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Recreating Lunar Environments by Fusion of Multimodal Data Using Machine Learning Models 利用机器学习模型融合多模态数据重建月球环境
Pub Date : 2022-11-01 DOI: 10.3390/ecsa-9-13326
Ana C. Castillo, Jesus A. Marroquin-Escobedo, Santiago Gonzalez-Irigoyen, Marlene Martinez-Santoyo, R. Villalpando-Hernandez, C. Vargas-Rosales
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引用次数: 0
VNetOS: Virtualised Distributed and Parallel Sensor Network Operating Environment for the IoT and SHM VNetOS:面向物联网和SHM的虚拟化分布式并行传感器网络运行环境
Pub Date : 2022-11-01 DOI: 10.3390/ecsa-9-13212
S. Bosse
: Dealing with distributed and parallel computing in strong heterogeneous environments, e.g., distributed sensor networks, is still a challenge at the algorithmic, communication, and application levels. Heterogeneity is related to different computer and network (communication) ar-chitectures. Virtualization can hide and unify heterogeneity. Besides inter-process communication and synchronization, the unified access and monitoring of computing nodes (devices, computers, processors) is required to handle distributed and parallel systems in a comfortable and easy-to-access manner. Especially in education, the access and control of a large set of computing nodes is difficult, which lowers the learning curve significantly. In this work, a unified distributed and parallel framework and Web tools are introduced using Virtual Machines (VM) and Web browsers to control them. The framework enables the control, monitoring, and study of distributed-parallel systems, especially addressing sensor networks and IoT networks. Nodes can be arranged in a graphical drawing world or script-based. Virtual network nodes are assigned to VM instances that can be created inside the browser using Web worker processes or can be attached to externally running VM instances via a Web control API. New VM instances or processes can be started and controlled instantly. The graphical UI provides access to the internal and external nodes, programming editors, and monitor shells. The VMs can be generic, but in this work there is a focus on JavaScript and Lua. The framework provides augmented virtuality, i.e., a coupling of physical and virtual worlds.
在强异构环境中处理分布式和并行计算,例如分布式传感器网络,在算法、通信和应用层面上仍然是一个挑战。异构性与不同的计算机和网络(通信)架构有关。虚拟化可以隐藏和统一异构。除了进程间通信和同步外,还需要对计算节点(设备、计算机、处理器)进行统一访问和监控,以便以舒适和易于访问的方式处理分布式和并行系统。特别是在教育领域,对大量计算节点的访问和控制是困难的,这大大降低了学习曲线。本文介绍了一个统一的分布式并行框架和Web工具,使用虚拟机和Web浏览器对其进行控制。该框架能够控制、监测和研究分布式并行系统,特别是寻址传感器网络和物联网网络。节点可以安排在图形绘图世界或基于脚本。虚拟网络节点被分配给VM实例,这些实例可以在浏览器中使用Web worker进程创建,也可以通过Web控制API附加到外部运行的VM实例。可以立即启动和控制新的虚拟机实例或进程。图形UI提供了对内部和外部节点、编程编辑器和监视器shell的访问。虚拟机可以是通用的,但在这项工作中,重点是JavaScript和Lua。该框架提供增强的虚拟性,即物理世界和虚拟世界的耦合。
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引用次数: 0
Light-Tunable Hybrid Organic–Inorganic Nanostructured Layers for Virtual Sensor Arrays 用于虚拟传感器阵列的光可调谐混合有机-无机纳米结构层
Pub Date : 2022-11-01 DOI: 10.3390/ecsa-9-13347
J. Burlachenko, I. Kruglenko, S. Capone, M. Epifani, P. Siciliano, B. Snopok
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引用次数: 0
Numerical Study of a PVDF-Based Strain Sensor for Damage Detection of an Asphalt Concrete Pavement Subject to Dynamic Loads 基于pvdf的沥青混凝土路面动载损伤传感器的数值研究
Pub Date : 2022-11-01 DOI: 10.3390/ecsa-9-13318
Jiayue Shen, Korkut Bekiroglu, Ali Tekeoglu, I. Boz, Weiru Chen, Minghao Geng
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引用次数: 0
Quinoline-Based Hydrazone Derivative as a Biocide Chemosensor: Synthesis and Sensing Studies 喹啉类腙衍生物作为杀菌剂化学传感器:合成与传感研究
Pub Date : 2022-11-01 DOI: 10.3390/ecsa-9-13199
R. P. Sousa, S. Costa, R. B. Figueira, M. Raposo
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引用次数: 0
Polarimetric Distortion Analysis of L- and S-Band Airborne SAR (LS-ASAR): A Precursor Study of the Spaceborne Dual-Frequency L- and S-Band NASA ISRO Synthetic Aperture Radar (NISAR) Mission L波段和s波段机载SAR (LS-ASAR)偏振畸变分析:星载双频L波段和s波段NASA ISRO合成孔径雷达(NISAR)任务的前导研究
Pub Date : 2022-11-01 DOI: 10.3390/ecsa-9-13186
S. V. N. Santhosh Kumar
: The polarimetric calibration (PolCal) is an essential process to ensure the minimization of distortions from airborne and spaceborne SAR data for scattering-based characterization of the targeted objects. The present study investigates the polarimetric distortions in the L-and S-band airborne dual-frequency SAR data. The L- and S-band airborne SAR (LS-ASAR) is a precursor mission of the spaceborne dual-frequency L- and S-band NASA ISRO Synthetic Aperture Radar (NISAR). The present work utilizes the LS-ASAR data acquired over the Rosamond Corner Reflector Array (RCRA). The polarimetric signature analysis of co-pol and cross-pol channels shows that perfect behavior is shown by the co-pol signature but the distortions could be easily identified in the cross-pol signatures.
偏振定标(PolCal)是确保机载和星载SAR数据对目标物体散射特征失真最小化的重要过程。本文研究了l波段和s波段机载双频SAR数据的极化畸变。L波段和s波段机载SAR (LS-ASAR)是NASA ISRO星载双频L波段和s波段合成孔径雷达(NISAR)的先导任务。本工作利用罗斯蒙德角反射器阵列(RCRA)上获得的LS-ASAR数据。对共pol信道和交叉pol信道的极化特征分析表明,共pol信道表现出完美的特性,但在交叉pol信道中容易识别出失真。
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引用次数: 1
Satellite Requirements for Observation of Close Proximity Celestial Bodies 近距离天体观测的卫星要求
Pub Date : 2022-11-01 DOI: 10.3390/ecsa-9-13329
Juan Misael Gongora-Torres, C. Vargas-Rosales, R. Villalpando-Hernandez, Julietth Fernanda Contreras-Venegas, Christian E. Duran-Bonilla
: Celestial bodies of our solar system remain as a major unexplored and unexploited reserve of natural resources available to humans. Furthermore, those constitute a valuable source of information about the origins and evolution of the solar system and an alternative to establish human settlements in the future. Observation and understanding of the land conditions of those celestial bodies is vital to learn more about those celestial bodies, to generate accurate maps of them, to look for natural resources of interest, and to evaluate the feasibility and help in the preparation of future land missions. A satellite constellation constitutes an important infrastructure element to observe those celestial bodies and to transmit the retrieved information back to Earth. Nonetheless, the operation of sensing satellites in other planets needs understanding of the requirements to perform such observations. In this paper we discuss those sensing requirements from the point of view of orbits and payload requirements for one of our closest neighbors of the solar system (Moon, Mars). To analyze the orbit of the sensing satellite, we discuss the required altitude to facilitate ground observation, the orbit’s conditions (such as radiation levels and orbital perturbations, among others), suitable orbit configurations, required number of satellites, and ways to estimate the required time to perform full observation of the celestial body. To evaluate suitable payloads, we discuss available information in the literature (such as known atmospheric and land conditions) to determine the best observation frequencies and determine the best kind of payload (such as sensors, a camera, or a lower frequency observation payload) to study that celestial body. Finally, we discuss some important considerations such as the requirements of satellite communication link to transmit the retrieved information back to Earth.
我们太阳系的天体仍然是人类可利用的未勘探和未开发的主要自然资源储备。此外,这些是关于太阳系起源和演变的宝贵资料来源,也是将来建立人类住区的另一种选择。观测和了解这些天体的陆地条件对于更多地了解这些天体,生成精确的天体地图,寻找感兴趣的自然资源,评估可行性并帮助准备未来的陆地任务至关重要。卫星星座是观测这些天体和将检索到的信息传回地球的重要基础设施。然而,在其他行星上进行遥感卫星的操作需要了解进行这种观测的要求。在本文中,我们从轨道和有效载荷要求的角度讨论了这些传感要求,为我们太阳系最近的邻居之一(月球,火星)。为了分析遥感卫星的轨道,我们讨论了便于地面观测所需的高度、轨道条件(如辐射水平和轨道扰动等)、合适的轨道配置、所需的卫星数量以及估算对天体进行全面观测所需时间的方法。为了评估合适的有效载荷,我们讨论了文献中的可用信息(如已知的大气和陆地条件),以确定最佳观测频率,并确定最佳有效载荷类型(如传感器、相机或低频观测有效载荷)来研究该天体。最后,我们讨论了一些重要的考虑因素,如卫星通信链路的要求,将检索到的信息传回地球。
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引用次数: 0
Assessment and Monitoring of Optically Active Water Quality Parameters on Wetland Ecosystems Based on Remote Sensing Approach: A Case Study on Harike and Keshopur Wetland over Punjab Region, India 基于遥感方法的湿地生态系统光活性水质参数评价与监测——以印度旁遮普地区Harike和Keshopur湿地为例
Pub Date : 2022-11-01 DOI: 10.3390/ecsa-9-13361
Mohit Arora, Ashwini N. Mudaliar, B. Pateriya
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引用次数: 0
A Wireless Resonant LC Sensor for Glucose Detection 用于葡萄糖检测的无线谐振LC传感器
Pub Date : 2022-11-01 DOI: 10.3390/ecsa-9-13365
Hong Li, Haibo Xu, Shiru Lin, Yi Jia
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引用次数: 0
Uncertainty Quantification at the Microscale: A Data-Driven Multi-Scale Approach 微尺度的不确定性量化:数据驱动的多尺度方法
Pub Date : 2022-11-01 DOI: 10.3390/ecsa-9-13351
J. P. Quesada-Molina, S. Mariani
: Data-driven formulations are currently developed and can result extremely helpful to deal with the complexity of the multi-physics governing the response of micro-electro-mechanical systems (MEMS) to the external stimuli. Such devices are in fact characterized by a hierarchy of length-and time-scales, which are difficult to fully account for in a purely model-based approach. In this work, we specifically refer to a (single-axis) Lorentz force micro-magnetometer designed for navi-gation purposes. Due to an alternating current flowing in a slender mechanical part (beam) and featuring an ad-hoc set frequency, the micro-system is driven into resonance so that its sensitivity to the magnetic field gets improved. A reduced-order physical model was formerly developed for the aforementioned movable part of the device; this model was then used to feed and speed up a multi-physics and multi-objective topology optimization procedure, aiming to design a robust and performing magnetometer. The stochastic effects, which are responsible for the scattering in the experimental data at the microscale, were not accounted for in such a model-based approach. A recently proposed formulation is here discussed and further extended to allow for such stochastic effects. The proposed multi-scale deep learning approach features: at the material scale, a convolutional neural network adopted to learn the scattering in the mechanical properties of polysilicon, induced by its morphology; at the device scale, two feed-forward neural networks, one adopted to upscale the mechanical properties while the other to learn a microstructure-informed mapping between the geometric imperfections induced by the microfabrication process and the effective response of the movable part of the magnetometer. The data-driven models are linked through the physical model to provide a kind of hybrid solution to the problem. Results relevant to different neural network architectures are here discussed, along with a proposal to frame the approach as a multi-fidelity, uncertainty quantification procedure.
数据驱动的公式目前正在开发,它可以非常有助于处理控制微机电系统(MEMS)对外部刺激响应的多物理场的复杂性。事实上,这些装置的特点是长度和时间尺度的层次结构,这在纯粹基于模型的方法中很难完全解释。在这项工作中,我们特别提到了为导航目的而设计的(单轴)洛伦兹力微磁力计。由于在细长的机械部件(梁)中流动的交流电具有特别设定的频率,因此微系统被驱动进入谐振状态,从而提高了其对磁场的灵敏度。以前为上述设备的可移动部分开发了一个降阶物理模型;然后利用该模型馈入并加速多物理场、多目标的拓扑优化过程,旨在设计出鲁棒性和高性能的磁强计。在这种基于模型的方法中,没有考虑到导致实验数据在微观尺度上散射的随机效应。这里讨论了最近提出的一个公式,并进一步扩展了它以允许这种随机效应。提出的多尺度深度学习方法的特点是:在材料尺度上,采用卷积神经网络来学习多晶硅的力学性能中由其形态引起的散射;在器件尺度上,采用两个前馈神经网络,一个用于提高机械性能,另一个用于学习微加工过程引起的几何缺陷与磁力计可移动部分的有效响应之间的微结构映射。数据驱动模型通过物理模型连接起来,为问题提供了一种混合解决方案。本文讨论了与不同神经网络架构相关的结果,并提出了将该方法框架为多保真度、不确定性量化程序的建议。
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The 9th International Electronic Conference on Sensors and Applications
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