基于CDMA智能网平台的增值业务--"位置服务"

本主要介绍基于CDMA智能网平台的一种增值业务——“位置服务”，阐述这个业务的网络结构、涉及到的技术问题和解决方案，并将这个整体思路投入到开发业务中去。     

图书馆图书管理系统分析与设计

图书馆作为一个专门收集、整理、保存、传播文献 ,并提供利用的科学、文化、教育机构 ,是人们获取知识的主要来源之一。发展至今已有数千年的历史。然而 ,在信息技术迅速普及的今天 ,越来越多的读者希望通过网络更方便、更快捷地进行图书的查阅、借阅 ,获取自己的所需 ,因此图书馆的信息化建设尤为重要     

WLAN网管系统的现状和发展

2001年以来，全球WLAN（无线局域网）市场应用的瓶颈被打破，中国也迎来了WLAN商业化运作和服务的春天，WLAN为我们带来了一种全新的、灵活的工作方式，同时也带来了新的挑战，本文将对WLAN网络管理的主要内容和具体指标进行阐述，并对各大厂商的WLAN网管产品进行简单介绍和比较。     

Toward Fuzzy Optimization without Mathematical Ambiguity

Fuzzy programming has been discussed widely in literature and applied in such various disciplines as operations research, economic management, business administration, and engineering. The main purpose of this paper is to present a brief review on fuzzy programming models, and classify them into three broad classes: expected value model, chance-constrained programming and dependent-chance programming. In order to solve general fuzzy programming models, a hybrid intelligent algorithm is also documented. Finally, some related topics are discussed.     

低潮时期宽带网建设需要注意的问题

宽带建设低潮期实际上是一次优胜劣汰的过程,宽带运营竞争的手段将由低价竞争转向服务质量和特色的竞争。应在重视宽带接入建设的同时,也要重视业务运营管理平台的建设。应从长远考虑,不应仅为了节省投资而降低对设备的要求。由于降低成本的主要方向是在线缆部分,因此EVDSL技术应用的速度将大大超过人们的预期。在网络建设上,IPDSLAM虽有优势但也有不足,还需要进一步的成熟、稳定和实践的检验。     

软交换技术的发展及其应用

软交换是下一代网络NGN的核心技术，本文从软交换的概念入手，介绍了软交换的基本功能及其优点．软交换的网络系统结构、软交换的对外接口等内容，并探讨了软交换技术的应用。     

Experimental analysis, modeling, and theoretical design of McMaster pore-filled nanofiltration membranes

A side-by-side comparison of the performance of McMaster pore-filled (MacPF) and commercial nanofiltration (NF) membranes is presented here. The single-salt and multi-component performance of these membranes is studied using experimental data and using a mathematical model. The pseudo two-dimensional model is based on the extended Nernst–Planck equation, a modified Poisson–Boltzmann equation, and hydrodynamic calculations. The model includes four structural properties of the membrane: pore radius, pure water permeability, surface charge density and the ratio of effective membrane thickness to water content. The analysis demonstrates that the rejection and transport mechanisms are the same in the commercial and MacPF membranes with different contributions from each type of mechanism (convection, diffusion and electromigration). Solute rejection in NF membranes is determined primarily by a combination of steric and electrostatic effects. The selectivity of MacPF membranes is primarily determined by electrostatic effects with a significantly smaller contribution of steric effects compared to commercial membranes. Hence, these membranes have the ability to reject ions while remaining highly permeable to low molecular weight organics. Additionally, a new theoretical membrane design approach is presented. This design procedure potentially offers the optimization of NF membrane performance by tailoring the membrane structure and operating variables to the specific process, simultaneously. The procedure is validated at the laboratory scale.     

Time Series Prediction Based on Chaotic Attractor

A new prediction technique is proposed for chaotic time series. The usefulness of the technique is thatit can kick off some false neighbor points which are not suitable for the local estimation of the dynamics systems. Atime-delayed embedding is used to reconstruct the underlying attractor, and the prediction model is based on the timeevolution of the topological neighboring in the phase space. We use a feedforward neural network to approximate thelocal dominant Lyapunov exponent, and choose the spatial neighbors by the Lyapunov exponent. The model is testedfor the Mackey-Glass equation and the convection amplitude of lorenz systems. The results indicate that this predictiontechnique can improve the prediction of chaotic time series.     

On effectiveness of wiretap programs in mapping social networks

Snowball sampling methods are known to be a biased toward highly connected actors and consequently produce core-periphery networks when these may not necessarily be present. This leads to a biased perception of the underlying network which can have negative policy consequences, as in the identification of terrorist networks. When snowball sampling is used, the potential overload of the information collection system is a distinct problem due to the exponential growth of the number of suspects to be monitored. In this paper, we focus on evaluating the effectiveness of a wiretapping program in terms of its ability to map the rapidly evolving networks within a covert organization. By running a series of simulation-based experiments, we are able to evaluate a broad spectrum of information gathering regimes based on a consistent set of criteria. We conclude by proposing a set of information gathering programs that achieve higher effectiveness then snowball sampling, and at a lower cost. Maksim Tsvetovat is an Assistant Professor at the Center for Social Complexity and department of Public and International Affairs at George Mason University, Fairfax, VA. He received his Ph.D. from the Computation, Organizations and Society program in the School of Computer Science, Carnegie Mellon University. His dissertation was centered on use of artificial intelligence techniques such as planning and semantic reasoning as a means of studying behavior and evolution of complex social networks, such as these of terrorist organizations. He received a Master of Science degree from University of Minnesota with a specialization in Artificial Intelligence and design of Multi-Agent Systems, and has also extensively studied organization theory and social science research methods. His research is centered on building high-fidelity simulations of social and organizational systems using concepts from distributed artificial intelligence and multi-agent systems. Other projects focus on social network analysis for mapping of internal corporate networks or study of covert and terrorist orgnaizations. Maksim’s vita and publications can be found on Kathleen M. Carley is a professor in the School of Computer Science at Carnegie Mellon University and the director of the center for Compuational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, both students and research staff. Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. She is the founding co-editor with Al. Wallace of the journal Computational Organization Theory and has co-edited several books and written over 100 articles in the computational organizations and dynamic network area. Her publications can be found at: http://www.casos.cs.cmu.edu/bios/carley/publications.php     

基于ANN的声目标识别系统及其DSP实时实现

介绍了一种基于人工神经网络(ANN)的声目标识别系统,概述了用MATLAB专用工具箱对神经网络权值进行训练及仿真的过程,叙述了ANN目标识别系统的数字信号处理器(DSP)实时实现过程,并着重分析定点实现过程中程序变量的定标、非线性运算的实现、溢出的处理等关键步骤.对不同字长的识别结果进行比较表明,基于定点实时实现的系统数据保持很高的精度,可以得到与浮点处理相同的识别率.