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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yi Feng Bohuan Xue Ming Liu Qijun Chen Rui Fan Robotics & Artificial Intelligence Laboratory (RAIL) the State Key Laboratory of Intelligent Autonomous Systems and Frontiers Science Center for Intelligent Autonomous Systems the College of Electronic & Information Engineering Tongji University Shanghai P. R. China Department of Computer Science & Engineering the Hong Kong University of Science and Technology (Guangzhou) Hong Kong SAR P. R. China Robotics & Autonomous Systems Thrust of the Systems Hub the Hong Kong University of Science and Technol-ogy (Guangzhou) Nansha Guangzhou P. R. China

Surface normal holds significant importance in visual environmental perception, serving as a source of rich geometric information. However, the state-of-the-art (SoTA) surface normal estimators (SNEs) generally suffer from an unsatisfactory trade-off between efficiency and accuracy. To resolve this dilemma, this paper first presents a superfast depth-to-normal translator (D2NT), which can directly translate depth images into surface normal maps without calculating 3D coordinates. We then propose a discontinuity-aware gradient (DAG) filter, which adaptively generates gradient convolution kernels to improve depth gradient estimation. Finally, we propose a surface normal refinement module that can easily be integrated into any depth-to-normal SNEs, substantially improving the surface normal estimation accuracy. Our proposed algorithm demonstrates the best accuracy among all other existing real-time SNEs and achieves the SoTA trade-off between efficiency and accuracy.

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Coverage and Rate Analysis for Distributed RISs-Assisted mmWave Communications

arXiv

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arXiv 2024年

作者： Xu, Yuan Huang, Chongwen Li, Wei Zhu, Yongxu Yang, Zhaohui He, Jiguang Yang, Jun Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310027 China School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore National Communications Research Laboratory Southeast University Nanjing210096 China Technology Innovation Institute 9639 Masdar City Abu Dhabi United Arab Emirates State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China Wireless Product R&D Institute ZTE Corporation Shenzhen518057 China Department of Electrical Engineering and Computer Science The KU 6G Center Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

The millimeter wave (mmWave) has received considerable interest due to its expansive bandwidth and high frequency. However, a noteworthy challenge arises from its vulnerability to blockages, leading to reduced coverage and achievable rates. To address these limitations, a potential solution is to deploy distributed reconfigurable intelligent surfaces (RISs), which comprise many low-cost and passively reflected elements, and can facilitate the establishment of extra communication links. In this paper, we leverage stochastic geometry to investigate the ergodic coverage probability and the achievable rate in both distributed RISs-assisted single-cell and multi-cell mmWave wireless communication systems. Specifically, we first establish the system model considering the stochastically distributed blockages, RISs and users by the Poisson point process. Then we give the association criterion and derive the association probabilities, the distance distributions, and the conditional coverage probabilities for two cases of associations between base stations and users without or with RISs. Finally, we use Campbell’s theorem and the total probability theorem to obtain the closed-form expressions of the ergodic coverage probability and the achievable rate. Simulation results verify the effectiveness of our analysis method, and demonstrate that by deploying distributed RISs, the ergodic coverage probability is significantly improved by approximately 50%, and the achievable rate is increased by more than 1.5 times. Copyright © 2024, The Authors. All rights reserved.

关键词： Millimeter waves

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Transition Metal-Vacancy Point Defects in Zinc Oxide as Deep-Level Spin Qubits

arXiv

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arXiv 2025年

作者： Zhang, Shimin Park, Taejoon Perez, Erik Li, Kejun Wang, Xingyi Wang, Yanyong Vega Bazantes, Jorge D. Zhang, Ruiqi Sun, Jianwei Fu, Kai-Mei C. Seo, Hosung Ping, Yuan Department of Materials Science and Engineering University of Wisconsin-Madison 53706 United States SKKU Advanced Institute of Nanotechnology Sungkyunkwan University Gyeonggi Suwon16419 Korea Republic of Department of Energy Systems Research Department of Physics Ajou University Gyeonggi Suwon16499 Korea Republic of Department of Physics University of California Santa CruzCA95064 United States Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States School of Science & Engineering Tulane University 6823 St Charles Ave New OrleansLA70118 United States Department of Physics University of Washington SeattleWA98195 United States Physical Sciences Division Pacific Northwest National Laboratory RichlandWA99352 United States Department of Quantum Information Engineering Sungkyunkwan University Gyeonggi Suwon16419 Korea Republic of Center for Quantum Information Korea Institute of Science and Technology Seoul02792 Korea Republic of Department of Physics University of Wisconsin-Madison 53706 United States Department of Chemistry University of Wisconsin-Madison 53706 United States

Wide band gap oxides are promising host materials for spin defect qubits, offering unique advantages such as a dilute nuclear spin environment. Zinc oxide (ZnO), in particular, can achieve exceptional high purity, which enables long spin coherence time. In this work, we theoretically search for deep-level point defects in ZnO with optimal physical properties for optically-addressable spin qubits. Using first-principles calculations, we predict the Molybdenum-vacancy complex defect (MoZnvO)2+ in ZnO to own promising spin and optical properties, including spin-triplet ground state, optical transition in the visible to near-infrared range with high quantum yield, allowed intersystem crossings with a sizable optically-detected magnetic resonance contrast, and long spin T2 and T∗2. Notably, we find the Huang-Rhys factor of the defect to be around 5, which is significantly smaller than the typical range of 10-30 for most known defects in ZnO. Furthermore, we compare the spin decoherence driven by the nuclear spin bath and paramagnetic impurity baths. We find that the paramagnetic impurities are very effective in causing spin decoherence even with very low concentrations, implying that they can likely dominate the spin decoherence in ZnO even after isotopic purification. Using the computed excited-state energies and kinetic rates as inputs, we predict the ODMR contrast and propose a new protocol for spin qubit initialization and readout, which could be generalized to other systems with forbidden axial intersystem crossings. © 2025, CC BY.

关键词： Zinc oxide

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Dual-UAV Aided Secure Dynamic G2U Communication

Dual-UAV Aided Secure Dynamic G2U Communication

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IEEE Symposium on computers and Communications (ISCC)

作者： Hongyue Kang Wei Li Jelena Mišić Vojislav B. Mišić Xiaolin Chang Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University China Beijing Jinghang Computation and Communication Research Institute China The Classified Information Carrier Safety Management Engineering Technology Research Center of Beijing Department of Computer Science Ryerson University Canada

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

Unmanned aerial vehicle (UAV) communication is easily wiretapped by malignant nodes due to the broadcast nature of line-of-sight (LoS) wireless channels. To tackle this problem, this paper investigates a dual-UAV aided secure dynamic ground-to-UAV (G2U) communication system. By dynamic, we mean UAVs communicate with moving ground devices (GDs). Our objective is maximizing the sum secrecy rate by the joint optimization of UAV trajectory and GDs transmit power. To achieve it, we first formulate this nonconvex optimization problem as a Constrained Markov Decision Process (CMDP) under the constraints of UAV flying speed, initial and final locations, limited energy, and average transmit power. Then, a Deep Deterministic Policy Gradient (DDPG) based deep reinforcement learning algorithm is designed, named SC-TDPC, to learn the optimal transmit power and UAV trajectory. The experiment results demonstrate that, compared to other benchmark schemes, SC-TDPC can efficiently enhance the UAV communication security in terms of sum secrecy rate.

关键词： Wireless communication Heuristic algorithms Reinforcement learning Benchmark testing Markov processes Autonomous aerial vehicles Trajectory

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Document image rectification method combined with semantic segmentation

Document image rectification method combined with semantic s...

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International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

作者： YongWei Wang ShuangShuang Xu Yao Xiao YiGuang Yang Hao Li RongZheng Yang CCCC Second Harbor Engineering Company LTD Hubei Wuhan China Key Laboratory of Large-span Bridge Construction Technology Hubei Wuhan China CCCC Highway Bridge National Engineering Research Centre Co. Ltd Beijing China School of Information Engineering Zhongnan University of Economics and Law Hubei Wuhan China Department of Computer Science and Artificial Intelligence Wuhan University Of Engineering Science Hubei Wuhan China Research and Development Center of Transport Industry of Intelligent Manufacturing Technologies of Transport Infrastructure Hubei Wuhan China

Proposed a method combined with semantic segmentation for document image rectification to address the limited applicability of traditional correction methods and difficulties in data annotation. Firstly, diverse experimental data are synthesized to create a target document image and its corresponding mask data. Secondly, a semantic segmentation model based on DeepLabV3 is constructed using pre-trained MobileNetV3, ResNet50, and ResNet101 as the backbone networks, respectively, to separate the document page area(ROI) from the image. Then, the document page area is corrected using corner point detection and perspective transformation to complete the document image rectification. The evaluation of the model shows that the DeepLabV3 model with MobileNetV3 as the backbone network has high processing efficiency, with an IOU of 0.983 and a Total Loss of 0.064 on the validation set. Test results demonstrate that the proposed method has better generalization capabilities and can be easily extended to practical engineering.

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Integrated Sensing and Communication Signals Toward 5G-A and 6G: A Survey

arXiv

引用

arXiv 2023年

作者： Wei, Zhiqing Qu, Hanyang Wang, Yuan Yuan, Xin Wu, Huici Du, Ying Han, Kaifeng Zhang, Ning Feng, Zhiyong The Key Laboratory of Universal Wireless Communications Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The National Engineering Lab for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Peng Cheng Laboratory Shenzhen518066 China Data61 CSIRO Sydney Australia The Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada The University of Science and Technology of China China Academy of Information and Communications Technology Beijing100191 China China Academy of Information and Communications Technology Beijing100191 China

Integrated sensing and communication (ISAC) has the advantages of efficient spectrum utilization and low hardware cost. It is promising to be implemented in the fifth-generation-advanced (5G-A) and sixth-generation (6G) mobile communication systems, having the potential to be applied in intelligent applications requiring both communication and high-accurate sensing capabilities. As the fundamental technology of ISAC, ISAC signal directly impacts the performance of sensing and communication. This article systematically reviews the literature on ISAC signals from the perspective of mobile communication systems, including ISAC signal design, ISAC signal processing and ISAC signal optimization. We first review the ISAC signal design based on 5G, 5G-A and 6G mobile communication systems. Then, radar signal processing methods are reviewed for ISAC signals, mainly including the channel information matrix method, spectrum lines estimator method and super resolution method. In terms of signal optimization, we summarize peak-to-average power ratio (PAPR) optimization, interference management, and adaptive signal optimization for ISAC signals. This article may provide the guidelines for the research of ISAC signals in 5G-A and 6G mobile communication *** Codes 94-02 Copyright © 2023, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach

Generating High-quality Movie Tags from Social Reviews: A Le...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zhenxiao Luo Guopin Tang Chen Wang Yipeng Zhou Xi Zheng Jessie Hui Wang Gang Liu Di Wu School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Faculty of Science and Engineering Macquarie University Sydney Australia Institute for Network Sciences and Cyberspace Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665417631

As a kind of keywords to describe video contents, tags are extremely beneficial for viewers to locate videos when they search the site. Additionally, tags also help video platform operators to better organize and recommend videos to platform users. Previous approaches are mainly based on manually tagging or tag propagation via video content analysis, which is time-consuming and resource-consuming. Especially, given the high volume of fresh movies generated per year, it is difficult to accurately tag all the movies manually. Moreover, video content analysis is also not easy considering typical features (e.g., long duration, complicated scenarios) of commercial movies. In this paper, we propose an automatic tagging algorithm called TagRec that exploits crowdsourced user reviews to generate accurate movie tags. We observe that user reviews contain rich information about movies (e.g., quality, actors) which can be learned to generate high-quality movie tags. Inspired by the above observation, we choose to transform the movie video tagging problem into a tag recommendation problem, in which tags are recommended to different movies by extracting knowledge from crowdsourced movie reviews. We take latent topics, tag co-occurrence probability and tag semantics into account, and formulate the problem as a recommendation optimization problem. We evaluate the performance of our pro-posed TagRec algorithm with a large-scale real-world dataset. Extensive experiments demonstrate that TagRec achieves 7.1 % and 9.6% improvement compared with other state-of-the-art methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain respectively.

关键词： Social computing Filtering Semantics Transforms Tagging Motion pictures Optimization

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Integrating AI for infectious disease prediction: A hybrid ANN-XGBoost model for leishmaniasis in Pakistan

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Acta Tropica 2025年 266卷

作者： Niu, Ben Qureshi, Humera Khan, Muhammad Imran Shah, Adil School of Computer Science and Software Engineering Shenzhen Institute of Information Technology Shenzhen China College of Management Shenzhen University Shenzhen China Division of Biostatistics Department of Population Health King Abdullah International Medical Research Center Riyadh Saudi Arabia King Saud Bin Abdulaziz University for Health Sciences Ministry of National Guard Health Affairs (MNGHA) Riyadh Saudi Arabia Health Department Khyber Pakhtunkhwa Peshawar Pakistan

Addressing leishmaniasis infection remains a substantial challenge in KP-Pakistan due to the increased infection prevalence. Understanding its spreading tool offerings is a major challenge. We essentially design effective approaches to pinpoint its emergence and implement upgraded management strategies. This study aims to assess the prevalence of leishmaniasis infection in KP's four high-endemic districts (Bannu, Karark, Lakki Marwat, and Dera Ismail Khan) and estimate the potential future incidence. We executed a broad logical evaluation on data obtained from the pertinent district health departments of KP, using a novel hybrid ANN-XGBoost approach. We assessed its performance by equating it with frequently used models for infectious disease forecasting over time, comprising the ARIMA, LSTM, ANN, and XGBoost. We evaluated the model's precision using manifold indicators: MAE, RMSE, and MAPE. We developed the models using Python 3.11 software. The results show that the hybrid model outperformed all other models, attaining an MAE score of 82.2, an RMSE of 111.6, and a MAPE of 10.6, validating superior forecast accuracy. According to our proposed model, about 1,017 new leishmaniasis cases are expected per month by December 2025. These findings provide valuable insights for disease monitoring and intervention in KP. Advanced machine learning techniques can help policymakers improve resource mapping and come up with targeted management measures to stop the spread of leishmaniasis. Subsequent research should include other environmental and socio-economic variables influencing illness spread to improve predictive models. © 2025

关键词： Infection Khyber pakhtunkhwa Leishmaniasis Pakistan Prediction

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Reasoning like human: Hierarchical reinforcement learning for knowledge graph reasoning 29

Reasoning like human: Hierarchical reinforcement learning fo...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Wan, Guojia Pan, Shirui Gong, Chen Zhou, Chuan Haffari, Gholamreza School of Computer Science Institute of Artificial Intelligence National Engineering Research Center for Multimedia Software Wuhan University China Faculty of Information Technology Monash University Australia School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computing Hong Kong Polytechnic University Hong Kong Academy of Mathematics and Systems Science Chinese Academy of Sciences China

ISBN: (纸本)9780999241165

Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge graph completion is to infer missing knowledge by multi-hop reasoning over the information found along other paths connecting a pair of entities. However, multi-hop reasoning is still challenging because the reasoning process usually experiences multiple semantic issue that a relation or an entity has multiple meanings. In order to deal with the situation, we propose a novel Hierarchical Reinforcement Learning framework to learn chains of reasoning from a Knowledge Graph automatically. Our framework is inspired by the hierarchical structure through which a human being handles cognitionally ambiguous cases. The whole reasoning process is decomposed into a hierarchy of two-level Reinforcement Learning policies for encoding historical information and learning structured action space. As a consequence, it is more feasible and natural for dealing with the multiple semantic issue. Experimental results show that our proposed model achieves substantial improvements in ambiguous relation tasks. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Knowledge graph

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Evaluation Method of Node Importance in Temporal Satellite networks Based on Time Slot Correlation

Research Square

引用

Research Square 2021年

作者： Xu, Rui Di, Xiaoqiang He, Xiongwen Qi, Hui School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China Jilin Province Key Laboratory of Network and Information Security Changchun University of Science and Technology Changchun130022 China Information Center Changchun University of Science and Technology Changchun130022 China Beijing Institute of Spacecraft System Engineering Beijing100094 China

Temporal satellite networks can accurately describe the dynamic process of satellite networks by considering the interaction relationship and interaction sequence between satellite nodes. In addition, the measurement of node importance in satellite networks plays a crucial role in understanding the structure and function of the network. The classical supra-adjacency matrix (SAM) temporal model identifies the key nodes in the temporal network to some extent, which ignores the differences of inter-layer connectivity relationships leading to the inability to reflect the dynamic variations of satellite nodes. Therefore, the evaluation method based on time slot correlation is proposed to measure the importance of satellite nodes in this paper. Firstly, the correlation coefficient of time slot nodes is defined to measure the coupling relationship of adjacent time slots. Secondly, the dynamic supra-adjacency matrix (DSAM) temporal network model is proposed considering the correlation between adjacent time slots and the characteristics of link time. Finally, the node importance ranking results in each time slot and a global perspective are obtained by utilizing the eigenvector centrality. Through experimental simulations of the Iridium and Orbcomm constellations, it is demonstrated that the DSAM method has a more accurate recognition rate and is more stable than the SAM method. © 2021, CC BY.

关键词： Satellites

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