随着物联网技术的快速发展,低功率广域网络(Low-Power Wide-Area Network,LPWAN)以其特有的优势得到广泛的应用。在众多的LPWAN技术应用场景中,地磁作为无线传感器被应用于各种停车管理的场景中,其中,国内停车行业关注度最高的是远距离...随着物联网技术的快速发展,低功率广域网络(Low-Power Wide-Area Network,LPWAN)以其特有的优势得到广泛的应用。在众多的LPWAN技术应用场景中,地磁作为无线传感器被应用于各种停车管理的场景中,其中,国内停车行业关注度最高的是远距离无线电(Long Range Radio,Lo Ra)和窄带物联网(Narrow Band Internet of Things,NB-Io T)两种通信技术的地磁。本文旨在从地磁的Lo Ra与NB-Io T通信技术方面进行系统的梳理和比较分析,为地磁的选择应用提供有力的技术参考和储备。展开更多
针对目前基于信道脉冲响应(Channel Impulse Response,CIR)的非视距(None Line of Sight,NLoS)/视距(Line of Sight,LoS)识别方法精度低、泛化能力差的问题,提出了一种多层卷积神经网络(Convolutional Neural Network,CNN)与通道注意力...针对目前基于信道脉冲响应(Channel Impulse Response,CIR)的非视距(None Line of Sight,NLoS)/视距(Line of Sight,LoS)识别方法精度低、泛化能力差的问题,提出了一种多层卷积神经网络(Convolutional Neural Network,CNN)与通道注意力模块(Channel Attention Module,CAM)相结合的NLoS/LoS识别方法。在多层CNN中嵌入CAM提取原始CIR的时域数据特征,利用全局平均池化层代替全连接层进行特征整合并分类输出。使用欧洲地平线2020计划项目eWINE公开的数据集进行不同结构模型和不同识别方法的对比实验,结果表明,所提出的CNN-CAM模型LoS和NLoS召回率分别达到了92.29%与87.71%,准确率达到了90.00%,F1分数达到了90.22%。与现有多种传统识别方法相比,均具有更好的识别效果。展开更多
A metropolitan city such as Los Angeles (LA) is an ideal study site with a very high population density, and it houses at least 3 treatment plants where sewage is treated preliminarily and then progressing to tertiary...A metropolitan city such as Los Angeles (LA) is an ideal study site with a very high population density, and it houses at least 3 treatment plants where sewage is treated preliminarily and then progressing to tertiary treatment before discharging into the LA River. We will gain a better understanding of the water quality in the LA River and the nitrate load in the watershed system by examining the influence of waste water treatment plants (WWTPs). The goal of this study is to pinpoint the exact source of nitrate in the LA River using the isotope signatures. We have selected sampling locations both upstream and downstream of the WWTP. This serves to monitor nitrate levels, aiding in the assessment of treatment plant effectiveness, pinpointing nitrate pollution sources, and ensuring compliance with environmental regulations. The research explores the isotopic composition of NO3 in relation to atmospheric nitrogen and Vienna Standard Mean Ocean Water, shedding light on the contributions from various sources such as manure, sewage, soil organic nitrogen, and nitrogen fertilizers. Specifically, there is a change in the δ15NAir value between the dry and wet seasons. The isotope values in the Tillman WWTP sample changed between dry and wet seasons. Notably, the presence of nitrate originating from manure and sewage is consistent across seasons, emphasizing the significant impact of anthropogenic and agricultural activities on water quality. This investigation contributes to the broader understanding of nitrogen cycling in urban water bodies, particularly in the context of wastewater effluent discharge. The findings hold implications for water quality management and highlight the need for targeted interventions to mitigate the impact of nitrogen-containing compounds on aquatic ecosystems. Overall, the study provides a valuable framework for future research and environmental stewardship efforts aimed at preserving the health and sustainability of urban water resources. This data informs decisions regarding additional treatment or mitigation actions to safeguard downstream water quality and ecosystem health.展开更多
文摘随着物联网技术的快速发展,低功率广域网络(Low-Power Wide-Area Network,LPWAN)以其特有的优势得到广泛的应用。在众多的LPWAN技术应用场景中,地磁作为无线传感器被应用于各种停车管理的场景中,其中,国内停车行业关注度最高的是远距离无线电(Long Range Radio,Lo Ra)和窄带物联网(Narrow Band Internet of Things,NB-Io T)两种通信技术的地磁。本文旨在从地磁的Lo Ra与NB-Io T通信技术方面进行系统的梳理和比较分析,为地磁的选择应用提供有力的技术参考和储备。
文摘针对目前基于信道脉冲响应(Channel Impulse Response,CIR)的非视距(None Line of Sight,NLoS)/视距(Line of Sight,LoS)识别方法精度低、泛化能力差的问题,提出了一种多层卷积神经网络(Convolutional Neural Network,CNN)与通道注意力模块(Channel Attention Module,CAM)相结合的NLoS/LoS识别方法。在多层CNN中嵌入CAM提取原始CIR的时域数据特征,利用全局平均池化层代替全连接层进行特征整合并分类输出。使用欧洲地平线2020计划项目eWINE公开的数据集进行不同结构模型和不同识别方法的对比实验,结果表明,所提出的CNN-CAM模型LoS和NLoS召回率分别达到了92.29%与87.71%,准确率达到了90.00%,F1分数达到了90.22%。与现有多种传统识别方法相比,均具有更好的识别效果。
文摘A metropolitan city such as Los Angeles (LA) is an ideal study site with a very high population density, and it houses at least 3 treatment plants where sewage is treated preliminarily and then progressing to tertiary treatment before discharging into the LA River. We will gain a better understanding of the water quality in the LA River and the nitrate load in the watershed system by examining the influence of waste water treatment plants (WWTPs). The goal of this study is to pinpoint the exact source of nitrate in the LA River using the isotope signatures. We have selected sampling locations both upstream and downstream of the WWTP. This serves to monitor nitrate levels, aiding in the assessment of treatment plant effectiveness, pinpointing nitrate pollution sources, and ensuring compliance with environmental regulations. The research explores the isotopic composition of NO3 in relation to atmospheric nitrogen and Vienna Standard Mean Ocean Water, shedding light on the contributions from various sources such as manure, sewage, soil organic nitrogen, and nitrogen fertilizers. Specifically, there is a change in the δ15NAir value between the dry and wet seasons. The isotope values in the Tillman WWTP sample changed between dry and wet seasons. Notably, the presence of nitrate originating from manure and sewage is consistent across seasons, emphasizing the significant impact of anthropogenic and agricultural activities on water quality. This investigation contributes to the broader understanding of nitrogen cycling in urban water bodies, particularly in the context of wastewater effluent discharge. The findings hold implications for water quality management and highlight the need for targeted interventions to mitigate the impact of nitrogen-containing compounds on aquatic ecosystems. Overall, the study provides a valuable framework for future research and environmental stewardship efforts aimed at preserving the health and sustainability of urban water resources. This data informs decisions regarding additional treatment or mitigation actions to safeguard downstream water quality and ecosystem health.
