowledge of current and future AI technologies and acquire the skills needed to address real-world problems. | |||||||||
| ECE5301 | Advanced Information Theory | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | No |
| The aims of this course are to introduce the principles and applications of information theory. This course will study how information is measured in terms of probability and entropy. A subset of the following is covered: entropy and information; theoretical limits of lossless data compression and practical algorithms; communication in the presence of noise - channel coding, Shannon's theorem on the properties of the entropy function and some of the best known data encryption techniques will also be presented. | |||||||||
| ECE5302 | Pattern Recognition | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | No |
| This lessen discusses basic technologies on processing and recognition of digital image patterns. Main subjects are statistical pattern recognition, supervised learning, linear discrimination functions, unsupervised learning, syntactic pattern recognition, parsing and grammers, graphical syntactic pattern recognition, grammatical inference, neural pattern recognition, and so on | |||||||||
| ECE5304 | Queueing Network | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | No |
| This course is on the theoretical foundations, models and techniques of queueing theory. Elementary through advanced queueing systems and networks will be covered. Topics to be covered : Markov chains, birth-death queueing models, imbedded-markov chain queueing models, queueing networks, sumulation of queueing models. | |||||||||
| ECE5576 | Advanced Network Design | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | No |
| This course is an advanced study of the current state-of-the-art in network design technologies. Emphasis is on design principles, managing the challenges required to apply these technologies to engineering problems and managing the infusion of new technologies as the state-of-the-art advances. Key areas of study include design methodologies and architectures, network technologies; queuing theory and traffic engineering; standards. System design will focus on planning for future technologies and integration challenges. Specific topics covered include MAC, TCP, IP Routing, MPLS Traffic Engineering. Multimedia protocol; Packet Classification; network security; | |||||||||
| ECE5605 | Advanced Database | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | No |
| In this course we introduce database design methodology fundamental for constructing information systems and investigate practical database design case study. The major contents that will be covered is the following: data modeling concepts, conceptual design methodologies, view design and integration, improving quality of database schema, high level design using ER model, logical design for relational model, reverse engineering, design theory for relational databases, physical database design, etc.. | |||||||||
| ECE5609 | Simulation Methodology | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | English | Yes |
| Based on DEVS(Discrete EVent System) formalism, the following topics are covered. Basic model definition, definition of I/O port for the model interaction and high level model construction, verification of models, abstract simulator, and hierarchical modular model construction. | |||||||||
| ECE5610 | Artificial Intelligent Based Simulation | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | English | Yes |
| The representation and construction of model structures and the management of models in a model base through the knowledge representation scheme of artificial intelligence. Also the state transition of complex models are represented by expert system production rules and the systems that cannot be represented by the simulation model alone are done by embedding the expert system to the simulation models. | |||||||||
| ECE5611 | Advanced Artificial Intelligence | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | No |
| The course objectives are to learn techniques and theory developed in major areas of Artificial Intelligence. The topics are: problem solving and search, logic and knowledge representation, reasoning, machine learning, soft computing, data mining and miscellaneous topics in the current research. | |||||||||
| ECE5612 | Computer Network Security | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | No |
| There will be studied on the computer network security in order to prevent and detect the computer crime which includes firewall system to prevent the third party from invading an internal network system, the several cryptogaphic techniques for confidentiality, integrity, and authentication on communication channel. chronous parallel architectures, and implementation of multilayer neural networks. | |||||||||
| ECE5613 | Special Topics on Information Security Theory | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | No |
| There will be studied on the classical cryptosystem, symmetric cryptosystem, public key cryptosystem, and zero-knowledge interactive proof system which are based on number theory, information theory and complexity theory. By using above cryptographic techniques, also, identification, authentication, digital signature, key management, and the security of communication and computer network will be lectured. | |||||||||
| ECE5614 | Computer Graphics Theory | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | No |
| This course presents basic techniques for computer graphics: display techniques, color and image processing, polygon filling, anti-aliasing, geometric transformation,3D viewing, hidden-surface removal, shading, etc. | |||||||||