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AI-driven drowned-detection system for rapid coastal rescue operations 用于快速海岸救援行动的人工智能驱动的溺水探测系统
Q3 REMOTE SENSING Pub Date : 2023-09-15 DOI: 10.1007/s41324-023-00549-7
Dileep P, M. Durairaj, Sharmila Subudhi, V V R Maheswara Rao, J. Jayanthi, D Suganthi
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引用次数: 0
Mobile wireless ad-hoc network routing protocols comparison for real-time military application 实时军事应用的移动无线自组网路由协议比较
Q3 REMOTE SENSING Pub Date : 2023-09-14 DOI: 10.1007/s41324-023-00535-z
Shams Al Ajrawi, Bang Tran
Abstract Mobile wireless Ad-hoc has become more popular because it forms quickly, has an easy setup, and has easy extensibility. The mobile ad-hoc wireless networks can be further classified according to their applications as follows: Regular user ad-hoc networks are commercial communication that applies to vehicles to help avoid collisions and accidents and live connections to transfer data from car to car. Another application is disaster rescue ad-hoc networking, usually used when a normal infrastructure network is destroyed by storms, earthquakes, tsunamis, etc. Nowadays, a lot of applications, particularly those related to the military and emergency situations, rely on mobile ad hoc wireless networks, where security needs are more challenging to provide than in regular networks. We present the tactical network needs for the military. This platform attempts to assess the possible advantages of mobile ad hoc networks in tactical military applications. This work proposes route discovery using reactive (on-demand) routing protocols where nodes need to just transfer data. This eliminates the requirement for each node to store and maintain any routing tables. This study presents and contrasts the benefits and drawbacks of two fundamental mobile ad hoc routing systems (AODV and DSR). Both protocols are On-Demand routing techniques, and when data needs to be sent, the discovery phase begins. The results of the simulation, the AODV routing approach outperforms the DSV routing method under identical simulated conditions.
摘要移动无线自组网由于其形成速度快、设置简单、易于扩展等特点而越来越受到人们的欢迎。移动自组织无线网络可以根据其应用进一步分类如下:普通用户自组织网络是应用于车辆的商业通信,以帮助避免碰撞和事故,以及在车与车之间传输数据的实时连接。另一个应用是灾难救援ad-hoc网络,通常在正常的基础设施网络被风暴、地震、海啸等破坏时使用。目前,许多应用,特别是与军事和紧急情况有关的应用,依赖于移动自组织无线网络,其安全需求比常规网络更具挑战性。我们提出了军队的战术网络需求。该平台试图评估移动自组织网络在战术军事应用中的可能优势。这项工作提出了使用响应(按需)路由协议的路由发现,节点只需要传输数据。这消除了每个节点存储和维护任何路由表的需求。本研究提出并对比了两种基本的移动自组织路由系统(AODV和DSR)的优缺点。这两种协议都是按需路由技术,当需要发送数据时,就开始发现阶段。仿真结果表明,在相同的仿真条件下,AODV路由方法优于DSV路由方法。
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引用次数: 0
Deep learning-based heat optimization techniques for forecasting indoor temperature changes 基于深度学习的室内温度变化预测热优化技术
Q3 REMOTE SENSING Pub Date : 2023-09-09 DOI: 10.1007/s41324-023-00546-w
Latika Jindal, Nitika Vats Doohan, Swati Vaidya, Hemlata Patel, Arpit Deo
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引用次数: 0
Mapping hotspots of tuberculosis cases with validation on site in Gombak, Selangor, Malaysia 在马来西亚雪兰莪州贡巴克绘制肺结核病例热点并进行现场验证
IF 2.4 Q3 REMOTE SENSING Pub Date : 2023-09-07 DOI: 10.1007/s41324-023-00545-x
Nur Adibah Mohidem, Malina Osman, Z. Hashim, F. Muharam, Saliza Mohd Elias, Aminuddin Ma’pol
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引用次数: 0
Spatiotemporal analysis of air pollutants and river turbidity over Varanasi region, India during COVID-19 second wave 2019冠状病毒病第二波期间印度瓦拉纳西地区空气污染物和河流浑浊度的时空分析
IF 2.4 Q3 REMOTE SENSING Pub Date : 2023-08-25 DOI: 10.1007/s41324-023-00543-z
Munizzah Salim, Shubham Bhattacharjee
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引用次数: 0
Deep convolutional neural network to predict ground water level 深度卷积神经网络预测地下水位
IF 2.4 Q3 REMOTE SENSING Pub Date : 2023-08-25 DOI: 10.1007/s41324-023-00537-x
A. S. Zamani, Aisha Hassan Abdalla Hashim, A. Gopi, K. Moholkar, Mohammed Rizwanullah, Rasool Altaee
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引用次数: 0
COVID-19: adverse population sentiment and place-based associations with socioeconomic and demographic factors COVID-19:不利的人口情绪及其与社会经济和人口因素的地方关联
IF 2.4 Q3 REMOTE SENSING Pub Date : 2023-08-23 DOI: 10.1007/s41324-023-00544-y
A. Hohl, Moongi Choi, Richard Medina, N. Wan, Ming Wen
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引用次数: 0
Spatial federated learning model for identification of corn leaf diseases 玉米叶片病害识别的空间联邦学习模型
IF 2.4 Q3 REMOTE SENSING Pub Date : 2023-07-24 DOI: 10.1007/s41324-023-00538-w
N. Patil, E. Kannan
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引用次数: 0
Spatio-temporal analysis of fire incidences in urban context: the case study of Mashhad, Iran 城市火灾事件的时空分析——以伊朗马什哈德为例
IF 2.4 Q3 REMOTE SENSING Pub Date : 2023-07-21 DOI: 10.1007/s41324-023-00540-2
Mohammad Mahdi Barati Jozan, A. Mohammadi, A. Lotfata, H. Tabesh, Behzad Kiani
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引用次数: 0
Comparative analysis of Air Quality Index prediction using deep learning algorithms 使用深度学习算法预测空气质量指数的比较分析
IF 2.4 Q3 REMOTE SENSING Pub Date : 2023-07-21 DOI: 10.1007/s41324-023-00541-1
Ankita Mishra, Yogesh Gupta
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引用次数: 1
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