Lower Bound Estimation of Hardware Resources for Scheduling in High-Level Synthesis

In high-level synthesis of VLSI circuits,good lower bound prediction can efficiently narrow down the large space of possible designs.Previous approaches predict the lower bound by relaxing or even ignoring the precedence constraints of the data flow graph (DFG),and result in inaccuracy of the lower bound.The loop folding and conditional branch were also not considered,In this paper,a new stepwise refinement algorithm is proposed.which takes consideration of precedence constraints of the DFG to estimate the lower bound of hardware resources under time constratints,Processing techniques to handle multi-cycle,chaining,pipelining,as well as loop folding and mutual exclusion among conditional branches are also incorporated in the algorithm.Experimental results show that the algorithm can produce a very tight and close to optimal lower bound in reasonable computation time.     

Best-Effort Patching for Multicast True VoD Service

A multicast Video-on-Demand (VoD) system allows clients to share a server stream by batching their requests, and hence, improves channel utilization. However, it is very difficult to equip such a VoD system with full support for interactive VCR functions which are important to a growing number of Internet applications. In order to eliminate service (admission) latency, patching was proposed to enable an existing multicast session to dynamically add new clients, and requests can be served without delay if patching channels are available. A true VoD (TVoD) service should support not only zero-delay client admission but also continuous VCR-like interactivity. However, the conventional patching is only suitable for admission control. We propose a new patching scheme, called Best-Effort Patching (BEP), that offers a TVoD service in terms of both request admission and VCR interactivity. Moreover, by using a novel dynamic merging algorithm, BEP significantly improves the efficiency of TVoD interactivity, especially for popular videos. We also model and evaluate the efficiency of the dynamic merging algorithm. It is shown that BEP outperforms the conventional TVoD interaction protocols.Huadong Ma received the B.S. degree in Mathematics from Henan Normal University in 1984, the M.S. degree in Computer Science from Shenyang Institute of Computing Technology, Chinese Academy of Science (CAS) in 1990 and the Ph.D. degree in Computer Science from Institute of Computing Technology, CAS, in 1995.He is a Professor with the School of Computer Science & Technology, Beijing University of Posts and Telecommunications, China. He visited UNU/IIST as research fellow in 1998 and 1999, respectively. From 1999 to 2000, he held a visiting position in the Real-Time Computing Laboratory in the Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor. His current research focuses on multimedia, networking, e-commerce and computergraphics, and he has published over 70papers and 3 books on these fields. He is member of IEEE and ACM.Kang G. Shin received the B.S. degree in Electronics Engineering from Seoul National University, Korea, in 1970, and both the M.S. and Ph.D. degrees in Electrical Engineering from Cornell University, Ithaca, New York in 1976 and 1978, respectively.He is the Kevin and Nancy OConnor Professor of Computer Scienceand Founding Director of the Real-Time Computing Laboratory in the Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor. His current research focuses on QoS-sensitive networking and computing as well as on embedded real-time OS, middleware and applications, all with emphasis on timeliness and dependability. He has supervised the completion of 49 Ph.D. theses, and authored/coauthored over 600 technical papers and numerous book chapters in the areas of distributed real-time computing and control, computer networking, fault-tolerant computing, and intelligent manufacturing. Dr. Shin is Fellow of IEEE and ACM, and member of the Korean Academy of Engineering.Weibiao Wu received the Ph.D. degree in statistics from the University of Michigan, Ann Arbor in 2001. He is currently an Assistant professor of statistics at the University of Chicago. His research interests include probabilistic network modelling and simulation, data-base compression, asymptotic theory and statistical inference of stochastic processes.     

An agent architecture for strategy-centric adaptive QoS control in flexible videoconference system

In this paper, we propose an agent architecture to improve flexibility of a videoconference system with strategy-centric adaptive QoS (Quality of Service) control mechanism. The proposed architecture realizes more flexibility by changing their QoS control strategies dynamically. To switch the strategies, system considers the properties of problems occurred on QoS and status of problem solving process. This architecture is introduced as a part of knowledge base of agent that deals with cooperation between software module of videoconference systems. We have implemented the mechanism, and our prototype system shows its capability of flexible problem solving against the QoS degradation, along with other possible problems within the given time limitation. Thus we confirmed that the proposed architecture can improve its flexibility of a videoconference system compared to traditional systems. Takuo Suganuma, Dr.Eng.: He is a research associate of Research Institute of Electrical Communication of Tohoku University. He received a Dr.Eng. degree from Chiba Institute of Technology in 1997. His research interests include agent-based computing and design methodology for distributed systems. He is a member of IPSJ, IEICE and IEEE. SungDoke Lee: He is a Ph.D. Student in the Graduate School of Information Sciences in Tohoku University. He received his MEng degree at Chonbuk National University, Korea in 1991. His research interests include Flexible Network and Knowledge of Agent. Tetsuo Kinoshita, Dr.Eng.: He is an associate professor of Research Institute of Electrical Communication of Tohoku University. He received a Dr.Eng. degree in information engineering from Tohoku University, Japan. His research interests include knowledge engineering, cooperative distributed processing and agent-based computing. He received the the IPSJ Best Paper Award in 1997, etc. He is a member of IPSJ, IEICE, JSAI, AAAI, ACM and IEEE. Norio Shiratori, Dr.Eng.: After receiving his Dr.Eng degree at Tohoku University, he joined the Research Institute of Electrical Communication of Tohoku University in 1977, and is now a professor at the same University. He has been engaged in research on distributed processing system, and flexible intelligent network. He received the 25th Anniversary of IPSJ Memorial Prize-Winning Paper Award in 1985, the 6th Telecommunications Advancement Foundation Incorporation Award in 1991, the Best Paper Award of ICOIN-9 in 1994, the IPSJ Best Paper Award in 1997, etc. He has been named a Fellow of the IEEE for his contributions to the field of computer communication networks.     

Efficient mining of association rules by reducing the number of passes over the database

This paper introduces a new algorithm of mining association rules.The algorithm RP counts the itemsets with different sizes in the same pass of scanning over the database by dividing the database into m partitions.The total number of pa sses over the database is only(k 2m-2)/m,where k is the longest size in the itemsets.It is much less than k .     

BCL—3：Aigh Performance Basic Communication Protocol for Commodity Superserver DAWNING—3000

This paper introduces the design and implemetation of BCL-3,a high performance low-level communication software running on a cluster of SMPs(CLUMPS) called DAWNING-3000,BCL-3 provides flexible and sufficient functionality to fulfill the communication requirements of fundamental system software developed for DAWNING-3000 while guaranteeing security,scalability,and reliability,Important features of BCL-3 are presented in the paper,including special support for SMP and heterogeneous network environment,semiuser-level communication,reliable and ordered data transfer and scalable flow control,The performance evaluation of BCL-3 over Myrinet is also given.     

Leakage current estimation of CMOS circuit with stack effect

Leakage current of CMOS circuit increases dramatically with the technology scaling down and has become a critical issue of high performance system. Subthreshold, gate and reverse biased junction band-to-band tunneling (BTBT) leakages are considered three main determinants of total leakage current. Up to now, how to accurately estimate leakage current of large-scale circuits within endurable time remains unsolved, even though accurate leakage models have been widely discussed. In this paper, the authors first dip into the stack effect of CMOS technology and propose a new simple gate-level leakage current model. Then, a table-lookup based total leakage current simulator is built up according to the model. To validate the simulator, accurate leakage current is simulated at circuit level using popular simulator HSPICE for comparison. Some further studies such as maximum leakage current estimation, minimum leakage current generation and a high-level average leakage current macromodel are introduced in detail. Experiments on ISCAS85 and ISCAS89 benchmarks demonstrate that the two proposed leakage current estimation methods are very accurate and efficient.     

An index and retrieval framework integrating perceptive features and semantics for multimedia databases

Typically, in multimedia databases, there exist two kinds of clues for query: perceptive features and semantic classes. In this paper, we propose a novel framework for multimedia databases index and retrieval integrating the perceptive features and semantic classes to improve the speed and the precision of the content-based multimedia retrieval (CBMR). We develop a semantics supervised clustering based index approach (briefly as SSCI): the entire data set is divided hierarchically into many clusters until the objects within a cluster are not only close in the perceptive feature space but also within the same semantic class, and then an index term is built for each cluster. Especially, the perceptive feature vectors in a cluster are organized adjacently in disk. So the SSCI-based nearest-neighbor (NN) search can be divided into two phases: first, the indexes of all clusters are scanned sequentially to get the candidate clusters with the smallest distances from the query example; second, the original feature vectors within the candidate clusters are visited to get search results. Furthermore, if the results are not satisfied, the SSCI supports an effective relevance feedback (RF) search: users mark the positive and negative samples regarded a cluster as unit instead of a single object; then the Bayesian classifiers on perceptive features and that on semantics are used respectively to adjust retrieval similarity distance. Our experiments show that SSCI-based searching was faster than VA⁺-based searching; the quality of the search result based on SSCI was better than that of the sequential search in terms of semantics; and a few cycles of the RF by the proposed approach can improve the retrieval precision significantly.

Blending parametric patches with subdivision surfaces

In this paper the problem of blending parametric surfaces using subdivision patches is discussed.A new approach,named removing-boundary,is presented to generate piecewise-smooth subdivision surfaces through discarding the outmost quadrilaterals of the open meshes derived by each subdivision step.Then the approach is employed both to blend parametric bicubic B-spline surfaces and to fill n-sided holes.It is easy to produce piecewise-smooth subdivision surfaces with both convex and concave corners on the boundary,and limit surfaces are guaranteed to be C^2 continuous on the boundaries except for a few singular points by the removing-boundary approach Thus the blending method is very efficient and the blending surface generated is of good effect.     

Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers

In this paper, we present a new method for fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. The proposed method considers the centroid points and the standard deviations of generalized trapezoidal fuzzy numbers for ranking generalized trapezoidal fuzzy numbers. We also use an example to compare the ranking results of the proposed method with the existing centroid-index ranking methods. The proposed ranking method can overcome the drawbacks of the existing centroid-index ranking methods. Based on the proposed ranking method, we also present an algorithm to deal with fuzzy risk analysis problems. The proposed fuzzy risk analysis algorithm can overcome the drawbacks of the one we presented in [7]. Shi-Jay Chen was born in 1972, in Taipei, Taiwan, Republic of China. He received the B.S. degree in information management from the Kaohsiung Polytechnic Institute, Kaohsiung, Taiwan, and the M.S. degree in information management from the Chaoyang University of Technology, Taichung, Taiwan, in 1997 and 1999, respectively. He received the Ph.D. degree at the Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, in October 2004. His research interests include fuzzy systems, multicriteria fuzzy decisionmaking, and artificial intelligence. Shyi-Ming Chen was born on January 16, 1960, in Taipei, Taiwan, Republic of China. He received the Ph.D. degree in Electrical Engineering from National Taiwan University, Taipei, Taiwan, in June 1991. From August 1987 to July 1989 and from August 1990 to July 1991, he was with the Department of Electronic Engineering, Fu-Jen University, Taipei, Taiwan. From August 1991 to July 1996, he was an Associate Professor in the Department of Computer and Information Science, National Chiao Tung University, Hsinchu, Taiwan. From August 1996 to July 1998, he was a Professor in the Department of Computer and Information Science, National Chiao Tung University, Hsinchu, Taiwan. From August 1998 to July 2001, he was a Professor in the Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. Since August 2001, he has been a Professor in the Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. He was a Visiting Scholar in the Department of Electrical Engineering and Computer Science, University of California, Berkeley, in 1999. He was a Visiting Scholar in the Institute of Information Science, Academia Sinica, Republic of China, in 2003. He has published more than 250 papers in referred journals, conference proceedings and book chapters. His research interests include fuzzy systems, information retrieval, knowledge-based systems, artificial intelligence, neural networks, data mining, and genetic algorithms. Dr. Chen has received several honors and awards, including the 1994 Outstanding Paper Award o f the Journal of Information and Education, the 1995 Outstanding Paper Award of the Computer Society of the Republic of China, the 1995 and 1996 Acer Dragon Thesis Awards for Outstanding M.S. Thesis Supervision, the 1995 Xerox Foundation Award for Outstanding M.S. Thesis Supervision, the 1996 Chinese Institute of Electrical Engineering Award for Outstanding M.S. Thesis Supervision, the 1997 National Science Council Award, Republic of China, for Outstanding Undergraduate Student's Project Supervision, the 1997 Outstanding Youth Electrical Engineer Award of the Chinese Institute of Electrical Engineering, Republic of China, the Best Paper Award of the 1999 National Computer Symposium, Republic of China, the 1999 Outstanding Paper Award of the Computer Society of the Republic of China, the 2001 Institute of Information and Computing Machinery Thesis Award for Outstanding M.S. Thesis Supervision, the 2001 Outstanding Talented Person Award, Republic of China, for the contributions in Information Technology, the 2002 Institute of information and Computing Machinery Thesis Award for Outstanding M.S. Thesis Supervision, the Outstanding Electrical Engineering Professor Award granted by the Chinese Institute of Electrical Engineering (CIEE), Republic of China, the 2002 Chinese Fuzzy Systems Association Best Thesis Award for Outstanding M.S. Thesis Supervision, the 2003 Outstanding Paper Award of the Technological and Vocational Education Society, Republic of China, the 2003 Acer Dragon Thesis Award for Outstanding Ph.D. Dissertation Supervision, the 2005 “Operations Research Society of Taiwan” Award for Outstanding M.S. Thesis Supervision, the 2005 Acer Dragon Thesis Award for Outstanding Ph.D. Dissertation Supervision, the 2005 Taiwan Fuzzy Systems Association Award for Outstanding Ph.D. Dissertation Supervision, and the 2006 “Operations Research Society of Taiwan” Award for Outstanding M.S. Thesis Supervision. Dr. Chen is currently the President of the Taiwanese Association for Artificial Intelligence (TAAI). He is a Senior Member of the IEEE, a member of the ACM, the International Fuzzy Systems Association (IFSA), and the Phi Tau Phi Scholastic Honor Society. He was an administrative committee member of the Chinese Fuzzy Systems Association (CFSA) from 1998 to 2004. He is an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics - Part C, an Associate Editor of the IEEE Computational Intelligence Magazine, an Associate Editor of the Journal of Intelligent & Fuzzy Systems, an Editorial Board Member of the International Journal of Applied Intelligence, an Editor of the New Mathematics and Natural Computation Journal, an Associate Editor of the International Journal of Fuzzy Systems, an Editorial Board Member of the International Journal of Information and Communication Technology, an Editorial Board Member of the WSEAS Transactions on Systems, an Editor of the Journal of Advanced Computational Intelligence and Intelligent Informatics, an Associate Editor of the WSEAS Transactions on Computers, an Editorial Board Member of the International Journal of Computational Intelligence and Applications, an Editorial Board Member of the Advances in Fuzzy Sets and Systems Journal, an Editor of the International Journal of Soft Computing, an Editor of the Asian Journal of Information Technology, an Editorial Board Member of the International Journal of Intelligence Systems Technologies and Applications, an Editor of the Asian Journal of Information Management, an Associate Editor of the International Journal of Innovative Computing, Information and Control, and an Editorial Board Member of the International Journal of Computer Applications in Technology. He was an Editor of the Journal of the Chinese Grey System Association from 1998 to 2003. He is listed in International Who's Who of Professionals, Marquis Who's Who in the World, and Marquis Who's Who in Science and Engineering.     

Genetic operators for combinatorial optimization in TSP and microarray gene ordering

This paper deals with some new operators of genetic algorithms and demonstrates their effectiveness to the traveling salesman problem (TSP) and microarray gene ordering. The new operators developed are nearest fragment operator based on the concept of nearest neighbor heuristic, and a modified version of order crossover operator. While these result in faster convergence of Genetic Algorithm (GAs) in finding the optimal order of genes in microarray and cities in TSP, the nearest fragment operator can augment the search space quickly and thus obtain much better results compared to other heuristics. Appropriate number of fragments for the nearest fragment operator and appropriate substring length in terms of the number of cities/genes for the modified order crossover operator are determined systematically. Gene order provided by the proposed method is seen to be superior to other related methods based on GAs, neural networks and clustering in terms of biological scores computed using categorization of the genes. Shubhra Sankar Ray is a Visiting Research Fellow at the Center for Soft Computing Research: A National Facility, Indian Statistical Institute, Kolkata, India. He received the M.Sc. in Electronic Science and M.Tech in Radiophysics & Electronics from University of Calcutta, Kolkata, India, in 2000 and 2002, respectively. Till March 2006, he had been a Senior Research Fellow of the Council of Scientific and Industrial Research (CSIR), New Delhi, India, working at Machine Intelligence Unit, Indian Statistical Institute, India. His research interests include bioinformatics, evolutionary computation, neural networks, and data mining. Sanghamitra Bandyopadhyay is an Associate Professor at Indian Statistical Institute, Calcutta, India. She did her Bachelors in Physics and Computer Science in 1988 and 1992 respectively. Subsequently, she did her Masters in Computer Science from Indian Institute of Technology (IIT), Kharagpur in 1994 and Ph.D in Computer Science from Indian Statistical Institute, Calcutta in 1998. She has worked in Los Alamos National Laboratory, Los Alamos, USA, in 1997, as a graduate research assistant, in the University of New South Wales, Sydney, Australia, in 1999, as a post doctoral fellow, in the Department of Computer Science and Engineering, University of Texas at Arlington, USA, in 2001 as a faculty and researcher, and in the Department of Computer Science and Engineering, University of Maryland Baltimore County, USA, in 2004 as a visiting research faculty. Dr. Bandyopadhyay is the first recipient of Dr. Shanker Dayal Sharma Gold Medal and Institute Silver Medal for being adjudged the best all round post graduate performer in IIT, Kharagpur in 1994. She has received the Indian National Science Academy (INSA) and the Indian Science Congress Association (ISCA) Young Scientist Awards in 2000, as well as the Indian National Academy of Engineering (INAE) Young Engineers' Award in 2002. She has published over ninety articles in international journals, conference and workshop proceedings, edited books and journal special issues and served as the Program Co-Chair of the 1st International Conference on Pattern Recognition and Machine Intelligence, 2005, Kolkata, India, and as the Tutorial Co-Chair, World Congress on Lateral Computing, 2004, Bangalore, India. She is on the editorial board of the International Journal on Computational Intelligence. Her research interests include Evolutionary and Soft Computation, Pattern Recognition, Data Mining, Bioinformatics, Parallel & Distributed Systems and VLSI. Sankar K. Pal (www.isical.ac.in/∼sankar) is the Director and Distinguished Scientist of the Indian Statistical Institute. He has founded the Machine Intelligence Unit, and the Center for Soft Computing Research: A National Facility in the Institute in Calcutta. He received a Ph.D. in Radio Physics and Electronics from the University of Calcutta in 1979, and another Ph.D. in Electrical Engineering along with DIC from Imperial College, University of London in 1982. He worked at the University of California, Berkeley and the University of Maryland, College Park in 1986-87; the NASA Johnson Space Center, Houston, Texas in 1990-92 & 1994; and in US Naval Research Laboratory, Washington DC in 2004. Since 1997 he has been serving as a Distinguished Visitor of IEEE Computer Society (USA) for the Asia-Pacific Region, and held seve ral visiting positions in Hong Kong and Australian universities. Prof. Pal is a Fellow of the IEEE, USA, Third World Academy of Sciences, Italy, International Association for Pattern recognition, USA, and all the four National Academies for Science/Engineering in India. He is a co-author of thirteen books and about three hundred research publications in the areas of Pattern Recognition and Machine Learning, Image Processing, Data Mining and Web Intelligence, Soft Computing, Neural Nets, Genetic Algorithms, Fuzzy Sets, Rough Sets, and Bioinformatics. He has received the 1990 S.S. Bhatnagar Prize (which is the most coveted award for a scientist in India), and many prestigious awards in India and abroad including the 1999 G.D. Birla Award, 1998 Om Bhasin Award, 1993 Jawaharlal Nehru Fellowship, 2000 Khwarizmi International Award from the Islamic Republic of Iran, 2000–2001 FICCI Award, 1993 Vikram Sarabhai Research Award, 1993 NASA Tech Brief Award (USA), 1994 IEEE Trans. Neural Networks Outstanding Paper Award (USA), 1995 NASA Patent Application Award (USA), 1997 IETE-R.L. Wadhwa Gold Medal, the 2001 INSA-S.H. Zaheer Medal, and 2005-06 P.C. Mahalanobis Birth Centenary Award (Gold Medal) for Lifetime Achievement . Prof. Pal is an Associate Editor of IEEE Trans. Pattern Analysis and Machine Intelligence, IEEE Trans. Neural Networks [1994–98, 2003–06], Pattern Recognition Letters, Neurocomputing (1995–2005), Applied Intelligence, Information Sciences, Fuzzy Sets and Systems, Fundamenta Informaticae, Int. J. Computational Intelligence and Applications, and Proc. INSA-A; a Member, Executive Advisory Editorial Board, IEEE Trans. Fuzzy Systems, Int. Journal on Image and Graphics, and Int. Journal of Approximate Reasoning; and a Guest Editor of IEEE Computer.     

Dyanmic Data Prefetching in Home—Based Software DSMs

A major overhead in software DSM(Distributed Shared Memory)is the cost of remote memory accesses necessitated by the protocol as well as induced by false sharing.This paper introduces a dynamic prefetching method implemented in the JIAJIA software DSM to reduce system overhead caused by remote accesses.The prefetching method records the interleaving string of INV(invalidation)and GETP (getting a remote page)operations for each cached page and analyzes the periodicity of the string when a page is invalidated on a lock or barrier.A prefetching request is issued after the lock or barrier if the periodicity analysis indicates that GETP will be the next operation in the string.Multiple prefetching requests are merged into the same message if they are to the same host,Performance evaluation with eight well-accepted benchmarks in a cluster of sixteen PowerPC workstations shows that the prefetching scheme can significantly reduce the page fault overhead and as a result achieves a performance increase of 15%-20% in three benchmarks and around 8%-10% in another three.The average extra traffic caused by useless prefetches is only 7%-13% in the evaluation.     

A New Approach to Humanitarian Demining

Landmines can deprive whole areas of valuable resources, and continue to kill and cause injuries years after the end of armed conflicts. Armored vehicles are used for mine clearance, but with limited reliability. The final inspection of minefields is still performed by human deminers exposed to potentially fatal accidents. The aim of this research is to introduce automation as a way to improve the final level of humanitarian demining. This paper addresses mobility and manipulation, while sensing, communication and visualization shall be discussed in detail in a subsequent paper. After analyzing the merits and limitations of previous works, a new approach to tele-operated demining is considered, using off-road buggies equipped with combustion engines, and taking into account actual field requirements. Control of the automated buggies on rough terrain is also discussed, as well as the development of a new weight-balanced manipulator for landmine clearance operations.Paulo Debenest received the B. Eng. degree in mechanical engineering (major in automation and systems) from Polytechnic School of the University of São Paulo (EPUSP), Brazil, in 1998, and the M. Eng. degree in mechanical and aerospace engineering from Tokyo Institute of Technology (Tokyo Tech), Japan, in 2002. He is currently working toward the Ph.D. degree in mechanical science engineering at Tokyo Tech and member of IEEE. His current research activities include development of demining robots and mechanical design of machines for field applications.Edwardo F. Fukushima is an assistant professor in the Department of Mechanical and Aerospace Engineering at Tokyo Institute of Technology (Tokyo Tech). He received the B. Eng. degree in electric engineering (major in electronics and telecommunications) from Federal Center of Technological Education of Paraná (CEFET-PR), Brazil, in 1989, and M. Eng. degree in mechanical science engineering from Tokyo Tech in 1993. In 1994 he became a research associate in the same institute. During Sept.–Dec. 2001 he has been a Visiting Researcher at Stanford University, and during Aug.–Sept. 2004 Visiting Scientist at University of Zurich. He is also member of RSJ. His current research activities include development of demining robots, design of controllers for intelligent robots, and development of new brushless motors and drives.Yuki Tojo is a masters course student in the Department of Mechanical and Aerospace Engineering at Tokyo Institute of Technology (Tokyo Tech). He received the B. Eng. degree in mechanical and aerospace engineering from Tokyo Tech in 2003. His research interests include design and control of weight-compensated manipulator on mobile platform. He is also member of RSJ.Shigeo Hirose was born in Tokyo in 1947. He received his B.Eng. Degree with First Class Honors in Mechanical Engineering from Yokohama National University in 1971, and his M. Eng. and Ph.D. Eng. Degrees in Control Engineering from Tokyo Institute of Technology in 1973 and 1976, respectively. From 1976 to 1979 he was a Research Associate, and from 1979 to 1992 an Associate Professor. Since 1992 he has been a Professor in the Department of Mechanical and Aerospace Engineering at the Tokyo Institute of Technology. Since 2002, he has been Honorary Professor in Shengyang Institute of Technology, the Chinese Academy of Sciences. Fellow of JSME and IEEE. He is engaged in creative design of robotic systems. Prof. Hirose has been awarded more than twenty prizes.     

An Efficient VLSI Architecture for Motion Compensation of AVS HDTV Decoder

In the part 2 of advanced Audio Video coding Standard （AVS-P2）, many efficient coding tools are adopted in motion compensation, such as new motion vector prediction, symmetric matching, quarter precision interpolation, etc. However, these new features enormously increase the computational complexity and the memory bandwidth requirement, which make motion compensation a difficult component in the implementation of the AVS HDTV decoder. This paper proposes an efficient motion compensation architecture for AVS-P2 video standard up to the Level 6.2 of the Jizhun Profile. It has a macroblock-level pipelined structure which consists of MV predictor unit, reference fetch unit and pixel interpolation unit. The proposed architecture exploits the parallelism in the AVS motion compensation algorithm to accelerate the speed of operations and uses the dedicated design to optimize the memory access. And it has been integrated in a prototype chip which is fabricated with TSMC 0.18-#m CMOS technology, and the experimental results show that this architecture can achieve the real time AVS-P2 decoding for the HDTV 1080i （1920 - 1088 4 ： 2 ： 0 60field/s） video. The efficient design can work at the frequency of 148.5MHz and the total gate count is about 225K.     

POTENTIAL: A highly adaptive core of parallel database system

POTENTIAL is a virtual database machine based on general computing platforms,especially parllel computing platforms.It provides a complete solution to high-performance database systems by a ‘virtual processor virtual data bus virtual memory‘ architecture.Virtual processors manage all CPU resources in the system,on which various operations are running.Virtual data bus is responsible for the management of data transmission between associated operations.which forms the higes of the entire system.Virtual memory provides efficient data storage and buffering mechanisms that conform to data reference behaviors in database systems.The architecture of POTENTIAL is very clear and has many good features,including high efficiency,high scalability,high extensibility,high portability,etc.     

On High Dimensional Projected Clustering of Data Streams

The data stream problem has been studied extensively in recent years, because of the great ease in collection of stream data. The nature of stream data makes it essential to use algorithms which require only one pass over the data. Recently, single-scan, stream analysis methods have been proposed in this context. However, a lot of stream data is high-dimensional in nature. High-dimensional data is inherently more complex in clustering, classification, and similarity search. Recent research discusses methods for projected clustering over high-dimensional data sets. This method is however difficult to generalize to data streams because of the complexity of the method and the large volume of the data streams.In this paper, we propose a new, high-dimensional, projected data stream clustering method, called HPStream. The method incorporates a fading cluster structure, and the projection based clustering methodology. It is incrementally updatable and is highly scalable on both the number of dimensions and the size of the data streams, and it achieves better clustering quality in comparison with the previous stream clustering methods. Our performance study with both real and synthetic data sets demonstrates the efficiency and effectiveness of our proposed framework and implementation methods.Charu C. Aggarwal received his B.Tech. degree in Computer Science from the Indian Institute of Technology (1993) and his Ph.D. degree in Operations Research from the Massachusetts Institute of Technology (1996). He has been a Research Staff Member at the IBM T. J. Watson Research Center since June 1996. He has applied for or been granted over 50 US patents, and has published over 75 papers in numerous international conferences and journals. He has twice been designated Master Inventor at IBM Research in 2000 and 2003 for the commercial value of his patents. His contributions to the Epispire project on real time attack detection were awarded the IBM Corporate Award for Environmental Excellence in 2003. He has been a program chair of the DMKD 2003, chair for all workshops organized in conjunction with ACM KDD 2003, and is also an associate editor of the IEEE Transactions on Knowledge and Data Engineering Journal. His current research interests include algorithms, data mining, privacy, and information retrieval.Jiawei Han is a Professor in the Department of Computer Science at the University of Illinois at Urbana–Champaign. He has been working on research into data mining, data warehousing, stream and RFID data mining, spatiotemporal and multimedia data mining, biological data mining, social network analysis, text and Web mining, and software bug mining, with over 300 conference and journal publications. He has chaired or served in many program committees of international conferences and workshops, including ACM SIGKDD Conferences (2001 best paper award chair, 1996 PC co-chair), SIAM-Data Mining Conferences (2001 and 2002 PC co-chair), ACM SIGMOD Conferences (2000 exhibit program chair), International Conferences on Data Engineering (2004 and 2002 PC vice-chair), and International Conferences on Data Mining (2005 PC co-chair). He also served or is serving on the editorial boards for Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, Journal of Computer Science and Technology, and Journal of Intelligent Information Systems. He is currently serving on the Board of Directors for the Executive Committee of ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). Jiawei has received three IBM Faculty Awards, the Outstanding Contribution Award at the 2002 International Conference on Data Mining, ACM Service Award (1999) and ACM SIGKDD Innovation Award (2004). He is an ACM Fellow (since 2003). He is the first author of the textbook “Data Mining: Concepts and Techniques” (Morgan Kaufmann, 2001).Jianyong Wang received the Ph.D. degree in computer science in 1999 from the Institute of Computing Technology, the Chinese Academy of Sciences. Since then, he ever worked as an assistant professor in the Department of Computer Science and Technology, Peking (Beijing) University in the areas of distributed systems and Web search engines (May 1999–May 2001), and visited the School of Computing Science at Simon Fraser University (June 2001–December 2001), the Department of Computer Science at the University of Illinois at Urbana-Champaign (December 2001–July 2003), and the Digital Technology Center and Department of Computer Science and Engineering at the University of Minnesota (July 2003–November 2004), mainly working in the area of data mining. He is currently an associate professor in the Department of Computer Science and Technology, Tsinghua University, Beijing, China.Philip S. Yuis the manager of the Software Tools and Techniques group at the IBM Thomas J. Watson Research Center. The current focuses of the project include the development of advanced algorithms and optimization techniques for data mining, anomaly detection and personalization, and the enabling of Web technologies to facilitate E-commerce and pervasive computing. Dr. Yu,s research interests include data mining, Internet applications and technologies, database systems, multimedia systems, parallel and distributed processing, disk arrays, computer architecture, performance modeling and workload analysis. Dr. Yu has published more than 340 papers in refereed journals and conferences. He holds or has applied for more than 200 US patents. Dr. Yu is an IBM Master Inventor.Dr. Yu is a Fellow of the ACM and a Fellow of the IEEE. He will become the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering on Jan. 2001. He is an associate editor of ACM Transactions of the Internet Technology and also Knowledge and Information Systems Journal. He is a member of the IEEE Data Engineering steering committee. He also serves on the steering committee of IEEE Intl. Conference on Data Mining. He received an IEEE Region 1 Award for “promoting and perpetuating numerous new electrical engineering concepts”. Philip S. Yu received the B.S. Degree in E.E. from National Taiwan University, Taipei, Taiwan, the M.S. and Ph.D. degrees in E.E. from Stanford University, and the M.B.A. degree from New York University.     

New algorithms for the Perspective-Three-Point Problem

In this paper,two approaches are used to solve the Perspective-Three-Point Problem(P3)):the symbolic computation approach and the geometric approach.In the symbolic computation approach,we use Wu-Ritt‘s zero decomposition algorithm to give a complete triangular decomposition for the P3P equation system.This decomposition provides the firest complete analytical solution to the P3P problem.In the geometric approach,we give some pure geometric criteria for the number of real physical solutions.The complete solution classification for two special cases with three and four paramters is also given.     

Formal Ontology: Foundation of Domain Knowledge Sharing and Reusing

Domain analysis is the activity of identifying and representing the relevant information in a domain,so that the information can be shared and reused in similar systems.But until now,no efficient approaches are available for capturing and representing the results of domain analysis and then for sharing and reusing the domain knowledge.This paper proposes an ontology-oriented approach for formalizing the domain models.The architecture for the multiple-layer structure of the domain knowledge base is also discussed.And finally,some genetic algorithm-based methods have been given for supporting the knowledge sharing and reusing.     

Ontology-Based Semantic Cache in AOKB

When querying on a large-scale knowledge base,a major technique of improving performance is to preload knowledge to minimize the number of roundtrips to the knowledge base.In this paper,an ontology-based semantic cache is proposed for an agent and ontology-oriented knowledge base (AOKB).In AOKB,an ontology is the collection of relationships between a group of knowledge units (agents and/or other sub-ontologies).When loading some agent A,its relationships with other knowledge units are examined,and those who have a tight semantic tie with A will be preloaded at the same time,including agents and sub-ontologies in the same ontology where A is.The proloaded agents and ontologies are saved at a semantic cache located in the memory.Test results show that up to 50% reduction in running time is achieved.     

Digital Image Watermarking Based on Discrete Wavelet Transform

This paper aims at digital watermark which is a new popular research topic recently,presents some methods to embed digital watermark based on modifying frequency coefficients in discrete wavelet transform(DWT) domian,Fist,the,the present progress of digital watermark is briefly introduced;after that,starting from Pitas‘s method and discarding his pseudo random number method,the authors use a digital image scrambling technology as preprocessing for digital watermarking ,Then the authors discuss how to embed a 1-bit digital image as watermark in frequency domain.Finally another digital watermarking method is given in which3-D DWT is used to transform a given digtial image .Based on the experimental results ,it is shown that the proposed methods are robust to a large extent.