Detecting how a vehicle is steered and then alarming drivers in real time is of utmost importance to the vehicle and the driver's safety, since fatal accidents are often caused by dangerous steering. Existing solu...
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We have constructed a new timescale, TT(IPTA16), based on observations of radio pulsars presented in the first data release from the International Pulsar Timing Array (IPTA). We used two analysis techniques with indep...
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Adding to societal changes today, are the miscellaneous big data produced in different fields. Coupled with these data is the appearance of risk management. Admittedly, to predict future trend by using these data is c...
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Adding to societal changes today, are the miscellaneous big data produced in different fields. Coupled with these data is the appearance of risk management. Admittedly, to predict future trend by using these data is conducive to make everything more efficient and easy. Now, no matter companies or individuals, they increasingly focus on identifying risks and managing them before risks. Effective risk management will lead them to deal with potential problems. This thesis focuses on risk management of flight delay area using big real time data. It proposes two different prediction models, one is called General Long Term Departure Prediction Model and the other is named as Improved Real time Arrival Prediction Model. By studying the main factors lead to flight delay, this thesis takes weather, carrier, National Aviation System, security and previous late aircraft as analysis factors. By utilizing our models can do not only long time but also short term flight delay predictions. The results demonstrate goodness of fit. Besides the theory part, it also presents a practical and beautiful web application for real time flight arrival prediction based on our second model.
Recent advances in in-vehicle technologies have paved way to a new era of connectivity. Vehicle manufacturers have already deployed various technologies for driving assistance, anti-theft, and infotainment. They are n...
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Recent advances in in-vehicle technologies have paved way to a new era of connectivity. Vehicle manufacturers have already deployed various technologies for driving assistance, anti-theft, and infotainment. They are n...
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ISBN:
(纸本)9781450319966
Recent advances in in-vehicle technologies have paved way to a new era of connectivity. Vehicle manufacturers have already deployed various technologies for driving assistance, anti-theft, and infotainment. They are now developing ways to interface mobile devices with vehicles and provide the customer's smartphone or tablet the ability to send/receive information to/from the car. However, such an integration introduces severe security risks to the vehicle. The in-vehicle network was originally designed to operate in a closed environment and thus, security was not of concern. It has now become an important issue due to an increasing number of external interfaces to the in-vehicle network. Several studies have already shown that an in-vehicle network can be easily compromised just by connecting cheap commercial devices and doing reverse engineering. Although research efforts have been made to secure in-vehicle networks, most of them focused on defining security requirements, or presenting attack scenarios without providing any feasible solution. Also, to the best of our knowledge, there hasn't been any study with a specific focus on understanding and analyzing the security aspects of integrating mobile devices with cars. In this paper, we define the integration model, present the attack scenarios, define the security objectives, and then propose a 3-step verification mechanism that meets our objectives.
A large-scale battery pack that consists of hundreds or thousands of battery cells must be carefully monitored. Due to the divergence of cell characteristics, every cell should be monitored periodically and accurately...
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A large-scale battery pack that consists of hundreds or thousands of battery cells must be carefully monitored. Due to the divergence of cell characteristics, every cell should be monitored periodically and accurately. There are two important issues in monitoring large-scale packs. First, sensing the health condition of battery cells must be timely to capture the turning point at which the battery condition abruptly changes. Failure to capture such an important event can cause irreversible damage to the battery, especially when its State-of-Charge (SoC) is very low. Second, the more the hardware components are used, the higher the failure rate the system will suffer. The frequency of monitoring battery cells, thus, should be adjustable to the underlying load demand, considering the fact that a low load demand has a minute impact on the battery condition. We propose to address these issues via an adaptive monitoring architecture, called ADMON. ADMON lowers the sensing latency effectively, making it effective to enhance the tolerance of physical cell failures. ADMON consists of sensing, path-switching, and computing systems. The sensing system collects data from a battery-cell array. The path-switching system effectively connects a specific sensor and a micro-controller that is part of the computing system. The path-switching system is characterized by three exclusive types of topology: n-tree-based, cascaded, and parallel. The computing system is synergistically combined with the other two systems while three policies specified in the computing system are applied. The ADMON architecture is shown to outperform a non-adaptive monitoring system with respect to the battery life by 67%.
The uncoordinated deployment of many high-bandwidth 802.11a/g/n access points (APs) in urban areas offers the potential for WLANs to be a strong complement to cellular networks in providing ubiquitous connectivity. Ho...
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The uncoordinated deployment of many high-bandwidth 802.11a/g/n access points (APs) in urban areas offers the potential for WLANs to be a strong complement to cellular networks in providing ubiquitous connectivity. However, given that the bandwidth of the backhaul links connected to these APs is often an order-of-magnitude lower than that of the WLAN channel, aggregating the throughput from multiple APs is often necessary in order for the client to achieve an acceptable level of network performance. In this paper, we present Sidekick - a simple and novel AP aggregation protocol that exploits effective communication between 802.11a/g/n nodes on partially overlapping channels to attain high aggregate throughput in the face of dynamic WLAN and backhaul link conditions. Sidekick is built upon Aileron, which provides an extremely reliable and low-overhead control channel over which the APs and clients can coordinate the aggregation process. The use of such a control channel over partially overlapping channels enables Sidekick to quickly respond to varying bandwidth availability and probe for new transmission opportunities with little overhead. Our evaluation results indicate that Sidekick can make more than 30% improvement in throughput over FatVAP in a variety of situations.
Conventional battery management systems (BMSs) for electric vehicles (EVs) are designed in an ad hoc way, causing the supply of EVs to fall behind the market demand. A well-designed and combined hardware-software arch...
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In cognitive radio networks (CRNs), spectrum sensing is key to opportunistic spectrum access while preventing any unacceptable interference to primary users' communications. Although cognitive radios function as s...
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ISBN:
(纸本)9781605587387
In cognitive radio networks (CRNs), spectrum sensing is key to opportunistic spectrum access while preventing any unacceptable interference to primary users' communications. Although cognitive radios function as spectrum sensors and move around, most, if not all, of existing approaches assume stationary spectrum sensors, thus providing inaccurate sensing results. As part of our effort to solve/alleviate this problem, we consider the impact of sensor mobility on spectrum sensing performance in a joint optimization framework for sensor cooperation and sensing scheduling. We show that sensor mobility increases spatio-temporal diversity in received primary signal strengths, and thus, improves the sensing performance. This is intuitively plausible, but have not been tested previously. Based on this observation, we propose a sensing strategy that minimizes the sensing overhead by finding an optimal combination of the number of sensors to cooperate and the number of times spectrum sensing must be scheduled. This result provides a useful insight to understand the spectrum sensing and its coupling with sensor mobility. Copyright 2009 ACM.
While mobile nodes (MNs) undergo handovers across inter-wireless access networks, their contexts must be propagated for seamless re-establishment of on-going application sessions, including IP header compression, secu...
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While mobile nodes (MNs) undergo handovers across inter-wireless access networks, their contexts must be propagated for seamless re-establishment of on-going application sessions, including IP header compression, secure mobile IP, authentication, authorization, and accounting services, to name a few. Routing contexts via an overlay network either on-demand or based on prediction of an MNs' mobility, introduces a new challenging requirement of context management. This paper proposes a context router (CXR) that manages contexts in an overlay network. A CXR is responsible for (1) monitoring of MNs' cross-handover, (2) analysis of MNs' movement patterns, and (3) context routing ahead of each MN's arrival at an AP or a network. The predictive routing of contexts is performed based on statistical learning of (dis)similarities between the patterns obtained from vector distance measurements. The proposed CXR has been evaluated on a prototypical implementation based on an MN mobility model in an emulated access network. Our evaluation results show that the prediction mechanisms applied on the CXR outperform a Kalman-filter-based method with respect to both prediction accuracy and computation performance.
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