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Journal articles on the topic 'Cloud overlay networks'

Author: Grafiati

Published: 31 August 2024

Last updated: 31 July 2025

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1

Ghazali, Osman, and Shahzada Khurram. "Enhanced IPFIX flow monitoring for VXLAN based cloud overlay networks." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5519. http://dx.doi.org/10.11591/ijece.v9i6.pp5519-5528.

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<span lang="EN-US">The demands for cloud computing services is rapidly growing due to its fast adoption and the migration of workloads from private data centers to cloud data centers. Many companies, small and large, prefer switching their data to the enterprise cloud environment rather than expanding their own data centers. As a result, the network traffic in cloud data centers is increasing rapidly. However, due to the dynamic resource provisioning and high-speed virtualized cloud networks, the traditional flow-monitoring systems is unable to provide detail visibility and information o

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Osman, Ghazali, and Khurram Shahzada. "Enhanced IPFIX flow monitoring for VXLAN based cloud overlay networks." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5519–28. https://doi.org/10.11591/ijece.v9i6.pp5519-5528.

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The demands for cloud computing services is rapidly growing due to its fast adoption and the migration of workloads from private data centers to cloud data centers. Many companies, small and large, prefer switching their data to the enterprise cloud environment rather than expanding their own data centers. As a result, the network traffic in cloud data centers is increasing rapidly. However, due to the dynamic resource provisioning and high-speed virtualized cloud networks, the traditional flow-monitoring systems is unable to provide detail visibility and information of traffic traversing the

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Wang, Jessie Hui, Jeffrey Cai, Jerry Lu, Kevin Yin, and Jiahai Yang. "Solving multicast problem in cloud networks using overlay routing." Computer Communications 70 (October 2015): 1–14. http://dx.doi.org/10.1016/j.comcom.2015.05.016.

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Wei, Ming, Ming Zhu, Yaoyuan Zhang, Jiaqi Sun, and Jiarong Wang. "Cyclic Global Guiding Network for Point Cloud Completion." Remote Sensing 14, no. 14 (2022): 3316. http://dx.doi.org/10.3390/rs14143316.

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The application of 3D scenes has gradually expanded in recent years. A 3D point cloud is unreliable when it is acquired because of the performance of the sensor. Therefore, it causes difficulties in utilization. Point cloud completion can reconstruct and restore sparse and incomplete point clouds to a more realistic shape. We propose a cyclic global guiding network structure and apply it to point cloud completion tasks. While learning the local details of the whole cloud, our network structure can play a guiding role and will not ignore the overall characteristics of the whole cloud. Based on

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Ramadan, Osama R. S., Mohamed Yasin I. Afifi, and Ahmed Yahya. "A Distributed Cloud Architecture Based on General De Bruijn Overlay Network." International Journal of Cloud Applications and Computing 14, no. 1 (2024): 1–19. http://dx.doi.org/10.4018/ijcac.339892.

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Distributed cloud systems enable the distribution of computing resources across various geographical locations. While offering benefits like accelerated content delivery, the scalability and coherence maintenance of these systems pose significant challenges. Recent studies reveal shortcomings in existing distributed system schemes to meet modern cloud application demands and maintain coherence among heterogeneous system elements. This paper proposes a service-oriented network architecture for distributed cloud computing networks. Using a De Bruijn network as a software-defined overlay network,

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KAWASHIMA, Ryota, and Hiroshi MATSUO. "Non-tunneling Overlay Approach for Virtual Tenant Networks in Cloud Datacenter." IEICE Transactions on Communications E97.B, no. 11 (2014): 2259–68. http://dx.doi.org/10.1587/transcom.e97.b.2259.

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Barabash, Kathy, David Breitgand, Etai Lev-Ran, Dean H. Lorenz, and Danny Raz. "A case for an open customizable cloud network." ACM SIGCOMM Computer Communication Review 52, no. 2 (2022): 56–62. http://dx.doi.org/10.1145/3544912.3544919.

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Cloud computing is transforming networking landscape over the last few years. The first order of business for major cloud providers today is to attract as many organizations as possible to their own clouds. To that end cloud providers offer a new generation of managed network solutions to connect the premises of the enterprises to their clouds. To serve their customers better and to innovate fast, major cloud providers are currently on the route to building their own "private Internets", which are idiosyncratic. On the other hand, customers that do not want to stay locked by vendors and who wa

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Qian, He, Wang Yong, Li Jia, and Cai Mengfei. "Publish/Subscribe and JXTA based Cloud Service Management with QoS." International Journal of Grid and High Performance Computing 8, no. 3 (2016): 24–37. http://dx.doi.org/10.4018/ijghpc.2016070102.

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How to manage cloud services efficiently is difficult for large scale of services with frequently changing Quality of Service (QoS) in cloud computing environment. A multiple-dimension publish/subscribe (pub/sub) and JXTA based cloud service management mechanism, consists of registry overlay, service publisher and subscriber, is proposed to manage cloud services with active QoS refreshing and fast subscribe capability. The registry overlay with multiple managers cooperating on JXTA, can manage large scale services discovery. The service model with QoS describes a formal model for pub/sub based

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Pham, Van-Nam, VanDung Nguyen, Tri D. T. Nguyen, and Eui-Nam Huh. "Efficient Edge-Cloud Publish/Subscribe Broker Overlay Networks to Support Latency-Sensitive Wide-Scale IoT Applications." Symmetry 12, no. 1 (2019): 3. http://dx.doi.org/10.3390/sym12010003.

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Computing services for the Internet-of-Things (IoT) play a vital role for widespread IoT deployment. A hierarchy of Edge-Cloud publish/subscribe (pub/sub) broker overlay networks that support latency-sensitive IoT applications in a scalable manner is introduced. In addition, we design algorithms to cluster edge pub/sub brokers based on topic similarities and geolocations to enhance data dissemination among end-to-end IoT devices. The proposed model is designed to provide low delay data dissemination and effectively save network traffic among brokers. In the proposed model, IoT devices running

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Gharib, Mohammed, Marzieh Malekimajd, and Ali Movaghar. "SLoPCloud: An Efficient Solution for Locality Problem in Peer-to-Peer Cloud Systems." Algorithms 11, no. 10 (2018): 150. http://dx.doi.org/10.3390/a11100150.

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Peer-to-Peer (P2P) cloud systems are becoming more popular due to the high computational capability, scalability, reliability, and efficient data sharing. However, sending and receiving a massive amount of data causes huge network traffic leading to significant communication delays. In P2P systems, a considerable amount of the mentioned traffic and delay is owing to the mismatch between the physical layer and the overlay layer, which is referred to as locality problem. To achieve higher performance and consequently resilience to failures, each peer has to make connections to geographically clo

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Wang, Lan. "THE RANDOM NEURAL NETWORK FOR COGNITIVE TRAFFIC ROUTING AND TASK ALLOCATION IN NETWORKS AND THE CLOUD." Probability in the Engineering and Informational Sciences 31, no. 4 (2017): 540–60. http://dx.doi.org/10.1017/s0269964817000183.

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G-Network queueing network models, and in particular the random neural network (RNN), are useful tools for decision making in complex systems, due to their ability to learn from measurements in real time, and in turn provide real-time decisions regarding resource and task allocation. In particular, the RNN has led to the design of the cognitive packet network (CPN) decision tool for the routing of packets in the Internet, and for task allocation in the Cloud. Thus in this paper, we present recent research on how to dynamically create the means for quality of service (QoS) to end users of the I

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Xia, Lei, Zheng Cui, John Lange, Yuan Tang, Peter Dinda, and Patrick Bridges. "Fast VMM-based overlay networking for bridging the cloud and high performance computing." Cluster Computing 17, no. 1 (2013): 39–59. http://dx.doi.org/10.1007/s10586-013-0274-7.

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Andre, Jean-Marc, Ulf Behrens, James Branson, et al. "Experience with dynamic resource provisioning of the CMS online cluster using a cloud overlay." EPJ Web of Conferences 214 (2019): 07017. http://dx.doi.org/10.1051/epjconf/201921407017.

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The primary goal of the online cluster of the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) is to build event data from the detector and to select interesting collisions in the High Level Trigger (HLT) farm for offline storage. With more than 1500 nodes and a capacity of about 850 kHEPSpecInt06, the HLT machines represent similar computing capacity of all the CMS Tier1 Grid sites together. Moreover, it is currently connected to the CERN IT datacenter via a dedicated 160 Gbps network connection and hence can access the remote EOS based storage with a high bandwidth.

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Li, Yanjun, Guoqing Zhang, and Guoqiang Zhang. "ISP-Friendly Data Scheduling by Advanced Locality-Aware Network Coding for P2P Distribution Cloud." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/968328.

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Peer-to-peer (P2P) file distribution imposes increasingly heavy traffic burden on the Internet service providers (ISPs). The vast volume of traffic pushes up ISPs’ costs in routing and investment and degrades their networks performance. Building ISP-friendly P2P is therefore of critical importance for ISPs and P2P services. So far most efforts in this area focused on improving the locality-awareness of P2P applications, for example, to construct overlay networks with better knowledge of the underlying network topology. There is, however, growing recognition that data scheduling algorithms also

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Benomar, Zakaria, Francesco Longo, Giovanni Merlino, and Antonio Puliafito. "Cloud-based Network Virtualization in IoT with OpenStack." ACM Transactions on Internet Technology 22, no. 1 (2022): 1–26. http://dx.doi.org/10.1145/3460818.

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In Cloud computing deployments, specifically in the Infrastructure-as-a-Service (IaaS) model, networking is one of the core enabling facilities provided for the users. The IaaS approach ensures significant flexibility and manageability, since the networking resources and topologies are entirely under users’ control. In this context, considerable efforts have been devoted to promoting the Cloud paradigm as a suitable solution for managing IoT environments. Deep and genuine integration between the two ecosystems, Cloud and IoT, may only be attainable at the IaaS level. In light of extending the

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Paiker, Nafize Rabbani, Jianchen Shan, Cristian Borcea, Narain Gehani, Reza Curtmola, and Xiaoning Ding. "Design and Implementation of an Overlay File System for Cloud-Assisted Mobile Apps." IEEE Transactions on Cloud Computing 8, no. 1 (2020): 97–111. http://dx.doi.org/10.1109/tcc.2017.2763158.

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Fu, Chunle, Bailing Wang, Hongri Liu, and Wei Wang. "Software-Defined Virtual Private Network for SD-WAN." Electronics 13, no. 13 (2024): 2674. http://dx.doi.org/10.3390/electronics13132674.

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Software-Defined Wide Area Networks (SD-WANs) are an emerging Software-Defined Network (SDN) technology to reinvent Wide Area Networks (WANs) for ubiquitous network interconnections in cloud computing, edge computing, and the Internet of Everything. The state-of-the-art overlay-based SD-WANs are simply conjunctions of Virtual Private Network (VPN) and SDN architecture to leverage the controllability and programmability of SDN, which are only applicable for specific platforms and do not comply with the extensibility of SDN. This paper motivates us to refactor traditional VPNs with SDN architect

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Mashreghi-Moghadam, Parisa, Tarek Ould-Bachir, and Yvon Savaria. "PrismParser: A Framework for Implementing Efficient P4-Programmable Packet Parsers on FPGA." Future Internet 16, no. 9 (2024): 307. http://dx.doi.org/10.3390/fi16090307.

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The increasing complexity of modern networks and their evolving needs demand flexible, high-performance packet processing solutions. The P4 language excels in specifying packet processing in software-defined networks (SDNs). Field-programmable gate arrays (FPGAs) are ideal for P4-based packet parsers due to their reconfigurability and ability to handle data transmitted at high speed. This paper introduces three FPGA-based P4-programmable packet parsing architectural designs that translate P4 specifications into adaptable hardware implementations called base, overlay, and pipeline, each optimiz

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Hossen, Rakib, Md Whaiduzzaman, Mohammed Nasir Uddin, et al. "BDPS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration." Information 12, no. 12 (2021): 517. http://dx.doi.org/10.3390/info12120517.

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The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture t

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Judijanto, Loso, Arnes Yuli Vandika, and Ardi Azhar Nampira. "Bibliometric Analysis of Artificial Intelligence Development in Customer Service Automation." West Science Interdisciplinary Studies 3, no. 04 (2025): 665–76. https://doi.org/10.58812/wsis.v3i04.1861.

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This study presents a comprehensive bibliometric analysis of scholarly literature on the development of artificial intelligence (AI) in customer service automation, based on data extracted from the Scopus database between 2000 and 2024. Using VOSviewer, the analysis maps the intellectual structure, thematic evolution, and collaborative networks within this rapidly growing research field. Findings reveal that core research themes revolve around customer satisfaction, chatbots, natural language processing, and machine learning—highlighting the shift from back-end AI infrastructure toward user-fa

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He, Tao, Kunxin Zhu, Zhipeng Chen, Ruomei Wang, and Fan Zhou. "Popularity-Guided Cost Optimization for Live Streaming in Mobile Edge Computing." Wireless Communications and Mobile Computing 2022 (January 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/5562995.

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Live streaming service usually delivers the content in mobile edge computing (MEC) to reduce the network latency and save the backhaul capacity. Considering the limited resources, it is necessary that MEC servers collaborate with each other and form an overlay to realize more efficient delivery. The critical challenge is how to optimize the topology among the servers and allocate the link capacity so that the cost will be lower with delay constraints. Previous approaches rarely consider server collaborations for live streaming service, and the scheduling delay is usually ignored in MEC, leadin

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Hao, Ruidong, Zhongwei Wei, Xu He, et al. "Robust Point Cloud Registration Network for Complex Conditions." Sensors 23, no. 24 (2023): 9837. http://dx.doi.org/10.3390/s23249837.

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Point cloud registration is widely used in autonomous driving, SLAM, and 3D reconstruction, and it aims to align point clouds from different viewpoints or poses under the same coordinate system. However, point cloud registration is challenging in complex situations, such as a large initial pose difference, high noise, or incomplete overlap, which will cause point cloud registration failure or mismatching. To address the shortcomings of the existing registration algorithms, this paper designed a new coarse-to-fine registration two-stage point cloud registration network, CCRNet, which utilizes a

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Piontek, Dennis, Luca Bugliaro, Marius Schmidl, Daniel K. Zhou, and Christiane Voigt. "The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development." Remote Sensing 13, no. 16 (2021): 3112. http://dx.doi.org/10.3390/rs13163112.

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Volcanic ash clouds are a threat to air traffic security and, thus, can have significant societal and financial impact. Therefore, the detection and monitoring of volcanic ash clouds to enhance the safety of air traffic is of central importance. This work presents the development of the new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) which is based on artificial neural networks, the thermal channels of the geostationary sensor MSG/SEVIRI and auxiliary data from a numerical weather prediction model. It derives a pixel classification as well as cloud top height

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Chen, Yi, Yong Wang, Jinlong Li, Yu Zhang, and Xiaorong Gao. "A Partial-to-Partial Point Cloud Registration Method Based on Geometric Attention Network." Journal of Sensors 2023 (October 27, 2023): 1–12. http://dx.doi.org/10.1155/2023/3427758.

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Partial point cloud registration is an important step in generating a full 3D model. Many deep learning-based methods show good performance for the registration of complete point clouds but cannot deal with the registration of partial point clouds effectively. Recent methods that seek correspondences over downsampled superpoints show great potential in partial point cloud registration. Therefore, this paper proposes a partial-to-partial point cloud registration network based on geometric attention (GAP-Net), which mainly includes a backbone network optimized by a spatial attention module and a

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Glazyrina, Natalya, Raikhan Muratkhan, Serik Eslyamov, et al. "Deep neural networks for removing clouds and nebulae from satellite images." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 5 (2024): 5390. http://dx.doi.org/10.11591/ijece.v14i5.pp5390-5399.

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This research paper delves into contemporary methodologies for eradicating clouds and nebulae from space images utilizing advanced deep learning technologies such as conditional generative adversarial networks (conditional GAN), cyclic generative adversarial networks (CycleGAN), and space-attention generative adversarial networks (space-attention GAN). Cloud cover presents a significant obstacle in remote sensing, impeding accurate data analysis across various domains including environmental monitoring and natural resource management. The proposed techniques offer novel solutions by leveraging

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Nie, Ziming, Qiao Wu, Chenlei Lv, et al. "SPU-IMR: Self-supervised Arbitrary-scale Point Cloud Upsampling via Iterative Mask-recovery Network." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 6236–44. https://doi.org/10.1609/aaai.v39i6.32667.

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Point cloud upsampling aims to generate dense and uniformly distributed point sets from sparse point clouds. Existing point cloud upsampling methods typically approach the task as an interpolation problem. They achieve upsampling by performing local interpolation between point clouds or in the feature space, then regressing the interpolated points to appropriate positions. By contrast, our proposed method treats point cloud upsampling as a global shape completion problem. Specifically, our method first divides the point cloud into multiple patches. Then a masking operation is applied to remove

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Chen, Yonghua, Filipe Aires, Jennifer A. Francis, and James R. Miller. "Observed Relationships between Arctic Longwave Cloud Forcing and Cloud Parameters Using a Neural Network." Journal of Climate 19, no. 16 (2006): 4087–104. http://dx.doi.org/10.1175/jcli3839.1.

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Abstract A neural network technique is used to quantify relationships involved in cloud–radiation feedbacks based on observations from the Surface Heat Budget of the Arctic (SHEBA) project. Sensitivities of longwave cloud forcing (CFL) to cloud parameters indicate that a bimodal distribution pattern dominates the histogram of each sensitivity. Although the mean states of the relationships agree well with those derived in a previous study, they do not often exist in reality. The sensitivity of CFL to cloud cover increases as the cloudiness increases with a range of 0.1–0.9 W m−2 %−1. There is a

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Zhu, Wen, Tianliang Chen, Beiping Hou, et al. "Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network." Applied Sciences 12, no. 3 (2022): 1570. http://dx.doi.org/10.3390/app12031570.

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Changes in clouds can affect the outpower of photovoltaics (PVs). Ground-based cloud images classification is an important prerequisite for PV power prediction. Due to the intra-class difference and inter-class similarity of cloud images, the classical convolutional network is obviously insufficient in distinguishing ability. In this paper, a classification method of ground-based cloud images by improved combined convolutional network is proposed. To solve the problem of sub-network overfitting caused by redundancy of pixel information, overlap pooling kernel is used to enhance the elimination

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Zhang, Hankui K., Dong Luo, and David P. Roy. "Improved Landsat Operational Land Imager (OLI) Cloud and Shadow Detection with the Learning Attention Network Algorithm (LANA)." Remote Sensing 16, no. 8 (2024): 1321. http://dx.doi.org/10.3390/rs16081321.

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Landsat cloud and cloud shadow detection has a long heritage based on the application of empirical spectral tests to single image pixels, including the Landsat product Fmask algorithm, which uses spectral tests applied to optical and thermal bands to detect clouds and uses the sun-sensor-cloud geometry to detect shadows. Since the Fmask was developed, convolutional neural network (CNN) algorithms, and in particular U-Net algorithms (a type of CNN with a U-shaped network structure), have been developed and are applied to pixels in square patches to take advantage of both spatial and spectral in

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Chernokulsky, A. V., and A. V. Eliseev. "Climatology of cloud overlap parameter." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 14, no. 1 (2017): 216–25. http://dx.doi.org/10.21046/2070-7401-2017-14-1-216-225.

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Zhang, Zheng, Zhiwei Xu, Chang’an Liu, Qing Tian, and Yongsheng Zhou. "Cloudformer V2: Set Prior Prediction and Binary Mask Weighted Network for Cloud Detection." Mathematics 10, no. 15 (2022): 2710. http://dx.doi.org/10.3390/math10152710.

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Cloud detection is an essential step in optical remote sensing data processing. With the development of deep learning technology, cloud detection methods have made remarkable progress. Among them, researchers have started to try to introduce Transformer into cloud detection tasks due to its excellent performance in image semantic segmentation tasks. However, the current Transformer-based methods suffer from training difficulty and low detection accuracy of small clouds. To solve these problems, this paper proposes Cloudformer V2 based on the previously proposed Cloudformer. For the training di

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Zhang, Chunjiao, Shenghua Xu, Tao Jiang, et al. "Integrating Normal Vector Features into an Atrous Convolution Residual Network for LiDAR Point Cloud Classification." Remote Sensing 13, no. 17 (2021): 3427. http://dx.doi.org/10.3390/rs13173427.

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LiDAR point clouds are rich in spatial information and can effectively express the size, shape, position, and direction of objects; thus, they have the advantage of high spatial utilization. The point cloud focuses on describing the shape of the external surface of the object itself and will not store useless redundant information to describe the occupation. Therefore, point clouds have become the research focus of 3D data models and are widely used in large-scale scene reconstruction, virtual reality, digital elevation model production, and other fields. Since point clouds have various charac

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Zhang, Jiazhe, Xingwei Li, Xianfa Zhao, and Zheng Zhang. "LLGF-Net: Learning Local and Global Feature Fusion for 3D Point Cloud Semantic Segmentation." Electronics 11, no. 14 (2022): 2191. http://dx.doi.org/10.3390/electronics11142191.

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Three-dimensional (3D) point cloud semantic segmentation is fundamental in complex scene perception. Currently, although various efficient 3D semantic segmentation networks have been proposed, the overall effect has a certain gap to 2D image segmentation. Recently, some transformer-based methods have opened a new stage in computer vision, which also has accelerated the effective development of methods in 3D point cloud segmentation. In this paper, we propose a novel semantic segmentation network named LLGF-Net that can aggregate features from both local and global levels of point clouds, effec

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Lim, Pheng-Un, Chang-Yeol Choi, and Hwang-Kyu Choi. "Cloud Assisted P2P Live Video Streaming over DHT Overlay Network." Transactions of The Korean Institute of Electrical Engineers 66, no. 1 (2017): 89–99. http://dx.doi.org/10.5370/kiee.2017.66.1.89.

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Gyasi, Emmanuel Kwabena, and Purushotham Swarnalatha. "Cloud-MobiNet: An Abridged Mobile-Net Convolutional Neural Network Model for Ground-Based Cloud Classification." Atmosphere 14, no. 2 (2023): 280. http://dx.doi.org/10.3390/atmos14020280.

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More than 60 percent of the global surface is covered by clouds, and they play a vital role in the hydrological circle, climate change, and radiation budgets by modifying shortwaves and longwave. Weather forecast reports are critical to areas such as air and sea transport, energy, agriculture, and the environment. The time has come for artificial intelligence-powered devices to take the place of the current method by which decision-making experts determine cloud types. Convolutional neural network models (CNNs) are starting to be utilized for identifying the types of clouds that are caused by

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Lagahit, M. L. R., Z. Li, K. Sakaguchi, and M. Matsuoka. "EXPLORING GROUND SEGMENTATION FROM LIDAR SCANNING-DERIVED IMAGES USING CONVOLUTIONAL NEURAL NETWORKS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W1-2023 (May 25, 2023): 221–26. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-221-2023.

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Abstract. Recent works have attempted to extract features such as road markings from sparse mobile LiDAR scanning point cloud-derived images via convolutional neural networks (CNN). In this paper, the use of such methods for ground segmentation was explored. To begin, point clouds from each channel will be projected onto the y-z plane to generate the images that will be used for training and testing the CNN model. Then, for the main workflow, the following steps were performed for each channel: (1) point cloud-to-image conversion; (2) CNN classification; and (3) image-to-point cloud projection

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Li, Wenwen, Yaxing Chen, Qianyue Fan, Meng Yang, Bin Guo, and Zhiwen Yu. "I-PAttnGAN: An Image-Assisted Point Cloud Generation Method Based on Attention Generative Adversarial Network." Remote Sensing 17, no. 1 (2025): 153. https://doi.org/10.3390/rs17010153.

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The key to building a 3D point cloud map is to ensure the consistency and accuracy of point cloud data. However, the hardware limitations of LiDAR lead to a sparse and uneven distribution of point cloud data in the edge region, which brings many challenges to 3D map construction, such as low registration accuracy and high construction errors in the sparse regions. To solve these problems, this paper proposes the I-PAttnGAN network to generate point clouds with image-assisted approaches, which aims to improve the density and uniformity of sparse regions and enhance the representation ability of

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Jing, Ran, Fuzhou Duan, Fengxian Lu, Miao Zhang, and Wenji Zhao. "An NDVI Retrieval Method Based on a Double-Attention Recurrent Neural Network for Cloudy Regions." Remote Sensing 14, no. 7 (2022): 1632. http://dx.doi.org/10.3390/rs14071632.

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NDVI is an important parameter for environmental assessment and precision agriculture that well-describes the status of vegetation. Nevertheless, the clouds in optical images often result in the absence of NDVI information at key growth stages. The integration of SAR and optical image features will likely address this issue. Although the mapping of different data sources is complex, the prosperity of deep learning technology provides an alternative approach. In this study, the double-attention RNN architecture based on the recurrent neural network (RNN) and attention mechanism is proposed to r

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Li, Xiaolong, Hong Zheng, Chuanzhao Han, et al. "SFRS-Net: A Cloud-Detection Method Based on Deep Convolutional Neural Networks for GF-1 Remote-Sensing Images." Remote Sensing 13, no. 15 (2021): 2910. http://dx.doi.org/10.3390/rs13152910.

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Clouds constitute a major obstacle to the application of optical remote-sensing images as they destroy the continuity of the ground information in the images and reduce their utilization rate. Therefore, cloud detection has become an important preprocessing step for optical remote-sensing image applications. Due to the fact that the features of clouds in current cloud-detection methods are mostly manually interpreted and the information in remote-sensing images is complex, the accuracy and generalization of current cloud-detection methods are unsatisfactory. As cloud detection aims to extract

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Latsch, Miriam, Andreas Richter, Henk Eskes, et al. "Intercomparison of Sentinel-5P TROPOMI cloud products for tropospheric trace gas retrievals." Atmospheric Measurement Techniques 15, no. 21 (2022): 6257–83. http://dx.doi.org/10.5194/amt-15-6257-2022.

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Abstract. Clouds have a strong impact on satellite measurements of tropospheric trace gases in the ultraviolet, visible, and near-infrared spectral ranges from space. Therefore, trace gas retrievals rely on information on cloud fraction, cloud albedo, and cloud height from cloud products. In this study, the cloud parameters from different cloud retrieval algorithms for the Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) are compared: the Optical Cloud Recognition Algorithm (OCRA) a priori cloud fraction, the Retrieval Of Cloud Information using Neural Networks (ROCINN)

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Gao, Lin, Chenxi Gai, Sijun Lu, and Jinyi Zhang. "MSACN: A Cloud Extraction Method from Satellite Image Using Multiscale Soft Attention Convolutional Neural Network." Applied Sciences 14, no. 8 (2024): 3285. http://dx.doi.org/10.3390/app14083285.

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In satellite remote sensing images, the existence of clouds has an occlusion effect on ground information. Different degrees of clouds make it difficult for existing models to accurately detect clouds in images due to complex scenes. The detection and extraction of clouds is one of the most important problems to be solved in the further analysis and utilization of image information. In this article, we refined a multi-head soft attention convolutional neural network incorporating spatial information modeling (MSACN). During the encoder process, MSACN extracts cloud features through a concurren

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Cynthia, Eka Pandu, Edi Ismanto, M. Imam Arifandy, et al. "Convolutional Neural Network and Deep Learning Approach for Image Detection and Identification." Journal of Physics: Conference Series 2394, no. 1 (2022): 012019. http://dx.doi.org/10.1088/1742-6596/2394/1/012019.

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Abstract There are many different varieties of clouds, each with a unique set of properties. As a result of this variability, it is difficult to discern these sorts of clouds. A database’s objects must be categorized using data categorization in order to be organized into multiple categories. This study made use of the Cirrus Cumulus Stratus Nimbus (CCSN) dataset, which falls under the low cloud category and includes photos of Cumulus (182 images), and Cumulonimbus (242 photographs), and Stratus (242 images) (202 images). A fast R-CNN detector with feature extraction = Resnet50 was used to cre

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Matama, Kazushige, Ren Goto, Chihiro Nishiwaki, and Katsuhiro Naito. "Extension Mechanism of Overlay Network Protocol to Support Digital Authenticates." Journal of Systemics, Cybernetics and Informatics 21, no. 1 (2023): 18–25. http://dx.doi.org/10.54808/jsci.21.01.18.

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Zero-trust security is a new security model that has recently received much attention. Since the model protects all resources, continuous authentication and authorization of resources are mandatory. Many enterprises currently use cloud systems to manage their resources and provide service. On the other hand, IoT systems typically require cooperation service among IoT devices. As a solution for redundant routes and load on the cloud, a peer-to-peer type system is a good candidate. On the contrary, it requires zero-trust security because each device should guarantee security. Since the authors h

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Wang, Kai, and Huanhuan Zhang. "RAAFNet: Reverse Attention Adaptive Fusion Network for Large-Scale Point Cloud Semantic Segmentation." Mathematics 12, no. 16 (2024): 2485. http://dx.doi.org/10.3390/math12162485.

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Point cloud semantic segmentation is essential for comprehending and analyzing scenes. However, performing semantic segmentation on large-scale point clouds presents challenges, including demanding high memory requirements, a lack of structured data, and the absence of topological information. This paper presents a novel method based on the Reverse Attention Adaptive Fusion network (RAAFNet) for segmenting large-scale point clouds. RAAFNet consists of a reverse attention encoder–decoder module, an adaptive fusion module, and a local feature aggregation module. The reverse attention encoder–dec

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Wang, Bo, Mingwei Zhou, Wei Cheng, et al. "An Efficient Cloud Classification Method Based on a Densely Connected Hybrid Convolutional Network for FY-4A." Remote Sensing 15, no. 10 (2023): 2673. http://dx.doi.org/10.3390/rs15102673.

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Understanding atmospheric motions and projecting climate changes depends significantly on cloud types, i.e., different cloud types correspond to different atmospheric conditions, and accurate cloud classification can help forecasts and meteorology-related studies to be more effectively directed. However, accurate classification of clouds is challenging and often requires certain manual involvement due to the complex cloud forms and dispersion. To address this challenge, this paper proposes an improved cloud classification method based on a densely connected hybrid convolutional network. A dens

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Chiu, J. C., A. Marshak, C. H. Huang, et al. "Cloud droplet size and liquid water path retrievals from zenith radiance measurements: examples from the Atmospheric Radiation Measurement Program and the Aerosol Robotic Network." Atmospheric Chemistry and Physics Discussions 12, no. 8 (2012): 19163–208. http://dx.doi.org/10.5194/acpd-12-19163-2012.

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Abstract. The ground-based Atmospheric Radiation Measurement Program (ARM) and NASA Aerosol Robotic Network (AERONET) routinely monitor clouds using zenith radiances at visible and near-infrared wavelengths. Using the transmittance calculated from such measurements, we have developed a new retrieval method for cloud effective droplet size and conducted extensive tests for non-precipitating liquid water clouds. The underlying principle is to combine a water-absorbing wavelength (i.e. 1640 nm) with a non-water-absorbing wavelength for acquiring information on cloud droplet size and optical depth

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Chiu, J. C., A. Marshak, C. H. Huang, et al. "Cloud droplet size and liquid water path retrievals from zenith radiance measurements: examples from the Atmospheric Radiation Measurement Program and the Aerosol Robotic Network." Atmospheric Chemistry and Physics 12, no. 21 (2012): 10313–29. http://dx.doi.org/10.5194/acp-12-10313-2012.

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Abstract. The ground-based Atmospheric Radiation Measurement Program (ARM) and NASA Aerosol Robotic Network (AERONET) routinely monitor clouds using zenith radiances at visible and near-infrared wavelengths. Using the transmittance calculated from such measurements, we have developed a new retrieval method for cloud effective droplet size and conducted extensive tests for non-precipitating liquid water clouds. The underlying principle is to combine a liquid-water-absorbing wavelength (i.e., 1640 nm) with a non-water-absorbing wavelength for acquiring information on cloud droplet size and optic

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Asvija B., Eswari R., and Bijoy M. B. "Security Threat Modelling With Bayesian Networks and Sensitivity Analysis for IAAS Virtualization Stack." Journal of Organizational and End User Computing 33, no. 4 (2021): 44–69. http://dx.doi.org/10.4018/joeuc.20210701.oa3.

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Designing security mechanisms for cloud computing infrastructures has assumed importance with the widespread adoption of public clouds. Virtualization security is a crucial component of the overall cloud infrastructure security. In this article, the authors employ the concept of Bayesian networks and attack graphs to carry out sensitivity analysis on the different components involved in virtualization security for infrastructure as a service (IaaS) cloud infrastructures. They evaluate the Bayesian attack graph (BAG) for the IaaS model to reveal the sensitive regions and thus help the administr

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HE, Minqi, Li LIU, Shang LI, Hao WU, and Dahu ZHU. "Multi-level filter network for low-overlap point cloud registration." Optics and Precision Engineering 32, no. 11 (2024): 1770–83. http://dx.doi.org/10.37188/ope.20243211.1770.

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HE, Minqi, Li LIU, Shang LI, Hao WU, and Dahu ZHU. "Multi-level filter network for low-overlap point cloud registration." Optics and Precision Engineering 32, no. 11 (2024): 1759–72. https://doi.org/10.37188/ope.20243211.1759.

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