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A survey for light field super-resolution

High-Confidence Computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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Networked Knowledge and Complex Networks:An Engineering View

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1366-1383页

作者： Jinhu Lü Guanghui Wen Ruqian Lu Yong Wang Songmao Zhang School of Automation Science and Electrical Engineering State Key Laboratory of Software Development EnvironmentBeihang UniversityBeijing 100083 Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China Department of Systems Science School of MathematicsSoutheast UniversityNanjing 211189China

Along with the development of information technologies such as mobile Internet,information acquisition technology,cloud computing and big data technology,the traditional knowledge engineering and knowledge-based software engineering have undergone fundamental changes where the network plays an increasingly important *** this context,it is required to develop new methodologies as well as technical tools for network-based knowledge representation,knowledge services and knowledge ***,the term“network”has different meanings in different ***,some breakthroughs in several bottleneck problems of complex networks promote the developments of the new methodologies and technical tools for network-based knowledge representation,knowledge services and knowledge *** paper first reviews some recent advances on complex networks,and then,in conjunction with knowledge graph,proposes a framework of networked knowledge which models knowledge and its relationships with the perspective of complex *** the unique advantages of deep learning in acquiring and processing knowledge,this paper reviews its development and emphasizes the role that it played in the development of knowledge ***,some challenges and further trends are discussed.

关键词： Complex network knowledge graph networked knowledge neural network

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UKF‐MOT:An unscented Kalman filter‐based 3D multi‐object tracker

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 1031-1041页

作者： Meng Liu Jianwei Niu Yu Liu Collective Intelligence&Collaboration Laboratory China North Artificial Intelligence and Innovation Research InstituteBeijingChina China North Vehicle Research Institute BeijingChina State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijingChina School of Computer Science and Engineering Beihang UniversityBeijingChina

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.

关键词： autonomous vehicle transportation

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Light field depth estimation:A comprehensive survey from principles to future

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High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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What makes for uniformity for non-contrastive self-supervised learning?

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Science China(Technological Sciences) 2022年第10期65卷 2399-2408页

作者： WANG YinQuan ZHANG XiaoPeng TIAN Qi Lü JinHu Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Huawei Inc. Shenzhen 518128China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development EnvironmentBeihang UniversityBeijing 100191China

Recent advances in self-supervised learning(SSL) have made remarkable progress, especially for contrastive methods that target pulling two augmented views of one image together and pushing the views of all other images away. In this setting, negative pairs play a key role in avoiding collapsed representation. Recent studies, such as those on bootstrap your own latent(BYOL)and SimSiam, have surprisingly achieved a comparable performance even without contrasting negative samples. However, a basic theoretical issue for SSL arises: how can different SSL methods avoid collapsed representation, and is there a common design principle? In this study, we look deep into current non-contrastive SSL methods and analyze the key factors that avoid *** achieve this goal, we present a new indicator of uniformity metric and study the local dynamics of the indicator to diagnose collapses in different scenarios. Moreover, we present some principles for choosing a good predictor, such that we can explicitly control the optimization process. Our theoretical analysis result is validated on some widely used benchmarks spanning differentscale datasets. We also compare recent SSL methods and analyze their commonalities in avoiding collapses and some ideas for future algorithm designs.

关键词： contrastive learning self-supervised learning representation uniformity dynamics

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software fault localization based on eigenvector centrality in complex network theory 24

Software fault localization based on eigenvector centrality ...

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24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Wu, Wentao Wang, Shihai Shao, Yuanxun Xie, Wandong School of Reliability and Systems Engineering Beihang University Beijing100191 China Science and Technology on Reliability and Environmental Engineering Laboratory China State Key Laboratory of Software Development Environment China Aerospace Science and Engineering Network Information Development CO. Ltd Beijing China Information Center of China North Industries Group Corporation Beijing China

ISBN: (纸本)9798350365658

software debugging plays a crucial role in fault localization tasks, and spectrum-based fault localization (SBFL) is a hot topic in software automation debugging research. However, existing SBFL technologies are generally limited by tie within ranks, where a large number of elements share the same suspiciousness, which in turn severely limits the performance of SBFL. To this end, we propose an SBFL model based on eigenvector centrality (FLEC). This algorithm first utilizes the statement coverage information in the program spectrum to construct a statement network, and adopts the correlation between statements as edge weights. Then, FLEC takes statement suspiciousness as node weight. On this basis, the algorithm utilizes the eigenvector centrality to calculate the weighted suspiciousness of each statement while considering both node importance and correlation between nodes. Finally, FLEC conducted experimental validation on 3 datasets of Defects4J, and the results showed an average improvement of 10.7% in ACC@N compared to the optimal baseline. © 2024 IEEE.

关键词： Program debugging

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Video-Based Jump Height Estimation in Athletic Performance Testing

Video-Based Jump Height Estimation in Athletic Performance T...

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2023 IEEE International Workshop on Sport, Technology and Research, STAR 2023

作者： Lokas, Ivan Vasilj, Matea Skender, Sven Vucetic, Vlatko Mihaldinec, Hrvoje Dzapo, Hrvoje Ultrax Technologies Software Development Zagreb Croatia Human Performance Laboratory Faculty of Kinesiology Zagreb Croatia Hardware Development Ultrax Technologies Zagreb Croatia Faculty of Electrical Engineering and Computing Department of Electronic Systems and Information Processing Zagreb Croatia

ISBN: (纸本)9798350316056

Jump height estimation is well-studied problem in sports science with a variety of methods proposed over the years. This paper introduces a portable, simple-to-setup and a cost-effective video-based jump height estimation method, and compares it with existing traditional methods that use expensive and specialized equipment. To enable more accurate comparison between different systems, a custom-designed Inertial Measurement Unit (IMU) was used as an additional sensor input. The analysis was performed on eleven female physically active handball players. The dataset consists of three most common vertical jump variations: squat jump, counter-movement jump and counter-movement jump with free arms. The analysis show that our video-based approach achieves comparable results to the traditional methods. © 2023 IEEE.

关键词： Cost effectiveness

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Computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

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2024 International Conference on Automation in Manufacturing, Transportation and Logistics, ICaMaL 2024

作者： Liang, Tailin Yuan, Zhaolin School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Beijing China Research Institute of Advanced Manufacturing Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350378658

For monitoring the paste concentration, existing techniques, such as ultrasonic concentration meters and neutron meters, suffer from radiation hazards and low precision in high concentrations. This paper proposes a novel non-contact concentration measurement method based on deep learning. With the dataset collected by preparing the paste with different concentrations and taking photographs from the paste surface, a convolutional neural network is trained to extract the features from images and predict the concentration. The experiments indicate that the measurement accuracy is close to 88.79 % for the manual stirring paste dataset and 91.42 % for the automatic stirring paste dataset, which are sufficient in industrial applications. As a substitution of traditional concentration measurement instruments, the proposed vision-based concentration measurement method is non-contact and its accuracy is sufficient in industrial applications. © 2024 IEEE.

关键词： Radiation hazards

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CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning 37

CLGT: A Graph Transformer for Student Performance Prediction...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Peng, Tianhao Liang, Yu Wu, Wenjun Ren, Jian Pengrui, Zhao Pu, Yanjun State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology China Institute of Artificial Intelligence Beihang University China

ISBN: (纸本)9781577358800

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Students

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Optimizing Link-Identified Forwarding Framework in LEO Satellite Networks 21

Optimizing Link-Identified Forwarding Framework in LEO Satel...

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21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Zhang, Hefan Wang, Zhiyuan Zhang, Shan Meng, Qingkai Luo, Hongbin School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Institute of Artificial Intelligence Beihang University Beijing China State Key Laboratory of Software Development Environment China State Key Laboratory of Virtual Reality Technology and Systems China

ISBN: (纸本)9783903176553

Low earth orbit (LEO) satellite networks have the potential to provide low-latency communication with global coverage. To unleash this potential, it is crucial to achieve efficient data delivery. In this paper, we analyze the topology characteristics of LEO satellite networks, and propose a source-route-style forwarding framework. Specifically, we leverage the deterministic neighbor relationship and identify all the unidirectional inter-satellite links (ISLs). Moreover, our framework utilizes the in-packet bloom filter (BF) to store the source-route-style forwarding information. This way, the source satellite could encode multiple ISL identifiers into the BF, which actually specifies the forwarding path. The intermediate satellites only need to check whether the outgoing ISLs are encoded and forward packets accordingly. Due to false positives caused by BF, the more ISLs are encoded at a time, the more redundant forwardings emerge. To reduce forwarding overhead, we take into account segment encoding, allowing the source and intermediate satellites to encode part of ISLs towards the destination. Overall, segment encoding seeks the right balance between forwarding overhead and encoding delay. We characterize a wide range of segment encoding policy in a unified framework, and derive the expected forwarding overhead in a closed-form. The segment encoding design is formulated as a binary non-linear programming, which is NP-hard. To overcome the challenge, we leverage its decomposable structure, and propose an efficient algorithm to solve it optimally. Finally, we validate our analytical results via packet-level experiments. Results also show that our proposed segment encoding policy significantly reduces the queuing delay compared to source encoding. © 2023 IFIP.

关键词： Satellites

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