检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Ma, Qian Gao, Wei Xiao, Qiang Ding, Lingsong Gao, Tianyi Zhou, Yajun Gao, Xinxin Yan, Tao Liu, Che Gu, Ze Kong, Xianghong Abbasi, Qammer H. Li, Lianlin Qiu, Cheng-Wei Li, Yuanqing Cui, Tie Jun Institute of Electromagnetic Space Southeast University Nanjing210096 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China State Key Laboratory of Millimeter Wave Southeast University Nanjing210096 China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou510330 China Research Center for Brain-Computer Interface Pazhou Lab Guangzhou510330 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Peking University Beijing100871 China

Brain-computer interfaces (BCIs), invasive or non-invasive, have projected unparalleled vision and promise for assisting patients in need to better their interaction with the surroundings. Inspired by the BCI-based rehabilitation technologies for nerve-system impairments and amputation, we propose an electromagnetic brain-computer-metasurface (EBCM) paradigm, regulated by human's cognition by brain signals directly and non-invasively. We experimentally show that our EBCM platform can translate human's mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively, which can be further processed and transported by an information metasurface in automated and wireless fashions. Directly wireless communications of the human minds are performed between two EBCM operators with accurate text transmissions. Moreover, several other proof-of-concept mind-control schemes are presented using the same EBCM platform, exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning, wave modulations, and pattern encoding. © 2022, CC BY.

关键词： Electrophysiology

来源：评论

学校读者我要写书评

暂无评论

Minimal 3D Dubins Path with Bounded Curvature and Pitch Angle

Minimal 3D Dubins Path with Bounded Curvature and Pitch Angl...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Petr Váňa Armando Alves Neto Jan Faigl Douglas G. Macharet Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Department of Electronic Engineering Universidade Federal de Minas Gerais MG Brazil Computer Vision and Robotics Laboratory (VeRLab) Universidade Federal de Minas Gerais MG Brazil

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

In this paper, we address the problem of finding cost-efficient three-dimensional paths that satisfy the maximum allowed curvature and the pitch angle of the vehicle. For any given initial and final configurations, the problem is decoupled into finding the horizontal and vertical parts of the path separately. Although the individual paths are modeled as two-dimensional Dubins curves using closed-form solutions, the final 3D path is constructed using the proposed local optimization to find a cost-efficient solution. Moreover, based on the decoupled approach, we provide a lower bound estimation of the optimal path that enables us to determine the quality of the found heuristic solution. The proposed solution has been evaluated using existing benchmark instances and compared with state-of-the-art approaches. Based on the reported results and lower bounds, the proposed approach provides paths close to the optimal solution while the computational requirements are in hundreds of microseconds. Besides, the proposed method provides paths with fewer turns than others, which make them easier to be followed by the vehicle's controller.

关键词： Three-dimensional displays Two dimensional displays Turning Path planning Atmospheric modeling Space vehicles Optimization

来源：评论

学校读者我要写书评

暂无评论

Targeted Attack for Deep Hashing Based Retrieval 16th

Targeted Attack for Deep Hashing Based Retrieval

引用

16th European Conference on computer vision, ECCV 2020

作者： Bai, Jiawang Chen, Bin Li, Yiming Wu, Dongxian Guo, Weiwei Xia, Shu-Tao Yang, En-Hui Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Peng Cheng Laboratory PCL Research Center of Networks and Communications Shenzhen China vivo AI Lab Shenzhen China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

ISBN: (纸本)9783030584511

The deep hashing based retrieval method is widely adopted in large-scale image and video retrieval. However, there is little investigation on its security. In this paper, we propose a novel method, dubbed deep hashing targeted attack (DHTA), to study the targeted attack on such retrieval. Specifically, we first formulate the targeted attack as a point-to-set optimization, which minimizes the average distance between the hash code of an adversarial example and those of a set of objects with the target label. Then we design a novel component-voting scheme to obtain an anchor code as the representative of the set of hash codes of objects with the target label, whose optimality guarantee is also theoretically derived. To balance the performance and perceptibility, we propose to minimize the Hamming distance between the hash code of the adversarial example and the anchor code under the (Formula Presented) restriction on the perturbation. Extensive experiments verify that DHTA is effective in attacking both deep hashing based image retrieval and video retrieval. © 2020, Springer Nature Switzerland AG.

关键词： Hamming distance

来源：评论

学校读者我要写书评

暂无评论

Towards deployment-centric multimodal AI beyond vision and language

arXiv

引用

arXiv 2025年

作者： Liu, Xianyuan Zhang, Jiayang Zhou, Shuo van der Plas, Thijs L. Vijayaraghavan, Avish Grishina, Anastasiia Zhuang, Mengdie Schofield, Daniel Tomlinson, Christopher Wang, Yuhan Li, Ruizhe van Zeeland, Louisa Tabakhi, Sina Demeocq, Cyndie Li, Xiang Das, Arunav Timmerman, Orlando Baldwin-McDonald, Thomas Wu, Jinge Bai, Peizhen Al Sahili, Zahraa Alwazzan, Omnia Do, Thao N. Suvon, Mohammod N.I. Wang, Angeline Cipolina-Kun, Lucia Moretti, Luigi A. Farndale, Lucas Jain, Nitisha Efremova, Natalia Ge, Yan Varela, Marta Lam, Hak-Keung Celiktutan, Oya Evans, Ben R. Coca-Castro, Alejandro Wu, Honghan Abdallah, Zahraa S. Chen, Chen Danchev, Valentin Tkachenko, Nataliya Lu, Lei Zhu, Tingting Slabaugh, Gregory G. Moore, Roger K. Cheung, William K. Charlton, Peter H. Lu, Haiping Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom School of Computer Science University of Sheffield Sheffield United Kingdom The Alan Turing Institute London United Kingdom Department of Metabolism Digestion and Reproduction Imperial College London London United Kingdom Department of Applied AI Simula Research Laboratory Oslo Norway Information School University of Sheffield Sheffield United Kingdom NHS England Leeds United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Engineering King’s College London London United Kingdom Department of Computing Science University of Aberdeen Aberdeen United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Department of Informatics King’s College London London United Kingdom Department of Earth Sciences University of Cambridge Cambridge United Kingdom Department of Computer Science University of Manchester Manchester United Kingdom Department of Computer Science Queen Mary University of London London United Kingdom Digital Environment Research Institute Queen Mary University of London London United Kingdom Department of Computer Science University of Bath Bath United Kingdom Department of Classics King’s College London London United Kingdom School of Electrical Electronic and Mechanical Engineering University of Bristol Bristol United Kingdom School of Engineering University of the West of England Bristol United Kingdom Cancer Research UK Scotland Institute Glasgow United Kingdom School of Business and Management Queen Mary University of London London United Kingdom City St George’s University of London London United Kingdom British Antarctic Survey Cambridge United Kingdom School of Health and Wellbeing University of Glasgow Glasgow United Kingdom Chief Data & AI Office Lloyds Banking Group Lo

Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most multimodal AI advances focus on models for vision and language data, while their deployability remains a key challenge. We advocate a deployment-centric workflow that incorporates deployment constraints early to reduce the likelihood of undeployable solutions, complementing data-centric and model-centric approaches. We also emphasise deeper integration across multiple levels of multimodality and multidisciplinary collaboration to significantly broaden the research scope beyond vision and language. To facilitate this approach, we identify common multimodal-AI-specific challenges shared across disciplines and examine three real-world use cases: pandemic response, self-driving car design, and climate change adaptation, drawing expertise from healthcare, social science, engineering, science, sustainability, and finance. By fostering multidisciplinary dialogue and open research practices, our community can accelerate deployment-centric development for broad societal impact. © 2025, CC BY.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Extensions of the open-source framework Aerostack 3.0 for the development of more interactive flights between UAVs

Extensions of the open-source framework Aerostack 3.0 for th...

引用

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Wojciech Giernacki Jacek Cieślak Martin Molina Pascual Campoy Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Poznań Poland Department of Artificial Intelligence Technical University of Madrid (UPM) Madrid Spain Computer Vision and Aerial Robotics (CVAR) Centre for Automation and Robotics Technical University of Madrid (UPM) Madrid Spain

ISBN: (数字)9781728142784

ISBN: (纸本)9781728142791

The basis for properly verified R&D works is to provide reliable prototyping tools at three most important stages: computer simulation, laboratory tests and real-world experiments. In the laboratory-limited conditions, particular importance is attributed to the first two stages, especially in the context of the safety development of autonomous flights of unmanned aerial vehicle (UAV) groups in various missions. The open-source framework Aerostack support those needs and its effectiveness has been proven in the International Micro Air Vehicle Indoor Competitions (IMAV 2013, 2016, 2017) and Mohammed Bin Zayed International robotics Challenge (MBZIRC 2020). In the paper, the exemplary functionalities for the new version of Aerostack Version 3.0 Distribution Sirocco (Aerial robotics framework for the industry), extended additionally with a library of new behaviors, are presented. The mission of UAVs can be developed fast and effectively in order to conduct test flights with real drones in lab, before one will decide to fly autonomously outdoor. The representative results obtained for low-cost *** 2.0 UAV models in two missions, are presented. The first mission is autonomous patrolling the area by a pair of UAVs, the second - intercepting the intruder in guarded area by the guard UAV.

关键词： Robots Trajectory Drones Libraries Software computer architecture

来源：评论

学校读者我要写书评

暂无评论

A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management

arXiv

引用

arXiv 2024年

作者： Boroujeni, Sayed Pedram Haeri Razi, Abolfazl Khoshdel, Sahand Afghah, Fatemeh Coen, Janice L. O’Neill, Leo Fule, Peter Watts, Adam Kokolakis, Nick-Marios T. Vamvoudakis, Kyriakos G. School of Computing Clemson University ClemsonSC29632 United States Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States NSF National Center for Atmospheric Research BoulderCO80301 United States Department of Environmental Science University of San Francisco San FranciscoCA94117 United States School of Forestry Northern Arizona University FlagstaffAZ86001 United States U.S.D.A. Forest Service Pacific Wildland Fire Science Laboratory SeattleWA98103 United States The Daniel Guggenheim School of Aerospace Engineering Georgia Institute of Technology AtlantaGA30332 United States

Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses in both human lives and forest wildlife. The increasing severity and frequency of wildfires across the globe have underscored the urgent need to improve public knowledge and advance existing techniques in wildfire management. Recently, the use of Artificial Intelligence (AI) in wildfires, propelled by the integration of Unmanned Aerial Vehicles (UAVs) and deep learning models, has created an unprecedented momentum to implement and develop more effective wildfire management. Although some of the existing survey papers have explored various learning-based approaches, a comprehensive review emphasizing the application of AI-enabled UAV systems and their subsequent impact on multi-stage wildfire management is notably lacking. This survey aims to bridge these gaps by offering a systematic review of the recent state-of-the-art technologies, highlighting the advancements of UAV systems and AI models from pre-fire, through the active-fire stage, to post-fire management. To this aim, we provide an extensive analysis of the existing remote sensing systems with a particular focus on the UAV advancements, device specifications, and sensor technologies relevant to wildfire management. We also examine the pre-fire and post-fire management approaches, including fuel monitoring, prevention strategies, as well as evacuation planning, damage assessment, and operation strategies. Additionally, we review and summarize a wide range of computer vision techniques in active-fire management, with an emphasis on Machine Learning (ML), Reinforcement Learning (RL), and Deep Learning (DL) algorithms for wildfire classification, segmentation, detection, and monitoring tasks. Ultimately, we underscore the substantial advancement in wildfire modeling through the integration of cutting-edge AI techniques and UAV-based data, providing novel insights and enhanced predictive capabilities to u

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

UNDERSTANDING DOMAIN RANDOMIZATION for SIM-TO-REAL TRANSFER

arXiv

引用

arXiv 2021年

作者： Chen, Xiaoyu Hu, Jiachen Jin, Chi Li, Lihong Wang, Liwei Key Laboratory of Machine Perception MOE School of Artificial Intelligence Peking University China Department of Electrical and Computer Engineering Princeton University United States Amazon United States International Center for Machine Learning Research Peking University China

Reinforcement learning encounters many challenges when applied directly in the real world. Sim-to-real transfer is widely used to transfer the knowledge learned from simulation to the real world. Domain randomization-one of the most popular algorithms for sim-to-real transfer-has been demonstrated to be effective in various tasks in robotics and autonomous driving. Despite its empirical successes, theoretical understanding on why this simple algorithm works is limited. In this paper, we propose a theoretical framework for sim-to-real transfers, in which the simulator is modeled as a set of MDPs with tunable parameters (corresponding to unknown physical parameters such as friction). We provide sharp bounds on the sim-to-real gap-the difference between the value of policy returned by domain randomization and the value of an optimal policy for the real world. We prove that sim-to-real transfer can succeed under mild conditionswithout any real-world training samples. Our theory also highlights the importance of using memory (i.e., history-dependent policies) in domain randomization. Our proof is based on novel techniques that reduce the problem of bounding the sim-to-real gap to the problem of designing efficient learning algorithms for infinite-horizon MDPs, which we believe are of independent interest. Copyright © 2021, The Authors. All rights reserved.

关键词： Random processes

来源：评论

学校读者我要写书评

暂无评论

Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

arXiv

引用

arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

P-WAE: Generalized patch-wasserstein autoencoder for anomaly screening

arXiv

引用

arXiv 2021年

作者： Zhang, Hui Chen, Yurong Wang, Yaonan Cao, Yihong Wu, Q.M. Jonathan Yang, Yimin National Engineering Laboratory of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha Hunan410082 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B3P4 Canada Department of Computer Science Lakehead University OntarioP7B 5E1 Canada Vector Institute TorontoM5G 1M1 Canada

To mitigate the inspector’s workload and improve the quality of the product, computer vision-based anomaly detection (AD) techniques are gradually deployed in real-world industrial scenarios. Recent anomaly analysis benchmarks progress to generative models. The aim is to model the defect-free distribution so that anomalies can be classified as out-of-distribution samples. Nevertheless, there are two disturbing factors that need researchers and deployers to prioritize: (i) the simplistic prior latent distribution inducing limited expressive capability;(ii) the collapsed mutual-dependent features resulting in poor generalization. In this paper, we propose a novel Patch-wise Wasserstein AutoEncoder (P-WAE) architecture to alleviate those challenges. In particular, a patch-wise variational inference model coupled with solving the jigsaw puzzle is designed, which is a simple yet effective way to increase the expressiveness and complexity of the latent manifold. This alleviates the blurry reconstruction problem. In addition, the Hilbert-Schmidt Independence Criterion (HSIC) bottleneck is introduced to constrain the over-regularization representation. Comprehensive experiments, conducted on the MVTec AD dataset, demonstrate the superior performance of our proposed method. © 2021, CC BY.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation

PolarNet: An Improved Grid Representation for Online LiDAR P...

引用

Conference on computer vision and Pattern Recognition (CVPR)

作者： Yang Zhang Zixiang Zhou Philip David Xiangyu Yue Zerong Xi Boqing Gong Hassan Foroosh Department of Computer Science University of Central Florida Computational and Information Sciences Directorate U.S. Army Research Laboratory Department of Electrical Engineering and Computer Sciences University of California Berkeley

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

The requirement of fine-grained perception by autonomous driving systems has resulted in recently increased research in the online semantic segmentation of single-scan LiDAR. Emerging datasets and technological advancements have enabled researchers to benchmark this problem and improve the applicable semantic segmentation algorithms. Still, online semantic segmentation of LiDAR scans in autonomous driving applications remains challenging due to three reasons: (1) the need for near-real-time latency with limited hardware, (2) points are distributed unevenly across space, and (3) an increasing number of more fine-grained semantic classes. The combination of the aforementioned challenges motivates us to propose a new LiDAR-specific, KNN-free segmentation algorithm - PolarNet. Instead of using common spherical or bird's-eye-view projection, our polar bird's-eye-view representation balances the points per grid and thus indirectly redistributes the network's attention over the long-tailed points distribution over the radial axis in polar coordination. We find that our encoding scheme greatly increases the mIoU in three drastically different real urban LiDAR single-scan segmentation datasets while retaining ultra low latency and near real-time throughput.

关键词： Three-dimensional displays Laser radar Semantics Image segmentation Two dimensional displays Neural networks Task analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：