A Novel Routing Method for Social Delay-Tolerant Networks

The lack of continuous connectivity and a complete path from source to destination makes node communication quite difficult in Delay-Tolerant Networks(DTNs). Most studies focus on routing problems in idealized network environments without considering social properties. Communication devices are carried by individuals in many DTNs; therefore, DTNs are unique social networks to some extent. To design efficient routing protocols for DTNs, it is important to analyze their social properties. In this paper, a more accurate and comprehensive metric for detecting the quality of the relationships between nodes is proposed, by considering the contact time, contact frequency, and contact regularity. An overlapping hierarchical community detection method is designed based on this new metric, and a tree structure is built. Furthermore, we exploit the overlapping community structure and the tree structure to provide message-forwarding paths from the source node to the destination node.The simulation results show that our Routing method based on Overlapping hierarchical Community Detection(ROCD) achieves better delivery rate than SimBet and Bubble Rap, the classic routing protocols, without affecting the average delay.     

基于无人机的三维MIMO信道建模与仿真分析

针对无人机通信环境特点以及多输入多输出(multiple input multiple output,MIMO)系统,将三维空间相关法建模和机身遮挡现象相结合,提出一种无人机的三维移动空间信道模型(three dimensional dynamic spatial channel model, 3DD SCM)。〖JP2〗推导了无人机天线阵列的相关性表达式,同时提出一种遮挡控制器来表示无人机与地面在通信过程中受到机身遮挡的影响。通过仿真分别分析了无人机不同飞行方向和机身遮挡对信道性能的影响,验证了该信道模型的正确性和有效性。该无人机通信模型可用于指导无人机天线设计,研究编码技术。     

An Efficient Multidimensional Fusion Algorithm for IoT Data Based on Partitioning

The Internet of Things (IoT) implies a worldwide network of interconnected objects uniquely addressable, via standard communication protocols. The prevalence of IoT is bound to generate large amounts of multisource, heterogeneous, dynamic, and sparse data. However, IoT offers inconsequential practical benefits without the ability to integrate, fuse, and glean useful information from such massive amounts of data. Accordingly, preparing us for the imminent invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve process efficiency and provide advanced intelligence. In order to determine an acceptable quality of intelligence, diverse and voluminous data have to be combined and fused. Therefore, it is imperative to improve the computational efficiency for fusing and mining multidimensional data. In this paper, we propose an efficient multidimensional fusion algorithm for IoT data based on partitioning. The basic concept involves the partitioning of dimensions (attributes), i.e., a big data set with higher dimensions can be transformed into certain number of relatively smaller data subsets that can be easily processed. Then, based on the partitioning of dimensions, the discernible matrixes of all data subsets in rough set theory are computed to obtain their core attribute sets. Furthermore, a global core attribute set can be determined. Finally, the attribute reduction and rule extraction methods are used to obtain the fusion results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is illustrated.     

轧制界面非稳态油膜润滑特性

以流体力学理论、轧制理论及Hill的特性曲线微分方程解法为基础,建立了轧制界面考虑入口板带厚度、轧辊半径发生波动下非稳态油膜厚度分布动力学模型,提出了油膜波动系数来反映界面油膜厚度绝对波动,并进行了相应的仿真分析.结果表明:非稳态条件下轧制界面油膜厚度分布性态会随时间不同而发生变化,不同时刻最小油膜厚度也会发生变化;界面油膜厚度的绝对波动会随着入口板带厚度、轧辊半径非均匀程度加剧而变大,而入口板带厚度、轧辊半径的变化频率对界面油膜厚度的绝对波动影响较小.     

Alkaline pressure oxidative leaching of bismuth-rich and arsenic-rich lead anode slime

A new alkaline pressure oxidative leaching process(with NaNO3 as the oxidant and NaOH as the alkaline reagent)is proposed herein to remove arsenic,antimony,and lead from bismuth-rich and arsenic-rich lead anode slime for bismuth,gold,and silver enrichment.The effects of the temperature,liquid-to-solid ratio,leaching time,and reagent concentration on the leaching ratios of arsenic,antimony,and lead were investigated to identify the optimum leaching conditions.The experimental results under optimized conditions indicate that the average leaching ratios of arsenic,antimony and lead are 95.36%,79.98%,63.08%,respectively.X-ray diffraction analysis indicated that the leaching residue is composed of Bi,Bi2O3,Pb2Sb2O7,and trace amounts of NaSb(OH)6.Arsenic,antimony,and lead are thus separated from lead anode slime as Na3AsO4·10H2O and Pb2Sb2O7.Scanning electron microscopy and energy-dispersive spectrometry imaging revealed that the samples undergo appreciable changes in their surface morphology during leaching and that the majority of arsenic,lead,and antimony is removed.X-ray photoelectron spectroscopy was used to demonstrate the variation in the valence states of the arsenic,lead,and antimony.The Pb(IV)and Sb(V)content was found to increase substantially with the addition of NaNO3.     

纳米MoS2的制备及应用

综述了纳米二硫化钼常用的几种制备方法:化学气相沉积法、高温硫化法、前驱体分解法、水热法、溶液法及物理方法,阐述了各种制备方法的研究现状及优缺点;介绍了纳米二硫化钼的应用现状及在电学方面的应用进展.     

Radiation hardness characterization of low gain avalanche detector prototypes for the high granularity timing detector

The high granularity timing detector (HGTD) is a crucial component of the ATLAS phase II upgrade to cope with the extremely high pile-up (the average number of interactions per bunch crossing can be as high as 200). With the precise timing information (σ_t～30 ps) of the tracks, the track-to-vertex association can be performed in the “4-D” space. The Low Gain Avalanche Detector (LGAD) technology is chosen for the sensors, which can provide the required timing resolution and good signal-to-noise ratio. Hamamatsu Photonics K.K. (HPK) has produced the LGAD with thicknesses of 35 μm and 50 μm. The University of Science and Technology of China(USTC) has also developed and produced 50 μm LGADs prototypes with the Institute of Microelectronics (IME) of Chinese Academy of Sciences. To evaluate the irradiation hardness, the sensors are irradiated with the neutron at the JSI reactor facility and tested at USTC. The irradiation effects on both the gain layer and the bulk are characterized by I-V and C-V measurements at room temperature (20 ℃) or ?30 ℃. The breakdown voltages and depletion voltages are extracted and presented as a function of the fluences. The final fitting of the acceptor removal model yielded the c-factor of 3.06×10?16 cm?2, 3.89×10?16 cm?2 and 4.12×10?16 cm?2 for the HPK-1.2, HPK-3.2 and USTC-1.1-W8, respectively, showing that the HPK-1.2 sensors have the most irradiation resistant gain layer. A novel analysis method is used to further exploit the data to get the relationship between the c-factor and initial doping density.