N-hop neighborhoods information is very useful in analytic tasks on large-scale graphs,like finding clique in a social network,recommending friends or advertising links according to one’s interests,predicting links a...N-hop neighborhoods information is very useful in analytic tasks on large-scale graphs,like finding clique in a social network,recommending friends or advertising links according to one’s interests,predicting links among websites and etc.To get the N-hop neighborhoods information on a large graph,such as a web graph,a twitter social graph,the most straightforward method is to conduct a breadth first search(BFS)on a parallel distributed graph processing framework,such as Pregel and GraphLab.However,due to the massive volume of message transfer,the BFS method results in high communication cost and has low efficiency.In this work,we propose a key/value based method,namely KVB,which perfectly fits into the prevailing parallel graph processing framework and computes N-hop neighborhoods on a large scale graph efficiently.Unlike the BFS method,our method need not transfer large amount of neighborhoods information,thus,significantly reduces the overhead on both the communication and intermediate results in the distributed framework.We formalize the N-hop neighborhoods query processing as an optimization problem based on a set of quantitative cost metrics of parallel graph processing.Moreover,we propose a solution to efficiently load only the relevant neighborhoods for computation.Specially,we prove the optimal partial neighborhoods load problem is NP-hard and carefully design a heuristic strategy.We have implemented our algorithm on a distributed graph frameworkSpark GraphX and validated our solution with extensive experiments over a number of real world and synthetic large graphs on a modest indoor cluster.Experiments show that our solution generally gains an order of magnitude speedup comparing to the state-of-art BFS implementation.展开更多
在车联网VANETs(vehicle ad hoc networks)中,车辆的高速移动、有限的无线资源以及不稳定的信号强度,给转发节点的选择机制提出挑战。为此,提出基于模糊逻辑的转发节点选择FLFNS(fuzzy logic-based forwarder nodes selection)算法。利...在车联网VANETs(vehicle ad hoc networks)中,车辆的高速移动、有限的无线资源以及不稳定的信号强度,给转发节点的选择机制提出挑战。为此,提出基于模糊逻辑的转发节点选择FLFNS(fuzzy logic-based forwarder nodes selection)算法。利用模糊逻辑算法选择下一跳转发节点,通过模糊逻辑理论,利用车间距离、车辆移动以及链路质量信息选择最优的转发节点。仿真结果表明,与Fuzzbr算法相比,FLFNS算法的端到端传输时延降低近50%,数据包传输成功率提高了10%。展开更多
简要介绍了UHF RFID国际标准ISO/IEC18000-6C、EPC Global C1G2及ETSI的空中射频接口要求,采用∑-△调制小数分频PLL频率合成方案,应用LMX2541及ADF4360-8芯片设计了一频率范围在860~960MHz内可跳变的UHF RFID读写器用频率合成器。仿真...简要介绍了UHF RFID国际标准ISO/IEC18000-6C、EPC Global C1G2及ETSI的空中射频接口要求,采用∑-△调制小数分频PLL频率合成方案,应用LMX2541及ADF4360-8芯片设计了一频率范围在860~960MHz内可跳变的UHF RFID读写器用频率合成器。仿真及实验结果表明,其各项指标均达到或超过ISO/IEC18000-6C、EPC Global C1G2及ETSI标准规定的要求,可以满足未来通用型UHF RFID读写器的应用需求。展开更多
We reanalyzed experimental data already published in Friedman J R, Zhang Y, Dai P, et al. Phys Rev B, 1996, 53（15）： 9528. Variable range hopping （VRH） conduction in the insulating three-dimensional n-CdSe samples...We reanalyzed experimental data already published in Friedman J R, Zhang Y, Dai P, et al. Phys Rev B, 1996, 53（15）： 9528. Variable range hopping （VRH） conduction in the insulating three-dimensional n-CdSe samples has been studied over the entire temperature range from 0.03 to 1 K. In the absence of a magnetic field, the low temperature conductivity a of the three samples （A, B and C） obeys the Mott VRH conduction with an appropriate temperature dependence in the prefactor （a = σ0 exp [- （T0/T）]^p with p ≈ 0.25）. This behavior can be explained by a VRH model where the transport occurs by hopping between localized states in the vicinity of the Fermi level, EF, without creation of the Coulomb gap （CG）. On the contrary, no Efros-Shklovskii VRH is observed, suggesting that the density is constant in the vicinity of the EF.展开更多
基金supported by the Natural Science Basic Research Plan in Shaanxi Province of China(2017JM6104)the National Natural Science Foundation of China(Grant Nos.61303037,61472321,61732014)+1 种基金the National Key Research and Development Program of China(2018YFB1003403),the National Basic Research Program(973 Program)of China(2012CB316203)the National High Technology Research and Development Program(863 Program)of China(2012AA011004).
文摘N-hop neighborhoods information is very useful in analytic tasks on large-scale graphs,like finding clique in a social network,recommending friends or advertising links according to one’s interests,predicting links among websites and etc.To get the N-hop neighborhoods information on a large graph,such as a web graph,a twitter social graph,the most straightforward method is to conduct a breadth first search(BFS)on a parallel distributed graph processing framework,such as Pregel and GraphLab.However,due to the massive volume of message transfer,the BFS method results in high communication cost and has low efficiency.In this work,we propose a key/value based method,namely KVB,which perfectly fits into the prevailing parallel graph processing framework and computes N-hop neighborhoods on a large scale graph efficiently.Unlike the BFS method,our method need not transfer large amount of neighborhoods information,thus,significantly reduces the overhead on both the communication and intermediate results in the distributed framework.We formalize the N-hop neighborhoods query processing as an optimization problem based on a set of quantitative cost metrics of parallel graph processing.Moreover,we propose a solution to efficiently load only the relevant neighborhoods for computation.Specially,we prove the optimal partial neighborhoods load problem is NP-hard and carefully design a heuristic strategy.We have implemented our algorithm on a distributed graph frameworkSpark GraphX and validated our solution with extensive experiments over a number of real world and synthetic large graphs on a modest indoor cluster.Experiments show that our solution generally gains an order of magnitude speedup comparing to the state-of-art BFS implementation.
文摘在车联网VANETs(vehicle ad hoc networks)中,车辆的高速移动、有限的无线资源以及不稳定的信号强度,给转发节点的选择机制提出挑战。为此,提出基于模糊逻辑的转发节点选择FLFNS(fuzzy logic-based forwarder nodes selection)算法。利用模糊逻辑算法选择下一跳转发节点,通过模糊逻辑理论,利用车间距离、车辆移动以及链路质量信息选择最优的转发节点。仿真结果表明,与Fuzzbr算法相比,FLFNS算法的端到端传输时延降低近50%,数据包传输成功率提高了10%。
文摘简要介绍了UHF RFID国际标准ISO/IEC18000-6C、EPC Global C1G2及ETSI的空中射频接口要求,采用∑-△调制小数分频PLL频率合成方案,应用LMX2541及ADF4360-8芯片设计了一频率范围在860~960MHz内可跳变的UHF RFID读写器用频率合成器。仿真及实验结果表明,其各项指标均达到或超过ISO/IEC18000-6C、EPC Global C1G2及ETSI标准规定的要求,可以满足未来通用型UHF RFID读写器的应用需求。
文摘We reanalyzed experimental data already published in Friedman J R, Zhang Y, Dai P, et al. Phys Rev B, 1996, 53（15）： 9528. Variable range hopping （VRH） conduction in the insulating three-dimensional n-CdSe samples has been studied over the entire temperature range from 0.03 to 1 K. In the absence of a magnetic field, the low temperature conductivity a of the three samples （A, B and C） obeys the Mott VRH conduction with an appropriate temperature dependence in the prefactor （a = σ0 exp [- （T0/T）]^p with p ≈ 0.25）. This behavior can be explained by a VRH model where the transport occurs by hopping between localized states in the vicinity of the Fermi level, EF, without creation of the Coulomb gap （CG）. On the contrary, no Efros-Shklovskii VRH is observed, suggesting that the density is constant in the vicinity of the EF.
