孪生导向锚框RPN网络实时目标跟踪

目的 区域推荐网络（region proposal network,RPN）与孪生网络（Siamese）相结合进行视频目标跟踪,显示了较高的准确性。然而,孪生RPN网络（Siamese region proposal network, SiamRPN）目标跟踪器依赖于密集的锚框策略,会产生大量冗余的锚框并影响跟踪的精度和速度。为了解决该问题,本文提出了孪生导向锚框RPN网络（Siamese-guided anchor RPN,Siamese GA-RPN）。方法 Siamese GA-RPN的主要思想是利用语义特征来指导锚框生成。其中导向锚框网络包括位置预测模块和形状预测模块,这两个模块分别利用孪生网络中CNN（convolutional neural network）产生的语义特征预测锚框的位置和长宽尺寸,减少了冗余锚框的产生。然后,进一步设计了特征自适应模块,利用每个锚框的形状信息,通过可变卷积层来修正跟踪目标的原始特征图,降低目标特征与锚框信息的不一致性,提高了目标跟踪的准确性。结果 在3个具有挑战性的视频跟踪基准数据集VOT（video object tracking）2015、VOT2016和VOT2017上进行了跟踪实验,测试了算法在目标快速移动、遮挡和光照等复杂场景下的跟踪性能,并与多种优秀算法在准确性和鲁棒性两个评价指标上进行定量比较。在VOT2015数据集上,本文算法与孪生RPN网络相比,准确性提高了1.72%,鲁棒性提高了5.17%;在VOT2016数据集上,本文算法与孪生RPN网络相比,准确性提高了3.6%,鲁棒性提高了6.6%;在VOT2017数据集上进行实时实验,本文算法表现出了较好的实时跟踪效果。结论 通过孪生导向锚框RPN网络提高了锚框生成的有效性,确保了特征与锚框的一致性,实现了对目标的精确定位,较好地解决了锚框尺寸对目标跟踪精度的影响。在目标尺度发生变化、遮挡、光照条件变化和目标快速运动等复杂场景下仍然表现出了较强的鲁棒性和适应性。     

论教育家校长的塑造力——以清华大学“永远的校长”梅贻琦为例

大学需要教育家校长,只有教育家校长才能以其特有的塑造力来创造大学。清华大学老校长梅贻琦正是凭借其大学的塑造力在动荡的时代造就了伟大的清华大学。他在各方面展现自己的塑造力,使清华大学在国内外独树一帜。在当今大学需要教育家办学的时代,我们的大学校长要学习梅贻琦先生,努力成为教育家校长,在各个方面展现出自己对大学的塑造能力,为大学发展贡献自己的力量,为我国造就世界一流大学。     

现代生态家纺产品与图案设计的发展趋势

随着我国经济的飞速发展,人民生活水平的日益提高以及国际市场需求的扩大,家居装饰已从单一的实用性为主朝着艺术性、个性化、多功能方向发展。家居艺术更加追求时代感和文化理念。同时生态家纺生产企业的数量正在不断的增加,生产规模的扩大和生产品质的提高,不仅需要引进先进的生产加     

多分量调频信号的阵列处理时频特征提取

在应用时频—MUSIC方法对多分量调频信号进行到达角估计的问题中,针对时频分布中交叉项的存在对估计性能的影响,本文提出了一种基于阵列信号处理抑制时频分布交叉项的新方法。该方法利用多信源在各阵元上的延迟对时频分布交叉项振荡相位的影响,联合运用多通道信号处理和图像处理的方法,采用二维乘法滤波,减小高频振荡交叉项在时频图像中的占据区域,从而达到抑制交叉项的目的。仿真实验显示该方法具有良好的效果。     

温敏型有机-无机杂化纳米微胶囊的渗透性能

将N-异丙基丙烯酰胺引入以正十六烷为模板的苯乙烯与甲基丙烯酸-3-三甲氧基硅丙酯细乳液共聚法制备纳米微胶囊的体系,得到一种温敏型有机-无机杂化纳米微胶囊。采用紫外可见分光光度计研究该种微胶囊在临界温度附近对甲酚红的载入和释放行为。结果表明N-异丙基丙烯酰胺的引入和温度均影响纳米微胶囊对甲酚红的载入与释放。微胶囊在临界温度以上具有一定渗透速率,临界温度以下渗透不明显,能较好地实现开-关式环境温敏型控制吸附和释放。     

数据采集系统的发展与应用

数据采集是对一个或多个信号获取对象信息的过程。数据采集器是一种具有实验室或现场进行实时数据采集、自动存储记录、信号预处理、即时显示、即时状态分析、自动传输等功能的自动化设备。本主要介绍了数据采集系统的最新发展、系统并行串行总线接口、系统通信的新技术、国内外常用的数据采集器及不同采集器的特点和存在的问题。在此基础上,介绍了数据采集监控系统在几个不同领域应用的实例。     

Research on topology control based on Voronoi diagram algorithm in FSO networks

Telecommunication Systems - In a wireless sensor network (WSN) where positioning information is not assumed or is partially available, efficient data access is a very challenging issue especially...     

Efficient and photostable CsPbl2Br solar cells realized by adding PMMA

Organic-inorganic hybrid perovskite materials demonstrate promising applications in high-efficiency perovskite solar cells (PSCs) with a certified power conversion efficiency (PCE) of 25.5％ (https://www.nrel.gov/pv/cell-efficiency.html).However, intrinsically volatile and thermally unstable nature of the organic cations result in poor thermal stability of organic-inorganic hybrid perovskite materials, hampering the commercialization of organic-inorganic hybrid PSCs[1].All-inorganic CsPbl3-xBrx (x =0-3) perovskites have been attracting great attention in recent years because of their higher thermal stability[2].Among the reported CsPbl3-xBrx perovskites, CsPbl2Br bears a reasonable balance between bandgap and phase stability, thus becomes the most extensively studied material[3-15].Though there are many works aiming at achieving high-efficiency CsPbl2Br PSCs, improving the photostability of CsPbl2Br PSCs is another key for commercialization of all-inorganic PSCs.Intriguingly, it has been reported that CsPbl2Br is susceptible to make light-induced phase segregation, i.e.severe segregation of CsPbl2Br to low-bandgap I-rich and wide-bandgap Br-rich domains via ion diffusion, leading to obvious current-voltage hysteresis and decrease of stabilized power output (SPO)[16-20].Such a light-induced phase segregation can be suppressed by optimizing the interface between perovskite layer and charge-transport layer[18, 19].     

Target Positioning Algorithm Based on RSS Fingerprints of SVM of Fuzzy Kernel Clustering

The positioning technology based on receive signal strength (RSS) fingerprints has become one of the hottest research spots with its advantages of simple deployment, low cost, and single parameter. However, in the limited space, the multipath and shadowing, result in poor separability of the fingerprint data, and low accuracy of target localization. In this paper, a novel RSS fingerprints positioning algorithm that is based on fuzzy kernel clustering SVM is proposed to combat the multipath and shadowing effects. The first step of the proposed positioning algorithm is to use kernel function to map the traditional fingerprints sample data to high-dimensional feature space to generate fuzzy classes. The second step is to generate binary-class SVM of fuzzy class based on the relationship between classes and internal discrete information of each class. After that, we can use the binary fuzzy class SVM to dichotomize the classified fingerprints in the first step, and combine these dichotomous SVMs into a handstand classification binary tree. And thus, the proposed positioning algorithm achieves quick and accurate positioning. Experimental results show that the positioning accuracy and locating stability of proposed positioning algorithm are improved by 38.73% and 59.26%, respectively, compared with the traditional RSS fingerprints algorithm.