Cache modeling and optimization for portable devices running MPEG-4 video decoder

There are increasing demands on portable communication devices to run multimedia applications. ISO (an International Organization for Standardization) standard MPEG-4 is an important and demanding multimedia application. To satisfy the growing consumer demands, more functions are added to support MPEG-4 video applications. With improved CPU speed, memory sub-system deficiency is the major barrier to improving the system performance. Studies show that there is sufficient reuse of values for caching that significantly reduce the memory bandwidth requirement for video data. Software decoding of MPEG-4 video data generates much more cache-memory traffic than required. Proper understanding of the decoding algorithm and the composition of its data set is obvious to improve the performance of such a system. The focus of this paper is cache modeling and optimization for portable communication devices running MPEG-4 video decoding algorithm. The architecture we simulate includes a digital signal processor (DSP) for running the MPEG-4 decoding algorithm and a memory system with two levels of caches. We use VisualSim and Cachegrind simulation tools to optimize cache sizes, levels of associativity, and cache levels for a portable device decoding MPEG-4 video. Abu Asaduzzaman is, currently, a PhD candidate in the department of Computer Science and Engineering (CSE), Florida Atlantic University (FAU), Boca Raton, Florida. He received his MS degree in computer engineering from FAU in 1997. Mr. Asaduzzaman worked for ECI Telecom as a software engineer from 1998 to 2001. From 2001 to 2003, he worked for BlueCross and BlueShield of Florida and SunPass (FDoT) as an IT Consultant. Currently, he is working as a research assistant at CSE Dept, FAU. His research interests include cache optimization, architecture exploration, embedded system evaluation, and networks-on-a-chip (NoC). He has published several research papers in these areas. Abu is a member of the honor society of Phi Kappa Phi, Tau Beta Pi, Upsilon Phi Epsilon, and the Association for Computing Machinery (ACM) FAU Chapter. Imad Mahgoub received the MS degree in applied mathematics and MS degree in electrical and computer engineering, both from North Carolina State University, Raleigh in 1983 and 1986 respectively and the PhD degree in computer engineering from the Pennsylvania State University, University Park, PA in 1989. Dr. Mahgoub joined Florida Atlantic University (FAU), Boca Raton, Florida in 1989. Currently he is a full professor of Computer Science and Engineering department and the director of the Mobile Computing Laboratory. His research interests include performance evaluation, mobile computing, sensor networks, and parallel and distributed processing. He has published over 80 research papers in these areas. He is the co-editor of the Mobile Computing Handbook and the Handbook of Sensor Networks. Dr. Mahgoub has served on the program committees of numerous conferences. He has been the vice-chair for the Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS) since 2003. He is a senior member of the IEEE. He is also a member of Tau Beta Pi, Upsilon Pi Epsilon, the IEEE Computer Society, and the ACM.     

An empirical study of predicting software faults with case-based reasoning

The resources allocated for software quality assurance and improvement have not increased with the ever-increasing need for better software quality. A targeted software quality inspection can detect faulty modules and reduce the number of faults occurring during operations. We present a software fault prediction modeling approach with case-based reasoning (CBR), a part of the computational intelligence field focusing on automated reasoning processes. A CBR system functions as a software fault prediction model by quantifying, for a module under development, the expected number of faults based on similar modules that were previously developed. Such a system is composed of a similarity function, the number of nearest neighbor cases used for fault prediction, and a solution algorithm. The selection of a particular similarity function and solution algorithm may affect the performance accuracy of a CBR-based software fault prediction system. This paper presents an empirical study investigating the effects of using three different similarity functions and two different solution algorithms on the prediction accuracy of our CBR system. The influence of varying the number of nearest neighbor cases on the performance accuracy is also explored. Moreover, the benefits of using metric-selection procedures for our CBR system is also evaluated. Case studies of a large legacy telecommunications system are used for our analysis. It is observed that the CBR system using the Mahalanobis distance similarity function and the inverse distance weighted solution algorithm yielded the best fault prediction. In addition, the CBR models have better performance than models based on multiple linear regression. Taghi M. Khoshgoftaar is a professor of the Department of Computer Science and Engineering, Florida Atlantic University and the Director of the Empirical Software Engineering Laboratory. His research interests are in software engineering, software metrics, software reliability and quality engineering, computational intelligence, computer performance evaluation, data mining, and statistical modeling. He has published more than 200 refereed papers in these areas. He has been a principal investigator and project leader in a number of projects with industry, government, and other research-sponsoring agencies. He is a member of the Association for Computing Machinery, the IEEE Computer Society, and IEEE Reliability Society. He served as the general chair of the 1999 International Symposium on Software Reliability Engineering (ISSRE’99), and the general chair of the 2001 International Conference on Engineering of Computer Based Systems. Also, he has served on technical program committees of various international conferences, symposia, and workshops. He has served as North American editor of the Software Quality Journal, and is on the editorial boards of the journals Empirical Software Engineering, Software Quality, and Fuzzy Systems. Naeem Seliya received the M.S. degree in Computer Science from Florida Atlantic University, Boca Raton, FL, USA, in 2001. He is currently a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. His research interests include software engineering, computational intelligence, data mining, software measurement, software reliability and quality engineering, software architecture, computer data security, and network intrusion detection. He is a student member of the IEEE Computer Society and the Association for Computing Machinery.     

Software quality analysis by combining multiple projects and learners

When building software quality models, the approach often consists of training data mining learners on a single fit dataset. Typically, this fit dataset contains software metrics collected during a past release of the software project that we want to predict the quality of. In order to improve the predictive accuracy of such quality models, it is common practice to combine the predictive results of multiple learners to take advantage of their respective biases. Although multi-learner classifiers have been proven to be successful in some cases, the improvement is not always significant because the information in the fit dataset sometimes can be insufficient. We present an innovative method to build software quality models using majority voting to combine the predictions of multiple learners induced on multiple training datasets. To our knowledge, no previous study in software quality has attempted to take advantage of multiple software project data repositories which are generally spread across the organization. In a large scale empirical study involving seven real-world datasets and seventeen learners, we show that, on average, combining the predictions of one learner trained on multiple datasets significantly improves the predictive performance compared to one learner induced on a single fit dataset. We also demonstrate empirically that combining multiple learners trained on a single training dataset does not significantly improve the average predictive accuracy compared to the use of a single learner induced on a single fit dataset.

The pairwise attribute noise detection algorithm

Analyzing the quality of data prior to constructing data mining models is emerging as an important issue. Algorithms for identifying noise in a given data set can provide a good measure of data quality. Considerable attention has been devoted to detecting class noise or labeling errors. In contrast, limited research work has been devoted to detecting instances with attribute noise, in part due to the difficulty of the problem. We present a novel approach for detecting instances with attribute noise and demonstrate its usefulness with case studies using two different real-world software measurement data sets. Our approach, called Pairwise Attribute Noise Detection Algorithm (PANDA), is compared with a nearest neighbor, distance-based outlier detection technique (denoted DM) investigated in related literature. Since what constitutes noise is domain specific, our case studies uses a software engineering expert to inspect the instances identified by the two approaches to determine whether they actually contain noise. It is shown that PANDA provides better noise detection performance than the DM algorithm. Jason Van Hulse is a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. His research interests include data mining and knowledge discovery, machine learning, computational intelligence and statistics. He is a student member of the IEEE and IEEE Computer Society. He received the M.A. degree in mathematics from Stony Brook University in 2000, and is currently Director, Decision Science at First Data Corporation. Taghi M. Khoshgoftaar is a professor at the Department of Computer Science and Engineering, Florida Atlantic University, and the director of the Empirical Software Engineering and Data Mining and Machine Learning Laboratories. His research interests are in software engineering, software metrics, software reliability and quality engineering, computational intelligence, computer performance evaluation, data mining, machine learning, and statistical modeling. He has published more than 300 refereed papers in these subjects. He has been a principal investigator and project leader in a number of projects with industry, government, and other research-sponsoring agencies. He is a member of the IEEE, the IEEE Computer Society, and IEEE Reliability Society. He served as the program chair and general chair of the IEEE International Conference on Tools with Artificial Intelligence in 2004 and 2005, respectively. Also, he has served on technical program committees of various international conferences, symposia, and workshops. He has served as North American editor of the Software Quality Journal, and is on the editorial boards of the journals Empirical Software Engineering, Software Quality, and Fuzzy Systems. Haiying Huang received the M.S. degree in computer engineeringfrom Florida Atlantic University, Boca Raton, Florida, USA, in 2002. She is currently a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. Her research interests include software engineering, computational intelligence, data mining, software measurement, software reliability, and quality engineering.     

Resource oriented selection of rule-based classification models: An empirical case study

The amount of resources allocated for software quality improvements is often not enough to achieve the desired software quality. Software quality classification models that yield a risk-based quality estimation of program modules, such as fault-prone (fp) and not fault-prone (nfp), are useful as software quality assurance techniques. Their usefulness is largely dependent on whether enough resources are available for inspecting the fp modules. Since a given development project has its own budget and time limitations, a resource-based software quality improvement seems more appropriate for achieving its quality goals. A classification model should provide quality improvement guidance so as to maximize resource-utilization. We present a procedure for building software quality classification models from the limited resources perspective. The essence of the procedure is the use of our recently proposed Modified Expected Cost of Misclassification (MECM) measure for developing resource-oriented software quality classification models. The measure penalizes a model, in terms of costs of misclassifications, if the model predicts more number of fp modules than the number that can be inspected with the allotted resources. Our analysis is presented in the context of our Rule-Based Classification Modeling (RBCM) technique. An empirical case study of a large-scale software system demonstrates the promising results of using the MECM measure to select an appropriate resource-based rule-based classification model. Taghi M. Khoshgoftaar is a professor of the Department of Computer Science and Engineering, Florida Atlantic University and the Director of the graduate programs and research. His research interests are in software engineering, software metrics, software reliability and quality engineering, computational intelligence applications, computer security, computer performance evaluation, data mining, machine learning, statistical modeling, and intelligent data analysis. He has published more than 300 refereed papers in these areas. He is a member of the IEEE, IEEE Computer Society, and IEEE Reliability Society. He was the general chair of the IEEE International Conference on Tools with Artificial Intelligence 2005. Naeem Seliya is an Assistant Professor of Computer and Information Science at the University of Michigan - Dearborn. He recieved his Ph.D. in Computer Engineering from Florida Atlantic University, Boca Raton, FL, USA in 2005. His research interests include software engineering, data mining and machine learnring, application and data security, bioinformatics and computational intelligence. He is a member of IEEE and ACM.     

A Prolog simulator for deterministic P systems with active membranes

In this paper we propose a new way to represent P systems with active membranes based on Logic Programming techniques. This representation allows us to express the set of rules and the configuration of the P system in each step of the evolution as literals of an appropriate language of first order logic. We provide a Prolog program to simulate, the evolution of these P systems and present some auxiliary tools to simulate the evolution of a P system with active membranes using 2-division which solves the SAT problem following the techniques presented in Reference.¹⁰ Andrés Cordón-Franco: He is a member of the Department of Computer Science and Artificial Intelligence at the University of Sevilla (Spain). He is also a member of the research group on Natural Computing of the University of Seville. His research interest includes Mathematical Logic, Logic in Computer Science, and Membrane Computing, both from a theoretical and from a practical (software implementation) point of view. Miguel A. Gutiérrez-Naranjo: He is an assistant professor in the Computer Science and Artificial Intelligence Department at University of Sevilla, Spain. He is also a member of the Research Group on Natural Computing of the University of Seville. His research interest includes Machine Learning, Logic Programming and Membrane Computing, both from a theoretical and a practical point of view. Mario J. Pérez-Jiménez, Ph.D.: He is professor of Department of Computer Science and Artificial Intelligence at University of Seville, where he is the head of the Group of Research on Natural Computing, He has published 8 books of Mathematics and Computation, and more than 90 scientific articles in prestigious scientific journals. He is member of European Molecular Computing Consortium. Fernando Sancho-Caparrini: He is a member of the Department of Computer Science and Artificial Intelligence at the University of Sevilla (Spain). He is also a member of the research group on Natural Computing of the University of Seville. His research interest includes Complex Systems, DNA Computing, Logic in Computer Science, and Membrane Computing, both from a theoretical and from a practical point of view.     

On the existence of delay-insensitive fair arbiters: Trace theory and its limitations

This paper considers the existence and formal specification of delay-insensitive fair arbiters. We show that the exact notion of fairness used is of critical importance because certain common notions are not delay-insensitive when used across independent interfaces. We further show that for the relevant notions of fairness, the existing trace theory of finite traces lacks the expressive power to specify a delay-insensitive fair arbiter (i.e. the specification of such a fair arbiter is also satisfied by an unfair arbiter). Based on this we extend trace theory to include infinite traces, and show by example the importance of including liveness in such a theory. The extended theory is sufficiently expressive to distinguish fair arbiters from unfair ones, and we use it to exhibit a delay-insensitive fair arbiter, thus establishing their existence. In addition our extended theory generalizes the existing trace theory by introducing a composition operator (C) that at once generalizes the existing operators and obviates the composability restrictions used by previous authors. Finally our extended theory introduces wire modules as an abstraction to capture the important role that transmission media properties play in circuit behavior. David L. Black is a graduate student and Ph.D. candidate in the Department of Computer Science, Carnegie-Mellon University, Pittsburgh, PA. His research interests include trace theory, temporal logic, and the specification, design and verification of asynchronous circuits. Mr. Black received the B.A. and M.A. degrees in Mathematics along with the B.S.E. (Computer Science and Engineering) degree in 1983 from the University of Pennsylvania, Philadelphia, PA. He also received the M.S. degree in computer science from Carnegie-Mellon University in 1985. Partial support of his graduate studies at Carnegie-Mellon has been provided by a R.K. Mellon fellowship. Mr. Black is also a member of Phi Beta Kappa, Tau Beta Pi, Eta Kappa Nu and Pi Mu Epsilon.     

A QoS-Enabled Transmission Scheme for MPEG Video Streaming

While MPEG is the de facto encoding standard for video services, online video streaming service is becoming popular over the open network such as the Internet. As the performance of open network is non-predictable and uncontrollable, the tuning of the quality of service (QoS) for on-line video streaming services is difficult. In order to provide better QoS for the delivery of videos, there are proposals of new encoding formats or new transmission protocols for on-line video streaming. However, these results are not compatible with popular video players or network protocols and hence these approaches are so far not very successful. We use another approach which tries to by-pass these problems. We designed a QoS Tuning Scheme and a QoS-Enabled Transmission Scheme for transmitting MPEG videos from video servers to clients. According to the traffic characteristics between the video server and each individual client, the QoS Tuning Scheme tunes the QoS to be delivered to each individual client on the fly. Furthermore, our QoS-Enabled Transmission Scheme can be applied over any protocol, such as HTTP which is the most popular protocol over the open network. With our transmission scheme, bandwidth can be better utilized by reducing transmitted frames which would have missed their deadlines and would eventually be discarded by the clients. This is achieved by sending frames according to their impact on the QoS in the playback under the allowed throughput. With these schemes, users can enjoy video streaming through their favorite video players and with the best possible QoS. In order to facilitate the real time QoS tuning, a metric, QoS-GFS, is developed. This QoS-GFS is extended from the QoS-Index, another metric which has taken human perspective in the measurement of video quality. Hence QoS-GFS is better than the common metrics which measures QoS by means of rate of transmission of bytes or MPEG frames. We designed and implemented a middleware to perform empirical tests of the proposed transmission scheme and QoS tuning scheme. Experiment results show that our schemes can effectively enhance the QoS for online MPEG video streaming services. The work reported in this paper was supported in part by the RGC Earmarked Research Grant under RGC HKBU 2074/01E, and by the FRG under FRG 00-01/I. Joseph Kee-Yin Ng received a B.Sc. in Mathematics and Computer Science, a M.Sc. in Computer Science, and a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in the years 1986, 1988, and 1993, respectively. Dr. Ng is currently an associate professor in the Department of Computer Science at Hong Kong Baptist University. His current research interests includes Real-Time Networks, Multimedia Communications, Ubiquitous/Pervasive Computing, Mobile and Location-aware Computing, Performance Evaluation, Parallel and Distributed Computing. Dr. Ng is the Technical Program Chair for TENCON 2006, General Chair for The 11th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2005), Program Vice Chair for The 11th International Conference on Parallel and Distributed Systems (ICPADS 2005), Program Area-Chair for The 18th & 19th International Conference on Advanced Information Networking and Applications (AINA 2004 & AINA 2005) and he had served as the General Co-Chair for The International Computer Congress 1999 & 2001 (ICC'99 & ICC'01), the Program Co-Chair for The Sixth International Conference on Real-Time Computing Systems and Applications (RTCSA'99) and the General Co-Chair for The 1999 and 2001 International Computer Science Conference (ICSC'99 & ICSC'01). Dr. Ng is a member of the Editorial Board of Journal of Pervasive Computing and Communications, Associate Editor of Real-Time Systems Journal and Journal of Mobile Multimedia. He is a guest editor of International Journal of Wireless and Mobile Computing for a special issue on Applications, Services, and Infrastructures for Wireless and Mobile Computing. Dr. Ng is currently the Region 10 Coordinator for the Chapter Activities Board of the IEEE Computer Society, and is the Coordinator of the IEEE Computer Society Distinguished Visitors Program (Asia/Pacific). He is a senior member of the IEEE and has been a member of the IEEE Computer Society since 1991. Dr. Ng has been an exco-member (1993–95), General Secretary (1995–1997), Vice-Chair (1997–1999), Chair (1999–2001) and is the immediate past Chairman of the IEEE, Hong Kong Section, Computer Chapter. Dr. Ng received the Certificate of Appreciation for Leadership and Service (2000–2001) from IEEE Region 10 and the IEEE Meritorious Service Award from IEEE Computer Society at 2004. He is also a member of the IEEE Communication Society, and ACM. Karl R.P.H. Leung received his Ph.D. from The University of Hong Kong. He is currently a Principal Lecturer in the Department of Information and Communications Technology at the Hong Kong Institute of Vocational Education (IVE). He is the founder of the Compuware Software Testing Laboratory in the IVE with a donation from the Compuware Asia Pacific Co. Ltd. His research areas include: domain modeling, mission critical software engineering methodology, secure workflow systems, GSM-based location estimation, and QoS of MPEG streaming. He is a Senior Member of the IEEE and IEEE Computer Society, and has held major office of the IEEE Hong Kong Section Computer Chapter. While he was the chairman in 1998, the Chapter won the IEEE Most Outstanding Computer Society Chapter Award. He is also a Chartered Engineer of Engineering Council (UK), a Chartered Information Systems Engineer of British Computer Society (UK), an Engineer of Hong Kong Institution of Engineers, Registered Professional Engineer (Information) of Hong Kong Engineers Registration Board, and a member of ACM, BCS, ACS, HKIE and HKCS. Calvin Kin Cheung Hui received a B.Sc. (First Class Honours) in Computer Science, and a M.Phil. degree in Computer Science from Hong Kong Baptist University in the years 1999, and 2002, respectively. Mr. Hui's research interests includes Real-Time Networks, VoD Systems, Video Streaming, Multimedia Communication, and Distributed Systems Performance Evaluation.     

Enabling MPEG-2 video playback in embedded systems through improved data cache efficiency

Digital video decoding, enabled by the MPEG-2 video standard, is an important future application for embedded systems, particularly personal digital assistants and other information appliances. Many such systems require portability and wireless communication capabilities, and thus face severe limitations in size and power consumption. This places a premium on integration and efficiency, and favors software solutions for video functionality over specialized hardware. Apart from computation, an equally important problem in video decoding is the data bandwidth and the need to insure adequate data supply. MPEG data sets are very large, and generate significant amounts of excess memory traffic for standard data caches, up to 100 times the amount required for decoding. Yet MPEG data has locality which caches can exploit if properly optimized, providing fast, flexible, and automatic data supply. We propose a set of enhancements which target the specific needs of the heterogeneous types within the MPEG decoder working set. These optimizations significantly improve the efficiency of small caches, reducing cache-memory traffic by almost 70%, and can make an enhanced 4-kB cache perform better than a standard 1 MB cache. This performance improvement can enable high-resolution, full frame rate video playback in cheaper, smaller systems than would otherwise be possible.     

Self-stabilizing depth-first token circulation on networks

Summary This paper proposes a self-stabilizing protocol which circulates a token on a connected network in nondeterministic depth-first-search order, rooted at a special node. Starting with any initial state in which the network may have no token at all or more than one token, the protocol eventually makes the system stabilize in states having exactly one circulating token. With a slight modification to the protocol —by removing nondeterminism in the search — a depth-first-search tree on the network can be constructed. The proposed protocol runs on systems that allow parallel operations. Shing-Tsaan Huang was born in Taiwan on September 4, 1949. He got his Ph.D. degree in 1985 from Department of Computer Science, University of Maryland at College Park. Before he pursued his Ph.D. degree, he had worked several years in the computer industry in Taiwan. Professor Huang is currently the chairman of the Department of Computer Science, Tsing Hua University, Taiwan, Republic of China. His research interests include interconnection networks, operating systems and distributed computing. He is a senior member of the IEEE Computer Society and a member of the Association for Computing Machinery. Nian-Shing Chen was born in Taiwan on October 21, 1961. He received his Ph.D. degree in computer science from National Tsing Hua University in 1990. Dr. Chen is currently an associate professor with the Department of Information Management at Sun Yat-Sen University of Taiwan. His research interests include distributed systems, computer networks, computer viruses and expert systems. He is a member of IEEE and Phi Tau Phi honorary society.This research is supported by National Science Council of the Republic of China under the contract NSC81-0408-E-007-05 and NSC82-0408-E-007-027     

Scheduling real-time requests in on-demand data broadcast environments

On-demand broadcast is an attractive data dissemination method for mobile and wireless computing. In this paper, we propose a new online preemptive scheduling algorithm, called PRDS that incorporates urgency, data size and number of pending requests for real-time on-demand broadcast system. Furthermore, we use pyramid preemption to optimize performance and reduce overhead. A series of simulation experiments have been performed to evaluate the real-time performance of our algorithm as compared with other previously proposed methods. The experimental results show that our algorithm substantially outperforms other algorithms over a wide range of workloads and parameter settings. The work described in this paper was partially supported by grants from CityU (Project No. 7001841) and RGC CERG Grant No. HKBU 2174/03E. This paper is an extended version of the paper “A preemptive scheduling algorithm for wireless real-time on-demand data broadcast” that appeared in the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications. Victor C. S. Lee received his Ph.D. degree in Computer Science from the City University of Hong Kong in 1997. He is now an Assistant Professor in the Department of Computer Science of the City University of Hong Kong. Dr. Lee is a member of the ACM, the IEEE and the IEEE Computer Society. He is currently the Chairman of the IEEE, Hong Kong Section, Computer Chapter. His research interests include real-time data management, mobile computing, and transaction processing. Xiao Wu received the B.Eng. and M.S. degrees in computer science from Yunnan University, Kunming, China, in 1999 and 2002, respectively. He is currently a Ph.D. candidate in the Department of Computer Science at the City University of Hong Kong. He was with the Institute of Software, Chinese Academy of Sciences, Beijing, China, between January 2001 and July 2002. From 2003 to 2004, he was with the Department of Computer Science of the City University of Hong Kong, Hong Kong, as a Research Assistant. His research interests include multimedia information retrieval, video computing and mobile computing. Joseph Kee-Yin NG received a B.Sc. in Mathematics and Computer Science, a M.Sc. in Computer Science, and a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in the years 1986, 1988, and 1993, respectively. Prof. Ng is currently a professor in the Department of Computer Science at Hong Kong Baptist University. His current research interests include Real-Time Networks, Multimedia Communications, Ubiquitous/Pervasive Computing, Mobile and Location- aware Computing, Performance Evaluation, Parallel and Distributed Computing. Prof. Ng is the Technical Program Chair for TENCON 2006, General Co-Chair for The 11th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2005), Program Vice Chair for The 11th International Conference on Parallel and Distributed Systems (ICPADS 2005), Program Area-Chair for The 18th & 19th International Conference on Advanced Information Networking and Applications (AINA 2004 & AINA 2005), General Co-Chair for The International Computer Congress 1999 & 2001 (ICC’99 & ICC’01), Program Co-Chair for The Sixth International Conference on Real-Time Computing Systems and Applications (RTCSA’99) and General Co-Chair for The 1999 and 2001 International Computer Science Conference (ICSC’99 & ICSC’01). Prof. Ng is a member of the Editorial Board of Journal of Pervasive Computing and Communications, Journal of Ubiquitous Computing and Intelligence, Journal of Embedded Computing, and Journal of Microprocessors and Microsystems. He is the Associate Editor of Real-Time Systems Journal and Journal of Mobile Multimedia. He is also a guest editor of International Journal of Wireless and Mobile Computing for a special issue on Applications, Services, and Infrastructures for Wireless and Mobile Computing. Prof. Ng is currently the Region 10 Coordinator for the Chapter Activities Board of the IEEE Computer Society, and is the Coordinator of the IEEE Computer Society Distinguished Visitors Program (Asia/Pacific). He is a senior member of the IEEE and has been a member of the IEEE Computer Society since 1991. Prof. Ng has been an Exco-member (1993–95), General Secretary (1995–1997), Vice-Chair (1997–1999), Chair (1999–2001) and the Past Chair of the IEEE, Hong Kong Section, Computer Chapter. Prof. Ng received the Certificate of Appreciation for Services and Contribution (2004) from IEEE Hong Kong Section, the Certificate of Appreciation for Leadership and Service (2000–2001) from IEEE Region 10 and the IEEE Meritorious Service Award from IEEE Computer Society at 2004. He is also a member of the IEEE Communication Society, ACM and the Founding Member for the Internet Society (ISOC)-Hong Kong Chapter.     

Improving WCET by applying worst-case path optimizations

It is advantageous to perform compiler optimizations that attempt to lower the worst-case execution time (WCET) of an embedded application since tasks with lower WCETs are easier to schedule and more likely to meet their deadlines. Compiler writers in recent years have used profile information to detect the frequently executed paths in a program and there has been considerable effort to develop compiler optimizations to improve these paths in order to reduce the average-case execution time (ACET). In this paper, we describe an approach to reduce the WCET by adapting and applying optimizations designed for frequent paths to the worst-case (WC) paths in an application. Instead of profiling to find the frequent paths, our WCET path optimization uses feedback from a timing analyzer to detect the WC paths in a function. Since these path-based optimizations may increase code size, the subsequent effects on the WCET due to these optimizations are measured to ensure that the worst-case path optimizations actually improve the WCET before committing to a code size increase. We evaluate these WC path optimizations and present results showing the decrease in WCET versus the increase in code size. A preliminary version of this paper entitled “Improving WCET by optimizing worst-case paths” appeared in the 2005 Real-Time and Embedded Technology and Applications Symposium. Wankang Zhao received his PhD in Computer Science from Florida State University in 2005. He was an associate professor in Nanjin University of Post and Telecommunications. He is currently working for Datamaxx Corporation. William Kreahling received his PhD in Computer Science from Florida State University in 2005. He is currently an assistant professor in the Math and Computer Science department at Western Carolina University. His research interests include compilers, computer architecture and parallel computing. David Whalley received his PhD in CS from the University of Virginia in 1990. He is currently the E.P. Miles professor and chair of the Computer Science department at Florida State University. His research interests include low-level compiler optimizations, tools for supporting the development and maintenance of compilers, program performance evaluation tools, predicting execution time, computer architecture, and embedded systems. Some of the techniques that he developed for new compiler optimizations and diagnostic tools are currently being applied in industrial and academic compilers. His research is currently supported by the National Science Foundation. More information about his background and research can be found on his home page, http://www.cs.fsu.edu/∼whalley. Dr. Whalley is a member of the IEEE Computer Society and the Association for Computing Machinery. Chris Healy earned a PhD in computer science from Florida State University in 1999, and is currently an associate professor of computer science at Furman University. His research interests include static and parametric timing analysis, real-time and embedded systems, compilers and computer architecture. He is committed to research experiences for undergraduate students, and his work has been supported by funding from the National Science Foundation. He is a member of ACM and the IEEE Computer Society. Frank Mueller is an Associate Professor in Computer Science and a member of the Centers for Embedded Systems Research (CESR) and High Performance Simulations (CHiPS) at North Carolina State University. Previously, he held positions at Lawrence Livermore National Laboratory and Humboldt University Berlin, Germany. He received his Ph.D. from Florida State University in 1994. He has published papers in the areas of embedded and real-time systems, compilers and parallel and distributed systems. He is a founding member of the ACM SIGBED board and the steering committee chair of the ACM SIGPLAN LCTES conference. He is a member of the ACM, ACM SIGPLAN, ACM SIGBED and the IEEE Computer Society. He is a recipient of an NSF Career Award.     

基于MPEG-4的数字监视系统解码器的设计

MPEG-4是基于第二代视音频编码技术制定的压缩标准,主要用于视频存贮、视频广播和视频流媒体;而数字监视系统则是将所有的视频信息输入计算机,由MPEG-4编码器将模拟视频信号转化成数字视频信号,完成视频信息的采集、浏览、传输等功能,其涉及到的技术主要有视频采集、数据存储、采样量化编码、解码、显示等技术。故又引入了H.264标准,该标准是由ITU-T与ISO/IEC联合进行开发的,主要用于实时视频通信。本文从MPEG-4编解码原理开始讲起,对MPEG-4多种编解码技术进行分析,同时又引入了数字监视系统解码器的模型,在此基础上,实现了数字监视系统解码器的方法。     

Image hiding scheme using modulus function and optimal substitution table

The simple least-significant-bit (LSB) substitution technique is the easiest way to embed secret data in the host image. To avoid image degradation of the simple LSB substitution technique, Wang et al. proposed a method using the substitution table to process image hiding. Later, Thien and Lin employed the modulus function to solve the same problem. In this paper, the proposed scheme combines the modulus function and the optimal substitution table to improve the quality of the stego-image. Experimental results show that our method can achieve better quality of the stego-image than Thien and Lin’s method does. The text was submitted by the authors in English. Chin-Shiang Chan received his BS degree in Computer Science in 1999 from the National Cheng Chi University, Taipei, Taiwan and the MS degree in Computer Science and Information Engineering in 2001 from the National Chung Cheng University, ChiaYi, Taiwan. He is currently a Ph.D. student in Computer Science and Information Engineering at the National Chung Cheng University, Chiayi, Taiwan. 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 the National Tsing Hua University, Hsinchu, Taiwan. He received his Ph.D. in computer engineering in 1982 from the 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 the 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 IEEE, a Fellow of 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. Yu-Chen Hu received his Ph.D. degree in Computer Science and Information Engineering from the Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan in 1999. Dr. Hu is currently an assistant professor in the Department of Computer Science and Information Engineering, Providence University, Sha-Lu, Taiwan. He is a member of the SPIE society and a member of the IEEE society. He is also a member of the Phi Tau Phi Society of the Republic of China. His research interests include image and data compression, information hiding, and image processing.     

Towards integrating semantics of multi-media resources and processes in e-Learning

Internet-based e-Learning has experienced a boom and bust situation in the past 10 years [32]. To bring in new forces to knowledge-oriented e-Learning, this paper addresses the semantic integration issue of multi-media resources and learning processes with theoretical learning supports in an integrated framework. This paper proposes a context-mediated approach that aims to enable semantic-based inter-operations across knowledge domains, even across the WWW and the Semantic Web [8]. The proposed semantic e-Learning framework enables intelligent operations of heterogeneous multi-media contents based on a generic semantic context intermediation model. This framework supports intelligent e-Learning with a knowledge network for knowledge object visualization, an enhanced Kolb's learning cycle [31] to guide learning practices, and a learning health care framework for personalized learning. W. Huang received his PhD in Computer Science from Nanjing University in 2001, MEng in Pattern Recognition and Intelligent Control and BEng in Automatic Control from Southeast University in 1998 and 1995, respectively. Dr. Huang is currently a senior lecturer with the Faculty of Computing, Information Systems and Mathematics at Kingston University London. Prior to this, he was a lecturer with the Centre for Internet Computing, The University of Hull, United Kingdom. Between October 2001 and September 2002, Dr. Huang was a post-doctoral research fellow at the University Lyon 1, France. His research interests include knowledge engineering and management, adaptive multimedia service, and pragmatic Semantic Web supporting technologies. His recent research focuses on semantic context aware computing and its applications in intelligent e-Services such as e-Learning and e-Enterprises. Dr. Huang is a member of ACM and IEEE Computer Society. E. Eze received his BS Degree in Computer Science from University of Nigeria in 1999. He is now a PhD student with the Centre for Internet Computing, The University of Hull, United Kingdom. His research interests include multimedia semantic modelling and representation and contextual knowledge engineering. D. Webster is currently studying for a PhD in Computer Science on the topic of trusted agents in the Semantic Web. He holds a 2-1 honours degree in Internet Computing from the University of Hull, UK and is a member of the British Computer Society. In addition to Web-based research, he also has an interest in graphics and has been involved in the development of graphics engines for video game projects on embedded and personal computing platforms.     

基于XvMC的MPEG-2硬件解码的研究与实现

研究了如何利用GPU来加速视频解码,概述了MPEG-2视频解码的系统框架,论述了MPEG-2视频解码在Linux下以XvMC(X video motion compensation)为API并基于通用可编程GPU的实现过程,重点讨论了MPEG-2视频解码中IDCT(inverse discrete cosine transform)和运动补偿的实现,提出了新的优化算法.MPEG-2视频解码算法具有一定的通用性,实验结果表明,与传统的解码方式相比,该解码器不仅能加速视频解码,还能有效降低CPU的利用率和电脑的功耗.     

An Integrated Framework for Semantic Annotation and Adaptation

Tools for the interpretation of significant events from video and video clip adaptation can effectively support automatic extraction and distribution of relevant content from video streams. In fact, adaptation can adjust meaningful content, previously detected and extracted, to the user/client capabilities and requirements. The integration of these two functions is increasingly important, due to the growing demand of multimedia data from remote clients with limited resources (PDAs, HCCs, Smart phones). In this paper we propose an unified framework for event-based and object-based semantic extraction from video and semantic on-line adaptation. Two cases of application, highlight detection and recognition from soccer videos and people behavior detection in domotic^* applications, are analyzed and discussed.Domotics is a neologism coming from the Latin word domus (home) and informatics.Marco Bertini has a research grant and carries out his research activity at the Department of Systems and Informatics at the University of Florence, Italy. He received a M.S. in electronic engineering from the University of Florence in 1999, and Ph.D. in 2004. His main research interest is content-based indexing and retrieval of videos. He is author of more than 25 papers in international conference proceedings and journals, and is a reviewer for international journals on multimedia and pattern recognition.Rita Cucchiara (Laurea Ingegneria Elettronica, 1989; Ph.D. in Computer Engineering, University of Bologna, Italy 1993). She is currently Full Professor in Computer Engineering at the University of Modena and Reggio Emilia (Italy). She was formerly Assistant Professor (‘93–‘98) at the University of Ferrara, Italy and Associate Professor (‘98–‘04) at the University of Modena and Reggio Emilia, Italy. She is currently in the Faculty staff of Computer Engenering where has in charges the courses of Computer Architectures and Computer Vision.Her current interests include pattern recognition, video analysis and computer vision for video surveillance, domotics, medical imaging, and computer architecture for managing image and multimedia data.Rita Cucchiara is author and co-author of more than 100 papers in international journals, and conference proceedings. She currently serves as reviewer for many international journals in computer vision and computer architecture (e.g. IEEE Trans. on PAMI, IEEE Trans. on Circuit and Systems, Trans. on SMC, Trans. on Vehicular Technology, Trans. on Medical Imaging, Image and Vision Computing, Journal of System architecture, IEEE Concurrency). She participated at scientific committees of the outstanding international conferences in computer vision and multimedia (CVPR, ICME, ICPR, ...) and symposia and organized special tracks in computer architecture for vision and image processing for traffic control. She is in the editorial board of Multimedia Tools and Applications journal. She is member of GIRPR (Italian chapter of Int. Assoc. of Pattern Recognition), AixIA (Ital. Assoc. Of Artificial Intelligence), ACM and IEEE Computer Society.Alberto Del Bimbo is Full Professor of Computer Engineering at the Università di Firenze, Italy. Since 1998 he is the Director of the Master in Multimedia of the Università di Firenze. At the present time, he is Deputy Rector of the Università di Firenze, in charge of Research and Innovation Transfer. His scientific interests are Pattern Recognition, Image Databases, Multimedia and Human Computer Interaction. Prof. Del Bimbo is the author of over 170 publications in the most distinguished international journals and conference proceedings. He is the author of the “Visual Information Retrieval” monography on content-based retrieval from image and video databases edited by Morgan Kaufman. He is Member of IEEE (Institute of Electrical and Electronic Engineers) and Fellow of IAPR (International Association for Pattern Recognition). He is presently Associate Editor of Pattern Recognition, Journal of Visual Languages and Computing, Multimedia Tools and Applications Journal, Pattern Analysis and Applications, IEEE Transactions on Multimedia, and IEEE Transactions on Pattern Analysis and Machine Intelligence. He was the Guest Editor of several special issues on Image databases in highly respected journals.Andrea Prati (Laurea in Computer Engineering, 1998; PhD in Computer Engineering, University of Modena and Reggio Emilia, 2002). He is currently an assistant professor at the University of Modena and Reggio Emilia (Italy), Faculty of Engineering, Dipartimento di Scienze e Metodi dell’Ingegneria, Reggio Emilia. During last year of his PhD studies, he has spent six months as visiting scholar at the Computer Vision and Robotics Research (CVRR) lab at University of California, San Diego (UCSD), USA, working on a research project for traffic monitoring and management through computer vision. His research interests are mainly on motion detection and analysis, shadow removal techniques, video transcoding and analysis, computer architecture for multimedia and high performance video servers, video-surveillance and domotics. He is author of more than 60 papers in international and national conference proceedings and leading journals and he serves as reviewer for many international journals in computer vision and computer architecture. He is a member of IEEE, ACM and IAPR.     

Hierarchies of parallel rewriting P systems — A survey

The paper is about some families of rewriting P systems, where the application of evolution rules is extended from the classical sequential rewriting to the parallel one (as, for instance, in Lindenmayer systems). As a result, consistency problems for the communication of strings may arise. Three variants of parallel rewriting P systems (already present in the literature) are considered here, together with the strategies they use to face the communication problem, and some parallelism methods for string rewriting are defined. We give a survey of all known results about each variant and we state some relations among the three variants, thus establishing hierarchies of parallel rewriting P systems. Various open problems related to the subject are also presented. Danicla Besozzi: She is assistant professor at the University of Milano. She received her M.S. in Mathematics (2000) from the University of Como and Ph.D. in Computer Science (2004) from the University of Milano. Her research interests cover topics in Formal Language Theory, Molecular Computing, Systems Biology. She is member of EATCS (European Association for Theoretical Computer Science) and EMCC (European Molecular Computing Consortium). Giancarlo Mauri: He is full professor of Computer Science at the University of Milano-Bicocca. His research interests are mainly in the area of theoretical computer science, and include: formal languages and automata, computational complexity, computational learning theory, soft computing techniques, cellular automata, bioinformatics and molecular computing. On these subjects, he published more than 150 scientific papers in international journals, contributed volumes and conference proceedings. Claudio Zandron: He received Ph.D. in Computer Science at the University of Milan, Italy, in 2001. Since 2002 he is assistant professor at the University of Milano-Bicocca, Italy. He is member of the EATCS (European Association for Theoretical Computer Science) and of EMCC (European Molecular Computing Consortium). His research interests are Molecular Computing (DNA and Membrane Computing) and Formal Languages.     

互联网上的MPEG-4视频解码自适应控制研究

针对互联网上的多媒体应用,提出了有限的系统资源条件下的MPEG-4视频解码控制方案。该方案既能保证视频流的解码,又能有效地利用网络带宽,从而实现了有服务质量保证的网络上的MPEG-4码流的实时传输和解码。