Meet-Cloud for Secure and Accurate Distribution of Negative Messages in Vehicular Ad hoc Network

Keeping Vehicular Ad hoc Network(VANET) from attacks requires secure and efficient distribution of information about bad entities. Negative messages are pieces of information that define the negative attributes of vehicles. By formally defining the negative message, we observe that accuracy is essential for its efficient distribution. We formally define the coverage percentage and accurate coverage percentage to describe the availability and distribution efficiency of negative message. These two metrics can jointly evaluate the performance of a distribution method. To obtain both high coverage percentage and high accurate coverage percentage, we propose meet-cloud, a scheme based on meet-table and cloud computing to securely and accurately distribute negative messages in VANET. A meet-table in a Road Side Unit(RSU) records the vehicles it encounters. All meettables are sent to cloud service to aggregate a global meet-table. The algorithm for distributing and redistributing negative messages are designed. Security analysis shows that meet-cloud is secure against fake and holding on to negative message attacks. Simulations and analysis demonstrate that meet-cloud is secure under denial of service and fake meet-table attacks. The simulation results also justify that meet-cloud outperforms the RSU broadcast and epidemic model.     

利用SRTM数据修测高山区地貌的探讨

在高海拔区域测制地形图,如青藏高原地区,所获取的遥感影像局部存在着大范围的高山阴影或云雪覆盖,造成立体观测地貌困难的现象,探讨应用SRTM数据对这类特殊区域地貌进行修测补绘。介绍了数据处理的作业过程,并对实验区SRTM与SPOT5影像立体测图成果进行实验对比分析,成果符合要求。     

Learning Multi Labels from Single Label——An Extreme Weak Label Learning Algorithm

This paper presents a novel algorithm for an extreme form of weak label learning, in which only one of all relevant labels is given for each training sample. Using genetic algorithm, all of the labels in the training set are optimally divided into several non-overlapping groups to maximize the label distinguishability in every group. Multiple classifiers are trained separately and ensembled for label predictions. Experimental results show significant improvement over previous weak label learning algorithms.     

基于小波变换的雷达目标回波信号提取算法

随着雷达技术的快速发展,低信噪比(signal-to-noise ratio, SNR)条件下的雷达目标信号提取越来越重要。传统的脉冲压缩算法在低SNR条件下作用不明显。提出了一种新的在低SNR条件下的雷达目标回波信号提取算法。该算法在传统脉冲压缩算法的基础之上,采用小波变换对脉冲压缩后的信号进行分解,提取信号高频信息的小波系数。再用改进的小波变换模极大值算法对信号的高频信息进行去噪,然后重构信号的高频信息,最后再重构完整的雷达目标回波信号。仿真结果表明,该算法能够有效地去除噪声,提取出雷达目标的回波信号,相比于脉冲压缩算法及其他算法性能得到了很大的提升。     

三维打印器件后处理液研究

分析了不同的填充强化剂(后处理A液)、不同的后处理B液基料、后处理B液中水性环氧乳液的掺量及后处理液浸泡时间对石膏基打印器件耐水性、抗压强度、尺寸偏差、表面微观结构的影响.结果表明:经纳米硅超硬固化剂处理后的打印器件抗压强度大幅提高,纳米硅超硬固化剂最适宜作为后处理A液;水性聚氨酯乳液PU-202B最适宜作为后处理B液的基料;后处理B液中加入水性环氧乳液,处理后打印器件的抗压强度增加、吸水率降低,但水性环氧乳液含量增加到一定程度后会出现尺寸偏差;后处理A、B液最佳浸泡时间分别为130 s和35 s;先浸泡后处理A液、后浸泡后处理B液的打印器件具有抗压强度高、吸水率小、表面较光滑、不脱粉等优良性能.     

梯度网络平稳点集合的Hopfield稳定性与吸引域

给出梯度神经网络模型的电路实现 ,证明了网络平稳点集合的全局 Hopfield稳定性。对各个平稳点吸引域的几何估计得到了相关结果 ,这为网络初始输入的数量与方向判定提供了基本的量度参数准则。     

Abrupt climate change：Debate or action

Global abrupt climate changes have been documented by various climate records, including ice cores, ocean sediment cores, lake sediment cores, cave deposits, loess deposits and pollen records. The climate system prefers to be in one of two stable states, i.e. interstadial or stadial conditions, but not in between. The transition between two states has an abrupt character. Abrupt climate changes are, in general, synchronous in the northern hemisphere and tropical regions. The timescale for abrupt climate changes can be as short as a decade. As the impacts may be potentially serious, we need to take actions such as reducing CO2 emissions to the atmosphere.     

用MUX实现组合逻辑的卡诺图列解法

本文采用卡诺图列解法来处理MUX组件的地址变量少于待发生逻辑函数变量的设计问题。方法简单易行、适用范围广。尤其是对于含有任意项的逻辑函数中任意项的处理,提供了寻找最佳方案的途径。     

高中学生的物理成绩的调查与分析

经过两年广泛的调查，对调查结果进行统计，按教育统计学相关分析的计算方法，逐条求出各单元的相应系数。并对量化的部分结果进行了初步分析，指出了高中学生学习物理困难的主要原因，以及进行矫治的初步措施。该调查采集面广，样本大，计算方法科学，量化结果数据可靠，所反映的信息具有一定的代表性，可供中学物理同仁在教学改革中参考。     

有关勾股定理、射影定理的研究性论题

本文对勾股定理、射影定理的研究性论题进行了研究。