Discipline-based instruction to promote interdisciplinary design of wearable and pervasive computing products

This paper reports on a design experience for undergraduates in computer engineering, industrial design, and marketing that focuses on pervasive computing devices. Across a broad range of targeted application areas and user groups, many of the student designs have been wearable computers. Consequently, our course will be of interest to the wearable computing community, particularly in terms of our aim of bridging the gap between design and engineering. For the two most recent offerings of the course, we have utilized external observers and surveyed the students in order to validate the impact of aspects of our process and changes to it. This paper is based upon 5 years of experience and 2 years of analysis of our course, and it presents an overview of our process with both qualitative and quantitative results from these two most recent offerings.     

Analysis,design,and experimental evaluation of power calculation in digital droop-controlled parallel microgrid inverters

Parallel operation of distributed generation is an important topic for microgrids,which can provide a highly reliable electric supply service and good power quality to end customers when the utility is unavailable.However,there is a well-known limitation:the power sharing accuracy between distributed generators in a parallel operation.Frequency and voltage droop is a well-established control method for improving power sharing performance.In this method,the active and reactive power calculations are used to adjust the frequency and amplitude of the output voltage.This paper describes the digital implementation of a droop method,and analyzes the influence of power calculation on droop method performance.According to the analysis,the performance of droop control in a digital control system is limited by the accuracy and speed of the power calculation method.We propose an improved power calculation method based on p-q theory to improve the performance of the droop control method,and we compare our new method with two traditional power calculation methods.Finally,simulation results and experimental results from a three single-phase 1-kW-inverter system are presented,which validate the performance of our proposed method.     

Efficient algorithms for counting parameterized list H-colorings

We study the fixed parameter tractability of the counting version of a parameterization of the restrictive list H-coloring problem. The parameterization is defined by fixing the number of preimages of a subset C of the vertices in H through a weight assignment K on C. We show the fixed parameter tractability of counting the number of list (H,C,K)-colorings, for the case in which (H,C,K) is simple. We introduce the concept of compactor and a new algorithmic technique, compactor enumeration, that allow us to design fixed parameter algorithms for parameterized counting problems.     

Explaining IS continuance in environments where usage is mandatory

Several research efforts over the last decade have attempted to explain user acceptance in mandated environments. This research is an attempt in the same direction. It addresses users’ satisfaction in mandated environments to further contribute to our understanding of how we can manage mandated use of information systems (IS) effectively beyond initial adoption. To better explain users’ IS continuance a revised post-acceptance model is proposed and empirically tested using the structural equation modelling technique. The results demonstrate the reliability and validity of the proposed measurement model and further demonstrate that confirmed expectations and ease of use perceptions explain 61% of the users’ satisfaction in this setting. Our findings have important implications for the management of users in mandated environments as well as for further research in the area of mandated use. To that end, we offer directions for future research.     

Optimal Trajectory Planning for Wheeled Mobile Robots Based on Kinematics Singularity

This research introduces a new optimality criterion for motion planning of wheeled mobile robots based on a cost index that assesses the nearness to singularity of forward and inverse kinematic models. Slip motions, infinite estimation error and impossible control actions are avoided escaping from singularities. In addition, high amplification of wheel velocity errors and high wheel velocity values are also avoided by moving far from the singularity. The proposed cost index can be used directly to complement path-planning and motion-planning techniques (e.g. tree graphs, roadmaps, etc.) in order to select the optimal collision-free path or trajectory among several possible solutions. To illustrate the applications of the proposed approach, an industrial forklift, equivalent to a tricycle-like mobile robot, is considered in a simulated environment. In particular, several results are validated for the proposed optimality criterion, which are extensively compared to those obtained with other classical optimality criteria, such as shortest-path, time-optimal and minimum-energy.     

A Secure Elliptic Curve-Based RFID Protocol

Nowadays, the use of Radio Frequency Identification (RFID) systems in industry and stores has increased. Nevertheless, some of these systems present privacy problems that may discourage potential users. Hence, high confidence and effient privacy protocols are urgently needed. Previous studies in the literature proposed schemes that are proven to be secure, but they have scalability problems. A feasible and scalable protocol to guarantee privacy is presented in this paper. The proposed protocol uses elliptic curve cryptography combined with a zero knowledge-based authentication scheme. An analysis to prove the system secure, and even forward secure is also provided. This work is supported by the Generalitat de Catalunya under Grant No. FIC 2007FIC 00880, and the projects of the Spanish MCyT MTM2007-66842-C02-02 and TIN2006-15662-C02-02.     

我的随身电脑——华硕Eee PC 1000HE上网本使用体会

瘦客户机 瘦客户机对于目前的医疗保健、金融、教育及其它行业来说．是一项良好的解决方案。不但经济实惠,也更便于IT管理人员进行管理。我们评测的是实达WT-8040瘦客户机。     

Robust network monitoring in the presence of non-cooperative traffic queries

We present the design of a predictive load shedding scheme for a network monitoring platform that supports multiple and competing traffic queries. The proposed scheme can anticipate overload situations and minimize their impact on the accuracy of the traffic queries. The main novelty of our approach is that it considers queries as black boxes, with arbitrary (and highly variable) input traffic and processing cost. Our system only requires a high-level specification of the accuracy requirements of each query to guide the load shedding procedure and assures a fair allocation of computing resources to queries in a non-cooperative environment. We present an implementation of our load shedding scheme in an existing network monitoring system and evaluate it with a diverse set of traffic queries. Our results show that, with the load shedding mechanism in place, the monitoring system can preserve the accuracy of the queries within predefined error bounds even during extreme overload conditions.     

Self-adaptive utility-based web session management

In the Internet, where millions of users are a click away from your site, being able to dynamically classify the workload in real time, and predict its short term behavior, is crucial for proper self-management and business efficiency. As workloads vary significantly according to current time of day, season, promotions and linking, it becomes impractical for some ecommerce sites to keep over-dimensioned infrastructures to accommodate the whole load. When server resources are exceeded, session-based admission control systems allow maintaining a high throughput in terms of properly finished sessions and QoS for a limited number of sessions; however, by denying access to excess users, the website looses potential customers.In the present study we describe the architecture of AUGURES, a system that learns to predict Web user’s intentions for visiting the site as well its resource usage. Predictions are made from information known at the time of their first request and later from navigational clicks. For this purpose we use machine learning techniques and Markov-chain models. The system uses these predictions to automatically shape QoS for the most profitable sessions, predict short-term resource needs, and dynamically provision servers according to the expected revenue and the cost to serve it. We test the AUGURES prototype on access logs from a high-traffic, online travel agency, obtaining promising results.