- 标题
- 摘要
- 关键词
- 实验方案
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A depolarisation lidar-based method for the determination of liquid-cloud microphysical properties
摘要： The fact that polarisation lidars measure a depolarisation signal in liquid clouds due to the occurrence of multiple scattering is well known. The degree of measured depolarisation depends on the lidar characteristics (e.g. wavelength and receiver field of view) as well as the cloud macrophysical (e.g. cloud-base altitude) and microphysical (e.g. effective radius, liquid water content) properties. Efforts seeking to use depolarisation information in a quantitative manner to retrieve cloud properties have been undertaken with, arguably, limited practical success. In this work we present a retrieval procedure applicable to clouds with (quasi-)linear liquid water content (LWC) profiles and (quasi-)constant cloud-droplet number density in the cloud-base region. Thus limiting the applicability of the procedure allows us to reduce the cloud variables to two parameters (namely the derivative of the liquid water content with height and the extinction at a fixed distance above cloud base). This simplification, in turn, allows us to employ a fast and robust optimal-estimation inversion using pre-computed look-up tables produced using extensive lidar Monte Carlo (MC) multiple-scattering simulations. In this paper, we describe the theory behind the inversion procedure and successfully apply it to simulated observations based on large-eddy simulation (LES) model output. The inversion procedure is then applied to actual depolarisation lidar data corresponding to a range of cases taken from the Cabauw measurement site in the central Netherlands. The lidar results were then used to predict the corresponding cloud-base region radar reflectivities. In non-drizzling condition, it was found that the lidar inversion results can be used to predict the observed radar reflectivities with an accuracy within the radar calibration uncertainty (2–3 dBZ). This result strongly supports the accuracy of the lidar inversion results. Results of a comparison between ground-based aerosol number concentration and lidar-derived cloud-droplet number densities are also presented and discussed. The observed relationship between the two quantities is seen to be consistent with the results of previous studies based on aircraft-based in situ measurements.
关键词： Monte Carlo simulations,cloud-base region,aerosol-cloud interactions,retrieval procedure,depolarisation lidar,liquid-cloud microphysical properties,multiple scattering,radar reflectivity
更新于2025-09-19 17:15:36
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Document Verification: A Cloud-Based Computing Pattern Recognition Approach to Chipless RFID
摘要： In this paper, we propose a novel means of verifying document originality using chipless RFID systems. The document sender prints a chipless RFID tag into the paper and does a frequency scanning in the 57–64 GHz spectrum of the document. The results of scattering parameters in individual step frequencies are stored in a cloud database, denoised and passed to pattern classi?ers, such as support vector machines or ensemble networks. These supervised learners train themselves based on these data on the remote/cloud computer. The document receiver veri?es this frequency ?ngerprint by using the same scanning method, sending the scattering parameters to the cloud server and getting the decoded data. Paper originality is veri?ed if the decoded data are as expected. The advantages of our cloud chipless RFID processing deployments are cost reduction and increased security and scalability.
关键词： chipless tag,classi?cation algorithms,Radio frequency identi?cation,support vector machines,pattern recognition,cloud computing,ensemble networks
更新于2025-09-19 17:15:36
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[IEEE 2018 Digital Image Computing: Techniques and Applications (DICTA) - Canberra, Australia (2018.12.10-2018.12.13)] 2018 Digital Image Computing: Techniques and Applications (DICTA) - Classifier-Free Extraction of Power Line Wires from Point Cloud Data
摘要： This paper proposes a classi?er-free method for extraction of power line wires from aerial point cloud data. It combines the advantages of both grid- and point-based processing of the input data. In addition to the non-ground point cloud data, the input to the proposed method includes the pylon locations, which are automatically extracted by a previous method. The proposed method ?rst counts the number of wires in a span between the two successive pylons using two masks: vertical and horizontal. Then, the initial wire segments are obtained and re?ned iteratively. Finally, the initial segments are extended on both ends and each individual wire points are modelled as a 3D polynomial curve. Experimental results show both the object-based completeness and correctness are 97%, while the point-based completeness and correctness are 99% and 88%, respectively.
关键词： power line,point cloud,extraction,wire,modelling
更新于2025-09-19 17:15:36
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[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Classification of LiDAR Point Cloud based on Multiscale Features and PointNet
摘要： Aiming at classifying the feature of LiDAR point cloud data in complex scenario, this paper proposed a deep neural network model based on multi-scale features and PointNet. The method improves the local feature of PointNet and realize automatic classification of LiDAR point cloud under the complex scene. Firstly, this paper adds multi-scale network on the basis of PointNet network to extract the local features of points. And then these local features of different scales are composed into a multi-dimensional feature through the fully connected layer, and combined with the global features extracted by PointNet, the scores of each point class are returned to complete the point cloud classification. The deep neural network model proposed in this paper is verified using the Semantic3D dataset and the Vaihingen dataset provided by ISPRS. The experimental results show that the proposed algorithm achieves higher classification accuracy compared with other neural networks used for point cloud classification.
关键词： Classification of point cloud,multi-scale features,PointNet,LiDAR
更新于2025-09-19 17:15:36
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[IEEE 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - Stuttgart, Germany (2018.11.20-2018.11.22)] 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - A Configurable Data Acquisition System for Various Working Conditions
摘要： Data collection and analysis of product are causing more and more attention with the rapid development of intelligent manufacturing. Faced with the problem that data acquisition system cannot be used universally, researchers have a long way to go. This paper designed a configurable data acquisition system for automatic filling lines, and the system can be used in other working lines without changing the hardware. The system is based on ARM and consists of three parts: data acquisition, server and cloud platform. Data acquisition part is responsible for acquiring data and sending data to host computer. Server part is used for receiving data from host computer and saving it in the database. Cloud platform serves for users and provides data analysis. The hardware design and software design of the acquisition board are also analyzed, meanwhile the communication protocol is made to ensure the data transmission. The data acquisition has been realized in this system and the whole process is running smoothly without problem.
关键词： ARM,data acquisition,communication protocol,cloud platform,configuration
更新于2025-09-19 17:15:36
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Clouds Classification from Sentinel-2 Imagery with Deep Residual Learning and Semantic Image Segmentation
摘要： Detecting changes in land use and land cover (LULC) from space has long been the main goal of satellite remote sensing (RS), yet the existing and available algorithms for cloud classification are not reliable enough to attain this goal in an automated fashion. Clouds are very strong optical signals that dominate the results of change detection if they are not removed completely from imagery. As various architectures of deep learning (DL) have been proposed and advanced quickly, their potential in perceptual tasks has been widely accepted and successfully applied to many fields. A comprehensive survey of DL in RS has been reviewed, and the RS community has been suggested to be leading researchers in DL. Based on deep residual learning, semantic image segmentation, and the concept of atrous convolution, we propose a new DL architecture, named CloudNet, with an enhanced capability of feature extraction for classifying cloud and haze from Sentinel-2 imagery, with the intention of supporting automatic change detection in LULC. To ensure the quality of the training dataset, scene classification maps of Taiwan processed by Sen2cor were visually examined and edited, resulting in a total of 12,769 sub-images with a standard size of 224 × 224 pixels, cut from the Sen2cor-corrected images and compiled in a trainset. The data augmentation technique enabled CloudNet to have stable cirrus identification capability without extensive training data. Compared to the traditional method and other DL methods, CloudNet had higher accuracy in cloud and haze classification, as well as better performance in cirrus cloud recognition. CloudNet will be incorporated into the Open Access Satellite Image Service to facilitate change detection by using Sentinel-2 imagery on a regular and automatic basis.
关键词： change detection,atrous convolution,CloudNet,cloud classification,semantic image segmentation,deep learning,land use and land cover,deep residual learning,Sentinel-2
更新于2025-09-19 17:15:36
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Impacts of distribution patterns of cloud optical depth on the calculation of radiative forcing
摘要： The gridding process applied to satellite-retrieved cloud properties results in the loss of certain information. In this study, we analyzed the error associated with using gridded cloud optical depth (τ) in calculating radiative forcing from the perspective of the distribution pattern of τ. Utilizing the simulated results from SBDART (Santa Babara DISORT Atmospheric Radiative Transfer), we calculated this error in ideal probability distribution functions (PDFs) of τ while keeping the average τ constant, and then used the τ retrieved from MODIS (Moderate Resolution Imaging Spectroradiometer) pixel-level observations to simulate real case studies. The results from both the ideal experiments and real case studies indicate that there is a large dependence of the error caused by gridding process on the PDF of τ. The greatest relative error occurs in the cases when τ fits a two-point or uniform distribution, reaching 10–20%, while this error is below 5% when τ follows a binomial distribution. From the analysis of MODIS pixel-level data from June 2016, we found that the PDFs of τ within one grid point (1° × 1°) could not be simply described by a normal distribution. Although using the logarithmic mean of τ controls the error effectively, the error can still be up to 4%. Our study suggests that using gridded data (especially the arithmetic mean) to calculate radiative forcing may result in uncertainty to a certain extent, which depends strongly on the distribution pattern of cloud properties within the grid point. The PDF of cloud properties should be comprehensively considered in the gridding process in the future.
关键词： Radiative forcing,Distribution pattern,Cloud optical depth,Grid,MODIS
更新于2025-09-19 17:15:36
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Use of Optical Quantum Sensors to Study Chemical Processes
摘要： In this paper, a vehicular cloud (VC) model is adopted where vehicles offer data as a service. We propose solutions for efficient data delivery based on transmission scheduling methods where vehicles gather data from their mounted sensors. This is done by first organizing vehicles into clusters, so that each cluster works as VC. A distributed D-hop cluster formation algorithm is presented to dynamically form vehicle clouds. The algorithm groups vehicles into non-overlapping clusters, which have adaptive sizes according to their mobility. VCs are created in such a way that each vehicle is at most D-hops away from a cloud coordinator (broker). Each vehicle chooses its broker based on relative mobility calculations within its D-hop neighbors. After cloud construction, a mathematical optimization scheduling algorithm is used to maximize throughput and minimize delay in delivering data from vehicles to their VC broker. Our proposed optimization model implements a contention-free-based medium access control where physical conditions of the channel are fully analyzed. Extensive simulations were performed for different scenarios to evaluate the performance of the proposed cloud formation and cloud-based transmission scheduling algorithms. Results show that VCs formed by our algorithms are more stable and provide higher data throughputs compared with others.
关键词： VANET,optimization,vehicular cloud,transmission scheduling,Cloud formation
更新于2025-09-19 17:13:59
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - High detectivity AlInSb mid-infrared photodiode sensors with dislocation filter layers for gas sensing application
摘要： An Enhanced-Internet that provides ultra-low-latency guaranteed-rate communications for Cloud Services is proposed. The network supports two traffic classes, the Smooth and Best-Effort classes. Smooth traffic flows receive low-jitter GR service over virtual-circuit-switched (VCS) connections with negligible buffering and queueing delays, up to 100% link utilizations, deterministic end-to-end quality-of-service (QoS) guarantees, and improved energy efficiency. End-to-end delays are effectively reduced to the fiber “time of flight.” A new router scheduling problem called the Bounded Normalized-Jitter integer-programming problem is formulated. A fast polynomial-time approximate solution is presented, allowing TDM-based router schedules to be computed in microseconds. We establish that all admissible traffic demands in any packet-switched network can be simultaneously satisfied with GR-VCS connections, with minimal buffering. Each router can use two periodic TDM-based schedules to support GR-VCS connections, which are updated automatically when the router's traffic rate matrix changes. The design of a Silicon-Photonics all-optical packet switch with minimal buffering is presented. The Enhanced-Internet can: 1) reduce router buffer requirements by factors of ; 2) increase the Internet's aggregate capacity; 3) lower the Internet's capital and operating costs; and 4) lower greenhouse gas emissions through improved energy efficiency.
关键词： energy efficiency,DiffServ,cloud,low latency,Buffer sizes,routing,data centers,Future Internet,cloud computing,quality of service (QoS),scheduling
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) - Westminster, CO, USA (2019.11.4-2019.11.7)] 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) - Design Technology for Scalable and Robust Photonic Integrated Circuits: Invited Paper
摘要： Taking full advantage of both heterogeneous networks and cloud access radio access networks, heterogeneous cloud radio access networks (H-CRANs) are presented to enhance both spectral and energy efficiencies, where remote radio heads (RRHs) are mainly used to provide high data rates for users with high quality of service (QoS) requirements, whereas the high-power node (HPN) is deployed to guarantee seamless coverage and serve users with low-QoS requirements. To mitigate the intertier interference and improve energy efficiency (EE) performances in H-CRANs, characterizing user association with RRH/HPN is considered in this paper, and the traditional soft fractional frequency reuse (S-FFR) is enhanced. Based on the RRH/HPN association constraint and the enhanced S-FFR, an energy-efficient optimization problem with the resource assignment and power allocation for the orthogonal-frequency-division-multiple-access-based H-CRANs is formulated as a nonconvex objective function. To deal with the nonconvexity, an equivalent convex feasibility problem is reformulated, and closed-form expressions for the energy-efficient resource allocation solution to jointly allocate the resource block and transmit power are derived by the Lagrange dual decomposition method. Simulation results confirm that the H-CRAN architecture and the corresponding resource allocation solution can enhance the EE significantly.
关键词： heterogeneous cloud radio access network (H-CRAN),green communication,Fifth-generation (5G),resource allocation,fractional frequency reuse (FFR)
更新于2025-09-19 17:13:59