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

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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

Simultaneous tactile exploration and grasp refinement for unknown objects

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arXiv 2021年

作者： de Farias, Cristiana Marturi, Naresh Stolkin, Rustam Bekiroglu, Yasemin The Extreme Robotics Laboratory School of Metallurgy and Materials University of Birmingham Birmingham United Kingdom Chalmers University of Technology Department of Electrical Engineering Automatic Control research group Sweden University College London Department of Computer Science Centre for Artificial Intelligence United Kingdom

This paper addresses the problem of simultaneously exploring an unknown object to model its shape, using tactile sensors on robotic fingers, while also improving finger placement to optimise grasp stability. In many situations, a robot will have only a partial camera view of the near side of an observed object, for which the far side remains occluded. We show how an initial grasp attempt, based on an initial guess of the overall object shape, yields tactile glances of the far side of the object which enable the shape estimate and consequently the successive grasps to be improved. We propose a grasp exploration approach using a probabilistic representation of shape, based on Gaussian Process Implicit Surfaces. This representation enables initial partial vision data to be augmented with additional data from successive tactile glances. This is combined with a probabilistic estimate of grasp quality to refine grasp configurations. When choosing the next set of finger placements, a bi-objective optimisation method is used to mutually maximise grasp quality and improve shape representation during successive grasp attempts. Experimental results show that the proposed approach yields stable grasp configurations more efficiently than a baseline method, while also yielding improved shape estimate of the grasped object. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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

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

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

On the Road to 6G: visions, Requirements, Key Technologies and Testbeds

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arXiv 2023年

作者： Wang, Cheng-Xiang You, Xiaohu Gao, Xiqi Zhu, Xiuming Li, Zixin Zhang, Chuan Wang, Haiming Huang, Yongming Chen, Yunfei Haas, Harald Thompson, John S. Larsson, Erik G. Di Renzo, Marco Tong, Wen Zhu, Peiying Shen, Xuemin Poor, H. Vincent Hanzo, Lajos The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211111 China The School of Information Science and Engineering The State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China The Pervasive Communication Research Center Purple Mountain Laboratories Nanjing211111 China The School of Engineering The University of Warwick CoventryCV4 7AL United Kingdom The LiFi Research and Development Center Department of Electronic and Electrical Engineering University of Strathclyde GlasgowG1 1XQ United Kingdom The Institute for Digital Communications School of Engineering University of Edinburgh EdinburghEH9 3JL United Kingdom Linköping University Linköping581 83 Sweden Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 Rue Joliot-Curie Gif-sur-Yvette91192 France The Wireless Advanced System and Competency Centre HUAWEI Technologies Co. Ltd. OttawaONK2K 3J1 Canada HUAWEI Technologies Canada Co. Ltd. OttawaONK2K 3J1 Canada The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed. © 2023, CC BY.

关键词： Network architecture

Depth-Awareness in a System for Mixed-Reality Aided Surgical Procedures 15th

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Depth-Awareness in a System for Mixed-Reality Aided Surgical...

15th International Conference on Intelligent Computing, ICIC 2019

作者： Sylos Labini, Mauro Gsaxner, Christina Pepe, Antonio Wallner, Juergen Egger, Jan Bevilacqua, Vitoantonio Institute for Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Electrical and Information Engineering Polytechnic University of Bari Bari Italy Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria

ISBN: (纸本)9783030267650

computer-assisted surgery is a trending topic in research, with many different approaches which aim at supporting surgeons in the operating room. Existing surgical planning and navigation solutions are often considered to be distracting, unintuitive or hard to interpret. In this work, we address this issue with an approach based on mixed reality devices like Microsoft HoloLens. We assess the depth sensing capabilities of Microsoft HoloLens, and the potential benefit they could bring to computer-assisted surgery applications. © 2019, Springer Nature Switzerland AG.

关键词： Mixed reality

System identification and feedback control for directed-energy, metal-based additive manufacturing 26

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System identification and feedback control for directed-ener...

26th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference, SFF 2015

作者： Seltzer, D.M. Wang, X. Nassar, A.R. Schiano, J.L. Reutzel, E.W. School of Electrical Engineering and Computer Science Pennsylvania State University University ParkPA16802 United States Department of Mechanical and Nuclear Engineering Pennsylvania State University University ParkPA16802 United States Applied Research Laboratory Pennsylvania State University University ParkPA16802 United States

Additive manufacturing of metal parts is a complex process where many variables determine part quality. In addition to manipulated process variables, such as travel speed, feedstock flow pattern, and energy distribution, other exogenous inputs also determine part quality. For example, changing build geometry and a growing global temperature. In addition, there are random external disturbances such as spatter on a cover lens. Both manipulated process variables and exogenous inputs affect dimensional tolerance, microstructure, and other properties that determine the final part quality. Our long term aim is to improve part quality through real-time regulation of measurable process variables using vision-based feedback control. As a starting point, we present a process model that relates scanning speed and laser power to build height and melt pool width. These results demonstrate the necessity for using multi-input multi-output feedback control techniques and provide information for refining the frame rate and spectral sensitivity of the imaging system. © SFF *** rights reserved.

关键词： Feedback control

Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6G

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arXiv 2022年

作者： Zhu, Guangxu Lyu, Zhonghao Jiao, Xiang Liu, Peixi Chen, Mingzhe Xu, Jie Cui, Shuguang Zhang, Ping Shenzhen Research Institute of Big Data Shenzhen518172 China Shenzhen518172 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables33146 United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Peng Cheng Laboratory Shenzhen518066 China

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead. Copyright © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

PuzzleTuning: Explicitly Bridge Pathological and Natural Image with Puzzles

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arXiv 2023年

作者： Zhang, Tianyi Lyu, Shangqing Lei, Yanli Chen, Sicheng Ying, Nan He, Yufang Liu, Zeyu Zhao, Yu Feng, Yunlu Jin, Yueming Lee, Hwee Kuan Zhang, Guanglei The Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing100191 China Singapore138410 Singapore The School of Microelectronics Xi’an Jiaotong University Xi’an710049 China The Department of Pathology Peking Union Medical College Hospital Beijing100006 China The Department of Gastroenterology Peking Union Medical College Hospital Beijing100006 China The Department of Biomedical Engineering National University of Singapore Singapore117417 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore117417 Singapore The School of Computing National University of Singapore Singapore117417 Singapore School of Biological Sciences Nanyang Technological University 639798 Singapore 169856 Singapore International Research Laboratory on Artifical Intelligence 138632 Singapore Singapore Institute for Clinical Sciences Singapore117609 Singapore

Pathological image analysis is a crucial field in computer vision. Due to the annotation scarcity in the pathological field, pre-training with self-supervised learning (SSL) is widely applied to learn on unlabeled images. However, the current SSL-based pathological pre-training: (1) does not explicitly explore the essential focuses of the pathological field, and (2) does not effectively bridge with and thus take advantage of the knowledge from natural images. To explicitly address them, we propose our large-scale PuzzleTuning framework, containing the following innovations. Firstly, we define three task focuses that can effectively bridge knowledge of pathological and natural domain: appearance consistency, spatial consistency, and restoration understanding. Secondly, we devise a novel multiple puzzle restoring task, which explicitly pre-trains the model regarding these focuses. Thirdly, we introduce an explicit prompt-tuning process to incrementally integrate the domain-specific knowledge. It builds a bridge to align the large domain gap between natural and pathological images. Additionally, a curriculum-learning training strategy is designed to regulate task difficulty, making the model adaptive to the puzzle restoring complexity. Experimental results show that our PuzzleTuning framework outperforms the previous state-of-the-art methods in various downstream tasks on multiple datasets. The code, demo, and pre-trained weights are available at https://***/sagizty/PuzzleTuning. © 2023, CC BY-NC-SA.

关键词： Image analysis

A reinforcement-learning-based energy-efficient framework for multi-task video analytics pipeline

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arXiv 2021年

作者： Zhao, Yingying Dong, Mingzhi Wang, Yujiang Feng, Da Lv, Qin Dick, Robert P. Li, Dongsheng Lu, Tun Gu, Ning Shang, Li School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Department of Computing Imperial College London London United Kingdom Software Service Company Limited Beijing China University of Colorado Boulder BoulderCO United States Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann ArborMI United States Microsoft Research Asia Shanghai China School of Computer Science Fudan University Shanghai China

Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its adoption in energy-constrained applications. Motivated by the observation of high and variable spatial redundancy and temporal dynamics in video data streams, we design and evaluate an adaptive-resolution optimization framework to minimize the energy use of multitask video analytics pipelines. Instead of heuristically tuning the input data resolution of individual tasks, our framework utilizes deep reinforcement learning to dynamically govern the input resolution and computation of the entire video analytics pipeline. By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency. Governed by reinforcement learning, optical flow is incorporated into the framework to minimize unnecessary spatio-temporal redundancy that leads to re-computation, while preserving accuracy. The proposed framework is applied to video instance segmentation which is one of the most challenging computer vision tasks, and achieves better energy efficiency than all baseline methods of similar accuracy on the YouTube-VIS dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency