2005 Graduate Catalog and 2004 Annual R & D Report - Sirindhorn ...

More documents

Recommendations

Info

2005 Graduate Catalog and 2004 Annual R & D Report Sirindhorn International Institute of Technology (SIIT) Computer Vision and Remote Sensing Depth estimation and motion analysis are the areas of my long-term research interests. Depth estimation is the central issue in dealing with stereo (or more) images, while motion analysis is concerned about the process of time-sequential images. I am particularly interested in the “correspondence problem” that has to be solved for estimating both depth and motion. Object Classification and Recognition Image understanding is another area of my interests. This category has various applications for bioinformatics and industry, such as security control, parts inspection, grading and sorting, etc. A popular research topic in bioinformatics is human face recognition which I have worked on. Shape analysis of rice grains is a current senior-project. Dr. Waree Kongprawechnon Assistant Professor B.Eng. (1 st Class Honors) in Electrical Engineering, Chulalongkorn University, Thailand M.Eng. in Control Engineering, Osaka University, Japan Ph.D. in Mathematical Engineering and Information Physics, University of Tokyo, Japan Areas of Specialization: The theory in H α control, the theory in Robust control. Research Interests: H ∞ Control The advent of H ∞ control was a truly remarkable innovation in multivariable theory. It eliminated the classical/modern dichotomy by formulating the design issues of classical control property and has solved it based on the state-space tool of modern theory. The theory-practice gap was no longer a significant issue at the beginning of the 1990s due to a number of successful applications of H ∞ control to real design problems, especially applications of H ∞ control based robust control theory. Robust Control No mathematical system can exactly model a physical system. Nowadays it is gradually being recognized that the real issue of control engineering we were faced with was the difficulty of modeling the plant to be controlled. For this reason we must be aware of how modeling errors might adversely affect the performance of a control system. Robust control theory became the most popular area that was expected to deal with model uncertainty. School of Information and Management Technology Faculty Members and Research Interests, 2005 Dr. Bunyarit Uyyanonvara Assistant Professor B.Sc. (1 st Class Honors) in Science (Physics), Prince of Songkhla University, Thailand PhD. in Image Processing, King's College, London, UK Areas of Specialization: Image processing, Texture segmentation, Relaxation labeling, Medical imaging Research Interests: Image Segmentation Using Texture and Relaxation Labeling Algorithms When normal density or intensity segmentation is not effective enough, a new representation of texture which is derived from the spatial energy of the texture is introduced in order to segment the given image. From the energy values, a 2D histogram of texture is generated. The texture histogram is used to discriminate textures and to retrieve image segmentation. In an attempt to assess the similarities in the regional areas, the property of adjacency could be useful. This characteristic of pixels is defined as a co-occurrence matrix, which is an important tool in Image Segmentation using Texture and Relaxation Labeling Algorithms. Medical Image Processing Taking advantage of the high capability of computers, offering advantages over film based systems, several image processing techniques are of interest, especially for medical purposes in order to get most of the information out of the given medical images. Essentially, medical imaging can make use of texture information, texture feature classification or texture segmentation because of the nature of the medical image itself. Medical assessment can then be made fully automated later on and this will lead to a reduction of human errors, increasing of consistency and repeatability. This can be distributed to the remote areas or hospitals that lack sophisticated treatment facilities or trained experts. 22
2005 Graduate Catalog and 2004 Annual R & D Report Sirindhorn International Institute of Technology (SIIT) Dr. Chawalit Jeenanunta Lecturer (Joined SIIT in October 2004) B.S. in Computer Science, University of Maryland, USA B.S. in Mathematics, University of Maryland, USA M.S. in Management Science, University of Maryland, USA Ph.D. in Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, USA Research Areas: Linear programming, Integer programming, Network optimization, Simulation. Research Interest: Large-Scale Simulation and Optimization Many problems in the real world are large and complex. Researchers in this field are trying to improve the algorithm and utilize available computational technology such as parallelism or grid computing to solve such problems where their resulting models are also very large. This technology also enables researchers to have a detail model which is close to the real world problem. Some examples of these problems are transportation problem in the urban area (where there consist of millions of people driving on thousands of streets), financial simulation, and bioinformatics. Dr. Cholwich Nattee Lecturer (Joined SIIT in May 2005) B.Eng. in Computer Engineering, Chulalongkorn University, Thailand M.Eng. in Computer Science, Tokyo Institute of Technology, Japan D.Eng. in Computer Science, Tokyo Institute of Technology, Japan Research Areas: Artificial intelligence, Machine learning, Knowledge discovery and Data mining, Artifcial Intelligence applications in distance learning and pattern recognition. Research Interest: Inductive Logic Programming for Structure- Activity Relationship Studies Nowadays, a vast amount of chemical compound structure information can be produced due to advances in High Throughput Screening technology that automates compound screening using the combination of robotics, image processing and pattern recognition. From these data, knowledge describing compound activities and characteristics from their structures is essential, since it can be used for predicting characteristics of unknown compounds for developing new drugs. Machine learning and data mining techniques have been applied in order to automatically obtain models describing the relations between structure and activity. However, traditional data mining algorithms have limitations on knowledge representations. Thus, complicated structures of chemical compounds cannot be handled efficiently. Extended from traditional machine learning techniques, Inductive Logic Programming (ILP) applies first-order logic for representing data. This allows complicated structures or relations among training examples to be denoted without losing any information. Moreover, learning results in the form of first-order rules, are comprehensible. The knowledge obtained can be easily explained to domain experts. Dr. Ekawit Nantajeewarawat Associate Professor B.Eng. in Computer Engineering, Chulalongkorn University, Thailand M.Eng. & D.Eng. in Computer Science, Asian Institute of Technology (AIT), Thailand Areas of Specialization: Knowledge representations, Computational logics, Computation theory, Programming paradigms, Object-oriented system analysis and design. Research Interests: Semantic Web Expectedly, Semantic Web technology will bring about large-scale heterogeneous Web knowledge bases with a qualitatively new level of service. The concept of ontology (domain theory) will play a key role as a formal, explicit specification of shared conceptualizations that describe the semantics of data on the Web. Grounded upon Description logics (DLs), the theory of XML declarative descriptions (XDD) and Resource Description Framework (RDF), formal ontology languages as well as meta-level representation of Web resources are investigated. The possibility of developing automated reasoning systems for Semantic Web is explored from both theoretical and practical viewpoints, e.g., a hybrid reasoning system comprising a DL-based reasoning component and a rule-based backward chaining component. Realization of the Semantic Web vision demands further research work on several other knowledge-representation-related issues. 23
Page 2 and 3: Board of Trustees Thammasat Univers
Page 4 and 5: 2005 Graduate Catalog and 2004 Annu
Page 6 and 7: 2005 Graduate Catalog Sirindhorn In
Page 48 and 49: 2004 Annual R & D Report Sirindhorn
Page 66 and 67: Access to SIIT at Rangsit ZONE A ZO

2005 Graduate Catalog and 2004 Annual R & D Report - Sirindhorn ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?