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UseGeo - A UAV-based multi-sensor dataset for geospatial research

ISPRS Open Journal of Photogrammetry and Remote Sensing 2024年 13卷

作者： Nex, F. Stathopoulou, E.K. Remondino, F. Yang, M.Y. Madhuanand, L. Yogender, Y. Alsadik, B. Weinmann, M. Jutzi, B. Qin, R. ITC Department of Earth Observation Science Faculty ITC University of Twente Enschede Netherlands 3D Optical Metrology (3DOM) Unit Bruno Kessler Foundation (FBK) Trento Italy Visual Computing Group University of Bath Bath United Kingdom Department of Physical Geography Faculty of Geosciences University of Utrecht Utrecht Netherlands Inter-university Department of Regional & Urban Studies and Planning (DIST) Politecnico di Torino Italy Institute of Photogrammetry and Remote Sensing Karlsruhe Institute of Technology Karlsruhe Germany Geospatial Data Analytics Laboratory The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States

3D reconstruction is a long-standing research topic in the photogrammetric and computer vision communities;although a plethora of open-source and commercial solutions for 3D reconstruction have been released in the last few years, several open challenges and limitations still exist. Undoubtedly, deep learning algorithms have demonstrated great potential in several remote sensing tasks, including image-based 3D reconstruction. State-of-the-art monocular and stereo algorithms leverage deep learning techniques and achieve increased performance in depth estimation and 3D reconstruction. However, one of the limitations of such methods is that they highly rely on large training sets that are often tedious to obtain;even when available, they typically refer to indoor, close-range scenarios and low-resolution images. Especially while considering UAV (Unmanned Aerial Vehicle) scenarios, such data are not available and domain adaptation is not a trivial challenge. To fill this gap, the UAV-based multi-sensor dataset for geospatial research (UseGeo - https://***/home) is introduced in this paper. It contains both image and LiDAR data and aims to support relevant research in photogrammetry and computer vision with a useful training set for both stereo and monocular 3D reconstruction algorithms. In this regard, the dataset provides ground truth data for both point clouds and depth maps. In addition, UseGeo can be also a valuable dataset for other tasks such as feature extraction and matching, aerial triangulation, or image and LiDAR co-registration. The paper introduces the UseGeo dataset and validates some state-of-the-art algorithms to assess their usability for both monocular and multi-view 3D reconstruction. © 2024 The Authors

关键词： 3D reconstruction Deep learning LiDAR Monocular depth estimation Multi-view stereo Stereo matching UAV

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X-ray quantification of oxygen groups on diamond surfaces for quantum applications

引用

Materials for Quantum Technology 2023年第4期3卷 045901-045901页

作者： Dontschuk, N. Rodgers, L.V.H. Chou, J.P. Evans, D.A. O’Donnell, K.M. Johnson, H.J. Tadich, A. Schenk, A.K. Gali, A. de Leon, N.P. Stacey, A. School of Physics University of Melbourne ParkvilleVIC3010 Australia Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Physics National Changhua University of Education Changhua Taiwan Department of Physics Aberystwyth University AberystwythSY23 3BZ United Kingdom Royal Melbourne Hospital University of Melbourne ParkvilleVIC3010 Australia Australian Synchrotron 800 Blackburn Road ClaytonVIC3168 Australia School of Computing Engineering and Mathematical Sciences La Trobe University BundooraVIC3086 Australia Wigner Research Centre for Physics POB 49 BudapestH-1525 Hungary Department of Atomic Physics Budapest University of Technology and Economics Műegyetem rakpart 3. BudapestH-1111 Hungary Princeton Plasma Physics Laboratory 100 Stellarator Road PrincetonNJ08544 United States School of Science RMIT University MelbourneVIC3000 Australia

Identifying the surface chemistry of diamond materials is increasingly important for device applications, especially quantum sensors. Oxygen-related termination species are widely used because they are naturally abundant, chemically stable, and compatible with stable nitrogen vacancy centres near the diamond surface. Diamond surfaces host a mixture of oxygen-related species, and the precise chemistry and relative coverage of different species can lead to dramatically different electronic properties, with direct consequences for near-surface quantum sensors. However, it is challenging to unambiguously identify the different groups or quantify the relative surface coverage. Here we show that a combination of x-ray absorption and photoelectron spectroscopies can be used to quantitatively identify the coverage of carbonyl functional groups on the { 100 } diamond surface. Using this method we reveal an unexpectedly high fraction of carbonyl groups ( > 9%) on a wide range of sample surfaces. Furthermore, through a combination of ab initio calculations and spectroscopic studies of engineered surfaces, we reveal unexpected complexities in the x-ray spectroscopy of oxygen terminated diamond surfaces. Of particular note, we find the binding energies of carbonyl-related groups on diamond differs significantly from other organic systems, likely resulting in previous misestimation of carbonyl fractions on diamond surfaces. © 2023 The Author(s). Published by IOP Publishing Ltd.

关键词： Oxygen

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GAMMA Challenge: Glaucoma grAding from Multi-Modality imAges

arXiv

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arXiv 2022年

作者： Wu, Junde Fang, Huihui Li, Fei Fu, Huazhu Lin, Fengbin Li, Jiongcheng Huang, Yue Yu, Qinji Song, Sifan Xu, Xinxing Xu, Yanyu Wang, Wensai Wang, Lingxiao Lu, Shuai Li, Huiqi Huang, Shihua Lu, Zhichao Ou, Chubin Wei, Xifei Liu, Bingyuan Kobbi, Riadh Tang, Xiaoying Lin, Li Zhou, Qiang Hu, Qiang Bogunović, Hrvoje Orlando, José Ignacio Zhang, Xiulan Xu, Yanwu State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China Singapore School of Informatics Xiamen University Xiamen China Shanghai Jiao Tong University Shanghai China Xi'an Jiaotong-Liverpool University Suzhou China Institute of High Performance Computing A*STAR Singapore Institute of Biomedical Engineering Chinese Academy of Medical Sciences Peking Union Medical College Tianjin China School of Medical Technology Beijing Institute of Technology Beijing China Department of Computing Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Weizhi Medical Technology Company Suzhou China École de Technologie Supérieure Montreal Montreal Canada DIAGNOS Inc. Quebec Canada Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Technology Co. Ltd. Shanghai China School of Information and Electronics Beijing Institute of Technology Beijing China Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology Medical University of Vienna Austria Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfill the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyze their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniqu

关键词： Grading

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Facilitating temporal synchronous target selection through user behavior modeling

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019年第4期3卷 1-24页

作者： Zhang, Tengxiang Yi, Xin Wang, Ruolin Gao, Jiayuan Wang, Yuntao Yu, Chun Li, Simin Shi, Yuanchun Tsinghua University Institute of Computing Technology Chinese Academy of Sciences Kexueyuan South Road Beijing100084 China Key Laboratory of Pervasive Computing Ministry of Education Beijing Key Lab of Networked Multimedia Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University China Tsinghua University China Global Innovation eXchange Institute Key Laboratory of Pervasive Computing Ministry of Education Beijing Key Lab of Networked Multimedia Department of Computer Science and Technology Tsinghua University Shuangqing Road Beijing100081 China Department of Electrical and Computing Engineering Beihang University Beijing100081 China Tsinghua University Shuangqing Road Beijing100081 China UCLA United States

Temporal synchronous target selection is an association-free selection technique: users select a target by generating signals (e.g., finger taps and hand claps) in sync with its unique temporal pattern. However, classical pattern set design and input recognition algorithm of such techniques did not leverage users' behavioral information, which limits their robustness to imprecise inputs. In this paper, we improve these two key components by modeling users' interaction behavior. In the first user study, we asked users to tap a finger in sync with blinking patterns with various period and delay, and modeled their finger tapping ability using Gaussian distribution. Based on the results, we generated pattern sets for up to 22 targets that minimized the possibility of confusion due to imprecise inputs. In the second user study, we validated that the optimized pattern sets could reduce error rate from 23% to 7% for the classical Correlation recognizer. We also tested a novel Bayesian, which achieved higher selection accuracy than the Correlation recognizer when the input sequence is short. The informal evaluation results show that the selection technique can be effectively scaled to different modalities and sensing techniques. Copyright © 2019 held by the owner/author(s).

关键词： Taps

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Quantum Statistical Transport Phenomena in Memristive computing Architectures

引用

Physical Review Applied 2021年第5期15卷 054030-054030页

作者： Christopher N. Singh Brian A. Crafton Mathew P. West Alex S. Weidenbach Keith T. Butler Allan H. MacDonald Arjit Raychowdury Eric M. Vogel W. Alan Doolittle L.F.J. Piper Wei-Cheng Lee Department of Physics Applied Physics and Astronomy Binghamton University Binghamton New York 13902 USA Materials Science and Technology Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia 30332 USA SciML Scientific Computing Department Rutherford Appleton Laboratory Didcot OX110QX United Kingdom Department of Physics University of Texas at Austin Austin Texas 78712-1081 USA WMG University of Warwick Coventry CV4 7AL United Kingdom

The advent of reliable nanoscale memristive components is promising for next-generation compute-in-memory paradigms; however, the intrinsic variability in these devices has prevented widespread adoption. Here, we show coherent electron wave functions play a pivotal role in the nanoscale transport properties of these emerging nonvolatile memories. By characterizing both filamentary and nonfilamentary memristive devices as disordered Anderson systems, the switching characteristics and intrinsic variability arise directly from the universality of electron transport in disordered media. Our framework suggests that localization phenomena in nanoscale solid-state memristive systems are directly linked to circuit-level performance. We discuss how quantum conductance fluctuations in the active layer set a lower bound on device variability. This finding implies that there is a fundamental quantum limit on the reliability of memristive devices and that electron coherence will play a decisive role in surpassing or maintaining Moore’s law with these systems.

关键词： Anderson localization Quantum transport Amorphous semiconductors Artificial neural networks Devices Transition metal oxides Density functional theory Linear response theory

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An efficient aggregate signature scheme for healthcare wireless sensor networks

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 1-15页

作者： Chen, Jian-Neng Zhou, Yu-Ping Huang, Zhen-Jie Wu, Tsu-Yang Zou, Fu-Min Tso, Raylin Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Zhangzhou363000 China Lab of Granular Computing Zhangzhou363000 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350008 China Department of Computer Science National Chengchi University Taipei Taiwan

In healthcare wireless sensor networks, there are a large number of sensors, which need to transmit a lot of information in real time. The aggregate signature scheme combines a great deal of signatures signed by different signers on different messages into one short signature, which greatly improves the efficiency of data transmission. In this paper, we present a scheme of certificate based aggregate signature, and prove its security in the random oracle model. The performance analysis shows that the scheme is efficient in sign and verify phase, and suitable for healthcare wireless sensor networks. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Wireless sensor networks

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GEOM-GCN: Geometric graph convolutional networks

arXiv

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arXiv 2020年

作者： Pei, Hongbin Wei, Bingzhe Chang, Kevin Chen-Chuan Lei, Yu Yang, Bo College of Computer Science and Technology Jilin University China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign United States Department of Computer Science University of Illinois at Urbana-Champaign United States Department of Computing Hong Kong Polytechnic University Hong Kong Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education China

Message-passing neural networks (MPNNs) have been successfully applied to representation learning on graphs in a variety of real-world applications. However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoods and lacking the ability to capture long-range dependencies in disassortative graphs. Few studies have noticed the weaknesses from different perspectives. From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses. The behind basic idea is the aggregation on a graph can benefit from a continuous space underlying the graph. The proposed aggregation scheme is permutation-invariant and consists of three modules, node embedding, structural neighborhood, and bi-level aggregation. We also present an implementation of the scheme in graph convolutional networks, termed Geom-GCN, to perform transductive learning on graphs. Experimental results show the proposed Geom-GCN achieved state-of-the-art performance on a wide range of open datasets of graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised tooth segmentation

arXiv

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arXiv 2024年

作者： Wang, Yaqi Zhang, Yifan Chen, Xiaodiao Wang, Shuai Qian, Dahong Ye, Fan Xu, Feng Zhang, Hongyuan Zhang, Qianni Wu, Chengyu Li, Yunxiang Cui, Weiwei Luo, Shan Wang, Chengkai Li, Tianhao Liu, Yi Feng, Xiang Zhou, Huiyu Liu, Dongyun Wang, Qixuan Lin, Zhouhao Song, Wei Li, Yuanlin Wang, Bing Wang, Chunshi Chen, Qiupu Li, Mingqian College of Media Engineering Communication University of Zhejiang Hangzhou China State Key Laboratory of Oral Diseases National Clinical Research Center for Oral Diseases West China Hospital of Stomatology Sichuan University Chengdu China Lishui University School of Medicine Hangzhou Geriatric Stomatology Hospital Hangzhou Dental Hospital Group Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China Suzhou Research Institute of Shandong University Suzhou China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China School of Biomedical Engineering Medical School Shenzhen University Shenzhen China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Department of Mechanical Electrical and Information Engineering Shandong University Weihai China School of Management Hangzhou Dianzi University Hangzhou China Department of Stomatology Sichuan Provincial People's Hospital University of Electronic Science and Technology of China Chengdu China School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Zeta Technology Co. Ltd. No. 1158 Zhangdong Road Pudong New Area Shanghai China China Academy of Information and Communications Technology Beijing China HangZhou Dianzi University Xiasha Higher Education Zone Hangzhou China Southeast University Sipailou Xuanwu District Nanjing China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Shanghai Jiao Tong University Shanghai China School of Computer Science and Technology Changchun University of Science and Technology Changchun China School of Artificial Intelligence Guilin University of Electronic Technology Guilin China University of Science and Technology of China Hefei China Hefei Institutes of Physical Science Chinese Academy of Sciences No. 350 Shushan

computer-aided design (CAD) tools are increasingly popular in modern dental practice, particularly for treatment planning or comprehensive prognosis evaluation. In particular, the 2D panoramic X-ray image efficiently detects invisible caries, impacted teeth and supernumerary teeth in children, while the 3D dental cone beam computed tomography (CBCT) is widely used in orthodontics and endodontics due to its low radiation dose. However, there is no open-access 2D public dataset for children's teeth and no open 3D dental CBCT dataset, which limits the development of automatic algorithms for segmenting teeth and analyzing diseases. The Semi-supervised Teeth Segmentation (STS) Challenge, a pioneering event in tooth segmentation, was held as a part of the MICCAI 2023 ToothFairy Workshop on the Alibaba Tianchi platform. This challenge aims to investigate effective semi-supervised tooth segmentation algorithms to advance the field of dentistry. In this challenge, we provide two modalities including the 2D panoramic X-ray images and the 3D CBCT tooth volumes. In Task 1, the goal was to segment tooth regions in panoramic X-ray images of both adult and pediatric teeth. Task 2 involved segmenting tooth sections using CBCT volumes. Limited labelled images with mostly unlabelled ones were provided in this challenge prompt using semi-supervised algorithms for training. In the preliminary round, the challenge received registration and result submission by 434 teams, with 64 advancing to the final round. This paper summarizes the diverse methods employed by the top-ranking teams in the STS MICCAI 2023 Challenge. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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e-SCP-ECG+v2 protocol: Expanding the e-SCP-ECG+ protocol 9th

e-SCP-ECG+v2 protocol: Expanding the e-SCP-ECG+ protocol

引用

9th International EAI Conference on Broadband Communications, Networks, and Systems, Broadnets 2018

作者： Mandellos, George J. Papaioannou, Maria Panagiotakopoulos, Theodor Lymberopoulos, Dimitrios K. Wire Communication Laboratory Electrical and Computer Engineering Department University of Patras Rion26504 Greece Mobile and Pervasive Computing Quality and Ambient Intelligence Laboratory School of Science and Technology Hellenic Open University Patras26335 Greece

ISBN: (纸本)9783030051945

Nowadays, the fast-growing technology market for sensors and smart devices allows anyone to constantly monitor important vital parameters, like electrocardiogram (ECG). Mobile health systems typically employ data transmission to the premises of the health service provider via appropriate telecommunication protocols, which often consider only vital parameters. However, these parameters are likely to vary, depending on each person’s exercise, psychological condition, habits, environmental conditions and many other factors. Thus, it is necessary to utilize a protocol that is able to carry additional data concerning factors that are tightly connected with the quality of vital sign measurements. This paper proposes an extended version of the e-SCP-ECG+ protocol, mainly used for ECG data transmission, in order to include several types of information that affect the quality of the obtained signal, as well as the diagnosis process. The aim is to offer medical experts with an enriched clinical view of the patients so as to diagnose a medical incident more accurately. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2019.

关键词： Electrocardiograms

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Quantum Software engineering: Roadmap and Challenges Ahead

引用

ACM Transactions on Software engineering and Methodology 2025年第5期34卷

作者： Murillo, Juan Manuel Garcia-Alonso, Jose Moguel, Enrique Barzen, Johanna Leymann, Frank Ali, Shaukat Yue, Tao Arcaini, Paolo Pérez-Castillo, Ricardo de Guzmán, Ignacio García-Rodríguez Piattini, Mario Ruiz-Cortés, Antonio Brogi, Antonio Zhao, Jianjun Miranskyy, Andriy Wimmer, Manuel University of Extremadura Cáceres Spain University of Stuttgart Stuttgart Germany Simula Research Laboratory Oslo Norway and Computer Science Oslo Metropolitan University Oslo Norway Computer Science and Engineering Beihang University Beijing China National Institute of Informatics Chiyoda-ku Japan Faculty of Social Sciences and Information Technologies University of Castilla-La Mancha Talavera de la Reina Spain Institute of Technologies and Information Systems University of Castilla-La Mancha Ciudad Real Spain University of Castilla-La Mancha Ciudad Real Spain Computer Languages and Systems University of Seville Seville Spain Department of Computer Science University of Pisa Pisa Italy Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Toronto Metropolitan University Toronto ON Canada Johannes Kepler University Linz Linz Austria

As quantum computers advance, the complexity of the software they can execute increases as well. To ensure this software is efficient, maintainable, reusable, and cost-effective—key qualities of any industry-grade software—mature software engineering practices must be applied throughout its design, development, and operation. However, the significant differences between classical and quantum software make it challenging to directly apply classical software engineering methods to quantum systems. This challenge has led to the emergence of Quantum Software engineering (QSE) as a distinct field within the broader software engineering landscape. In this work, a group of active researchers analyze in depth the current state of QSE research. From this analysis, the key areas of QSE are identified and explored in order to determine the most relevant open challenges that should be addressed in the next years. These challenges help identify necessary breakthroughs and future research directions for advancing QSE. © 2025 Copyright held by the owner/author(s).

关键词： open challenges QSE Quantum computing Quantum Software engineering

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