CVT (Centroidal Voronoi Tessellation)-based remeshing optimizes mesh quality via the Voronoi-Delaunay framework, optimizing vertex distribution and generating regular triangles. Current CVT-based approaches can fall into two categories. The former are exact methods, such as Geodesic CVT and Restricted Voronoi Diagrams(RVD), which ensure high quality but require significant computation. The latter are approximate methods, which reduce computational complexity yet compromise quality. To address this tradeoff, we propose a CVT-based surface remeshing method that balances optimization between quality and efficiency via curvature-adaptive multi-clipping of 3D Centroidal Voronoi cells using original surface facets. The core idea of the method is that we adaptively adjust the number of clipping times according to local curvature, and use the angular relationship between the normal vectors of neighboring facets to represent the magnitude of local curvature. Experimental results demonstrate the effectiveness of our method.

Inverse rendering is widely used in digital twins, virtual asset generation, and VR/AR. When reconstructing scene illumination, predominant methods rely on environment maps, which simplify illumination into a distant uniform source, struggling to accurately model spatially varying effects. Inspired by the rapid development and representational power of 3D Gaussian Splatting (3DGS), we propose GS-SVIR, a novel inverse rendering framework that leverages 3DGS for the modeling of spatially varying illumination. The proposed framework is structured around three main parts. Firstly, we introduce a novel lighting representation termed ”Gaussian-emitter,” which models the scene illumination as a collection of 3D Gaussians in space, thereby effectively overcoming the limitation of environment maps in modeling spatially varying and near-field lighting. Secondly, to enable high-fidelity rendering under this lighting model, we employ a differentiable ray tracer that calculates direct illumination from the Gaussian emitters. Thirdly, we account for global illumination by approximating multiple-bounce indirect lighting using spherical harmonics coefficients stored within each Gaussian, which significantly enhances the realism of the reconstructed materials and lighting. Experimental results on benchmark datasets demonstrate that our method achieves an average improvement of 4% in material reconstruction accuracy and 3% in rendering quality compared to environment-map-based methods.

Abstract Creating floorplans lays the foundation for architectural design and scene modeling. We propose a novel framework for generating diverse high‐quality floorplans under predefined constraints. Central to our method is an iterative refinement process for optimizing the bounding boxes of rooms and the floorplan semantics image, which defines a vector floorplan together. Vector floorplans can be generated through a learning‐based refinement process. Our framework supports various constraints, such as floorplan boundaries, topological graphs, and bubble diagrams. Extensive experiments demonstrate that our method is superior to state‐of‐the‐art techniques, particularly in generating a wider variety of solutions that cater to various architectural needs.

In large-scale urban areas, edge computing, with flexible and low-latency services, enriches various pioneering applications such as the internet of vehicles (IoVs) and smart cities. These location-sensitive applications raise a critical concern, i.e., the mismatch between the spatial distributions of computing requirements and computing capacities. And this mismatch gives a huge challenge for load balancing among edge servers. However, existing approaches do not account for this spatial unevenness, which undermines the high-quality implementations of urban edge computing systems. Regarding this load balancing problem, we propose a novel Power Diagram based Edge Balancing (PDEB) approach, pursuing computing capacities self-adapted with computing requirements via the power diagram. This paper makes three key contributions. First, we propose a distribution modeling framework that formulates spatial mismatch as an optimization problem. Second, we introduce a decentralized power diagram constructed from modeled distributions, serving as the mathematical foundation for decentralized edge coordination. Third, we develop a structured pairing and scheduling strategy based on the power diagram to proactively redistribute load across edge servers. Experimentally, PDEB achieves$42.78\%$better load balance and$16.69\%$lower queuing delay than leading baselines, validating its theoretical and practical advantages.

Automating architectural floorplan design is vital for housing and interior design, offering a faster, cost-effective alternative to manual sketches by architects. However, existing methods, including rule-based and learning-based approaches, face challenges in design complexity and constrained generation with extensive post-processing, and tend to obvious geometric inconsistencies such as misalignment, overlap, and gaps. In this work, we propose a novel generative framework for vector floorplan design via structural graph generation, called GSDiff, focusing on wall junction generation and wall segment prediction to capture both geometric and semantic aspects of structural graphs. To improve the geometric rationality of generated structural graphs, we propose two innovative geometry enhancement methods. In wall junction generation, we propose a novel alignment loss function to improve geometric consistency. In wall segment prediction, we propose a random self-supervision method to enhance the model’s perception of the overall geometric structure, thereby promoting the generation of reasonable geometric structures. Employing the diffusion model and the Transformer model, as well as the geometry enhancement strategies, our framework can generate wall junctions, wall segments and room polygons with structural and semantic information, resulting in structural graphs that accurately represent floorplans. Extensive experiments show that the proposed method surpasses existing techniques, enabling free generation and constrained generation, marking a shift towards structure generation in architectural design.

Automated vector floorplan generation is valuable for designers to explore potential spatial designs. However, existing learning-based methods rely on complex post-processing or optimization to obtain plausible vector floorplans, which disrupts the end-to-end design flow. In this paper, we propose FloorplanSBS, a patch-based segmentation framework for directly synthesizing vector floorplans. Our method leverages the strengths of box-based representation and segmentation-based generation, following a division-and-labeling scheme. The framework operates in two stages: given input design constraints, a division model first divides the design space into rectangular patches, followed by a labelling model that assigns semantic labels to each patch. FloorplanSBS supports constraints such as boundaries and layout graphs. Extensive evaluations show that it surpasses state-of-the-art methods in generating high-quality vector floorplans. With its end-to-end neural framework, FloorplanSBS eliminates the need for post-processing, offering a simple, efficient, and user-friendly tool for vector floorplan design.

Abstract Point cloud normal estimation underpins many 3D vision and graphics applications. Precise normal estimation in regions of sharp curvature and high‐frequency variation remains a major bottleneck; existing learning‐based methods still struggle to isolate fine geometry details under noise and uneven sampling. We present FAHNet, a novel frequency‐aware hierarchical network that precisely tackles those challenges. Our Frequency‐Aware Hierarchical Geometry (FAHG) feature extraction module selectively amplifies and merges cross‐scale cues, ensuring that both fine‐grained local features and sharp structures are faithfully represented. Crucially, a dedicated Frequency‐Aware geometry enhancement (FA) branch intensifies sensitivity to abrupt normal transitions and sharp features, preventing the common over‐smoothing limitation. Extensive experiments on synthetic benchmarks (PCPNet, FamousShape) and real‐world scans (SceneNN) demonstrate that FAHNet outperforms state‐of‐the‐art approaches in normal estimation accuracy. Ablation studies further quantify the contribution of each component, and downstream surface reconstruction results validate the practical impact of our design.

Ultrasound examination has become a vital mid-term prenatal screening method due to its numerous benefits. However, identifying fetal ultrasound sections poses significant challenges and requires the expertise of experienced doctors, especially when dealing with unfavorable fetal positions and image artifacts. The shortage of skilled doctors and the complexity of these screenings highlight the necessity of artificial intelligence to support medical professionals. Most current research targets a few specific fetal ultrasound sections for particular tasks. In reality, mid-pregnancy level II ultrasound assessments involve around ten different fetal sections. Therefore, there is a critical need for algorithms that can recognize these level II fetal sections. Furthermore, the standard ultrasound planes located in the same region exhibit a high degree of similarity. The traditional fetal slice algorithm such as ResNet generates feature maps that are sparse matrices. As a result, employing global average pooling (GAP) layers can lead to significant information loss, making it challenging to effectively differentiate between similar views. In this study, we introduce a deep-learning network designed for second-trimester anatomy ultrasound standard plane recognition. Our approach incorporates a novel spectral pooling paradigm. We introduce singular pooling to enhance information extraction from feature maps and integrate this singular pooling layer into ResNet. This methodology is tested on our dataset, which includes ten distinct standard fetal ultrasound sections from second-trimester level II examinations. Our model achieves exceptional performance, with an accuracy of 92.07% and an F1-Score of 0.919. The implementation of singular pooling significantly improves the original ResNet model. This method can effectively aid in the identification of standard plane recognition in second-trimester anatomy ultrasounds. Furthermore, we validated the efficacy of our model by applying it to two additional datasets, thereby further demonstrating its efficiency and applicability. Our code is available at https://github.com/sysll/vectore/tree/ master.

With the rapid development of Mobile Edge Computing (MEC), various heterogeneous applications have being deployed on edge servers in close proximity to end-users for the low-latency responses. In this circumstance, since the resources on edge servers are limited, it is critical to deploy these applications on suitable edge servers. However, due to the heterogeneity of the applications and the mobility of end-users in real MEC circumstances, the requests each edge server received may undergo temporal fluctuations in both views of quantity and type. In other words, it is crucial to re-deploy these heterogeneous applications to match these dynamic circumstances, instead of permanent deployments without adjustments. Nevertheless, frequent re-deployment causes service interruptions and resource wastage, leading to system instability. Existing approaches struggle to handle redeployment effectively in heterogeneous, dynamic, and stability-critical MEC environments. In this paper, we first formulate the Edge Application Re-Deployment problem on the basis of constrained multi-objective optimization and prove its$\mathcal {NP}$-hardness. Then we propose an optimal re-deployment approach based on the Integer Programming technique for small-scale edge application re-deployment scenarios. And we also propose a Decompose-Solve-Merge approximation approach which balances the effectiveness and efficiency with a configurable parameter for large-scale scenarios. Extensive experiments on a real-world data set evaluate our novel approaches against four existing representative approaches. Additionally, we perform the ablation experiment to validate the effectiveness of our approaches and explore the impact of configurable parameter on the performance. The results show the superior performance of our approaches on re-deployment in terms of heterogeneous, dynamic, and stability.

Abstract Recognizing the detailed information embedded in rasterized floorplans is at the research forefront in the community of computer graphics and vision. With the advent of deep neural networks, automatic floorplan recognition has made tremendous breakthroughs. However, co‐recognizing both the structures and semantics of floorplans through one neural network remains a significant challenge. In this paper, we introduce a novel framework Raster‐to‐Graph, which automatically achieves structural and semantic recognition of floorplans. We represent vectorized floorplans as structural graphs embedded with floorplan semantics, thus transforming the floorplan recognition task into a structural graph prediction problem. We design an autoregressive prediction framework using the neural network architecture of the visual attention Transformer, iteratively predicting the wall junctions and wall segments of floorplans in the order of graph traversal. Additionally, we propose a large‐scale floorplan dataset containing over 10,000 real‐world residential floorplans. Our autoregressive framework can automatically recognize the structures and semantics of floorplans. Extensive experiments demonstrate the effectiveness of our framework, showing significant improvements on all metrics. Qualitative and quantitative evaluations indicate that our framework outperforms existing state‐of‐the‐art methods. Code and dataset for this paper are available at: https://github.com/HSZVIS/Raster-to-Graph .

Forecasting human trajectory is an essential technology in intelligent surveillance systems, robot navigation systems, autonomous driving systems, etc. Most of the trajectory prediction models based on RNN and Transformers use autoregressive methods to generate future trajectories, which may accumulate displacement errors and are inefficient for training and testing. To address these problems, we propose a novel decoder named MRG decoder, which introduces a Mapping-Refinement-Generation structure to generate trajectory in a non-autoregressive manner. Furthermore, we design the MRGTraj trajectory prediction model based on the proposed MRG decoder. Firstly, we employ a Transformer as an encoder to extract encoded features from the past trajectory. Secondly, we introduce an interaction-aware latent code generator to learn a Gaussian distribution from the social context among pedestrians for latent code sampling. Finally, we feed the encoded features to the MRG decoder and sample the latent code multiple times from the learned Gaussian distribution, providing additional inputs to the MRG decoder to generate multiple socially acceptable future trajectories. Experimental results on two public datasets, ETH and UCY, validate the effectiveness of the MRGTraj model. Besides, the MRGTraj model achieves superior prediction performance, with improvements of 13.21% on FDE metrics and a 71.29% speed-up compared to state-of-the-art models. The code is available athttps://github.com/wisionpeng/MRGTraj.

Edge computing deploys edges close to end-users to provide highly accessible resources and latency-sensitive services. It is invaluable for urban crowd/hazard management services, e.g., real-time dynamic route planning and hazard monitoring/analysis, etc. However, in such scenarios, various types of urban hazards jeopardize the usability of edge servers. Worsely, these hazards could be integrated, like gas fires caused by urban earthquakes. In this regard, the formulation of usability risks that servers face is intractable due to the complexity, incomplete real-time data and insufficient expert knowledge of these integrated hazards. Therefore, we innovatively define the usability risks asServer Hazard Riskmodel from the view of the spatial data field by utilizingInformation Diffusion techniquewhich can overcome the adverse conditions above. Then we involve it to formulate theServer Hazard Risk User Allocation(SR-UA) problem, and analyze three typical solutions from the perspective of optimality and efficiency, which are the Lexicographic Goal Programming approach (SR-UA-LGP), the Approximation approach (SR-UA-A) and the Particle Swarm Optimization-based approach (SR-UA-PSO). The extensive experiments based on two real-world datasets illustrate the superior performance of our model and solutions.

Abstract Bubble diagrams serve as a crucial tool in the field of architectural planning and graphic design. With the surge of Artificial Intelligence Generated Content (AIGC), there has been a continuous emergence of research and development efforts focused on utilizing bubble diagrams for layout design and generation. However, there is a lack of research efforts focused on bubble diagram generation. In this paper, we propose a novel generative model, BubbleFormer, for generating diverse and plausible bubble diagrams. BubbleFormer consists of two improved Transformer networks: NodeFormer and EdgeFormer. These networks generate nodes and edges of the bubble diagram, respectively. To enhance the generation diversity, a VAE module is incorporated into BubbleFormer, allowing for the sampling and generation of numerous high‐quality bubble diagrams. BubbleFormer is trained end‐to‐end and evaluated through qualitative and quantitative experiments. The results demonstrate that BubbleFormer can generate convincing and diverse bubble diagrams, which in turn drive downstream tasks to produce high‐quality layout plans. The model also shows generalization capabilities in other layout generation tasks and outperforms state‐of‐the‐art techniques in terms of quality and diversity. In previous work, bubble diagrams as input are provided by users, and as a result, our bubble diagram generative model fills a significant gap in automated layout generation driven by bubble diagrams, thereby enabling an end‐to‐end layout design and generation. Code for this paper is at https://github.com/cgjiahui/BubbleFormer .

Abstract Triangular meshes of superior quality are important for geometric processing in practical applications. Existing approximative CVT‐based remeshing methodology uses planar polygonal facets to fit the original surface, simplifying the computational complexity. However, they usually do not consider surface curvature. Topological errors and outliers can also occur in the close sheet surface remeshing, resulting in wrong meshes. With this regard, we present a novel method named PowerRTF, an extension of the restricted tangent face (RTF) in conjunction with the power diagram, to better approximate the original surface with curvature adaption. The idea is to introduce a weight property to each sample point and compute the power diagram on the tangent face to produce area‐controlled polygonal facets. Based on this, we impose the variable‐capacity constraint and centroid constraint to the PowerRTF, providing the trade‐off between mesh quality and computational efficiency. Moreover, we apply a normal verification‐based inverse side point culling method to address the topological errors and outliers in close sheet surface remeshing. Our method independently computes and optimizes the PowerRTF per sample point, which is efficiently implemented in parallel on the GPU. Experimental results demonstrate the effectiveness, flexibility, and efficiency of our method.

Abstract This study proposes a multi‐scale transformation method based on morphological reconstruction and membership filtering, termed as MMFuse, to fuse infrared and visible images. This method employs a fuzzy c‐means clustering algorithm for multi‐scale decomposition by introducing morphological reconstruction operations and modifying member partitions to ensure noise resistance and image detail preservation. In addition, the MMFuse utilises the image attributes of layers as their fusion weights at each scale for adaptive feature fusion, which reduces the difficulty of manual adjustment of fusion weights. Moreover, on the basis of histogram enhancement, a visible image enhancement method is proposed, which can help exploit additional texture details in low‐light visible images and transfer these details to the fused image. The experiments performed on public datasets indicates that the MMFuse can generate sharp and clean fused images with high robustness and good fusion results for the images corrupted by different noises. Moreover, the results of this method appear as high‐quality visible images with clear highlighted infrared targets.

In the era of smart healthcare tremendous growth, plenty of smart devices facilitate cognitive computing for the purposes of lower cost, smarter diagnostic, etc. Android system has been widely used in the field of IoMT, and as the main operating system. However, Android malware is becoming one major security concern for healthcare, by the serious threat for our medical software assets, like the leakage of private information, the abusing of critical operations, etc. Unfortunately, the existing methods focus on building sustainable classification models, without fully considering system API which is the key to model aging. Compared to the traditional methods, we apply the lifeCycle of API as temporal metric. In addition to the temporal view, the “sizes” of the APPs are utilized as spatial metric in the spatial view. Based on this, we firstly discuss the temporal and spatial metrics together in terms of clustering, and then propose our novel framework-TSDroid. In this framework, we use TS-based clustering algorithm to obtain clustering subsets to enhance the detection capability. We have carried out an experimental verification on three existing excellent methods (i.e., Drebin, HinDroid, and DroidEvolver) and obtain good promotion effects by our framework.

Edge computing is a promising solution to enabling highly accessible resources and latency-sensitive services for nearby users. In public safety, it can provide critical support for urban crowd/hazard management services, such as real-time path planning, hazard warning, etc. In a crowd/hazard scenario, crowds can be allocated to nearby edge servers for obtaining real-time support, e.g., evacuation instructions for those who want to evacuate and crowd flow updates for those who want to rescue, etc. In such scenarios, the behaviors of different roles (like rescuers and evacuees) and the positive/negative interactions among them must be considered in user allocation for reducing injuries and fatalities. In this paper, these issues are defined as a novelRole-Based Criticality(RBC) model to describe the fatal risks of different roles in the crowd/hazard scenarios. Based on the model, theRole-Based User Allocation(RUA) problem is formulated. To tackle this problem, we devise an optimal solution named RUA-ILP based on Integer Linear Programming. To accommodate large-scale scenarios, we propose two representative approximate approach named RUA-A and RUA-GA to ensure efficient and effectiveness user allocation respectively. They can maximize the overall role-based criticality which can reduce injuries and fatalities in crowd/hazard scenarios by theoretical proofing and extensive experiments conducted on a real-world dataset.

Edge computing provides a novel computing paradigm by deploying services on edge servers to serve nearby end-users with low latency. In this regard, a suitable allocation strategy is crucial that maximizes the number of users served at the minimum overall cost, which is referred to as the Edge User Allocation (EUA) problem. However, when edge computing meets public safety, some critical issues have not been fully considered by existing EUA approaches. Among these issues, the levels of danger to individuals quantitatively indicate whether individuals are in danger in an emergency. Hence, the inclusion of these levels impacts the priority for allocating resources in the EUA problem. In this paper, these levels are defined as individual criticalities formally. Then, we take them into account to formulate the novel CRiticality-EUA (CR-EUA) problem, and prove its NP-hardness. To solve this problem, an optimal approach, named CR-EUA-O, is proposed by utilizing the Integer Programming technique. Furthermore, we propose an approach with a proven approximation ratio, named CR-EUA-H, as an effective and efficient solution. Experiments are conducted on a real-world dataset to evaluate our approaches against four representative approaches. The results show the superior performance of our approaches in the overall criticality and execution time.

Floorplan generation has drawn widespread interest in the community. Recent learning-based methods for generating realistic floorplans have made significant progress while a complex heuristic post-processing is still necessary to obtain desired results. In this paper, we propose a novel wall-oriented method, called WallPlan , for automatically and efficiently generating plausible floorplans from various design constraints. We pioneer the representation of the floorplan as a wall graph with room labels and consider the floorplan generation as a graph generation. Given the boundary as input, we first initialize the boundary with windows predicted by WinNet. Then a graph generation network GraphNet and semantics prediction network LabelNet are coupled to generate the wall graph progressively by imitating graph traversal. WallPlan can be applied for practical architectural designs, especially the wall-based constraints. We conduct ablation experiments, qualitative evaluations, quantitative comparisons, and perceptual studies to evaluate our method's feasibility, efficacy, and versatility. Intensive experiments demonstrate our method requires no post-processing, producing higher quality floorplans than state-of-the-art techniques.

With the remarkable development of sensor devices and the Internet of Things (IoT), today's researchers can easily know what changes have taken place in the real world by acquiring a 3-D model. Conversely, a large amount of image data promotes the development of perceptual computing technology. In this article, we focus on modeling 3-D scenes from the multisource image data obtained from the IoT with cameras. Although great progress has been made in 3-D reconstruction, it is still challenging to recover the 3-D model from IoT data because the captured images are usually noisy, incomplete, varying scale, and with repetitive structures or features. In this article, we propose an accurate 3-D reconstruction method under IoT environments for perceptual computing of the scene. This method consists of sparse, dense, and surface reconstruction processes, which can gradually recover high-quality geometric models from the image data and efficiently deal with various repetitive structures. By analyzing the reconstructed model, we can detect the changes of scenes. We evaluate the proposed method on the benchmark data sets (i.e., tanks and temples) and publicly available data sets(in which samples usually contain repeated structures, lighting change, and different scales). Experimental results show that the proposed method outperforms the state-of-the-art methods according to the standard evaluation metric. We also use our method to enhance the real scenes with virtual objects, thus producing promising results.

Benefits from artificial intelligence and the Internet of Vehicles (IoV), Management of modern transportation have great progress, especially in urban areas. However, traditional traffic video analysis and visualization are usually conducted in offsite and textural environments, i.e., text and number, which do not promote user's sensorial perception and interaction. Thus, the problem that how to use modern novel techniques to analyze traffic video for improving intelligent transportation is so emergency. In this paper, we introduce a joint 3D reconstruction and object tracking approach to traffic video analysis under the IoV environment, which is an integrative framework and consists of 3D reconstruction, object detection, and visual tracking. The 3D reconstruction system is connected to the Internet of Vehicles and integrated into the system to retrieve image data for recovering the 3D model of vehicles, and then, visualizing vehicle trajectories in real-time by augmented reality. And the system can also locate the vehicle's position in real-time. The experiments in both laboratory and practice show great feedback, which will effectively contribute to intelligent transportation.

Abstract Review helpfulness prediction aims to prioritize online reviews by quality. Existing methods largely combine review texts and star ratings for helpfulness prediction. However, star ratings are used in a way that has either little representation capacity or limited interaction with review texts. As a result, rating information has yet to be fully exploited during the combination. This paper aims to overcome the two drawbacks. A deep interactive architecture is proposed to learn the text–rating interaction (TRI) for helpfulness modeling. TRI enlarges the representation capacity of star ratings while enhancing the influence of rating information on review texts. TRI is evaluated on six real-world domains of the Amazon 5-Core dataset. Extensive experiments demonstrate that TRI can better predict review helpfulness and beat the state of the art. Ablation studies and qualitative analysis are provided to further understand model behaviors and the learned parameters.

In this paper, we propose a novel fault diagnosis (FD) approach for micro-electromechanical systems (MEMS) inertial sensors that recognize the fault patterns of MEMS inertial sensors in an end-to-end manner. We use a convolutional neural network (CNN)-based data-driven method to classify the temperature-related sensor faults in unmanned aerial vehicles (UAVs). First, we formulate the FD problem for MEMS inertial sensors into a deep learning framework. Second, we design a multi-scale CNN which uses the raw data of MEMS inertial sensors as input and which outputs classification results indicating faults. Then we extract fault features in the temperature domain to solve the non-uniform sampling problem. Finally, we propose an improved adaptive learning rate optimization method which accelerates the loss convergence by using the Kalman filter (KF) to train the network efficiently with a small dataset. Our experimental results show that our method achieved high fault recognition accuracy and that our proposed adaptive learning rate method improved performance in terms of loss convergence and robustness on a small training batch.

This study focuses on the land surface ecosystem change in Ordos, Inner Mongolia from 1994 to 2015. Due to the typical climate, geographic location and abundant mineral resources, this region is an ecological vulnerable zone. Landsat images were collected to detect the change of vegetation coverage and waterbody distribution considering the global climate change and the anthropogenic drivers. During the process, the dimidiate pixel model and tasseled cap transformation technology were adopted. The result shows that the evolution characteristics of vegetation and waterbody have a similar trend, which is decreasing from 1994 to 2000 and continuous increasing from 2000 to 2015. We found that the changes of this study area in vegetation and waterbody are mainly dominated by precipitation under the influence of global climate change.

As a challenging problem, image haze removal plays an important role in computer vision applications. The dark channel prior has been widely studied for haze removal since it is simple and effective; however, it still suffers from over‐saturation, artefacts and dark‐look. To resolve these problems, this study proposes a method of single image haze removal using content‐adaptive dark channel and post enhancement. The main contributions of this work are as follows: first, an associative filter, which can transfer the structures of a reference image and the grey levels of a coarse image to the filtering output, is employed to compute the dark channel efficiently and effectively. Secondly, the dark channel confidence is utilised to restrict the dark channel based on the content of the image. Finally, a post enhancement method is devised to map the luminance of the restored haze‐free image with the preservation of local contrast. Experimental results demonstrate that the proposed method significantly improves the visibility of the hazy image.

In this paper a three-phase four-leg voltage source inverter operating in island mode is described. The four-leg inverter is implemented by using a delta/wye or ZigZag transformer to meet isolation requirement. The control scheme includes an inner current loop providing the capability of fast current limiting and outer voltage loop. Digital sliding mode control is used for the inner current loop which requires higher bandwidth. The voltage loop is implemented in synchronous frame with selected harmonics cancellation for both positive and negative sequence components. Simulation and test of a 125 kW inverter at various operation conditions are presented to verify the validity of the control method.

Maximal clearance is an important property that is highly desirable in multi-agent motion planning. However, it is also inherently difficult to attain. We propose a novel approach to achieve maximal clearance by exploiting the ability of evenly distributing a set of points by a centroidal Voronoi tessellation (CVT). We adapt the CVT framework to multi agent motion planning by adding an extra time dimension and optimize the trajectories of the agents in the augmented domain. As an optimization framework, our method can work naturally on complex regions. We demonstrate the effectiveness of our algorithm in achieving maximal clearance in motion planning with some examples.

Constraint information is crucial content of CSCD. It becomes more complicated and various with the expanding application and hard to manage. In order to control collaborative design flow efficiently, detect constraint conflicts early and decrease contradiction extension speed, intuitive and vivid constraint information visualization method is created. This paper discusses detailed signification and formalized description of constraint information visualization in CSCD based on classification and representations, puts forward two key procedures in visualization: one is counterpart modes between constraint information and available visualization techniques. The other one is harmony system of assessment based on constraint harmony concept. Crucial steps of constraint information visualization in CSCD such as constraint information mapping, dynamic track have been pointed out as well. The application of constraint information in collaborative design will give an important direction to complicated system control and complicated constraints management

Template technology has a great contribution to domain of design, especially computer aided design. Based on existing technologies and methodology, authors have applied the concept of cooperative template to the study of visual design environment. A framework of visual design environment of cooperative template is introduced including cooperative template description, element constraint, working flow and parallel mechanism. Several instances like Die cooperative & Sofa are provided to demonstrate the features and utility of the design environment. As a new concept, result of application makes authors believe that cooperative template play an important role in information-reducing, relation-order and design efficiency in CSCD environment.