一种基于混沌和矩阵变换的序列密码

基于混沌的加密或扩频通信是当前的热门研究课题。但有限精度效应使得混沌序列密码存在密钥空间过小及周期退化的缺陷。本文通过引入一类特殊的（0,1）-矩阵类及三种矩阵变换,在二维混沌序列控制下不断地进行矩阵变换并以某种特殊方式扫描矩阵而产生N相伪随机序列。该序列满足作为密码序列的各项要求。实验结果同样显示该序列具有均匀分布、伪随机性好、复杂度高等优良统计特性,与理论分析的结论相符。     

Application of the Stream Evolution Model to a Volcanically Disturbed River: The North Fork Toutle River,Washington State,USA

In this study, a recently revised version of the channel evolution model, named the Stream Evolution Model (SEM), was applied to the upper North Fork Toutle River disrupted by the deposition of a 2.5‐km³ debris avalanche during the catastrophic eruption of Mount St. Helens in 1980. The results show that, in the first few years following the eruption, upstream channel reaches generally incised, evolving in SEM Stage 4 (i.e. degradation and widening), while downstream reaches aggraded, evolving in Stage 5 (i.e. aggradation and widening). However, starting in the late‐1980s, this simple pattern was disrupted by incision in the downstream reaches, which seemed to propagate upstream. Since the 1990s, lateral channel adjustments have become predominant as rates of vertical adjustment have slowed and river valley top widths relaxed to asymptotic values. Spatial and temporal sequences of channel evolution have tended to follow the sequences of stages expected according to the SEM, although these sequences have been frequently disrupted by renewed incision, secondary cycles of adjustment and the impacts of local geologic, geomorphic and hydraulic conditions. Within the quasi‐full SEM cycles, stages 4 and 5 were sometimes repeated, while stage 6 (quasi‐equilibrium) was sometimes omitted, and stage 8 (anabranching) only occurred in the downstream braided/anabranching reaches. According to the SEM, degradation, widening and lateral activity (stages 4 and 7) are forecast to continue until transverse valley profiles and channel planforms stabilize and floodplain and terrace surfaces are fully colonized by vegetation. Copyright © 2017 John Wiley & Sons, Ltd.     

INTELLIGENT CONTROL SYSTEM FOR STREAM SAMPLING

This article introduces an intelligent sampling controller to assist the actions taken by a plant operator to correct sampling conditions. This hybrid system includes a sampling error filter (SEF), a sampling performance indexer (SPI), a sampling correctness inspector (SCI), and a sampling error evaluator (SEE). First, the SEF upgrades the measured variables in a mineral-processing system by material balance. Then, the SPI employs fuzzy logic to assess the sampling performance. In addition, the expert system SCI checks the correctness of the sampling process. Finally, the sampling error is computed by the expert system SEE.     

USGS Streamflow Data and Modeling Sand-Bed Rivers

United States Geological Survey streamflow data are commonly used for hydraulic model calibration and boundary conditions. The transitory nature of sand-bed rivers’ bathymetry is problematic for the traditional automated stream gauging methods used by the USGS. This note seeks to assess the limitations of streamflow measurements for use in hydraulic models. An overview of USGS rating-curve development and use is presented with a focus on the specific challenges of sand-bed rivers. Measurements from three consecutive USGS gauges for a storm event on the Rio Grande in Albuquerque, New Mexico, illustrate the outlined problems with rating curves. These gauges are utilized to study the impact of uncertainty in rating-curve discharges on hydraulic model results. A one dimensional hydraulic model of the study reach indicates up to 25% reduction in the calculated flow depth if questionable rating-curve discharges are used as model input. Recommendations for using USGS streamflow data in hydraulic models are outlined.     

Multivariable stream data classification using motifs and their temporal relations

Multivariable stream data is becoming increasingly common as diverse types of sensor devices and networks are deployed. Building accurate classification models for such data has attracted a lot of attention from the research community. Most of the previous works, however, relied on features extracted from individual streams, and did not take into account the dependency relations among the features within and across the streams. In this work, we propose new classification models that exploit temporal relations among features. We showed that consideration of such dependencies does significantly improve the classification accuracy. Another benefit of employing temporal relations is the improved interpretability of the resulting classification models, as the set of temporal relations can be easily translated to a rule using a sequence of inter-dependent events characterizing the class. We evaluated the proposed scheme using different classification models including the Naive Bayesian, TFIDF, and vector distance models. We showed that the proposed model can be a useful addition to the set of existing stream classification algorithms.     

基于网格的流媒体服务QoS管理框架及实现

流媒体服务是Internet上一类高带宽需求和高实时性约束的应用，对服务质量（Quality of Service，QoS）有较高的要求。流媒体服务的发展导致传统的QoS管理框架难以适应平台的异构性和复杂性。本文提出了一种基于网格的流媒体服务QoS管理框架，为由异构的系统构成的流媒体服务提供集成的、平台无关的QoS管理机制。在谈框架的基础上，我们设计了一个基于网格的流媒体服务QoS管理系统。     

利用JDBC类型4驱动程序远程存取SQL Server 2000 BLOB数据

介绍SQLServer2000BLOB对象的概念,实现了用JDBC类型4驱动程序远程存取SQLServer2000BLOB对象。     

“傲视”——基于P2P技术的实时流媒体系统

在本篇论文中介绍了一个开发成功的，基于P2P技术的实时流媒体系统——“傲视”。该系统在数据传输方式上利用了P2P的原理．即每一个用户在接收服务器数据的同时，相互之间进行一定数据交换，这一原理类似于著名的软件BitTorrent（BT）。由于在数据传榆过程中。用户之间可以相互交换数据，而并不需要一定从服务器获取数据，因此使得该系统具有两个明显的技术优势：1．降低媒体源的硬件要求。2．降低信号源的输出带宽要求。经过校内测试，利用该系统只需要普通的个人电脑（P4 1．6G，256M内存）利用学生宿舍的100M带宽的网口．就可以在校园网中对全校上百用户进行多媒体网络广播（548Kbps）。实现学生个人电台、个人电视台功能。     

Query indexing with containment-encoded intervals for efficient stream processing

Many continual range queries can be issued against data streams. To efficiently evaluate continual queries against a stream, a main memory-based query index with a small storage cost and a fast search time is needed, especially if the stream is rapid. In this paper, we study a CEI-based query index that meets both criteria for efficient processing of continual interval queries. This new query index is an indirect indexing approach. It centres around a set of predefined virtual containment-encoded intervals, or CEIs. The CEIs are used to first decompose query intervals and then perform efficient search operations. The CEIs are defined and labeled such that containment relationships among them are encoded in their IDs. The containment encoding makes decomposition and search operations efficient; from the encoding of the smallest CEI containing a data point, the encodings of other containing CEIs can be easily derived. Closed-form formulae for the bounds of the average index storage cost are derived. Simulations are conducted to evaluate the effectiveness of the CEI-based query index and to compare it with alternative approaches. The results show that the CEI-based query index significantly outperforms existing approaches in terms of both storage cost and search time. Kun-Lung Wu received the B.S. degree in electrical engineering from the National Taiwan University, Taipei, Taiwan, the M.S. and Ph.D. degrees in computer science from the University of Illinois at Urbana–Champaign. He is with the IBM Thomas J. Watson Research Center, currently a member of the Software Tools and Techniques Group. His current research interests include data streams, continual queries, mobile computing, Internet technologies and applications, database systems and distributed and parallel computing. He has published extensively and holds various patents in these areas. Dr. Wu is a Senior Member of the IEEE Computer Society and a member of the ACM. He was an Associate Editor for the IEEE Transactions on Knowledge and Data Engineering, 2000–2004. He was the general chair for the 3rd International Workshop on e-Commerce and Web-Based Information Systems (WECWIS 2001). He has served as an organising and program committee member on various conferences. He has received various IBM awards, including IBM Corporate Environmental Affair Excellence Award, Research Division Award and Invention Achievement Awards. He received a best paper award from IEEE EEE 2004. He is an IBM Master Inventor. Shyh-Kwei Chen received the B.S. degree in computer science and information engineering from National Taiwan University, Taipei, Taiwan, in 1983, the M.S. degree in computer science from the University of Minnesota, Minneapolis, in 1987, and the Ph.D. degree in computer science from University of Illinois at Urbana–Champaign, in 1994. Dr. Chen has been with the IBM Thomas J. Watson Research Center, Yorktown Heights, New York since October 1994, where he is currently a research staff member. His current research interests include XML, electronic commerce, business performance management, data engineering and compilers. He is a member of the ACM, the IEEE and the IEEE Computer Society. Philip S. Yu received the B.S. degree in electrical engineering from National Taiwan University, the M.S. and Ph.D. degrees in electrical engineering from Stanford University, and the M.B.A. degree from New York University. He is with the IBM Thomas J. Watson Research Center and is currently manager of the Software Tools and Techniques group. His research interests include data mining, Internet applications and technologies, database systems, multimedia systems, parallel and distributed processing and performance modelling. Dr. Yu has published more than 400 papers in refereed journals and conferences. He holds or has applied for more than 250 US patents. Dr. Yu is a Fellow of the ACM and a Fellow of the IEEE. He is an associate editor of ACM Transactions on Internet Technology. He is a member of the IEEE Data Engineering steering committee and is also on the steering committee of IEEE Conference on Data Mining. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001–2004), an editor and advisory board member of IEEE Transactions on Knowledge and Data Engineering and also a guest coeditor of the special issue on mining of databases. He had also served as an associate editor of Knowledge and Information Systems. In addition to serving as program committee member on various conferences, he was the program cochair of the 11th International Conference on Data Engineering, the 6th Pacific Area Conference on Knowledge Discovery and Data Mining, and the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, and the program chair of the 2nd International Workshop on Research Issues on Data Engineering: Transaction and Query Processing, the PAKDD Workshop on Knowledge Discovery from Advanced Databases and the 2nd International Workshop on Advanced Issues of E-Commerce and Web-based Information Systems. He served as the general chair of the 14th International Conference on Data Engineering and the general cochair of the 2nd IEEE International Conference on Data Mining. He has received several IBM honours, including two IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, two Research Division Awards and the 81st Plateau of Invention Achievement Awards. He received an Outstanding Contributions Award from IEEE International Conference on Data Mining in 2003 and also an IEEE Region 1 Award for “promoting and perpetuating numerous new electrical engineering concepts” in 1999. Dr. Yu is an IBM Master Inventor and was recognised as one of the IBM's 10 top leading inventors in 1999.     

网络直播系统建设方案

本文探讨了网络直播方案的构建方法，提供了一种基于Internet和Web平台，采用流式媒体实现全球现场直播的技术方法。