Cloud Computing has become a vital component of modern digital infrastructure, offering users remote access to a plethora of online services and resources. Concurrently, the rise of the Internet of Things (IoT) has pa...
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In a number of industries, including computer graphics, robotics, and medical imaging, three-dimensional reconstruction is essential. In this research, a CNN-based Multi-output and Multi-Task Regressor with deep learn...
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作者：
El-Houari, HamzaUniversity Moulay Ismail
Faculty of Sciences and Techniques Research Laboratory “Applied Mathematics Computer Science and Systems” Errachidia Morocco
This paper aims to show that there exists a weak solution to the following quasilinear system driven by the M-Laplacian (Formula presented.) where Ω is a bounded open subset in RN and (-Δm) is the M-Laplacian operat...
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The implementation of digital industrial technologies marks a pivotal step towards maximizing automation in production processes, aligning with the principles of Industry 4.0 and the NDE 4.0 paradigms. One key area of...
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Visual localization and object detection both play important roles in various *** many indoor application scenarios where some detected objects have fixed positions,the two techniques work closely ***,few researchers ...
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Visual localization and object detection both play important roles in various *** many indoor application scenarios where some detected objects have fixed positions,the two techniques work closely ***,few researchers consider these two tasks simultaneously,because of a lack of datasets and the little attention paid to such *** this paper,we explore multi-task network design and joint refinement of detection and *** address the dataset problem,we construct a medium indoor scene of an aviation exhibition hall through a semi-automatic *** dataset provides localization and detection information,and is publicly available at https://***/drive/folders/1U28zk0N4_I0db zkqyIAK1A15k9oUKOjI?usp=sharing for benchmarking localization and object detection *** this dataset,we have designed a multi-task network,JLDNet,based on YOLO v3,that outputs a target point cloud and object bounding *** dynamic environments,the detection branch also promotes the perception of *** includes image feature learning,point feature learning,feature fusion,detection construction,and point cloud ***,object-level bundle adjustment is used to further improve localization and detection *** test JLDNet and compare it to other methods,we have conducted experiments on 7 static scenes,our constructed dataset,and the dynamic TUM RGB-D and Bonn *** results show state-of-the-art accuracy for both tasks,and the benefit of jointly working on both tasks is demonstrated.
作者：
高旭峰王琦张世杰洪瑞金张大伟Shanghai Key Laboratory of Modern Optic Systems
Engineering Research Center of Optical Instrument and SystemMinistry of Education and Shanghai Key Laboratory of Modern Optical SystemsSchool of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China
Color filters in different surroundings inherently suffer from angular sensitivity,which hinders their practical ***,we present an angle-insensitive plasmonic filter that can produce different color responses to diffe...
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Color filters in different surroundings inherently suffer from angular sensitivity,which hinders their practical ***,we present an angle-insensitive plasmonic filter that can produce different color responses to different surrounding *** color filters are based on a two-dimensional periodically and randomly distributed silver nanodisk array on a silica *** proposed plasmonic color filters not only produce bright colors by altering the diameter of the Ag nanodisk,but also achieve continuous color palettes by changing the surrounding *** to the weak coupling between the metallic nanodisks,the plasmonic color filters can enable good incident angle-insensitive properties(up to 30°).The strategy presented here could exhibit robust and promising applicability in anti-counterfeiting and imaging technologies.
This survey paper aims to show methods to analyze and classify field satellite images using deep learning and machine learning *** of deep learning-based Convolutional Neural Network(CNN)technology to harvest fields f...
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This survey paper aims to show methods to analyze and classify field satellite images using deep learning and machine learning *** of deep learning-based Convolutional Neural Network(CNN)technology to harvest fields from satellite images or generate zones of interest were among the planned application scenarios(ROI).Using machine learning,the satellite image is placed on the input image,segmented,and then *** contem-porary categorization,field size ratio,Local Binary Pattern(LBP)histograms,and color data are taken into *** satellite image localization has several practical applications,including pest management,scene analysis,and field *** relationship between satellite images in a specific area,or contextual information,is essential to comprehending the field in its whole.
Network embedding(NE)tries to learn the potential properties of complex networks represented in a low-dimensional feature ***,the existing deep learningbased NE methods are time-consuming as they need to train a dense...
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Network embedding(NE)tries to learn the potential properties of complex networks represented in a low-dimensional feature ***,the existing deep learningbased NE methods are time-consuming as they need to train a dense architecture for deep neural networks with extensive unknown weight parameters.A sparse deep autoencoder(called SPDNE)for dynamic NE is proposed,aiming to learn the network structures while preserving the node evolution with a low computational *** tries to use an optimal sparse architecture to replace the fully connected architecture in the deep autoencoder while maintaining the performance of these models in the dynamic ***,an adaptive simulated algorithm to find the optimal sparse architecture for the deep autoencoder is *** performance of SPDNE over three dynamical NE models(*** architecture-based deep autoencoder method,DynGEM,and ElvDNE)is evaluated on three well-known benchmark networks and five real-world *** experimental results demonstrate that SPDNE can reduce about 70%of weight parameters of the architecture for the deep autoencoder during the training process while preserving the performance of these dynamical NE *** results also show that SPDNE achieves the highest accuracy on 72 out of 96 edge prediction and network reconstruction tasks compared with the state-of-the-art dynamical NE algorithms.
Multi‐object tracking in autonomous driving is a non‐linear *** better address the tracking problem,this paper leveraged an unscented Kalman filter to predict the object's *** the association stage,the Mahalanob...
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Multi‐object tracking in autonomous driving is a non‐linear *** better address the tracking problem,this paper leveraged an unscented Kalman filter to predict the object's *** the association stage,the Mahalanobis distance was employed as an affinity metric,and a Non‐minimum Suppression method was designed for *** the detections fed into the tracker and continuous‘predicting‐matching’steps,the states of each object at different time steps were described as their own continuous *** conducted extensive experiments to evaluate tracking accuracy on three challenging datasets(KITTI,nuScenes and Waymo).The experimental results demon-strated that our method effectively achieved multi‐object tracking with satisfactory ac-curacy and real‐time efficiency.
In this paper, a hollow-core anti-resonant optical fibre containing a semi-elliptical nested tube is proposed, which has the characteristics of single-polarization, large bandwidth, single-mode and low confinement los...
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