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[IEEE 2018 20th International Conference on Transparent Optical Networks (ICTON) - Bucharest (2018.7.1-2018.7.5)] 2018 20th International Conference on Transparent Optical Networks (ICTON) - Elastic Networks Thematic Network Results II: Edge Computing and Wireless Support
摘要： This paper overviews the approach of the Elastic Networks research network to address the challenges posed by the convergence of networking and computing in the edge for a number of scenarios. Each section of the paper covers one topic addressed by the network. Firstly, we address reliability in Fog-to-Cloud computing (F2C). Then, an architecture for a residential SDN gateway featuring NFV is presented. In addition, switching based on SDN for data centres is studied for which a high-speed flexible packet matching in hardware is proposed. Resiliency in SDN controller location is also addressed. Finally, a technique for delay analysis in a single node C-RAN scenario is described.
关键词： NFV,SDN,C-RAN,fog-to-cloud,elastic networks
更新于2025-09-10 09:29:36
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Uplink Channel Estimation and Data Transmission in Millimeter-Wave CRAN with Lens Antenna Arrays
摘要： Millimeter-wave (mmWave) communication and network densification hold great promise for achieving high-rate communication in next-generation wireless networks. Cloud radio access network (CRAN), in which low-complexity remote radio heads (RRHs) coordinated by a central unit (CU) are deployed to serve users in a distributed manner, is a cost-effective solution to achieve network densification. However, when operating over a large bandwidth in the mmWave frequencies, the digital fronthaul links in a CRAN would be easily saturated by the large amount of sampled and quantized signals to be transferred between RRHs and the CU. To tackle this challenge, we propose in this paper a new architecture for mmWave-based CRAN with advanced lens antenna arrays at the RRHs. Due to the energy focusing property, lens antenna arrays are effective in exploiting the angular sparsity of mmWave channels, and thus help in substantially reducing the fronthaul rate and simplifying the signal processing at the multi-antenna RRHs and the CU, even when the channels are frequency-selective. We consider the uplink transmission in a mmWave CRAN with lens antenna arrays and propose a low-complexity quantization bit allocation scheme for multiple antennas at each RRH to meet the given fronthaul rate constraint. Further, we propose a channel estimation technique that exploits the energy focusing property of the lens array and can be implemented at the CU with low complexity. Finally, we compare the proposed mmWave CRAN using lens antenna arrays with a conventional CRAN using uniform planar arrays at the RRHs, and show that the proposed design achieves significant throughput gains, yet with much lower complexity.
关键词： lens antenna array,antenna selection,fronthaul constraint,millimeter-wave communication,quantization bit allocation,Cloud radio access network,channel estimation
更新于2025-09-10 09:29:36
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Community-scale multi-level post-hurricane damage assessment of residential buildings using multi-temporal airborne LiDAR data
摘要： Building damage assessment is a critical task following major hurricane events. Use of remotely sensed data to support building damage assessment is a logical choice considering the di?culty of gaining ground access to the impacted areas immediately after hurricane events. However, a remote sensing based damage assessment approach is often only capable of detecting severely damaged buildings. In this study, an airborne LiDAR based approach is proposed to assess multi-level hurricane damage at the community scale. In the proposed approach, building clusters are ?rst extracted using a density-based algorithm. A novel cluster matching algorithm is proposed to robustly match post-event and pre-event building clusters. Multiple features including roof area and volume, roof orientation, and roof shape are computed as building damage indicators. A hierarchical determination process is then employed to identify the extent of damage to each building object. The results of this study suggest that our proposed approach is capable of 1) recognizing building objects, 2) extracting damage features, and 3) characterizing the extent of damage to individual building properties.
关键词： Hurricane damage assessment,Point cloud processing,Geometric computing,Airborne LiDAR,Data clustering
更新于2025-09-10 09:29:36
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Integration of point cloud data and hyperspectral imaging as a data gathering methodology for refurbishment projects using building information modelling (BIM)
摘要： Purpose – Building information modelling (BIM) is a digital representation of the physical and functional characteristics of a building. Its use offers a range of bene?ts in terms of achieving the ef?cient design, construction, operation and maintenance of buildings. Applying BIM at the outset of a new build project should be relatively easy. However, it is often problematic to apply BIM techniques to an existing building, for example, as part of a refurbishment project or as a tool supporting the facilities management strategy, because of inadequacies in the previous management of the dataset that characterises the facility in question. These inadequacies may include information on as built geometry and materials of construction. By the application of automated retrospective data gathering for use in BIM, such problems should be largely overcome and signi?cant bene?ts in terms of ef?ciency gains and cost savings should be achieved. Design/methodology/approach – Laser scanning can be used to collect geometrical and spatial information in the form of a 3D point cloud, and this technique is already used. However, as a point cloud representation does not contain any semantic information or geometrical context, such point cloud data must refer to external sources of data, such as building speci?cation and construction materials, to be in used in BIM. Findings – Hyperspectral imaging techniques can be applied to provide both spectral and spatial information of scenes as a set of high-resolution images. Integrating of a 3D point cloud into hyperspectral images would enable accurate identi?cation and classi?cation of surface materials and would also convert the 3D representation to BIM. Originality/value – This integrated approach has been applied in other areas, for example, in crop management. The transfer of this approach to facilities management and construction would improve the ef?ciency and automation of the data transition from building pathology to BIM. In this study, the technological feasibility and advantages of the integration of laser scanning and hyperspectral imaging (the latter not having previously been used in the construction context in its own right) is discussed, and an example of the use of a new integration technique is presented, applied for the ?rst time in the context of buildings.
关键词： Laser scanning,Information modelling,Refurbishment,BIM,Point cloud,Hyperspectral imaging
更新于2025-09-10 09:29:36
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Framework for automated registration of UAV and UGV point clouds using local features in images
摘要： Automatically registering 3D point clouds generated by unmanned aerial and ground vehicles (UAVs and UGVs) is challenging, as data is acquired at different locations with different sensors, consequently resulting in different spatial scales and occlusions. To address these problems, this study proposes a framework for the automated registration of UAV and UGV point clouds using 2D local feature points in the images taken from UAVs and UGVs. This study first conducted field experiments by varying the angles of the UAV camera to identify the optimal angle with which to detect sufficient points matching with the images taken by the UGV. As a result, this study identified that a combination of UAV images taken at 30° and 90° is appropriate for generating a sufficient number of matching points and attaining a reasonable level of precision. The UAV and UGV point clouds were initially scaled and registered with a transformation matrix computed from the 3D points corresponding to the 2D feature matching points. The initially aligned point clouds were subsequently adjusted by the Iterative Closest Point (ICP) algorithm, resulting in the root mean square error (RMSE) of 0.112 m. This promising result indicates that full automation of spatial data collection and registration from a scattered environment (e.g., construction or disaster sites) by UAVs and UGVs is feasible without human intervention.
关键词： UGV,UAV,Registration,Point cloud,Drone,Mobile robot,Automation
更新于2025-09-10 09:29:36
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AIP Conference Proceedings [Author(s) SolarPACES 2017: International Conference on Concentrating Solar Power and Chemical Energy Systems - Santiago, Chile (26–29 September 2017)] - Optimisation of aiming strategies in solar tower power plants
摘要： Inclement weather effects have a direct impact on the efficiency of a Solar Power Tower plant and have the potential to damage the receiver by flash heating. An optimised aiming strategy for the heliostat field mitigates the risk of receiver damage and maximises plant efficiency. A stochastic integer programming approach is applied to optimise the aiming strategy of the heliostat field, with uncertainty in the cloud location, size and density. The optimisation technique is demonstrated with a test case and results are presented for near real-time simulation of the optimal aiming strategy.
关键词： Solar Power Tower,stochastic integer programming,cloud uncertainty,heliostat field,aiming strategy
更新于2025-09-10 09:29:36
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A Geometry-Based Point Cloud Reduction Method for Mobile Augmented Reality System
摘要： In this paper, a geometry-based point cloud reduction method is proposed, and a real-time mobile augmented reality system is explored for applications in urban environments. We formulate a new objective function which combines the point reconstruction errors and constraints on spatial point distribution. Based on this formulation, a mixed integer programming scheme is utilized to solve the points reduction problem. The mobile augmented reality system explored in this paper is composed of the offline and online stages. At the offline stage, we build up the localization database using structure from motion and compress the point cloud by the proposed point cloud reduction method. While at the online stage, we compute the camera pose in real time by combining an image-based localization algorithm and a continuous pose tracking algorithm. Experimental results on benchmark and real data show that compared with the existing methods, this geometry-based point cloud reduction method selects a point cloud subset which helps the image-based localization method to achieve higher success rate. Also, the experiments conducted on a mobile platform show that the reduced point cloud not only reduces the time consuming for initialization and re-initialization, but also makes the memory footprint small, resulting a scalable and real-time mobile augmented reality system.
关键词： augmented reality,point cloud reduction,structure from motion,mobile platform
更新于2025-09-10 09:29:36
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Development of deep learning architecture for automatic classification of outdoor mobile LiDAR data
摘要： This paper proposes a deep convolutional neural network (CNN) architecture for automatic classification of mobile laser scanning (MLS) data obtained for outdoor environment, which are characterized by noise, clutter, large size and larger quantum of information. The developed architecture introduces a look up table (LUT) based approach, which retains the geometry of the input MLS point cloud while rescaling. Further, with the voxelisation of the input MLS sample, the ambiguity of selecting one out of multiple point values within a voxel is resolved. The performance of the architecture is evaluated on MLS data of outdoor environment in two instances, first using tree and non-tree classes (non-tree class has objects like electric pole, wire, low vegetation, wall, house and ground) and then with tree and electric pole classes. Additional testing is carried out by mixing the outdoor MLS data of tree and electric pole classes with three classes of indoor objects, taken from Modelnet dataset, thereby assessing the architecture efficacy over an ensemble of three-dimensional (3D) datasets. Classification of tree and non-tree classes, followed by tree and electric pole classes from MLS samples result in total accuracies of 86.0%, 90.0% respectively and kappa values of 72.0%, 78.7% respectively. Moreover, for the combinations of MLS and Modelnet classes, the classification results are promising, reaching a total accuracy of 95.2% and kappa of 92.5%. The LUT based approach has shown better classification over the traditional rescaling approach for the MLS dataset, resulting in an enhancement up to 9.0% and 18.0% in total accuracy and kappa, respectively. With different varieties of tree, non-tree and electric pole samples, the proposed architecture has shown its potential for automatic classification of MLS data with high accuracy. This study further reveals that the accuracy of classification is improved by introducing more spatial features in the input layer. The accuracies produced in this work can be further improved with the availability of better hardware resources.
关键词： outdoor environment,deep learning,mobile laser scanning,point cloud,convolutional neural network,classification
更新于2025-09-10 09:29:36
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A Spectral–Temporal Patch-Based Missing Area Reconstruction for Time-Series Images
摘要： Clouds, cloud shadows (CCS), and numerous other factors will cause a missing data problem in passive remote sensing images. A well-known reconstruction method is the selection of a similar pixel (with an additional clear reference image) from the remaining clear part of an image to replace the missing pixel. Due to the merit of filling the missing value using a pixel acquired on the same image with the same sensor and the same date, this method is suitable for time-series applications when a time-series profile-based similar measure is utilized for selecting the similar pixel. Since the similar pixel is independently selected, the improper reference pixel or various accuracies obtained by different land covers causes the problem of salt-and-pepper noise in the reconstructed part of an image. To overcome these problems, this paper presents a spectral–temporal patch (STP)-based missing area reconstruction method for time-series images. First, the STP, the pixels of which have similar spectral and temporal evolution characteristics, is extracted using multi-temporal image segmentation. However, some STP have Missing Observations (STPMO) in the time series, which should be reconstructed. Next, for an STPMO, the most similar STP is selected as the reference STP; then, the mean and standard deviation of the STPMO is predicted using a linear regression method with the reference STP. Finally, the textural information, which is denoted by the spatial configuration of color or intensities of neighboring pixels, is extracted from the clear temporal-adjacent STP and “injected” into the missing area to obtain synthetic cloud-free images. We performed an STP-based missing area reconstruction experiment in Jiangzhou, Chongzuo, Guangxi with time-series images acquired by wide field view (WFV) onboard Chinese Gao Fen 1 on 12 different dates. The results indicate that the proposed method can effectively recover the missing information without salt-and-pepper noise in the reconstructed area; also, the reconstructed part of the image is consistent with the clear part without a false edge. The results confirm that the spectral information from the remaining clear part of the same image and textural information from the temporal-adjacent image can create seamless time-series images.
关键词： missing area reconstruction,cloud-free time-series image,cloud and cloud shadow,multi-temporal image segmentation
更新于2025-09-09 09:28:46
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Cloud height and tracking accuracy of three all sky imager systems for individual clouds
摘要： Solar irradiance nowcasts can be derived with sky images from all sky imagers (ASI) by detecting and analyzing transient clouds, which are the main contributor of intra-hour solar irradiance variability. The accuracy of ASI based solar irradiance nowcasting systems depends on various processing steps. Two vital steps are the cloud height detection and cloud tracking. This task is challenging, due to the atmospheric conditions that are often complex, including various cloud layers moving in di?erent directions simultaneously. This challenge is addressed by detecting and tracking individual clouds. For this, we developed two distinct ASI nowcasting approaches with four or two cameras and a third hybridized approach. These three systems create individual 3-D cloud models with unique attributes including height, position, size, optical properties and motion. This enables us to describe complex multi-layer conditions. In this paper, derived cloud height and motion vectors are compared with a reference ceilometer (height) and shadow camera system (motion) over a 30 day validation period. The validation data set includes a wide range of cloud heights, cloud motion patterns and atmospheric conditions. Furthermore, limitations of ASI based nowcasting systems due to image resolution and image perspective constrains are discussed. The most promising system is found to be the hybridized approach. This approach uses four ASIs and a voxel carving based cloud modeling combined with a cloud segmentation independent stereoscopic cloud height and tracking detection. We observed for this approach an overall mean absolute error of 648 m for the height, 1.3 m/s for the cloud speed and 16.2° for the motion direction.
关键词： Cloud height,Cloud tracking,Nowcasting,Irradiance map,All sky imager,3-D cloud modeling
更新于2025-09-09 09:28:46