3D Tiles-Based High-Efficiency Visualization Method for Complex BIM Models on the Web

Geographic data visualization is an important research area of Web Geographic Information System (GIS). Owing to the detailed subassemblies and exhaustive knowledge database, building information modeling (BIM) plays an important role in geospatial research and industries. The integration of BIM and GIS contributes to the smooth visualization, quick construction, and efficient management of geographic data. However, there are very few methods that can yield high-efficiency data transmission and visualization for complex BIM models while maintaining the integrity of the internal subassembly structure and attributes. To overcome this issue, this paper proposes a 3D Tiles-based visualization method for complex BIM models on the Web-based 3D model viewer. This method is adopted to partition the BIM model according to its assembly without simplifying the BIM model, by using a tiling method for 3D models based on a degraded R-tree, which accounts for the size of tiles. Subsequently, we introduce the “Mask Filter,” a level of detail method that is used to layer the BIM model. Conducting a series of contrast experiments, the result indicates that this method is efficient and feasible, which significantly improves visualization performance of complex BIM with mass data in the geospatial scene and facilitates the integration of Building Information Modeling and Geographic Information System

SADNet: Space-aware DeepLab network for Urban-Scale point clouds semantic segmentation

Semantic segmentation of urban-scale point clouds can effectively assist people in understanding and perceiving 3D urban scenes. Although a considerable number of deep learning models for the semantic segmentation of point clouds have been proposed, some methods are plagued by information loss caused by sampling and insufficient perception of the spatial relationship between points. To address this issue, this paper proposes an end-to-end space-aware DeepLab deep learning network, named SADNet. In the SADNet, a space-aware attentive residual module (SARM) is incorporated to extract rich point cloud features with the assistance of perceiving spatial relationships between points. Then, in combination with a point cloud atrous spatial pyramid pooling module (PCaspp), SADNet extracts multiscale point cloud features while effectively avoiding information loss from pooling and downsampling. Finally, a dilated local feature extraction (DLFE) module is designed to enhance the detection ability for small objects by dilating the feature map. Furthermore, to validate the superiority of the SADNet, extensive experiments are conducted on two publicly available benchmarks, Sensaturban and Hessigheim 3D. The results demonstrate the state-of-the-art performance on both datasets, which achieves the mean IoU of 66.8% on Sensaturban and overall accuracy of 91.77%, mean F1-score of 82.81% on Hessigheim 3D. Overall, SADNet is a promising approach for urban-scale point cloud semantic segmentation and has the potential to enhance understanding and perception of real-world urban scenes

PAPR reduction based on deep autoencoder for VLC DCO-OFDM system

International audienc

Deep Convolutional Auto-Encoder based Indoor Light Positioning Using RSS Temporal Image

International audienc

New Insight into the Formation Mechanism of PCDD/Fs from 2‑Chlorophenol Precursor

Chlorophenols are known as precursors of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs). The widely accepted formation mechanism of PCDD/Fs always assumes chlorophenoxy radicals as key and important intermediates. Based on the results of density functional theory calculations, the present work reports new insight into the formation mechanism of PCDD/Fs from chlorophenol precursors. Using 2-chlorophenol as a model compound of chlorophenols, we find that apart from the chlorinated phenoxy radical, the chlorinated phenyl radical and the chlorinated α-ketocarbene also have great potential for PCDD/F formation, which has scarcely been considered in previous literature. The calculations on the self- and cross-coupling reactions of the chlorophenoxy radical, the chlorinated phenyl radical and the chlorinated α-ketocarbene show that the formations of 1-MCDD, 1,6-DCDD, 4,6-DCDF, and 4-MCDF are both thermodynamically and kinetically favorable. In particular, some pathways involving the chlorinated phenyl radicals and the chlorinated α-ketocarbene are even energetically more favorable than those involving the chlorophenoxy radical. The calculated results may improve our understanding for the formation mechanism of PCDD/Fs from chlorophenol precursors and be informative to environmental scientists

Gold Enhanced Graphene-Based Photodetector on Optical Fiber with Ultrasensitivity over Near-Infrared Bands

Graphene has been widely used in photodetectors; however its photoresponsivity is limited due to the intrinsic low absorption of graphene. To enhance the graphene absorption, a waveguide structure with an extended interaction length and plasmonic resonance with light field enhancement are often employed. However, the operation bandwidth is narrowed when this happens. Here, a novel graphene-based all-fiber photodetector (AFPD) was demonstrated with ultrahigh responsivity over a full near-infrared band. The AFPD benefits from the gold-enhanced absorption when an interdigitated Au electrode is fabricated onto a Graphene-PMMA film covered over a side-polished fiber (SFP). Interestingly, the AFPD shows a photoresponsivity of >1 × 104 A/W and an external quantum efficiency of >4.6 × 106% over a broadband region of 980–1620 nm. The proposed device provides a simple, low-cost, efficient, and robust way to detect optical fiber signals with intriguing capabilities in terms of distributed photodetection and on-line power monitoring, which is highly desirable for a fiber-optic communication system