57,260 research outputs found

    On the Collaboration of an Automatic Path-Planner and a Human User for Path-Finding in Virtual Industrial Scenes

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    This paper describes a global interactive framework enabling an automatic path-planner and a user to collaborate for finding a path in cluttered virtual environments. First, a collaborative architecture including the user and the planner is described. Then, for real time purpose, a motion planner divided into different steps is presented. First, a preliminary workspace discretization is done without time limitations at the beginning of the simulation. Then, using these pre-computed data, a second algorithm finds a collision free path in real time. Once the path is found, an haptic artificial guidance on the path is provided to the user. The user can then influence the planner by not following the path and automatically order a new path research. The performances are measured on tests based on assembly simulation in CAD scenes

    Collaborative signal and information processing for target detection with heterogeneous sensor networks

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    In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield

    DCDIDP: A distributed, collaborative, and data-driven intrusion detection and prevention framework for cloud computing environments

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    With the growing popularity of cloud computing, the exploitation of possible vulnerabilities grows at the same pace; the distributed nature of the cloud makes it an attractive target for potential intruders. Despite security issues delaying its adoption, cloud computing has already become an unstoppable force; thus, security mechanisms to ensure its secure adoption are an immediate need. Here, we focus on intrusion detection and prevention systems (IDPSs) to defend against the intruders. In this paper, we propose a Distributed, Collaborative, and Data-driven Intrusion Detection and Prevention system (DCDIDP). Its goal is to make use of the resources in the cloud and provide a holistic IDPS for all cloud service providers which collaborate with other peers in a distributed manner at different architectural levels to respond to attacks. We present the DCDIDP framework, whose infrastructure level is composed of three logical layers: network, host, and global as well as platform and software levels. Then, we review its components and discuss some existing approaches to be used for the modules in our proposed framework. Furthermore, we discuss developing a comprehensive trust management framework to support the establishment and evolution of trust among different cloud service providers. © 2011 ICST

    Addressing Colorado's Primary Care Provider Shortage

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    Outlines projected shortages of physicians, physician assistants, and advance practice nurses in family practice, internal medicine, and pediatrics and potential impact on health, healthcare access, and the economy. Recommends policy interventions

    Evidence-Based Skin Champion Program Reduces Pressure Injuries in a Pediatric Hospital

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    Prevention of pressure injuries (PIs) in pediatric patients is an important nurse-sensitive quality goal. The PI rate at a large urban pediatric hospital triggered a call to action by the Chief Nursing Officer to establish a Hospital Acquired PI (HAPI) Task Force which identified the Skin Champion program as a key improvement strategy. The goals of the Skin Champion program are to lower the rate of HAPIs, empower front line care providers to implement evidence-based care bundles, achieve consistency of practice, and provide resource availability at the point of care. The implementation of the Skin Champion quality improvement program achieved an 85% reduction in severe harm and “reportable HAPI incidence, which is lower than the HAPI national average in pediatric patients (Solutions for Patient Safety, 2018), and an increase in nurse compliance with the HAPI prevention bundle. The HAPI incidence rate has remained near 0.05 per 1000 patient days

    Collaborative spectrum sensing optimisation algorithms for cognitive radio networks

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    The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance
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