3,887 research outputs found

    Centroidal localization game

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    One important problem in a network is to locate an (invisible) moving entity by using distance-detectors placed at strategical locations. For instance, the metric dimension of a graph GG is the minimum number kk of detectors placed in some vertices {v1,,vk}\{v_1,\cdots,v_k\} such that the vector (d1,,dk)(d_1,\cdots,d_k) of the distances d(vi,r)d(v_i,r) between the detectors and the entity's location rr allows to uniquely determine rV(G)r \in V(G). In a more realistic setting, instead of getting the exact distance information, given devices placed in {v1,,vk}\{v_1,\cdots,v_k\}, we get only relative distances between the entity's location rr and the devices (for every 1i,jk1\leq i,j\leq k, it is provided whether d(vi,r)>d(v_i,r) >, <<, or == to d(vj,r)d(v_j,r)). The centroidal dimension of a graph GG is the minimum number of devices required to locate the entity in this setting. We consider the natural generalization of the latter problem, where vertices may be probed sequentially until the moving entity is located. At every turn, a set {v1,,vk}\{v_1,\cdots,v_k\} of vertices is probed and then the relative distances between the vertices viv_i and the current location rr of the entity are given. If not located, the moving entity may move along one edge. Let ζ(G)\zeta^* (G) be the minimum kk such that the entity is eventually located, whatever it does, in the graph GG. We prove that ζ(T)2\zeta^* (T)\leq 2 for every tree TT and give an upper bound on ζ(GH)\zeta^*(G\square H) in cartesian product of graphs GG and HH. Our main result is that ζ(G)3\zeta^* (G)\leq 3 for any outerplanar graph GG. We then prove that ζ(G)\zeta^* (G) is bounded by the pathwidth of GG plus 1 and that the optimization problem of determining ζ(G)\zeta^* (G) is NP-hard in general graphs. Finally, we show that approximating (up to any constant distance) the entity's location in the Euclidean plane requires at most two vertices per turn

    Intelligent Image Capturing Alarm System Using Raspberry Pi

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    Home surveillance system assumes an essential part in this present day living style to help recognizing illegal activities. In this proposed paper, an intelligent image capturing alarm system to protect locker was developed. Raspberry Pi 2 is used as the main controller (server). At the point when any conceivable intrusion is identified, a webcam installed to Raspberry Pi 2 will capture the picture of the intruder. In the meantime, the spotlight or light of the house which represented by an LED will be turned "ON" alongside an alarm sound from a buzzer which is fixed as an output. Taking everything into account, this improvement offers reasonable and easy to use surveillance alarm system

    Analytical/ML Mixed Approach for Concurrency Regulation in Software Transactional Memory

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    In this article we exploit a combination of analytical and Machine Learning (ML) techniques in order to build a performance model allowing to dynamically tune the level of concurrency of applications based on Software Transactional Memory (STM). Our mixed approach has the advantage of reducing the training time of pure machine learning methods, and avoiding approximation errors typically affecting pure analytical approaches. Hence it allows very fast construction of highly reliable performance models, which can be promptly and effectively exploited for optimizing actual application runs. We also present a real implementation of a concurrency regulation architecture, based on the mixed modeling approach, which has been integrated with the open source Tiny STM package, together with experimental data related to runs of applications taken from the STAMP benchmark suite demonstrating the effectiveness of our proposal. © 2014 IEEE
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