Beam me up, doctor McCoy [pervasive computing]

Interplanetary explorers in science fiction have long worn communicators to keep in voice contact with team members and deliver crucial information just when needed. Wearers simply ask the badge to contact appropriate individuals by name, function, or location-voila, they are connected. Moreover, the badges respond only to their owners, not to nearby casual conversations. They are always light, small, and casually portable. Best yet they are no longer fiction. To see how this concept translates to a real-life working organization with pressing scheduled and a busy staff, I spoke with representatives from Vocera Communications, which has developed a working communicator badge system for mobile users, and from St. Vincent's Hospital in Birmingham, Alabama, which has deployed the Vocera system. As with other pervasive systems we've covered, this one delivers information directly to mobile workers, at the point of service, thus saving numerous trips to distant telephones, terminals, and PCs to get needed data and messages.     

Transforming homo economicus into homo ludens: A field experiment on gamification in a utilitarian peer-to-peer trading service

During recent years, the addition of game mechanisms to non-game services has gained a relatively large amount of attention. Popular discussion connects gamification to successful marketing and increased profitability through higher customer engagement, however, there is a dearth of empirical studies that confirm such expectations. This paper reports the results of a field experiment, which gamifies a utilitarian peer-to-peer trading service by implementing the game mechanism of badges that users can earn from a variety of tasks. There were 3234 users who were randomly assigned to treatment groups and subjected to different versions of the badge system in a 2 × 2 design. The results show that the mere implementation of gamification mechanisms does not automatically lead to significant increases in use activity in the studied utilitarian service, however, those users who actively monitored their own badges and those of others in the study showed increased user activity.     

Modeling and Tracking of Transaction Flow Dynamics for Fault Detection in Complex Systems

With the prevalence of Internet services and the increase of their complexity, there is a growing need to improve their operational reliability and availability. While a large amount of monitoring data can be collected from systems for fault analysis, it is hard to correlate this data effectively across distributed systems and observation time. In this paper, we analyze the mass characteristics of user requests and propose a novel approach to model and track transaction flow dynamics for fault detection in complex information systems. We measure the flow intensity at multiple checkpoints inside the system and apply system identification methods to model transaction flow dynamics between these measurements. With the learned analytical models, a model-based fault detection and isolation method is applied to track the flow dynamics in real time for fault detection. We also propose an algorithm to automatically search and validate the dynamic relationship between randomly selected monitoring points. Our algorithm enables systems to have self-cognition capability for system management. Our approach is tested in a real system with a list of injected faults. Experimental results demonstrate the effectiveness of our approach and algorithms     

Information systems for enhancing social experience in a conference context: A sensor based approach

We combine Auto Identification and Web technologies in an extensible on-site event support system for enhancing experience of conference organizers as well as participants. Our system enables users to authenticate themselves using RFID badges and to access interactive, Web-based services via a touchscreen PC. The developed services aim at supporting social interactions of participants, and thus validate the promising usage directions of the combination of offline social networks and the online social Web. The main hypothesis of our work is that: ??A sensor based information system can truly enhance social conference experience.?? Technically, we have investigated employment of Web 2.0 technologies in social, sensor and mobile technologies enabled systems at conferences and events. This article gives an overview of the overall system and its evaluation via a user survey and usage log data collected during the Extended Semantic Web Conference (ESWC) 2010 and similar international conferences, altogether with several hundred participants.     

Tracking urban geo-topics based on dynamic topic model

Modern cities are facing critical environmental and social problems that are difficult to solve using conventional planning approaches due to the cities’ magnitude and complexity. Recent developments in sensing technologies and urban computing, however, integrate new data resources and technologies to tackle these challenges. Popular social networking platforms such as Twitter provide new data sources on important events (e.g., cultural activities, political campaigns, accidents, crises) providing rich knowledge about urban systems and human dynamics. This research is intended to develop a method for effectively monitoring important information during such events and helping with planning and policymaking. We use semantically similar and geographically close geo-topics to represent important local events. This research proposes a data-driven system for detecting and tracking the semantic, spatial, and temporal dynamics of these geo-topics, specifically designed for geo-tagged tweets. The system consists of data preprocessing, geo-topic generation, and geo-topic tracking modules. The preprocessing module can remove robotic and semantically trivial texts. In the geo-topic generation module, we use spatial factors to measure the spatial impacts of geo-tagged tweets by applying an exponential decay function to the pairwise distances between tweets. We then improve the dynamic topic model (DTM) by embedding the spatial factors to enable the generation of geo-topics in semantic, spatial, and temporal dimensions simultaneously. The geo-topic tracking module monitors semantic change by detecting changes in certain keywords’ probabilities and the volumes of tweets belonging to different geo-topics. This module also uses radius of gyration and trajectory-pattern mining to track and analyze the movement patterns of geo-topics. We employed the tracking system in three disaster cases in different U.S. cities to track small-scale emergencies and crises. These implementations demonstrated the effectiveness of the system for identifying and tracking geo-topics at fine temporal and geographic scales. The system also has strong potential in creating planning-related analyses for policy makers, improving the situational awareness of the general public, and serving as a basis for urban information systems that contribute to smart, agile, and resilient city developments.     

单片机的选择和串行通信协议的设计

目前,射频识别技术（RFID）在国外发展非常迅速,该技术被广泛应用于工业自动化、商业自动化、交通监控、高速公路自动收费系统、停车场管理系统等领域。将远距离射频识别技术与无线通信技术、单片机控制技术、计算机串口通信技术结合在一起,为实现体育赛事中径赛记圈自动化提出了系统解决方案。首次将无线通信技术应用于径赛记圈工作中,制定了适合运动场地内信号传送的小型通信网络,并编制了通信协议。该项目的研究可以实现径赛记圈工作的自动化,提高记圈的准确性,因此,该项目的研究切实可行,且应用前景巨大。     

Automated detection of unusual events on stairs

This paper presents a method for automatically detecting unusual human events on stairs from video data. The motivation is to provide a tool for biomedical researchers to rapidly find the events of interest within large quantities of video data. Our system identifies potential sequences containing anomalies, and reduces the amount of data that needs to be searched by a human. We compute two sets of features from a video of a person descending a stairwell. The first set of features are the foot positions and velocities. We track both feet using a mixed state particle filter with an appearance model based on histograms of oriented gradients. We compute expected (most likely) foot positions given the state of the filter at each frame. The second set of features are the parameters of the mean optical flow over a foreground region. Our final classification system inputs these two sets of features into a hidden Markov model (HMM) to analyse the spatio-temporal progression of the stair descent. A single HMM is trained on sequences of normal stair use, and a threshold on sequence likelihoods is used to detect unusual events in new data. We demonstrate our system on a data set with five people descending a set of stairs in a laboratory environment. We show how our system can successfully detect nearly all anomalous events, with a low false positive rate. We discuss limitations and suggest improvements to the system.     

A Super-Element of Track-Wheel-Terrain Interaction for Dynamic Simulation of Tracked Vehicles

A track-wheel-terrain interaction model is presented in this paper, which can be used as a “force” super-element in a multibody dynamics code for dynamic simulation of tracked vehicles. This model employs a nonlinear finite element representation for the track segment that is in contact with the terrain and roadwheels, which can be used to simulate two different track systems, namely a continuous rubber band track and a multi-pitched metallic track, provided the finite element mesh in the track model is properly defined. The new track model accounts for the tension variations along the track (due to the non-uniformly distributed normal pressure and traction), track extensibility, and geometrically large (nonlinear) track deflections. A new solution algorithm is then proposed that includes an adaptive meshing method for representing track movement during the simulation for the multi-pitch tracks. Doing so produces a track model that captures high-frequency content of the track-wheel-terrain interaction, and it can more accurately describe the mechanics of a multi-pitch track as the vehicle negotiates rough terrain. The resulting track-wheel-terrain model combines approximate and known constitutive laws for terrain response with the new track representation, which allows the computation of the normal and shear forces, as well as the passage frequency, at the track-terrain interface. The track model and solution algorithm are further illustrated in this paper using a simple two-wheel system model and a full vehicle model of an M1A1 tank.     

Scalable robot fault detection and identification

Experience has shown that even carefully designed and tested robots may encounter anomalous situations. It is therefore important for robots to monitor their state so that anomalous situations may be detected in a timely manner. Robot fault diagnosis typically requires tracking a very large number of possible faults in complex non-linear dynamic systems with noisy sensors. Traditional methods either ignore the uncertainty or use linear approximations of non-linear system dynamics. Such approximations are often unrealistic, and as a result faults either go undetected or become confused with non-fault conditions.Probability theory provides a natural representation for uncertainty, but an exact Bayesian solution for the diagnosis problem is intractable. Monte Carlo approximations have demonstrated considerable success in application domains such as computer vision and robot localization and mapping. But, classical Monte Carlo methods, such as particle filters, can suffer from substantial computational complexity. This is particularly true with the presence of rare, yet important events, such as many system faults.This paper presents an algorithm that provides an approach for computationally tractable fault diagnosis. Taking advantage of structure in the domain it dynamically concentrates computation in the regions of state space that are currently most relevant without losing track of less likely states. Experiments with a dynamic simulation of a six-wheel rocker-bogie rover show a significant improvement in performance over the classical approach.     

Detecting spatial patterns of rivermouth processes using a geostatistical framework for near-real-time analysis

This paper proposes a geospatial analysis framework and software to interpret water-quality sampling data from towed undulating vehicles in near-real time. The framework includes data quality assurance and quality control processes, automated kriging interpolation along undulating paths, and local hotspot and cluster analyses. These methods are implemented in an interactive Web application developed using the Shiny package in the R programming environment to support near-real time analysis along with 2- and 3-D visualizations. The approach is demonstrated using historical sampling data from an undulating vehicle deployed at three rivermouth sites in Lake Michigan during 2011. The normalized root-mean-square error (NRMSE) of the interpolation averages approximately 10% in 3-fold cross validation. The results show that the framework can be used to track river plume dynamics and provide insights on mixing, which could be related to wind and seiche events.     

An on-line customer information system at a gas-utility company

Since about 1970, there has been a significant trend among large organizations in moving from a manual system or semiautomatic system that first prepares source data and then processes it in a batch environment to an online automatic system that receives and processes data entered directly at the office. This paper describes the course of events during the development of such an on-line system that processes customer orders at a large gas utility company. One significant outcome of the conversion from the previous manual system to the on-line system was a large reduction in the work force required for processing customer order; this resulted in great savings in labor and material costs.     

How do Virtual Badges Incentivize Voluntary Contributions to Online Communities?

Online communities rely heavily on badges to incentivize users to participate in voluntary activities. We examine the relationship between earned badges/pursued badges and contributions in the Stack Overflow question and answer community. Our main results reveal that different levels of earned and pursued badges increase the amount of subsequent answering activity. In addition, earned gold badges are the most influential in the motivation of users while earned bronze badges are the least impactful. Furthermore, the effect of badges involves positive spillovers from higher levels of earned badges to lower ones. Our findings offer important theoretical and managerial implications for online communities.     

SEEVis: A Smart Emergency Evacuation Plan Visualization System with Data-Driven Shot Designs

Despite the significance of tracking human mobility dynamics in a large-scale earthquake evacuation for an effective first response and disaster relief, the general understanding of evacuation behaviors remains limited. Numerous individual movement trajectories, disaster damages of civil engineering, associated heterogeneous data attributes, as well as complex urban environment all obscure disaster evacuation analysis. Although visualization methods have demonstrated promising performance in emergency evacuation analysis, they cannot effectively identify and deliver the major features like speed or density, as well as the resulting evacuation events like congestion or turn-back. In this study, we propose a shot design approach to generate customized and narrative animations to track different evacuation features with different exploration purposes of users. Particularly, an intuitive scene feature graph that identifies the most dominating evacuation events is first constructed based on user-specific regions or their tracking purposes on a certain feature. An optimal camera route, i.e., a storyboard is then calculated based on the previous user-specific regions or features. For different evacuation events along this route, we employ the corresponding shot design to reveal the underlying feature evolution and its correlation with the environment. Several case studies confirm the efficacy of our system. The feedback from experts and users with different backgrounds suggests that our approach indeed helps them better embrace a comprehensive understanding of the earthquake evacuation.     

基于加速度映射的跳跃机器人稳定性控制

针对腿式机器人,讨论了腿式机器人稳定性控制与稳定性判定准则,基于稳定约束条件零力矩点(ZMP),提出一种干扰下的动态稳定性控制方法.落地冲击是跳跃机器人必须考虑的外干扰.所提出的稳定性控制方法是基于驱动性能约束下腿式跳跃机器人的动态平衡,运用ZMP平面加速度正交映射方法,进行地面冲击力干扰下的稳定性控制,在满足轨迹跟踪的同时保证了系统的动态平衡性.跳跃运动过程中的仿真结果表明,该方法是一种有效可行的腿式机器人抗干扰控制策略.     

Localization methods for a mobile robot in urban environments

This paper addresses the problems of building a functional mobile robot for urban site navigation and modeling with focus on keeping track of the robot location. We have developed a localization system that employs two methods. The first method uses odometry, a compass and tilt sensor, and a global positioning sensor. An extended Kalman filter integrates the sensor data and keeps track of the uncertainty associated with it. The second method is based on camera pose estimation. It is used when the uncertainty from the first method becomes very large. The pose estimation is done by matching linear features in the image with a simple and compact environmental model. We have demonstrated the functionality of the robot and the localization methods with real-world experiments.     

nSpace and GeoTime: a VAST 2006 case study.

The nSpace and GeoTime visual analytic tools enable investigation and understanding of complex, ambiguous situations. nSpace is a system of systems supporting the whole analytic workflow. GeoTime provides insight into events and behaviors in time and space. Junior analysts used the analytic capabilities that these tools support to investigate the 2006 Visual Analytics Science and Technology contest data set and to produce an intelligence assessment report. Their work won first place in the corporate category. This article is a detailed case study describing how the nSpace and GeoTime tools created an analytical environment that enabled two novice analysts to examine a scenario, discover patterns, trace connections, assess evidence, visually represent meaningful hypotheses with associated evidence, track progress, collaborate with others, and then produce a final report. nSpace and GeoTime are developed in collaboration with analysts to support the investigation of large and complex multidimensional data sets.     

Dynamic Event Generation for Runtime Checking using the JDI

Approaches to runtime checking have to track the execution of a software system and therefore have to deal with generating and processing execution events. Often these techniques are applied at the code level – either by inserting new source code prior to the compilation or by modifying the target code, e.g. Java byte code, before running the program.The jassda [4,3] framework and tool enable runtime checking of Java programs against a CSP-like specification. For generating events it uses the Java Debug Interface (JDI) and thus no modifications to the code are necessary. Another advantage is that events are generated on demand, i.e. dynamically at runtime it is determined which events to generate for the current debug run without modifying the program itself. This paper shows how this event generation is done by the jassda framework.     

QR码在防伪技术中的应用

二维条码作为一种应用于存储和检索系统中的技术,广泛应用于身份鉴别。将QR码和图像处理技术相结合,构造了一个基于学生证的防伪系统。首先将文本信息嵌入到QR条码中,然后去除格式信息以减少信息量;接着将校徽图标二值化;最后将QR条码和图标嵌入到人脸图像中频系数的关系中,以实现证件的防伪目的。水印嵌入位置由密码控制,增强了算法的保密性和灵活性,同时不可感知性和鲁棒性可调。实验表明,该方法可有效抵抗常见攻击。     

基于车辆响应计算的地铁轨道平顺状态评价

针对目前轨道检测数据以幅值超限统计的方式评价轨道平顺状态而未考虑轮轨关系及车辆实际响应的问题, 提出一种基于车辆响应计算的地铁轨道平顺状态评价方法。该方法引入随机振动虚拟激励法对车辆动力学模型的响应谱进行计算, 再由响应谱计算得到平稳性指标, 指出在车辆动力学参数稳定状态下, 平稳性指标可用于对轨道平顺状态的判定。不同车速下高级别轨道谱的平稳性指标均优于低级别轨道谱, 结果表明该方法可对轨道的整体状态进行量化评价。该算法可用于深入分析轨检数据, 并为轨道状态评价提供新的思路。     

半张量积理论在网络遥操作系统中的应用

研究了网络遥操作系统的半张量积建模问题.网络遥操作系统中存在人机交互以及大时延事件,这使得系统中既有离散事件动态又有连续动态,当系统的离散事件动态与底层连续动态相互作用耦合强烈时,系统的性能、行为分析变得更为复杂.本文通过引入半张量积给出了一种新的半张量积混杂建模方法,它把一类特定事件系统建模为布尔系统,通过半张量积方法得到类似于离散动态系统的方程.它将系统中离散事件和连续动态相互耦合的地方表达为半张量积的形式,最后经过扩张简化得到完整统一的系统数学模型,它将有利于进一步分析、计算.本文给出的半张量积建模方法和步骤具有一定的通用性和一般性.此外本文还对基于半张量积的混杂网络遥操作系统模型进行了基于事件控制和时间控制的仿真.仿真结果表明,半张量积建模方法完全可信、可行,基于事件控制的方法在网络遥操作控制中明显优于基于时间控制的方法.