协同虚拟环境中方向更新消息滤波机制研究

方向更新消息滤波机制是协同虚拟环境(Collaborative Virtual Environments，以下简称CVE)中的一个重要研究课题，Dead-Reckoning(以下简称DR)技术是一种有效的更新消息滤波机制。然而，以前的DR技术主要集中在位置更新消息滤波，现有的方向DR算法一般基于固定阈值，方向固定阈值机制(Fixed Threshold Mechanism for Orientations，以下简称FTMO)的缺点将在本文讨论．提出一个方向可变阈值机制(Variable Threshold Mechanism for Orientation，以下简称VTMO)．VTMO首先计算最近平均角速度，并基于最近平均角速度改变方向阈值，从而在保持一定的方向精确度的情况下，减少需发送的方向更新消息的数量。     

A monitoring system for detecting repeated packets with applications to computer worms

We present a monitoring system which detects repeated packets in network traffic, and has applications including detecting computer worms. It uses Bloom filters with counters. The system analyzes traffic in routers of a network. Our preliminary evaluation of the system involved traffic from our internal lab and a well known historical data set. After appropriate configuration, no false alarms are obtained under these data sets and we expect low false alarm rates are possible in many network environments. We also conduct simulations using real Internet Service Provider topologies with realistic link delays and simulated traffic. These simulations confirm that this approach can detect worms at early stages of propagation. We believe our approach, with minor adaptations, is of independent interest for use in a number of network applications which benefit from detecting repeated packets, beyond detecting worm propagation. These include detecting network anomalies such as dangerous traffic fluctuations, abusive use of certain services, and some distributed denial-of-service attacks. P. van Oorschot(Ph.D. Waterloo, 1988) is a Professor in the School of Computer Science at Carleton University, and Canada Research Chair in Network and Software Security. He is the founding director of Carleton's Digital Security Group. He has worked in research and development in cryptography and network security, including at Bell-Northern Research (Ottawa), and Entrust Technologies (Ottawa) as VP and Chief Scientist. He is coauthor of the standard reference Handbook of Applied Cryptography. His current research interests include authentication and identity management, network security, software protection, and security infrastructures. J.-M. Robertis a Principal Security Researcher at Alcatel in Ottawa, Ontario. His research interests are network and telecom infrastructure security, focusing mainly on denial-of-service attacks and worm propagation. Previously, Dr. Robert worked as Security Director for the North American Development Center of Gemplus International as well as Professor at the Université du Québec à Chicoutimi. Dr. Robert received a Ph.D. in Computer Science from McGill University. M. Vargas Martinis an Assistant Professor at the University of Ontario Institute of Technology (Oshawa, Canada), with faculty appointments in Business and Information Technology, as well as Engineering and Applied Science. He was previously a post-doctoral researcher at Carleton University supported in part by Alcatel Canada. He holds a Ph.D. in Computer Science (Carleton University, 2002), a Masters degree in Electrical Engineering (Cinvestav, Mexico, 1998), and a Bachelor of Computer Science (Universidad Autónoma de Aguascalientes, Mexico, 1996). His current research interests include network and host-based intrusion detection and reaction, mitigation of denial-of-service (DoS) and distributed DoS attacks, Web modeling and optimization, Internet connectivity, and interconnection protocols.     

Object extraction from an image of wear particles on a complex background

The object extraction of a debris image is an important basic task in identifying wear particles in ferrographic analysis. However, there is some difficulty in object extraction because of noise jamming in the original debris image. In the present study, two methods of image enhancement—weighted mean filtering and adaptive median filtering—were applied in order to improve the image quality. Then, the adaptive thresholding selection method was used, which is based on an improved debris image. Finally, the effective segmentation of the debris image and the automatic extraction of debris objects were realized. At the same time, targetting the characteristics of low proportion of an object in the total image, a novel method of adaptive thresholding selection was put forward, which is based on the Ostu thresholding method. The segmentation results along with the debris image prove that the current method can give more precise and accurate segmentation of objects than the classical methods. The results also showed that methods in the present paper were concise and effective, which provides an important basis for the further study of debris recognition, fault diagnosis, and condition monitoring of machines. The text was submitted by the authors in English. Xianguo Hu (born 1963), PhD, is a professor at the School of Mechanical and Automotive Engineering at the Hefei University of Technology, China. He received his BS and MS in Powder Metallurgy Material and Mechanics (Tribology) from the Hefei University of Technology in 1985 and 1988, respectively. His PhD degree was awarded at Szent Istvan University, Hungary, in 2002. As a visiting scientist, he conducted research at the Technical University of Budapest, Hungary, and the Technical University of Berlin, Germany, from 1994 to 1997. His research areas include wear debris analysis, optimal tribological design, friction and wear mechanisms, etc. He is the author or coauthor of more than 100 published technical papers. Peng Huang (born 1981) is an MS student at the School of Mechanical and Automotive Engineering of Hefei University of Technology, China. His main focus is on wear debris analysis. Shousen Zheng (born 1963) is an associate professor at the School of Engineering, SunYat-Sen University, China. He received his BS, MS, and PhD in Mechanical Engineering from Hefei University of Technology in 1985, 1988, and 2001, respectively. From 1988 to 2004, he was employed at the Department of Mechanical Engineering at the Hefei University of Technology. In 2005, he moved to the current university. His research interests include computer language, auto CAD/CAM, wear debris analysis, etc. He is the author or coauthor of more than 40 published technical papers.     

Detecting feature interaction in CPL

This article addresses the problem of detecting feature interactions in the area of telephony systems design. The proposed approach consists of two phases: filtering and testing. The filtering phase detects possible interactions by identifying incoherencies in a logic specification of the main elements of the features, consisting of preconditions, triggers, results and constraints. If incoherencies are identified, then an interaction is suspected, test cases corresponding to the suspected interaction are generated and testing is applied to see if the interaction actually exists. Two case studies, carried out on established benchmarks, show that this approach gives good results in practice. Nicolas Gorse received a Master of Computer Science from the University of Ottawa, School of Information Technology and Engineering in 2001.He is currently a Ph.D. candidate in the Département d'Informatique et Recherche Opérationnelle of the Université de Montréal. His research interests relate to formal methods and their application in the design and verification of complex electronic systems at high levels of abstraction. Luigi Logrippo received a degree in law from the University of Rome (Italy) in 1961, and in the same year he started a career in computing. He worked for several computer companies and in 1969 he obtained a Master of Computer Science from the University of Manitoba, followed by a Ph.D. of Computer Science from the University of Waterloo in 1974.He was with the University of Ottawa for 29 years, where he was Chair of the Computer Science Department for 7 years. In 2002 he moved to the Université du Québec en Outaouais, Département d'Informatique et Ingénierie, while remaining associated with the University of Ottawa as an Adjunct Professor.His interest area is formal and logic-based methods and their applications in the design of communications systems. For a number of years he worked on the development of tools and methods for the language LOTOS. Current research deals with the formal analysis of advanced communications services made possible by internet telephony, of the policies that govern them, and of their interactions, in application areas such as presence features and e-commerce contracts. Jacques Sincennes is a research programmer/systems analyst at the University of Ottawa, School of Information Technology and Engineering. He has held this position for the past 17 years. He is coauthor of a number of papers and a patent application.An erratum to this article is available at .     

Multiple-Morphs Adaptive Stream Architecture

In modern VLSI technology, hundreds of thousands of arithmetic units fit on a 1cm^2 chip. The challenge is supplying them with instructions and data. Stream architecture is able to solve the problem well. However, the applications suited for typical stream architecture are limited. This paper presents the definition of regular stream and irregular stream, and then describes MASA （Multiple-morphs Adaptive Stream Architecture） prototype system which supports different execution models according to applications＇ stream characteristics. This paper first discusses MASA architecture and stream model, and then explores the features and advantages of MASA through mapping stream applications to hardware. Finally MASA is evaluated by ten benchmarks. The result is encouraging.     

Robust H∞ Filter Design for 2D Discrete Systems in Roesser Model

This paper investigates the robust H∞ filtering problem for uncertain two-dimensional (2D) systems described by the Roesser model. The parameter uncertainties considered in this paper are assumed to be of polytopie type. A new structured polynomi-ally parameter-dependent method is utilized, which is based on homogeneous polynomially parameter-dependent matrices of arbitrary degree. The proposed method includes results in the quadratic framework and the linearly parameter-dependent framework as special cases for zeroth degree and first degree, respectively. A numerical example illustrates the feasibility and advantage of the proposed filter design methods.     

Visual wheel sinkage measurement for planetary rover mobility characterization

Wheel sinkage is an important indicator of mobile robot mobility in natural outdoor terrains. This paper presents a vision-based method to measure the sinkage of a rigid robot wheel in rigid or deformable terrain. The method is based on detecting the difference in intensity between the wheel rim and the terrain. The method uses a single grayscale camera and is computationally efficient, making it suitable for systems with limited computational resources such as planetary rovers. Experimental results under various terrain and lighting conditions demonstrate the effectiveness and robustness of the algorithm. Christopher Brooks is a graduate student in the Mechanical Engineering department of the Massachusetts Institute of Technology. He received his B.S. degree with honor in engineering and applied science from the California Institute of Technology in 2000, and his M.S. degree from the Massachusetts Institute of Technology in 2004. He is a student collaborator on the Mars Exploration Rover science mission. His research interests include mobile robot control, terrain sensing, and their application to improving autonomous robot mobility. He is a member of Tau Beta Pi. Karl Iagnemma is a research scientist in the Mechanical Engineering department of the Massachusetts Institute of Technology. He received his B.S. degree summa cum laude in mechanical engineering from the University of Michigan in 1994, and his M.S. and Ph.D. from the Massachusetts Institute of Technology, where he was a National Science Foundation graduate fellow, in 1997 and 2001, respectively. He has been a visiting researcher at the Jet Propulsion Laboratory. His research interests include rough-terrain mobile robot control and motion planning, robot-terrain interaction, and robotic mobility analysis. He is author of the monograph Mobile Robots in Rough Terrain: Estimation, Motion Planning, and Control with Application to Planetary Rovers (Springer, 2004). He is a member of IEEE and Sigma Xi. Steven Dubowsky received his Bachelor's degree from Rensselaer Polytechnic Institute of Troy, New York in 1963, and his M.S. and Sc.D. degrees from Columbia University in 1964 and 1971. He is currently a Professor of Mechanical Engineering at M.I.T and Director of the Mechanical Engineering Field and Space Robotics Laboratory. He has been a Professor of Engineering and Applied Science at the University of California, Los Angeles, a Visiting Professor at Cambridge University, Cambridge, England, and Visiting Professor at the California Institute of Technology. During the period from 1963 to 1971, he was employed by the Perkin-Elmer Corporation, the General Dynamics Corporation, and the American Electric Power Service Corporation. Dr. Dubowsky's research has included the development of modeling techniques for manipulator flexibility and the development of optimal and self-learning adaptive control procedures for rigid and flexible robotic manipulators. He has authored or co-authored nearly 300 papers in the area of the dynamics, control and design of high performance mechanical and electromechanical systems. Professor Dubowsky is a registered Professional Engineer in the State of California and has served as an advisor to the National Science Foundation, the National Academy of Science/Engineering, the Department of Energy, and the US Army. He is a fellow of the ASME and IEEE and is a member of Sigma Xi and Tau Beta Pi.     

A New Algorithm for Generalized Optimal Discriminant Vectors

A study has been conducted on the algorithm of solving generalized optimal set of discriminant vectors in this paper.This paper proposes an analytical algorithm of solving generalized optimal set of discriminant vectors theoretically for the first time.A lot of computation time can be saved because all the generalized optimal ests of discriminant vectors can be obtained simultaneously with the proposed algorithm,while it needs no iterative operations .The proposed algorithm can yield a much higher recognition rate.Furthermore,the proposed algorithm overcomes the shortcomings of conventional human face recognition algorithms which were effective for small sample size problems only.These statements are supported by the numerical simulation experiments on facial database of ORL.     

Node similarity in the citation graph

Published scientific articles are linked together into a graph, the citation graph, through their citations. This paper explores the notion of similarity based on connectivity alone, and proposes several algorithms to quantify it. Our metrics take advantage of the local neighborhoods of the nodes in the citation graph. Two variants of link-based similarity estimation between two nodes are described, one based on the separate local neighborhoods of the nodes, and another based on the joint local neighborhood expanded from both nodes at the same time. The algorithms are implemented and evaluated on a subgraph of the citation graph of computer science in a retrieval context. The results are compared with text-based similarity, and demonstrate the complementarity of link-based and text-based retrieval. Wangzhong Lu holds a Bachelor's degree from Hefei University of Technology (1993), and a Master's degree from Dalhousie University (2001), both in computer science. From 1993 to 1999 he worked as a developer with China National Computer Software and Technical Service Corp. in Beijing. From 2001 to 2005 he held industrial positions as a senior software architect in Atlantic Canada. He is currently with DST Systems, Charlotte, NC, as a senior data architect. Jeannette Janssen's research area is applied graph theory. She has worked on the problem of frequency assignment in cellular and digital broadcasting networks. Her current interest is in graph theory applied to the World Wide Web and other networked information spaces. Dr. Janssen did her Master's studies at Eindhoven University of Technology in the Netherlands, and her doctorate at Lehigh University, USA. She is currently an associate professor at Dalhousie University, Canada. Evangelos Milios received a diploma in electrical engineering from the National Technical University of Athens, and Master's and Ph.D. degrees in electrical engineering and computer science from the Massachusetts Institute of Technology. He held faculty positions at the University of Toronto and York University. He is currently a professor of computer science at Dalhousie University, Canada, where he was Director of the Graduate Program. He has served on the committees of the ACM Dissertation Award, and the AAAI/SIGART Doctoral Consortium. He has worked on the interpretation of visual and range signals for landmark-based positioning, navigation and map construction in single- and multi-agent robotics. His current research activity is centered on Networked Information Spaces, Web information retrieval, and aquatic robotics. He is a senior member of the IEEE. Nathalie Japkowicz is an associate professor at the School of Information Technology and Engineering of the University of Ottawa. She obtained her Ph.D. from Rutgers University, her M.Sc. from the University of Toronto, and her B.Sc. from McGill University. Prior to joining the University of Ottawa, she taught at Ohio State University and Dalhousie University. Her area of specialization is Machine Learning and her most recent research interests focused on the class imbalance problem. She made over 50 contributions in the form of journal articles, conference articles, workshop articles, magazine articles, technical reports or edited volumes. Yongzheng Zhang obtained a B.E. in computer applications from Southeast University, China, in 1997 and a M.S. in computer science from Dalhousie University in 2002. From 1997 to 1999 he was an instructor and undergraduate advisor at Southeast University. He also worked as a software engineer in Ricom Information and Telecommunications Co. Ltd., China. He is currently a Ph.D. candidate at Dalhousie University. His research interests are in the areas of Information Retrieval, Machine Learning, Natural Language Processing, and Web Mining, particularly centered on Web Document Summarization. A paper based on his Master's thesis received the best paper award at the 2003 Canadian Artificial Intelligence conference.     

S-Club: an overlay-based efficient service discovery mechanism in CROWN Grid

Information service plays a key role in grid system, handles resource discovery and management process. Employing existing information service architectures suffers from poor scalability, long search response time, and large traffic overhead. In this paper, we propose a service club mechanism, called S-Club, for efficient service discovery. In S-Club, an overlay based on existing Grid Information Service (GIS) mesh network of CROWN is built, so that GISs are organized as service clubs. Each club serves for a certain type of service while each GIS may join one or more clubs. S-Club is adopted in our CROWN Grid and the performance of S-Club is evaluated by comprehensive simulations. The results show that S-Club scheme significantly improves search performance and outperforms existing approaches. Chunming Hu is a research staff in the Institute of Advanced Computing Technology at the School of Computer Science and Engineering, Beihang University, Beijing, China. He received his B.E. and M.E. in Department of Computer Science and Engineering in Beihang University. He received the Ph.D. degree in School of Computer Science and Engineering of Beihang University, Beijing, China, 2005. His research interests include peer-to-peer and grid computing; distributed systems and software architectures. Yanmin Zhu is a Ph.D. candidate in the Department of Computer Science, Hong Kong University of Science and Technology. He received his B.S. degree in computer science from Xi’an Jiaotong University, Xi’an, China, in 2002. His research interests include grid computing, peer-to-peer networking, pervasive computing and sensor networks. He is a member of the IEEE and the IEEE Computer Society. Jinpeng Huai is a Professor and Vice President of Beihang University. He serves on the Steering Committee for Advanced Computing Technology Subject, the National High-Tech Program (863) as Chief Scientist. He is a member of the Consulting Committee of the Central Government’s Information Office, and Chairman of the Expert Committee in both the National e-Government Engineering Taskforce and the National e-Government Standard office. Dr. Huai and his colleagues are leading the key projects in e-Science of the National Science Foundation of China (NSFC) and Sino-UK. He has authored over 100 papers. His research interests include middleware, peer-to-peer (P2P), grid computing, trustworthiness and security. Yunhao Liu received his B.S. degree in Automation Department from Tsinghua University, China, in 1995, and an M.A. degree in Beijing Foreign Studies University, China, in 1997, and an M.S. and a Ph.D. degree in computer science and engineering at Michigan State University in 2003 and 2004, respectively. He is now an assistant professor in the Department of Computer Science and Engineering at Hong Kong University of Science and Technology. His research interests include peer-to-peer computing, pervasive computing, distributed systems, network security, grid computing, and high-speed networking. He is a senior member of the IEEE Computer Society. Lionel M. Ni is chair professor and head of the Computer Science and Engineering Department at Hong Kong University of Science and Technology. Lionel M. Ni received the Ph.D. degree in electrical and computer engineering from Purdue University, West Lafayette, Indiana, in 1980. He was a professor of computer science and engineering at Michigan State University from 1981 to 2003, where he received the Distinguished Faculty Award in 1994. His research interests include parallel architectures, distributed systems, high-speed networks, and pervasive computing. A fellow of the IEEE and the IEEE Computer Society, he has chaired many professional conferences and has received a number of awards for authoring outstanding papers.     

In-pipe robot based on selective drive mechanism

This paper presents an in-pipe robot, called MRINSPECT V (Multifunctional Robotic crawler for In-pipe inSPECTion V), which is under development for the inspection of pipelines with a nominal 8-inch inside diameter. To travel freely in every pipeline element, the robot adopts a differential driving mechanism that we have developed. Furthermore, by introducing clutches in transmitting driving power to the wheels, MRINSPECT V is able to select the suitable driving method according to the shape of the pipeline and save the energy to drive in pipelines. In this paper, the critical points in the design and construction of the proposed robot are described with the preliminary results that yield good mobility and increased efficiency. Recommended by Editorial Board member Dong Hwan Kim under the direction of Editor Jae-Bok Song. This work was supported by the Postdoctoral Research Program of Sungkyunkwan University (2008). Se-gon Roh received the B.S., M.S., and Ph.D degrees in Mechatronics Engineering from Sungkyunkwan University, Korea, in 1997, 1999, and 2006 respectively, and is currently a Researcher of the School of Mechanical Engineering also at Sungkyunkwan University. His research interests include mechanism design, applications of mobile robots, and in-pipe robots. Do Wan Kim received the B.S. degree in Mechanical Engineering from Sungkyunkwan University, Korea, in 2007. He is currently working toward a M.S. degree in Mechanical Engineering also at Sungkyunkwan University. His research interests include field robotics, in-pipe robots, and autonomous mobile robots. Jung-Sub Lee received the B.S. degree in Mechanical Engineering in 2008 from Sungkyunkwan University, Suwon, Korea, where he is currently working toward a M.S. degree in mechatronics engineering. His research interests include robot mechanism design, automation, and in-pipe robot. Hyungpil Moon received the B.S. and M.S. degrees in Mechanical Engineering from POSTECH in 1996 and 1998 respectively, and Ph.D. degree in Mechanical Engineering from University of Michigan in 2005. He joined the faculty of School of Mechanical Engineering in Sungkyunkwan University as a Full-time Lecturer in 2008. He was a Post-doctoral fellow at Carnegie Mellon University, Robotics Institute until November 2007. His research interests include distributed manipulation, multiple robot navigation, SLAM, and biomimetic robotics. Hyouk Ryeol Choi received the B.S. degree from Seoul National University in 1984, the M.S. degree from Korea Advanced Technology of Science and Technology (KAIST) in 1986, and the Ph.D. degree from Pohang University of Science and Technology (POSTECH) in 1994, Korea. Since 1995, he has been with Sungkyunkwan University, where he is currently a Professor of the School of Mechanical Engineering. He worked as an Associate Engineer with LG Electronics Central Research Laboratory from 1986 to 1989. From 1993 to 1995, he was with Kyoto University as a grantee of a scholarship from the Japanese Educational Ministry. He visited Advanced Institute of Industrial Science Technology (AIST), Japan as the JSPS Fellow, from 1999 to 2000. He is now an Associate Editor of IEEE Transactions on Robotics, International Journal of Control, System, Automation(IJCAS), and International Journal of Intelligent Service Robots (JISR). His interests include dexterous mechanisms, field applications of robots, and artificial muscle actuator.     

Provisioning QoS guarantee by multipath routing and reservation in Ad hoc networks

In this paper, a QoS multipath source routing protocol (QoS-MSR) is proposed for ad hoc networks. It can collect QoS information through route discovery mechanism of multipath source routing (MSR) and establish QoS route with reserved bandwidth. In order to reserve bandwidth efficiently, a bandwidth reservation approach called the multipath bandwidth splitting reservation (MBSR) is presented, under which the overall bandwidth request is split into several smaller bandwidth requests among multiple paths. In simulations, the anthors introduce Insignia, an in-bind signaling system that supports QoS in ad hoc networks, and extend it to multipath Insignia (M-Insignia) with QoS-MSR and MBSR. The results show that QoS-MSR routing protocol with the MBSR algorithm can improve the call admission ratio of QoS traffic, the packet delivery ratio, and the end-to-end delay of both best-effort traffic and QoS traffic. Therefore, QoS-MSR with MBSR is an efficient mechanism that supports QoS for ad hoc networks.     

Hiding a halftone secret image in two camouflaged halftone images

In this paper, we shall propose a method to hide a halftone secret image into two other camouflaged halftone images. In our method, we adjust the gray-level image pixel value to fit the pixel values of the secret image and two camouflaged images. Then, we use the halftone technique to transform the secret image into a secret halftone image. After that, we make two camouflaged halftone images at the same time out of the two camouflaged images and the secret halftone image. After overlaying the two camouflaged halftone images, the secret halftone image can be revealed by using our eyes. The experimental results included in this paper show that our method is very practicable. The text was submitted by the authors in English. Wei-Liang Tai received his BS degree in Computer Science in 2002 from Tamkang University, Tamsui, Taiwan, and his MS degree in Computer Science and Information Engineering in 2004 from National Chung Cheng University, Chiayi, Taiwan. He is currently a PhD student of Computer Science and Information Engineering at National Chung Cheng University. His research fields are image hiding, digital watermarking, and image compression. Chi-Shiang Chan received his BS degree in Computer Science in 1999 from National Cheng Chi University, Taipei, Taiwan, and his MS degree in Computer Science and Information Engineering in 2001 from National Chung Cheng University, Chiayi, Taiwan. He is currently a PhD student of Computer Science and Information Engineering at National Chung Cheng University. His research fields are image hiding and image compression. Chin-Chen Chang received his BS degree in Applied Mathematics in 1977 and his MS degree in Computer and Decision Sciences in 1979, both from National Tsing Hua University, Hsinchu, Taiwan. He received his PhD in Computer Engineering in 1982 from National Chiao Tung University, Hsinchu, Taiwan. During the academic years of 1980–1983, he was on the faculty of the Department of Computer Engineering at National Chiao Tung University. From 1983–1989, he was on the faculty of the Institute of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan. From 1989 to 2004, he has worked as a professor in the Institute of Computer Science and Information Engineering at National Chung Cheng University, Chiayi, Taiwan. Since 2005, he has worked as a professor in the Department of Information Engineering and Computer Science at Feng Chia University, Taichung, Taiwan. Dr. Chang is a fellow of the IEEE, a fellow of the IEE, and a member of the Chinese Language Computer Society, the Chinese Institute of Engineers of the Republic of China, and the Phi Tau Phi Society of the Republic of China. His research interests include computer cryptography, data engineering, and image compression.     

Periodic use of time-varying state feedbacks for the receding horizon control of bilinear systems

This paper provides a receding horizon control method for SISO bilinear systems in the presence input constraints. Periodically-invariant sets are derived for a bilinear system with respect to a series of time-varying state feedback gains. The dual-mode control strategy is adopted and the periodically-invariant sets are used as target invariant sets. The state feedback gains used to define the target invariant sets are also used to render degrees of freedom to steer the current state into the target set. The region of attraction for the proposed algorithm is enlarged significantly with an extension of the horizon of periodicity while the on-line. Recommended by Editorial Board member Young Soo Suh under the direction of Editor Jae Weon Choi. This project is conducted through the Practical Application Project of Advanced Microsystems Packaging Program of Seoul Technopark, funded by the Ministry of Knowledge Economy. Young Il Lee was born in Korea in 1963. He received his B.Sc., M.S. and Ph.D. in Control and Instrumentation from Seoul National University. He is currently a Professor of the Dept. of Control and Instrumentation, Seoul National University of Technology. He spent two years at Oxford University as a Visiting Research Fellow. Basil Kouvaritakis was born in Athens, Greece in 1948. He was awarded First-Class Honours in Electrical Engineering from the Manchester Institute of Science and Technology, where he also received his Master and Doctorate. He is currently a Professor in Engineering at the Department of Engineering Science and a Tutorial Fellow at St Edmund Hall, Oxford University. Mark Cannon was born in England in 1971. He received M.Eng. and D.Phil. degrees from Oxford University and S.M. from Massachusetts Institute of Technology. He is currently a University Lecturer in Engineering and Fellow of St. Peter’s College, Oxford University.     

Achieving dynamic, multi-commander, multi-mission planning and execution

The Multi-Agent Distributed Goal Satisfaction (MADGS) system facilitates distributed mission planning and execution in complex dynamic environments with a focus on distributed goal planning and satisfaction and mixed-initiative interactions with the human user. By understanding the fundamental technical challenges faced by our commanders on and off the battlefield, we can help ease the burden of decision-making. MADGS lays the foundations for retrieving, analyzing, synthesizing, and disseminating information to commanders. In this paper, we present an overview of the MADGS architecture and discuss the key components that formed our initial prototype and testbed. Eugene Santos, Jr. received the B.S. degree in mathematics and Computer science and the M.S. degree in mathematics (specializing in numerical analysis) from Youngstown State University, Youngstown, OH, in 1985 and 1986, respectively, and the Sc.M. and Ph.D. degrees in computer science from Brown University, Providence, RI, in 1988 and 1992, respectively. He is currently a Professor of Engineering at the Thayer School of Engineering, Dartmouth College, Hanover, NH, and Director of the Distributed Information and Intelligence Analysis Group (DI²AG). Previously, he was faculty at the Air Force Institute of Technology, Wright-Patterson AFB and the University of Connecticut, Storrs, CT. He has over 130 refereed technical publications and specializes in modern statistical and probabilistic methods with applications to intelligent systems, multi-agent systems, uncertain reasoning, planning and optimization, and decision science. Most recently, he has pioneered new research on user and adversarial behavioral modeling. He is an Associate Editor for the IEEE Transactions on Systems, Man, and Cybernetics: Part B and the International Journal of Image and Graphics. Scott DeLoach is currently an Associate Professor in the Department of Computing and Information Sciences at Kansas State University. His current research interests include autonomous cooperative robotics, adaptive multiagent systems, and agent-oriented software engineering. Prior to coming to Kansas State, Dr. DeLoach spent 20 years in the US Air Force, with his last assignment being as an Assistant Professor of Computer Science and Engineering at the Air Force Institute of Technology. Dr. DeLoach received his BS in Computer Engineering from Iowa State University in 1982 and his MS and PhD in Computer Engineering from the Air Force Institute of Technology in 1987 and 1996. Michael T. Cox is a senior scientist in the Intelligent Distributing Computing Department of BBN Technologies, Cambridge, MA. Previous to this position, Dr. Cox was an assistant professor in the Department of Computer Science & Engineering at Wright State University, Dayton, Ohio, where he was the director of Wright State’s Collaboration and Cognition Laboratory. He received his Ph.D. in Computer Science from the Georgia Institute of Technology, Atlanta, in 1996 and his undergraduate from the same in 1986. From 1996 to 1998, he was a postdoctoral fellow in the Computer Science Department at Carnegie Mellon University in Pittsburgh working on the PRODIGY project. His research interests include case-based reasoning, collaborative mixed-initiative planning, intelligent agents, understanding (situation assessment), introspection, and learning. More specifically, he is interested in how goals interact with and influence these broader cognitive processes. His approach to research follows both artificial intelligence and cognitive science directions.     

Adaptive filtering under minimum information divergence criterion

Traditional filtering theory is always based on optimization of the expected value of a suitably chosen function of error, such as the minimum mean-square error (MMSE) criterion, the minimum error entropy (MEE) criterion, and so on. None of those criteria could capture all the probabilistic information about the error distribution. In this work, we propose a novel approach to shape the probability density function (PDF) of the errors in adaptive filtering. As the PDF contains all the probabilistic information, the proposed approach can be used to obtain the desired variance or entropy, and is expected to be useful in the complex signal processing and learning systems. In our method, the information divergence between the actual errors and the desired errors is chosen as the cost function, which is estimated by kernel approach. Some important properties of the estimated divergence are presented. Also, for the finite impulse response (FIR) filter, a stochastic gradient algorithm is derived. Finally, simulation examples illustrate the effectiveness of this algorithm in adaptive system training. Recommended by Editorial Board member Naira Hovakimyan under the direction of Editor Jae Weon Choi. This work was supported in part by the National Natural Science Foundation of China under grants 50577037 and 60604010. Badong Chen received the B.S. and M.S. degrees in Control Theory and Engineering from Chongqing University, Chongqing, China, in 1997 and 2003, respectively, and the Ph.D. degree in Computer Science and Technology from Tsinghua University, Beijing China, in 2008. He is currently a Postdoctor of the Institute of Manufacturing Engineering, Department of Precision Instruments and Mechanology, Tsinghua University, Beijing, China. His research interests are in signal processing, adaptive control, and information theoretic aspects of control systems. Yu Zhu received the B.S. of Radio Electronics in 1983 at Beijing Normal University, and the M.S. of Computer Applications in 1993, and the Ph.D. of Mechanical Design and Theory in 2001 at China University of Mining & Technology. He is now a Professor of the Institute of Manufacturing Engineering of Department of Precision and Mechanology of Tsinghua University. His current research interests are parallel machanism and theory, two photon micro-fabrication, ultra-precision motion system and motion control. Jinchun Hu received the Ph.D. in Control Science and Engineering from Nanjing University of Science and Technology, Nanjing, China, in 1998. Since then, he has been a postdoctoral researcher in Nanjing University of Aeronautics and Astronautics in 1999 and Tsinghua University in 2002 respectively. His research interests are in flight control, aerial Robot and intelligent control. Dr. Hu is currently an Associate Professor of the Department of Computer Science and Technology of Tsinghua University, Beijing, China. Zengqi Sun received the B.S. degree from the Department of Automatic Control, Tsinghua University, Beijing, China, in 1966 and the Ph.D. degree in Control Engineering from the Chalmas University of Technology, Sweden, in 1981. He is currently a Professor of the Department of Computer Science and Technology, Tsinghua University, Beijing, China. He is the author or coauthor of more than 100 paper and eight books on control and robotics. His research interests include robotics, intelligent control, fuzzy system, neural networks, and evolutionary computation.     

Two fusion predictors for discrete-time linear systems with different types of observations

New fusion predictors for linear dynamic systems with different types of observations are proposed. The fusion predictors are formed by summation of the local Kalman filters/predictors with matrix weights depending only on time instants. The relationship between fusion predictors is established. Then, the accuracy and computational efficiency of the fusion predictors are demonstrated on the first-order Markov process and the GMTI model with multisensor environment. Recommended by Editorial Board member Lucy Y. Pao under the direction of Editor Young Il Lee. This work was partially supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korean government (MOST), No. R01-2007-000-20227-0 and the Center for Distributed Sensor Network at GIST. Ha-Ryong Song received the B.S. degree in Control and Instrumentation Engineering from the Chosun University, Korea, in 2006, the M.S. degree in School of Information and Mechatronics from the Gwangju Institute of Science and Technology, Korea, in 2007. He is currently a Ph.D. candidate in Gwangju Institute of Science and Technology. His research interests include estimation, target tracking systems, data fusion, nonlinear filtering. Moon-Gu Jeon received the B.S. degree in architectural engineering from the Korea University, Korea in 1988. He then received both the M.S. and Ph.D. degrees in computer science and scientific computation from the University of Minnesota in 1999 and 2001, respectively. Currently, he is an Associate Professor at the School of Information and Mechatronics of the Gwangju Institute of Science and Technology (GIST). His current research interests are in machine learning and pattern recognition and evolutionary computation. Tae-Sun Choi received the B.S. degree in Electrical Engineering from the Seoul National University, Seoul, Korea, in 1976, the M.S. degree in Electrical Engineering from the Korea Advanced Institute of Science and Technology, Seoul, Korea, in 1979, and the Ph.D. degree in Electrical Engineering from the State University of New York at Stony Brook, in 1993. He is currently a Professor in the School of Information and Mechatronics at Gwangju Institute of Science and Technology, Korea. His research interests include image processing, machine/robot vision, and visual communications. Vladimir Shin received the B.Sc. and M.Sc. degrees in Applied Mathematics from Moscow State Aviation Institute, in 1977 and 1979, respectively. In 1985 he received the Ph.D. degree in Mathematics at the Institute of Control Science, Russian Academy of Sciences, Moscow. He is currently an Associate Professor at Gwangju Institute of Science and Technology, South Korea. His research interests include estimation, filtering, tracking, data fusion, stochastic control, identification, and other multidimensional data processing methods.     

Super-Resolution Reconstruction of Image Sequence Using Multiple Motion Estimation Fusion

Super-resolution reconstruction algorithm produces a high-resolution image from a low-resolution image sequence. The accuracy and the stability of the motion estimation (ME) are essential for the whole restoration. In this paper, a new super-resolution reconstruction algorithm is developed using a robust ME method, which fuses multiple estimated motion vectors within the sequence. The new algorithm has two major improvements compared with the previous research. First, instead of only two frames, the whole sequence is used to obtain a more accurate and stable estimation of the motion vector of each frame; second, the reliability of the ME is quantitatively measured and introduced into the cost function of the reconstruction algorithm. The algorithm is applied to both synthetic and real sequences, and the results are presented in the paper.     

Secure web transaction with anonymous mobile agent over internet

A major problem of mobile agents is their apparent mability to authenticate transactions in hostile environments,In this paper,a new secure anonymous mobile agent scheme is proposed for the prevention of agent tempering without compromising the mobility or autonomy of the agent.in the scheme,a mobile agent can produce valid signature on website‘s bid(it means to transact a contact with the web site)on behalf of its customer ,without-revealing the customer‘s real private key.In addition,the anonymity of the customer is also achieved when its agent tansacts with the websites.Furthermore,the customer who issues a malicious agent or denies the transaction can be identified and detected by Agent Management Center(AMC).Thererfore,the scheme is practical in the future elecronic commerce over Internet.