智能超表面赋能移动边缘计算部分任务卸载策略

针对移动边缘计算(MEC)任务卸载性能易受障碍物阻挡影响的问题,该文提出一种双智能超表面(RIS) 赋能的移动边缘计算任务部分卸载框架。首先,分析两个RIS之间的反射对链路增益的影响。其次,联合考虑终端用户的发射功率、终端用户的卸载速率、任务卸载量、卸载时间的分配以及RIS相移约束,旨在建立一个能耗最小化优化问题。最后,采用交替迭代算法,将原非凸问题分解为两个子问题,并利用Dinkelbach方法和最优性条件进行求解。仿真结果验证了所提算法的快速收敛特性以及在降低系统能耗方面的有效性。     

可见光通信中融合VOOK和分层OFDM的高效频谱混合调制方法

为了满足可见光通信(VLC)的照明和高速率数据传输的双重需求,该文提出一种融合可变开关键控(VOOK)和分层正交频分复用(OFDM)的高效频谱混合调制。首先针对分层OFDM调制方法设计了相应的VOOK信号,避免了VOOK对分层OFDM信息传输的干扰。然后,为了保证混合调制信号工作在发光二极管(LED)的线性区间,提出一种全新的重构分层光OFDM(RLO-OFDM),并构建VOOK-RLO-OFDM混合调制方法,实现了调光控制和高效频谱数据传输的双重功能。混合调制能够并行地进行VOOK和RLO-OFDM的信号检测,同时,RLO-OFDM的信号检测可以采用标准的OFDM接收机,无须依赖串行干扰消除方式,显著地降低了接收复杂度和处理时延。仿真结果表明,所提方法能够实现线性调光控制,且具有高效的频谱效率。     

Real-time Safety Helmet-wearing Detection Based on Improved YOLOv5

Safety helmet-wearing detection is an essential part of the intelligent monitoring system. To improve the speed and accuracy of detection, especially small targets and occluded objects, it presents a novel and efficient detector model. The underlying core algorithm of this model adopts the YOLOv5 (You Only Look Once version 5) network with the best comprehensive detection performance. It is improved by adding an attention mechanism, a CIoU (Complete Intersection Over Union) Loss function, and the Mish activation function. First, it applies the attention mechanism in the feature extraction. The network can learn the weight of each channel independently and enhance the information dissemination between features. Second, it adopts CIoU loss function to achieve accurate bounding box regression. Third, it utilizes Mish activation function to improve detection accuracy and generalization ability. It builds a safety helmet-wearing detection data set containing more than 10,000 images collected from the Internet for preprocessing. On the self-made helmet wearing test data set, the average accuracy of the helmet detection of the proposed algorithm is 96.7%, which is 1.9% higher than that of the YOLOv5 algorithm. It meets the accuracy requirements of the helmet-wearing detection under construction scenarios.     

Deep Convolutional Neural Network Approach for COVID-19 Detection

Coronavirus disease 2019 (Covid-19) is a life-threatening infectious disease caused by a newly discovered strain of the coronaviruses. As by the end of 2020, Covid-19 is still not fully understood, but like other similar viruses, the main mode of transmission or spread is believed to be through droplets from coughs and sneezes of infected persons. The accurate detection of Covid-19 cases poses some questions to scientists and physicians. The two main kinds of tests available for Covid-19 are viral tests, which tells you whether you are currently infected and antibody test, which tells if you had been infected previously. Routine Covid-19 test can take up to 2 days to complete; in reducing chances of false negative results, serial testing is used. Medical image processing by means of using Chest X-ray images and Computed Tomography (CT) can help radiologists detect the virus. This imaging approach can detect certain characteristic changes in the lung associated with Covid-19. In this paper, a deep learning model or technique based on the Convolutional Neural Network is proposed to improve the accuracy and precisely detect Covid-19 from Chest Xray scans by identifying structural abnormalities in scans or X-ray images. The entire model proposed is categorized into three stages: dataset, data pre-processing and final stage being training and classification.     

石墨烯海绵的制备与性能及在环境修复中的应用研究进展

石墨烯海绵是一种新型的多孔三维结构材料,具有比表面积大、吸附能力强、力学强度高、易于制备等优良特性。这些独特的性质使得石墨烯海绵吸附材料成为环境污染处理中的热点,同时也在很多领域里有着广泛的应用。概述了石墨烯海绵的两种不同制备方法及其对环境中的有机溶剂、重金属离子、抗生素以及染料的吸附研究,并进一步展望了石墨烯海绵在其他领域的潜在应用。     

Localization using GPS and VISION aided INS with an image database and a network of a ground-based reference station in outdoor environments

GPS/INS integrated systems do not guarantee robustness and accuracy of localization, because GPS has vulnerability to external disturbances. However, the overall performance and reliability of the system can be significantly improved by fusing multiple sensors with a different operating principle. In outdoor environments where GPS may be blocked, there are many features compared to the open space and these features can provide much information for UGV localization. Thus, this paper proposes an improved localization algorithm based on the hierarchical federation of three measurement layers, i.e., GPS, INS, and visual localization, to overcome the shortcomings of GPS/INS integrated systems. The proposed algorithm automatically switches the operation modes according to GPS status and a network of a ground-based reference station. A vocabulary tree with SURF is used in the visual localization method. In the data fusion of visual localization and INS, an asynchronous and time-delayed data fusion algorithm is presented because visual localization is always time-delayed compared with INS. By using DGPS to obtain the reference position under the dynamic conditions of the reference station, the restrictions of the conventional DGPS are overcome and all UGVs within WiBro communication range of the reference station can accurately estimate the position with a common GPS. The experiment results with a predefined path demonstrate enhancement of the robustness and accuracy of localization in outdoor environments.     

基于MATLAB的同步发电机PSS与励磁系统仿真

在分析同步发电机励磁控制系统模型的基础上,研究了以电力系统稳定器（Power System Stabilizer—PSS）为辅助控制的同步发电机励磁控制方式,并基于MATLAB／SIMULINK构建了包含PSS的励磁控制系统的仿真模型。根据仿真结果分析在电力系统出现不同故障（三线短路以及断线故障）情况下带有PSS的励磁控制系统的控制性能,并与不加载PSS的励磁系统进行了比较,得到了在同步机励磁系统中加载PSS的必要性,为励磁系统的设计提供了依据。     

无纠缠量子秘密共享中酉操作的选择

文中针对不使用纠缠态的量子秘密共享方案,提出了一种Bell态替换攻击策略,并定量分析了当量子秘密共享方案采用常见的几种酉操作组合时,这种攻击的最小失败概率,从而得到酉操作的选择和Bell态替换攻击效果之间的若干关系.对于量子秘密共享方案的设计和实施中,如何选择酉操作以保证通信安全的问题,文中的工作具有重要的指导作用.     

片相似性各项异性扩散图像去噪

提出了一种基于片相似性的各项异性扩散图像去噪方法.传统的各项异性图像去噪方法都是基于单个像素点的灰度相似性(或梯度信息),不能很好地保持弱梯度边缘和纹理等细节信息.基于片相似性的非局部图像去噪方法由于利用了邻域像素的灰度相似性,而能够很好地保持纹理等细节信息.将片相似性思想引入到各项异性扩散中,利用片相似性构造扩散函数,同时将片相似性各项异性扩散模型扩展到彩色图像的去噪.实验结果表明,提出的改进方法能很好地保持纹理等细节信息,不存在各项异性扩散普遍存在的明显的阶梯效应,同时比非局部图像去噪方法速度快.医学图像去噪实例也表明所提出方法具有很好的应用前景.     

高精度pH测量仪研究

研究并设计了一种工业用高精度酸碱度(pH)测量仪.该pH测量仪的研制是以低功耗单片机MSP430和pH电极传感器为核心器件,算法上采用软件二点标定和温度补偿法.该pH测量仪具有两点标定、自动温度补偿、历史数据存储、实时数据传输等功能,解决了pH测量仪的高精度、低功耗、历史数据存储和实时数据传输等问题.该pH测量仪已经在工业生产中得到了应用.