Interactive learning and management of visual information via human-like software robot

To achieve smooth real-world interaction between people and computers, we developed a system that displays a three-dimensional computer-graphic human-like image from the waist up (anthropomorphic software robot: hereinafter “robot”) on the display, that interactively sees and hears, and that has fine and detailed control functions such as facial expressions, line of sight, and pointing at targets with its finger. The robot visually searches and identifies persons and objects in real space that it has learned in advance (registered space, which was our office in this case), manages the history information of the places and times it found objects and/or persons, and tells the user, indicating their three-dimensional positions with line of sight and its finger. It interactively learns new objects and persons with line of with their names and owners. By using this function, the robot can engage in simple dialogue (do a task) with the user. Osamu Hasegawa, Ph.D.: He received the B.E. and M.E. degrees from the Science University of Tokyo, in 1988, 1990 respectively. He received Ph.D. degree from the University of Tokyo, in 1993. Currently, he is a senior research scientist at the Electrotechnical Laboratory (ETL), Tsukuba, Japan. His research interests include Computer Vision and Multi-modal Human Interface. Dr. Hasegawa is a member of the AAAI, the Institute of Electronics, Information and Communication Engineers, Japan (IEICE), Information Processing Society of Japan and others. Katsuhiko Sakaue, Ph.D.: He received the B.E., M.E., and Ph.D. degrees all in electronic engineering from the University of Tokyo, in 1976, 1978 and 1981, respectively. In 1981, he joined the Electrotechnical Laboratory, Ministry of International Trade and Industry, and engaged in researches in image processing and computer vision. He received the Encouragement Prize in 1979 from IEICE, and the Paper Award in 1985 from Information Processing Society of Japan. He is a member of IEICE, IEEE, IPSJ, ITE. Satoru Hayamizu, Ph.D.: He is a leader of Interactive Intermodal Integration Lab. at Electrotechnical Laboratory. He received the B.E., M.E., Ph.D. degrees from Tokyo University. Since 1981, he has been working on speech recognition, spoken dialogue, and communication with artifacts. From 1989 to 1990, he was a visiting scholar in Carnegie Mellon University and in 1994 a visiting scientist in LIMSI/CNRS.     

User-independent gesture recognition by relative-motion extraction and discriminant analysis

We propose a new method for user-independent gesture recognition from time-varying images. The method uses relative-motion extraction and discriminant analysis for providing online learning/recognition abilities. Efficient and robust extraction of motion information is achieved. The method is computationally inexpensive which allows real-time operation on a personal computer. The performance of the proposed method has been tested with several data sets and good generalization abilities have been observed: it is robust to changes in background and illumination conditions, to users’ external appearance and changes in spatial location, and successfully copes with the non-uniformity of the performance speed of the gestures. No manual segmentation of any kind, or use of markers, etc. is necessary. Having the above-mentioned features, the method could be successfully used as a part of more refined human-computer interfaces. Bisser R. Raytchev: He received his BS and MS degrees in electronics from Tokai University, Japan, in 1995 and 1997 respectively. He is currently a doctoral student in electronics and information sciences at Tsukuba University, Japan. His research interests include biological and computer vision, pattern recognition and neural networks. Osamu Hasegawa, Ph.D.: He received the B.E. and M.E. degrees in Mechanical Engineering from the Science University of Tokyo, in 1988, 1990 respectively. He received Ph.D. degree in Electrical Engineering from the University of Tokyo, in 1993. Currently, he is a senior research scientist at the Electrotechnical Laboratory (ETL), Tsukuba, Japan. His research interests include Computer Vision and Multi-modal Human Interface. Dr. Hasegawa is a member of the AAAI, the Institute of Electronics, Information and Communication Engineers, Japan (IEICE), Information Processing Society of Japan and others. Nobuyuki Otsu, Ph.D.: He received B.S., Mr. Eng. and Dr. Eng. in Mathematical Engineering from the University of Tokyo in 1969, 1971, and 1981, respectively. Since he joined ETL in 1971, he has been engaged in theoretical research on pattern recognition, multivariate data analysis, and applications to image recognition in particular. After taking positions of Head of Mathematical Informatics Section (since 1985) and ETL Chief Senior Scientist (since 1990), he is currently Director of Machine Understanding Division since 1991, and concurrently a professor of the post graduate school of Tsukuba University since 1992. He has been involved in the Real World Computing program and directing the R&D of the project as Head of Real World Intelligence Center at ETL. Dr. Otsu is members of Behaviormetric Society and IEICE of Japan, etc.     

Evolvable hardware: A robot navigation system testbed

Recently there has been great interest in the design and study of evolvable systems based on Artificial Life principles in order to monitor and control the behavior of physically embedded systems such as mobile robots, plants and intelligent home devices. At the same time new integrated circuits calledsoftware-reconfigurable devices have been introduced which are able to adapt their hardware almost continuously to changes in the input data or processing. When the configuration phase and the execution phase are concurrent, the software-reconfigurable device is calledevolvable hardware (EHW). This paper examines an evolutionary navigation system for a mobile robot using a Boolean function approach implemented on gate-level evolvable hardware (EHW). The task of the mobile robot is to reach a goal represented by a colored ball while avoiding obstacles during its motion. We show that the Boolean function approach using dedicated evolution rules is sufficient to build the desired behavior and its hardware implementation using EHW allows to decrease the learning time for on-line training. We demonstrate the effectiveness of the generalization ability of the Boolean function approach using EHW due to its representation and evolution mechanism. The results show that the evolvable hardware configuration learned off-line in a simple environment creates a robust robot behavior which is able to perform the desired behaviors in more complex environments and which is insensitive to the gap between the real and simulated world. Didier Keymeulen, Ph.D.: He currently works as a senior research engineer at the Computer Science Division of Electrotechnical Laboratory, AIST, MITI, Japan. His research interests are in the design of adaptive physically embedded systems using biologically inspired complex dynamical systems. He studied electrical and computer science engineering at the Universite Libre de Bruxelles in 1987. He obtained his M. Sc. and PH. D. in Computer Science from the Artificial Intelligence Laboratory of the Vrije Universiteit Brussel, directed by Dr. Luc Steels, respectively in 1991 and 1994. He was the Belgium laureate of the Japanese JSPS Postdoctoral Fellowship for Foreign Researchers in 1995. Masaya Iwata, Ph.D.: He currently works as a researcher at the Computer Science Division of Electrotechnical Laboratory, AIST, MITI, Japan. His research interests are in developing adaptive hardware devices using genetic algorithms, and in their applications to pattern recognition and image compression. He received his B. E. in 1988, his M. E. in 1990, and his Ph. D. in 1993 in applied physics from the Osaka University. He was a postdoctoral fellow in optical computing at ONERA-CERT, Toulouse, France in 1993. Kenji Konaka: He is currently working as a software research engineer at the Humanoid Interaction Laboratory of the Intelligent Systems Division of Electrotechnical Laboratory, AIST, MITI, Japan. His current research interest is on real-time vision-based mobile robots working in cooperative mode. He has developped a highly interactive distributed real-time software and hardware platform for controlling a group of robots. Yasuo Kuniyoshi, Ph.D.: He is currently a senior research scientist and head of the Humanoid Interaction Laboratory at the Intelligent Systems Division of Electrotechnical Laboratory, AIST, MITI, Japan. His current research interest is on emergence of stable structures out of complex sensory-motor interactions by a humanoid robot. He received IJCAI93 Outstanding Paper A ward and several other awards in the field of intelligent robotics. He received the B. Eng. in applied physics in 1985, M. Eng. and Ph. D. in information engineering in 1988 and 1991 respectively, all from the University of Tokyo. Tetsuya Higuchi, Ph.D.: He heads the Evolvable Systems Laboratory in Electrotechnical Laboratory, AIST, MITI, Japan. He received B. E., M. E., Ph. D. degrees all in electrical engineering from Keio University in 1978, 1980, and 1984, respectively. His current interests include envolvable hardware systems, parallel processing architecture in artificial intelligence, and adaptive systems. He is also in charge of the adaptive devices group in the MITI national project, Real World Computing Project.     

Toward a real-world knowledge medium: Building a guidance system for exhibition tours

We propose a notion of a real-world knowledge medium by presenting our ongoing project to build a guidance system for exhibition tours. In order to realize a knowledge medium usable in the real world, we focus on the context-awareness of users and their environments. Our system is a personal mobile assistant that provides visitors touring exhibitions with information based on their spatial/temporal locations and individual interests. We also describe an application of knowledge sharing used in the actual exhibition spaces. Yasuyuki Sumi, Ph.D.: He has been a researcher at ATR Media Integration & Communications Research Laboratories since 1995. His research interests include knowledge-based systems, creativity supporting systems, and their applications for facilitating human collaboration. He received his B. Eng. degree from Waseda University in 1990, and M. Eng. and D. Eng. degrees in information engineering from the University of Tokyo in 1992 and 1995, respectively. He is a member of Institutes of Electronics, Information and Communication Engineers (IEICE) of Japan, the Information Processing Society of Japan (IPSJ), the Japanese Society for Artificial Intelligence (JSAI), and American Association for Artificial Intelligence (AAAI). Kenji Mase, Ph.D.: He received the B.S. degree in Electrical Engineering and the M.S. and Ph.D. degrees in Information Engineering from Nagoya University in 1979, 1981 and 1992 respectively. He has been with ATR (Advanced Telecommunications Research Institute) Media Integration & Communications Research Laboratories since 1995 and is currently the head of Department 2. He joined the Nippon Telegraph and Telephone Corporation (NTT) in 1981 and had been with the NTT Human Interface Laboratories. He was a visiting researcher at the Media Laboratory, MIT in 1988–1989. His research interests include image sequence processing of human actions, computer graphics, computer vision, artificial intelligence and their applications for computer-aided communications and human-machine interfaces. He is a member of the Information Processing Society of Japan (IPSJ), Institutes of Electronics, Information and Communication Engineers (IEICE) of Japan and IEEE Computer Society.     

Cooperation of categorical and behavioral learning in a practical solution to the abstraction problem

Real robots should be able to adapt autonomously to various environments in order to go on executing their tasks without breaking down. They achieve this by learning how to abstract only useful information from a huge amount of information in the environment while executing their tasks. This paper proposes a new architecture which performs categorical learning and behavioral learning in parallel with task execution. We call the architectureSituation Transition Network System (STNS). In categorical learning, it makes a flexible state representation and modifies it according to the results of behaviors. Behavioral learning is reinforcement learning on the state representation. Simulation results have shown that this architecture is able to learn efficiently and adapt to unexpected changes of the environment autonomously. Atsushi Ueno, Ph.D.: He is a research associate in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the Nara Institute of Science and Technology (NAIST). He received the B.E., the M.E., and the Ph.D. degrees in aeronautics and astronautics from the University of Tokyo in 1991, 1993, and 1997 respectively. His research interest is robot learning and autonomous systems. He is a member of Japan Association for Artificial Intelligence (JSAI). Hideaki Takeda, Ph.D.: He is an associate professor in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the Nara Institute of Science and Technology (NAIST). He received his Ph.D. in precision machinery engineering from the University of Tokyo in 1991. He has conducted research on a theory of intelligent computer-aided design systems, in particular experimental study and logical formalization of engineering design. He is also interested in multiagent architectures and ontologies for knowledge base systems.     

An efficient method for geo-referenced video mosaicing for environmental monitoring

Environmental monitoring applications require seamless registration of optical data into large area mosaics that are geographically referenced to the world frame. Using frame-by-frame image registration alone, we can obtain seamless mosaics, but it will not exhibit geographical accuracy due to frame-to-frame error accumulation. On the other hand, the 3D geo-data from GPS, a laser profiler, an INS system provides a globally correct track of the motion without error propagation. However, the inherent (absolute) errors in the instrumentation are large for seamless mosaicing. The paper describes an effective two-track method for combining two different sources of data to achieve a seamless and geo-referenced mosaic, without 3D reconstruction or complex global registration. Experiments with real airborne video images show that the proposed algorithms are practical in important environmental applications. Zhigang Zhu received his B.E., M.E. and Ph.D. degrees, all in computer science from Tsinghua University, Beijing, in 1988, 1991 and 1997, respectively. He is currently an associate professor in the Department of Computer Science, the City College of the City University of New York. Previously, he was an associate professor at Tsinghua University, and a senior research fellow at the University of Massachusetts, Amherst. His research interests include 3D computer vision, HCI, virtual/augmented reality, video representation, and various applications in education, environment, robotics, surveillance and transportation. He has published over 90 technical papers in the related fields. He is a member of IEEE and ACM. Edward M. Riseman received his B.S. degree from Clarkson College of Technology in 1964 and his M.S. and Ph.D. degrees in electrical engineering from Cornell University in 1966 and 1969, respectively. He joined the Computer Science Department at UMass-Amherst as assistant professor in 1969, has been a professor since 1978, and served as chairman of the department from 1981 to 1985. Professor Riseman has conducted research in computer vision, artificial intelligence, learning, and pattern recognition, and has more than 200 publications. He has co-directed the Computer Vision Laboratory since its inception in 1975. Professor Riseman has been on the editorial boards of Computer Vision and Image Understanding (CVIU) from 1992 to 1997 and of the International Journal of Computer Vision (IJCV) from 1987 to the present. He is a senior member of IEEE, and a fellow of AAAI. Allen R. Hanson received his B.S. degree from Clarkson College of Technology in 1964 and his M.S. and Ph.D. degrees in electrical engineering from Cornell University in 1966 and 1969, respectively. He joined the Computer Science Department at UMass-Amherst as an associate professor in 1981, and has been a professor there since 1989. Professor Hanson has conducted research in computer vision, artificial intelligence, learning, and pattern recognition, and has more than 150 publications. He is co-director of the Computer Vision Laboratory at UMass-Amherst, and has been on the editorial boards of the following journals: Computer Vision, Graphics and Image Processing 1983–1990, Computer Vision, Graphics, and Image Processing—Image Understanding 1991–1994, and Computer Vision and Image Understanding 1995–present. Howard Schultz received a M.S. degree in physics from UCLA in 1974 and a Ph.D. in physical oceanography from the University of Michigan in 1982. Currently, he is a senior research fellow with the Computer Science Department at the University of Massachusetts, Amherst. His research interests include quantitative methods for image understanding and remote sensing. The current focus of his research activities are on developing automatic techniques for generating complex, 3D models from sequences of images. This research has found application in a variety of programs including real-time terrain modeling and video aided navigation. He is a member of the IEEE, the American Geophysical Union, and the American Society of Photogrammetry and Remote Sensing.     

A challenge towards next-generation research infrastructure for advanced life science

Recently, life scientists have expressed a strong need for computational power sufficient to complete their analyses within a realistic time as well as for a computational power capable of seamlessly retrieving biological data of interest from multiple and diverse bio-related databases for their research infrastructure. This need implies that life science strongly requires the benefits of advanced IT. In Japan, the Biogrid project has been promoted since 2002 toward the establishment of a next-generation research infrastructure for advanced life science. In this paper, the Biogrid strategy toward these ends is detailed along with the role and mission imposed on the Biogrid project. In addition, we present the current status of the development of the project as well as the future issues to be tackled. Haruki Nakamura, Ph.D.: He is Professor of Protein Informatics at Institute for Protein Research, Osaka University. He received his B.S., M.A. and Ph.D. from the University of Tokyo in 1975, 1977 and 1980 respectively. His research field is Biophysics and Bioinformatics, and has so far developed several original algorithms in the computational analyses of protein electrostatic features and folding dynamics. He is also a head of PDBj (Protein Data Bank Japan) to manage and develop the protein structure database, collaborating with RCSB (Research Collaboratory for Structural Bioinformatics) in USA and MSD-EBI (Macromolecular Structure Database at the European Bioinformatics Institute) in EU. Susumu Date, Ph.D.: He is Assistant Professor of the Graduate School of Information Science and Technology, Osaka University. He received his B.E., M.E. and Ph.D. degrees from Osaka University in 1997, 2000 and 2002, respectively. His research field is computer science and his current research interests include application of Grid computing and related information technologies to life sciences. He is a member of IEEE CS and IPSJ. Hideo Matsuda, Ph.D.: He is Professor of the Department of Bioinformatic Engineering, the Graduate School of Information Science and Technology, Osaka University. He received his B.S., M.Eng. and Ph.D. degrees from Kobe University in 1982, 1984 and 1987 respectively. For M.Eng. and Ph.D. degrees, he majored in computer science. His research interests include computational analysis of genomic sequences. He has been involved in the FANTOM (Functional Annotation of Mouse) Project for the functional annotation of RIKEN mouse full-length cDNA sequences. He is a member of ISCB, IEEE CS and ACM. Shinji Shimojo, Ph.D.: He received M.E. and Ph.D. degrees from Osaka University in 1983 and 1986 respectively. He was an Assistant Professor with the Department of Information and Computer Sciences, Faculty of Engineering Science at Osaka University from 1986, and an Associate Professor with Computation Center from 1991 to 1998. During the period, he also worked as a visiting researcher at the University of California, Irvine for a year. He has been Professor with Cybermedia Center (then Computation Center) at Osaka University since 1998. His current research work focus on a wide variety of multimedia applications, peer-to-peer communication networks, ubiquitous network systems and Grid technologies. He is a member of ACM, IEEE and IEICE.     

A futures market-based model for dynamic network resource allocation

Management of telecommunication network requires quick, continuous and decentralized allocation of network bandwidth to various sorts of demands. So as to achieve the efficient network resource allocation, this paper describes a market-based model combining futures market with the agent-based approach. That is, utilization time is divided into many timeslots, and futures markets in hereafter use of bandwidth are opened. In our model, all market participants (software agents) observe only market prices and decide to buy or sell bandwidth trying to maximize their utilities over time so that they can secure enough network resources. The authors discuss network resource allocation through simulation using the proposed model. Masayuki Ishinishi, Ph.D.: He graduated from National Defense Academy in 1995 and 2000. He received the B.E. (1995) and M.E.(2000) degrees in computer science from National Institution for Academic Degrees (NIAD). He received his Ph.D. degree from Tokyo Institute of Technology in 2003. He has been a communications officer at Air Communications and Systems Wing in Japan Air Self-Defence Force (JASDF) since 2003. His research interests include information assurance, agent-based modeling and simulation, multi-agent system and market-based control. He is a member of IEEJ, IPSJ and JSAI. Yuhsuke Koyama, Ph.D.: He received the B.Econ., M.Econ., and Ph.D. degrees in economics from Kyoto University, in 1996, 1998, 2002, respectively. He has been a research associate of Tokyo Institute of Technology since 2002. His research field is evolutionary economics, mathematical sociology and experimental economics. He is a member of JAFEE, JAMS, JASESS and JASAG. Hiroshi Deguchi, Ph.D.: He received his Ph.D. degree in systems science from Tokyo Institute of Technology, in 1986. He also received the Dr. Econ. degree from Kyoto University in 2001. He has been a Professor of Tokyo Institute of Technology since 2002. His research field is evolutionary economics, computational organization theory, agent-based modeling, social system theory, gaming simulation, and philosophy of science. He is a member of SICE, JAMS, IPSJ, PHSC, JASAG and JAFEE. Hajime Kita, Ph.D.: He received the B.E., M.E., and Ph.D. degrees in electrical engineering from Kyoto University, in 1982, 1984, 1991, respectively. He has been a Professor of Kyoto University since 2003, His research field is systems science/engineering, and his research interests are evolutionary computation, neural networks and socio-economic analysis of energy systems, and agentbased modeling. He is a member of IEEJ, IEICE, ISCIE, JNNS, JSER, ORSJ and SICE.     

Learning-based tracking of complex non-rigid motion

This paper describes a novel method for tracking complex non-rigid motions by learning the intrinsic object structure. The approach builds on and extends the studies on non-linear dimensionality reduction for object representation, object dynamics modeling and particle filter style tracking. First, the dimensionality reduction and density estimation algorithm is derived for unsupervised learning of object intrinsic representation, and the obtained non-rigid part of object state reduces even to 2-3 dimensions. Secondly the dynamical model is derived and trained based on this intrinsic representation. Thirdly the learned intrinsic object structure is integrated into a particle filter style tracker. It is shown that this intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle filter style tracker more robust and reliable.Extensive experiments are done on the tracking of challenging non-rigid motions such as fish twisting with selfocclusion, large inter-frame lip motion and facial expressions with global head rotation. Quantitative results are given to make comparisons between the newly proposed tracker and the existing tracker. The proposed method also has the potential to solve other type of tracking problems.     

GigaE PM: a high performance communication facility using a Gigabit Ethernet

A high performance communication facility, called theGigaE PM, has been designed and implemented for parallel applications on clusters of computers using a Gigabit Ethernet. The GigaE PM provides not only a reliable high bandwidth and low latency communication, but also supports existing network protocols such as TCP/IP. A reliable communication mechanism for a parallel application is implemented on the firmware on a NIC while existing network protocols are handled by an operating system kernel. A prototype system has been implemented using an Essential Communications Gigabit Ethernet card. The performance results show that a 58.3 μs round trip time for a four byte user message, and 56.7 MBytes/sec bandwidth for a 1,468 byte message have been achieved on Intel Pentium II 400 MHz PCs. We have implemented MPICH-PM on top of the GigaE PM, and evaluated the NAS parallel benchmark performance. The results show that the IS class S performance on the GigaE PM is 1.8 times faster than that on TCP/IP. Shinji Sumimoto: He is a Senior Researcher of Parallel and Distributed System Software Laboratory at Real World Computing Partnership, JAPAN. He received BS degree in electrical engineering from Doshisha University. His research interest include parallel and distributed systems, real-time systems, and high performance communication facilities. He is a member of Information Processing Society of Japan. Hiroshi Tezuka: He is a Senior Researcher of Parallel and Distributed System Software Laboratory at Real World Computing Partnership, JAPAN. His research interests include real-time systems and operating system kernel. He is a member of the Information Processing Society of Japan, and Japan Society for Software Science and Technology. Atsushi Hori, Ph.D.: He is a Senior Researcher of Parallel and Distributed System Software Laboratory at Real World Computing Partnership, JAPAN. His current research interests include parallel operating system. He received B.S. and M.S. degrees in Electrical Engineering from Waseda University, and received Ph.D. from the University of Tokyo. He worked as a researcher in Mitsubishi Research Institute from 1981 to 1992. Hiroshi Harada: He is a Senior Researcher of Parallel and Distributed System Software Laboratory at Real World Computing Partnership, JAPAN. His research interests include distributed/parallel systems and distributed shared memory. He received BS degree in physics from Science University of Tokyo. He is a member of ACM and Information Processing Society of Japan. Toshiyuki Takahashi: He is a Researcher at Real World Computing Partnership since 1998. He received his B.S. and M.S. from the Department of Information Sciences of Science University of Tokyo in 1993 and 1995. He was a student of the Information Science Department of the University of Tokyo from 1995 to 1998. His current interests are in meta-level architecture for programming languages and high-performance software technologies. He is a member of Information Processing Society of Japan. Yutaka Ishikawa, Ph.D.: He is the chief of Parallel and Distributed System Software Laboratory at Real World Computing Partnership, JAPAN. He is currently temporary retirement from Electrotechnical Laboratory, MITI. His research interests include distributed/parallel systems, object-oriented programming languages, and real-time systems. He received the B.S., M.S. and Ph.D degrees in electrical engineering from Keio University. He is a member of the IEEE Computer Society, ACM, Information Processing Society of Japan, and Japan Society for Software Science and Technology.     

RHiNET-2/SW: a high-throughput, compact network-switch using 8.8-gbit/s optical interconnection

We have developed a high-throughput, compact network switch (the RHiNET-2/SW) for a distributed parallel computing system. Eight pairs of 800-Mbit/s×12-channel optical interconnection modules and a CMOS ASIC switch are integrated on a compact circuit board. To realize high-throughput (64 Gbit/s) and low-latency network, the SW-LSI has a customized high-speed LVDS I/O interface, and a high-speed internal SRAM memory in a 784-pin BGA one-chip package. We have also developed device implementation technologies to overcome the electrical problems (loss and crosstalk) caused by such high integration. The RHiNET-2/SW system enables high-performance parallel processing in a distributed computing environment. Shinji Nishimura: He is a researcher in the Department of Network System at the Central Research Laboratory, Hitachi Ltd., at Tokyo. He obtained his bachelors degree in Electronics Engineering from the University of Tokyo in 1989, and his M.E. from the University of Tokyo in 1991. He joined a member of the Optical Interconnection Hitachi Laboratory from 1992. His research interests are in hardware technology for the optical interconnection technologies in the computer and communication systems. Katsuyoshi Harasawa: He is a Senior Enginner of Hitachi Communication Systems Inc. He obtained his bachelors degree in Electrical Engineering from Tokyo Denki University. He is a chief of development of the devices and systems for the optical telecommunication. He was engaged in Development of Optical Reciever and Transmitter module. He joined RWCP project from 1997. His research interests are in hardward technology for optical interconnection in distributed parallel computing system (RHiNET). Nobuhiro Matsudaira: He is a engineer in the Hitachi Communication Systems, Inc. He obtained his bachelors degree in Mercantile Marine Engineering from the Kobe University of Mercantile Marine in 1986. He was engaged in Development of Optical Reciever and Transmitter module at 2.4 Gbit/s to 10Gbit/s. He joined RWCP project from 1998. His reserch interests are in hardware technology for the optical interconnection technology in the computer and communication systems. Shigeto Akutsu: He is a staff in Hitachi Communication Systems Inc. He obtained his bachelors degree in Electronics from Kanagawa University, Japan in 1998. His research interests are hardware technology for the optical interconnection technology in the computer and communication systems. Tomohiro Kudoh, Ph.D.: He received Ph.D. degree from Keio University, Japan in 1992. He has been chief of the parallel and distributed architecture laboratory, Real World Computing Partnership since 1997. His research interests include the area of parallel processing and network for high performance computing. Hiroaki Nishi: He received B.E., M.E. from Keio University, Japan, in 1994, 1996, respectively. He joined Parallel & Distributed Architecture Laboratory, Real World Computing Partnership in 1999. He is currently working on his Ph.D. His research interests include area of interconnection networks. Hideharu Amano, Ph.D.: He received Ph.D. degree from Keio University, Japan in 1986. He is now an Associate Professor in the Department of Information and Computer Science, Keio University. His research interests include the area of parallel processing and reconfigurable computing.     

Anti-aliased rendering of water surface

Water surface is one of the most important components of landscape scenes. When rendering spacious water surface such as that of the lakes and reservoirs, aliasing and/or moiré artifacts frequently occur in the regious far from the viewpoint. This is because water surface consists of stochastic water waves which are usually modeled by periodic bump mapping. The incident rays on the water surface are actually scattered by the bumped waves, and the reflected rays at each sample point are distributed in a solid angle. To get rid of the artifacts of moiré pattern, we estimate this solid angle of reflected rays and trace these rays. An image-based accelerating method is adopted so that the contribution of each reflected ray can be quickly obtained without elaborate intersection calculation. We also demonstrate anti-aliased shadows of sunlight and skylight on the water surface. Both the rendered images and animations show excellent effects on the water surface of a reservoir. The first, third and fifth co-authors were partially supported by the National Natural Science Foundation of China (Grant Nos. 60021201 and 60373035), Key Research Project of Ministry of Education (Grant No.01094) and the National Grand Fundamental Research 973 Program of China (Grant No.2002CB312102). Xue-Ying Qin is an associated professor of State Key Laboratory of CAD&CG, Zhejiang University. She received her Ph.D. degree from Hiroshima University in 2001, B.S. and M.S. degrees in Mathematics from Peking University in 1988 and from Zhejiang University in 1991, respectively. Her research interests include computer graphics, visions and image processing. Eihachiro Nakamae is currently Chairman of Sanei Co. He was granted the title of emeritus professor from both Hiroshima University and Hiroshima Institute of Technology. He was appointed as a researcher associate at Hiroshima University in 1956, a professor from 1968 to 1992 and an associated researcher at Clarkson College of Technology, Potsdam, N.Y., from 1973 to 1974. He was a professor at Hiroshima Prefectural University from 1992 to 1995 and a professor at Hiroshima Institute of Technology from a996 to the end of March 1999. He received his B.E., M.E., and Ph.D. degrees in electrical engineering in 1954, 1956, and 1967 from Waseda University. His research interests include computer graphics, image processing and electric machinery. He is a member of IEEE, ACM, CGS, Eurographics, IEE of Japan, and IPS of Japan. Wei Hua received his Ph.D. degree in applied mathematics from Zhejiang University in 2002. He joined the CAD&CG State Key Lab in 2002. His main interests include real-time simulation and rendering, virtual reality and software engineering. Yasuo Nagai is now an associate professor of Hiroshima Institute of Technology. He was appointed a researcher associate at Hiroshima Institute of Technology in 1965, and an associate professor in 1984. His research interests include computer graphics and image processing. He is a member of IEE, IEICE, IPSJ, and ITE of Japan. Qun-Sheng Peng was born in 1947. He received his Ph.D. degree in computer science from the University of East Anglia, U.K., in 1983. He is a professor and his research interests include computer graphics, computer animation, virtual reality, and point-based modeling and rendering.     

A program specialiser for meta-level compositions of logic programs

Metal-level compositions of object logic programs are naturally implemented by means of meta-programming techniques. Metainterpreters defining program compositions however suffer from a computational overhead that is due partly to the interpretation layer present in all meta-programs, and partly to the specific interpretation layer needed to deal with program compositions. We show that meta-interpreters implementing compositions of object programs can be fruitfully specialised w.r.t. meta-level queries of the form Demo (E, G), where E denotes a program expression and G denotes a (partially instantiated) object level query. More precisely, we describe the design and implementation of declarative program specialiser that suitably transforms such meta-interpreters so as to sensibly reduce — if not to completely remove — the overhead due to the handling of program compositions. In many cases the specialiser succeeds in eliminating also the overhead due to meta-interpretation. Antonio Brogi, Ph.D.: He is currently assistant professor in the Department of Computer Science at the University of Pisa, Italy. He received his Laurea Degree in Computer Science (1987) and his Ph. D. in Computer Science (1993) from the University of Pisa. His research interests include programming language design and semantics, logic programming, deductive databases, and software coordination. Simone Contiero: He is currently a Ph. D. student at the Department of Computer Science, University of Pisa (Italy). He received his Laurea Degree in Computer Science from the University of Pisa in 1994. His research interests are in high-level programming languages, metaprogramming and logic-based coordination of software.     

A study on effects of market systems in an artificial market

In an artificial market approach with multi-agent systems, the static equilibrium concept is often used in market systems to approximate continuous market auctions. However, differences between the static equilibrium concept and continuous auctions have not been discussed in the context of an artificial market study. In this paper, we construct an artificial market model with both of them, namely, the Itayose and Zaraba method, and show simple characteristic differences between these methods based on computer simulations. The result indicates the further need to model the market system by studying artificial markets. Hidenori Kawamura, Ph.D.: He received Ph.D. degree from Division of Systems and Information Engineering, Graduate School of Engineering, Hokkaido University, Japan in 2000. He is currently an instructor in Graduate School of Information Science and Technology, Hokkaido University, Japan. His research interests include multiagent systems, mass user support, artificial intelligence, complex systems, and tourism informatics. He is a member of IPSJ, JSAI, IEICE, ORSJ, JSTI and AAAI. Yasushi Okada, Ph.D.: He is a master course student in Graduate School of Engineering, Hokkaido University, Japan. He studies multiagent systems. Azuma Ohuchi, Ph.D.: He received his Ph.D. degree in 1974 from Hokkaido University. He is currently the professor in Graduate School of Information Science and Technology, Hokkaido University Japan. His research interstes include systems information engineering, artificial intelligence, complex systems, tourism informatics and medical systems. He is a member of the IPSJ, JSAI, IEEJ, ORSJ, Soc. Contr. Eng., Jap. OR Soc., Soc. Med. Informatics, Hosp. Manag., JSTI and IEEE-SMC. Koichi Kurumatani, Ph.D.: He received his Ph.D. Degree in 1989 from The University of Tokyo. He is currently a leader of Multiagent Research Team in Cyber Assist Research Center (CARC), National Institute of Advanced Industrial Science and Technology (AIST), Japan. His research interests include multiagent systems and mass user support. He is a member of JSAI, IPSJ, JSTI and AAAI.     

A grid-oriented genetic algorithm framework for bioinformatics

In this paper, we propose a framework for enabling for researchers of genetic algorithms (GAs) to easily develop GAs running on the Grid, named “Grid-Oriented Genetic algorithms (GOGAs)”, and actually “Gridify” a GA for estimating genetic networks, which is being developed by our group, in order to examine the usability of the proposed GOGA framework. We also evaluate the scalability of the “Gridified” GA by applying it to a five-gene genetic network estimation problem on a grid testbed constructed in our laboratory. Hiroaki Imade: He received his B.S. degree in the department of engineering from The University of Tokushima, Tokushima, Japan, in 2001. He received the M.S. degree in information systems from the Graduate School of Engineering, The University of Tokushima in 2003. He is now in Doctoral Course of Graduate School of Engineering, The University of Tokushima. His research interests include evolutionary computation. He currently researches a framework to easily develop the GOGA models which efficiently work on the grid. Ryohei Morishita: He received his B.S. degree in the department of engineering from The University of Tokushima, Tokushima, Japan, in 2002. He is now in Master Course of Graduate School of Engineering, The University of Tokushima, Tokushima. His research interest is evolutionary computation. He currently researches GA for estimating genetic networks. Isao Ono, Ph.D.: He received his B.S. degree from the Department of Control Engineering, Tokyo Institute of Technology, Tokyo, Japan, in 1994. He received Ph.D. of Engineering at Tokyo Institute of Technology, Yokohama, in 1997. He worked as a Research Fellow from 1997 to 1998 at Tokyo Institute of Technology, and at University of Tokushima, Tokushima, Japan, in 1998. He worked as a Lecturer from 1998 to 2001 at University of Tokushima. He is now Associate Professor at University of Tokushima. His research interests include evolutionary computation, scheduling, function optimization, optical design and bioinformatics. He is a member of JSAI, SCI, IPSJ and OSJ. Norihiko Ono, Ph.D.: He received his B.S. M.S. and Ph.D. of Engineering in 1979, 1981 and 1986, respectively, from Tokyo Institute of Technology. From 1986 to 1989, he was Research Associate at Faculty of Engineering, Hiroshima University. From 1989 to 1997, he was an associate professor at Faculty of Engineering, University of Tokushima. He was promoted to Professor in the Department of Information Science and Intelligent Systems in 1997. His current research interests include learning in multi-agent systems, autonomous agents, reinforcement learning and evolutionary algorithms. Masahiro Okamoto, Ph.D.: He is currently Professor of Graduate School of Systems Life Sciences, Kyushu University, Japan. He received his Ph.D. degree in Biochemistry from Kyushu University in 1981. His major research field is nonlinear numerical optimization and systems biology. His current research interests cover system identification of nonlinear complex systems by using evolutional computer algorithm of optimization, development of integrated simulator for analyzing nonlinear dynamics and design of fault-tolerant routing network by mimicking metabolic control system. He has more than 90 peer reviewed publications.     

Automatic model-based 3D object recognition by combining feature matching with tracking

We propose a vision-based robust automatic 3D object recognition, which provides object identification and 3D pose information by combining feature matching with tracking. For object identification, we propose a robust visual feature and a probabilistic voting scheme. An initial object pose is estimated using correlations between the model image and the 3D CAD model, which are predefined, and the homography, byproduct of the identification. In tracking, a Lie group formalism is used for robust and fast motion computation. Experimental results show that object recognition by the proposed method improves the recognition range considerably. Sungho Kim received the B.S. degree in Electrical Engineering from Korea University, Korea in 2000 and the M.S. degree in Electrical Engineering and Computer Science from Korea Advanced Institute of Science and Technology, Korea in 2002. He is currently pursuing his Ph.D. at the latter institution, concentrating on 3D object recognition and tracking. In So Kweon received the Ph.D. degree in robotics from Carnegie Mellon University, Pittsburgh, PA, in 1990. Since 1992, he has been a Professor of Electrical Engineering at KAIST. His current research interests include human visual perception, object recognition, real-time tracking, vision-based mobile robot localization, volumetric 3D reconstruction, and camera calibration. He is a member of the IEEE, and Korea Robotics Society (KRS).     

Using discriminant analysis for multi-class classification: an experimental investigation

Many supervised machine learning tasks can be cast as multi-class classification problems. Support vector machines (SVMs) excel at binary classification problems, but the elegant theory behind large-margin hyperplane cannot be easily extended to their multi-class counterparts. On the other hand, it was shown that the decision hyperplanes for binary classification obtained by SVMs are equivalent to the solutions obtained by Fisher's linear discriminant on the set of support vectors. Discriminant analysis approaches are well known to learn discriminative feature transformations in the statistical pattern recognition literature and can be easily extend to multi-class cases. The use of discriminant analysis, however, has not been fully experimented in the data mining literature. In this paper, we explore the use of discriminant analysis for multi-class classification problems. We evaluate the performance of discriminant analysis on a large collection of benchmark datasets and investigate its usage in text categorization. Our experiments suggest that discriminant analysis provides a fast, efficient yet accurate alternative for general multi-class classification problems. Tao Li is currently an assistant professor in the School of Computer Science at Florida International University. He received his Ph.D. degree in Computer Science from University of Rochester in 2004. His primary research interests are: data mining, machine learning, bioinformatics, and music information retrieval. Shenghuo Zhu is currently a researcher in NEC Laboratories America, Inc. He received his B.E. from Zhejiang University in 1994, B.E. from Tsinghua University in 1997, and Ph.D degree in Computer Science from University of Rochester in 2003. His primary research interests include information retrieval, machine learning, and data mining. Mitsunori Ogihara received a Ph.D. in Information Sciences at Tokyo Institute of Technology in 1993. He is currently Professor and Chair of the Department of Computer Science at the University of Rochester. His primary research interests are data mining, computational complexity, and molecular computation.     

Mobile robot localization based on effective combination of vision and range sensors

Most localization algorithms are either range-based or vision-based, but the use of only one type of sensor cannot often ensure successful localization. This paper proposes a particle filter-based localization method that combines the range information obtained from a low-cost IR scanner with the SIFT-based visual information obtained from a monocular camera to robustly estimate the robot pose. The rough estimation of the robot pose by the range sensor can be compensated by the visual information given by the camera and the slow visual object recognition can be overcome by the frequent updates of the range information. Although the bandwidths of the two sensors are different, they can be synchronized by using the encoder information of the mobile robot. Therefore, all data from both sensors are used to estimate the robot pose without time delay and the samples used for estimating the robot pose converge faster than those from either range-based or vision-based localization. This paper also suggests a method for evaluating the state of localization based on the normalized probability of a vision sensor model. Various experiments show that the proposed algorithm can reliably estimate the robot pose in various indoor environments and can recover the robot pose upon incorrect localization. Recommended by Editorial Board member Sooyong Lee under the direction of Editor Hyun Seok Yang. This research was conducted by the Intelligent Robotics Development Program, one of the 21st Century Frontier R&D Programs funded by the Ministry of Knowledge Economy of Korea. Yong-Ju Lee received the B.S. degree in Mechanical Engineering from Korea University in 2004. He is now a Student for Ph.D. of Mechanical Engineering from Korea University. His research interests include mobile robotics. Byung-Doo Yim received the B.S. degree in Control and Instrumentation Engineering from Seoul National University of Technology in 2005. Also, he received the M.S. degree in Mechatroncis Engineering from Korea University in 2007. His research interests include mobile robotics. Jae-Bok Song received the B.S. and M.S. degrees in Mechanical Engineering from Seoul National University in 1983 and 1985, respectively. Also, he received the Ph.D. degree in Mechanical Engineering from MIT in 1992. He is currently a Professor of Mechanical Engineering, Korea University, where he is also the Director of the Intelligent Robotics Laboratory from 1993. His current research interests lie mainly in mobile robotics, safe robot arms, and design/control of intelligent robotic systems.     

Multimedia technologies for structuring and retrieval of TV news

Because of the media digitization, a large amount of information such as speech, audio and video data is produced everyday. In order to retrieve data from these databases quickly and precisely, multimedia technologies for structuring and retrieving of speech, audio and video data are strongly required. In this paper, we overview the multimedia technologies such as structuring and retrieval of speech, audio and video data, speaker indexing, audio summarization and cross media retrieval existing today for TV news detabase. The main purpose of structuring is to produce tables of contents and indices from audio and video data automatically. In order to make these technologies feasible, first, processing units such as words on audio data and shots on video data are extracted. On a second step, they are meaningfully integrated into topics. Furthermore, the units extracted from different types of media are integrated for higher functions. Yasuo Ariki, Ph.D.: He is a Professor in the Department of Electronics and Informatics at the Ryukoku University. He received his B.E., M.E. and Ph.D. in information science from Kyoto University in 1974, 1976 and 1979, respectively. He had been an Assistant in Kyoto University from 1980 to 1990, and stayed at Edinburgh University as visiting academic from 1987 to 1990. His research interests are in speech and image recognition and in information retrieval and database. He is a member of IPSJ, IEICE, ASJ, Soc. Artif. Intel. and IEEE.     

Balance control of a biped robot using camera image of reference object

This paper presents a new balance control scheme for a biped robot. Instead of using dynamic sensors to measure the pose of a biped robot, this paper uses only the visual information of a specific reference object in the workspace. The zero moment point (ZMP) of the biped robot can be calculated from the robot’s pose, which is measured from the reference object image acquired by a CCD camera on the robot’s head. For balance control of the biped robot a servo controller uses an error between the reference ZMP and the current ZMP, estimated by Kalman filter. The efficiency of the proposed algorithm has been proven by the experiments performed on both flat and uneven floors with unknown thin obstacles. Recommended by Editorial Board member Dong Hwan Kim under the direction of Editor Jae-Bok Song. This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD). This research was supported by the MKE(The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA(Institute for Information Technology Advancement) (IITA-2008-C1090-0803-0006). Sangbum Park received the B.S. and M.S. degrees from Electronic Engineering of Soongsil University, Seoul, Korea, in 2004 and 2006 respectively. He has been with School of Electronic Engineering, Soongsil University since 2006, where he is currently pursuing a Ph.D. His current research interests include biped walking robot, robotics vision. Youngjoon Han received the B.S., M.S. and Ph.D. degrees in Electronic Engineering from Soongsil University, Seoul, Korea, in 1996, 1998, and 2003, respectively. He is currently an Assistant Professor in the School of Electornic Engineering at Soongsil University. His research interests include robot vision system, and visual servo control. Hernsoo Hahn received the B.S. and M.S. degrees in Electronic Engineering at Soongsil University and Younsei University, Korea in 1982 and 1983 respectively. He received the Ph.D. degree in Computer Engineering from University of Southern California in 1991, and became an Assistant Professor at the School Electroncis Engneering in Soongsil University in 1992. Currently, he is a Professor. His research interests include application of vision sensors to mobile robots and measurement systems.