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     

Battery-Aware Routing for Streaming Data Transmissions in Wireless Sensor Networks

Recent technological advances have made it possible to support long lifetime and large volume streaming data transmissions in sensor networks. A major challenge is to maximize the lifetime of battery-powered sensors to support such transmissions. Battery, as the power provider of the sensors, therefore emerges as the key factor for achieving high performance in such applications. Recent study in battery technology reveals that the behavior of battery discharging is more complex than we used to think. Battery powered sensors might waste a huge amount of energy if we do not carefully schedule and budget their discharging. In this paper we study the effect of battery behavior on routing for streaming data transmissions in wireless sensor networks. We first give an on-line computable energy model to mathematically model battery discharge behavior. We show that the model can capture and describe battery behavior accurately at low computational complexity and thus is suitable for on-line battery capacity computation. Based on this battery model we then present a battery-aware routing (BAR) protocol to schedule the routing in wireless sensor networks. The routing protocol is sensitive to the battery status of routing nodes and avoids energy loss. We use the battery data from actual sensors to evaluate the performance of our protocol. The results show that the battery-aware protocol proposed in this paper performs well and can save a significant amount of energy compared to existing routing protocols for streaming data transmissions. Network lifetime is also prolonged with maximum data throughput. As far as we know, this is the first work considering battery-awareness with an accurate analytical on-line computable battery model in sensor network routing. We believe the battery model can be used to explore other energy efficient schemes for wireless networks as well.     

精品光网络建设策略--智能光网络的引入

简要介绍了ASON技术的特点、ASON的引入为新一代光网络带来的好处及衡量网络质量的一些参数和注意事项。     

基于无线收发数传模块PTR2030的温度测控系统设计

PTR2030是超小型、超低功耗、高速率的无线数传MODEM模块。它性能优异，是目前低功率无线数传应用方面的理想器件。文中介绍了PTR2030的主要特点、引脚功能、软件设计、硬件连接方法及具体应用电路。     

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.     

面向WLAN的MPC852T嵌入式系统设计

MPC852T是Motorola新近推出的MPC866/MPC859T家族中面向低端的一款通信处理器.嵌入式Linux由于其费用低廉及开放源码而具有广阔的发展前景.文中在分析了MPC852T通信处理器和嵌入式Linux操作系统的基础上,将二者结合给出了一种嵌入式硬件系统的设计与实现,以及该硬件系统在无线局域网WLAN中的一种应用.实践证明,该硬件系统具有性能稳定、廉价、可扩展性强、适用面广等优点.     

关于本地传输网网络优化的探讨

根据长期工作经验,对现有网络存在的问题进行分析,提出对本地传输网网络优化的有关建议。     

一种具有弹性服务质量要求的多业务最优接纳控制策略

随着无线网络所支持的业务种类的增加和具有弹性服务质量要求的业务的大量出现，与服务质量保证密切相关的呼叫接纳控制问题成了近年来无线网络研究的热点之一。本文研究了基于准马尔可夫决策过程方法的多业务最优呼叫接纳控制问题。根据业务的特点，首先引入了带宽分配满意度函数和收益率函数，在此基础上，提出了基于带宽分配满意度的最优带宽分配算法和基于准马尔可夫决策过程方法的最优呼叫接纳控制策略。计算结果表明，本文方案能够在对各类业务的呼叫阻塞率进行适当权衡的前提下，进一步提高网络的期望收益率和期望带宽利用率，同时满足了各类业务的最低服务质量要求。