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23rd International Conference on Computational Science and Its Applications, ICCSA 2023

作者： de Fátima Pina, Maria Andrade, Marina Alexandra ISCTE - IUL University Institute of Lisbon Av. das Forças Armadas Lisbon1649-026 Portugal ISLA - Santarém Superior Institute of Management and Administration of Santarém Rua Teixeira Guedes 31 Santarém2000-029 Portugal ISR - UC Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Rua Silvio Lima - Pólo II Coimbra3030-290 Portugal ISTAR - IUL Information Sciences and Technologies and Architecture Research Center University Institute of Lisbon Av. das Forças Armadas Lisbon1649-026 Portugal

ISBN: (纸本)9783031371288

Interpolating data and the application of data mining techniques in nonlinear manifolds plays a significant role in different areas of knowledge, ranging from computer vision and robotics, to industrial and medical requests, and these growing number of applications have sparked the research interest of the scientific community to these topics. The Generalized Essential manifold, briefly, Essential manifold, consisting of the product of the Grassmann manifold of all k-dimensional subspaces of Rn and the Lie group of rotations in Rn, for instance, plays an important role in the problem of recovering the structure and motion from a sequence of images, also known as stereo matching, which is a crucial problem in image processing and computer vision. A well-known recursive procedure to generate interpolating polynomial curves in Euclidean spaces is the classical De Casteljau algorithm, which is a simple and powerful tool widely used in the field of computer Aided Geometric Design, particularly because it is essentially geometrically based. This algorithm has been generalized to geodesically complete Riemannian manifolds. Thus, having this in mind, in this work we present all the ingredients for a detailed implementation of the generalized De Casteljau algorithm to generate geometric cubic polynomials in the Essential manifold preparing the ground to solve different real interpolation problems in this manifold. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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A Survey on computer vision based Human Analysis in the COVID-19 Era

arXiv

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arXiv 2022年

作者： Eyiokur, Fevziye Irem Kantarcı, Alperen Erakın, Mustafa Ekrem Damer, Naser Ofli, Ferda Imran, Muhammad Križaj, Janez Salah, Albert Ali Waibel, Alexander Štruc, Vitomir Ekenel, Hazım Kemal Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Department of Computer Engineering Istanbul Technical University Istanbul Turkey Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Qatar Computing Research Institute HBKU Doha Qatar Faculty of Electrical Engineering University of Ljubljana Tržaška cesta 25 Ljubljana1000 Slovenia Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Computer Engineering Boğaziçi University Istanbul Turkey Carnegie Mellon University Pittsburgh United States

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for comp

关键词： COVID-19

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FLAME 3 Dataset: Unleashing the Power of Radiometric Thermal UAV Imagery for Wildfire Management

arXiv

引用

arXiv 2024年

作者： Hopkins, Bryce O’Neill, Leo Marinaccio, Michael Rowell, Eric Parsons, Russell Flanary, Sarah Nazim, Irtija Seielstad, Carl Afghah, Fatemeh Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC United States Pacific Southwest Research Station US Forest Services ReddingCA United States Desert Research Institute RenoNV United States US Forest Service Rocky Mountain Research Station Fire Sciences Laboratory MissoulaMT United States Department of Mechanical Engineering Clemson University ClemsonSC United States Department of Forest Management University of Montana MissoulaMT United States

The increasing accessibility of radiometric thermal imaging sensors for unmanned aerial vehicles (UAVs) offers significant potential for advancing AI-driven aerial wildfire management. Radiometric imaging provides per-pixel temperature estimates, a valuable improvement over non-radiometric data that requires irradiance measurements to be converted into visible images using RGB color palettes. Despite its benefits, this technology has been underutilized largely due to a lack of available data for researchers. This study addresses this gap by introducing methods for collecting and processing synchronized visual spectrum and radiometric thermal imagery using UAVs at prescribed fires. The included imagery processing pipeline drastically simplifies and partially automates each step from data collection to neural network input. Further, we present the FLAME 3 dataset, the first comprehensive collection of side-by-side visual spectrum and radiometric thermal imagery of wildland fires. Building on our previous FLAME 1 and FLAME 2 datasets, FLAME 3 includes radiometric thermal Tag Image File Format (TIFFs) and nadir thermal plots, providing a new data type and collection method [1], [2]. This dataset aims to spur a new generation of machine learning models utilizing radiometric thermal imagery, potentially trivializing tasks such as aerial wildfire detection, segmentation, and assessment. A single-burn subset of FLAME 3 for computer vision applications is available on Kaggle here, with the full 6 burn set available to readers upon request. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Insights from an Industrial Collaborative Assembly Project: Lessons in research and Collaboration

arXiv

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arXiv 2022年

作者： Chen, Tan Huang, Zhe Motes, James Geng, Junyi Ta, Quang Minh Dinkel, Holly Abdul-Rashid, Hameed Myers, Jessica Mun, Ye-Ji Lin, Wei-Che Huang, Yuan-Yung Liu, Sizhe Morales, Marco Amato, Nancy M. Driggs-Campbell, Katherine Bretl, Timothy The Coordinated Science Laboratory The University of Illinois Urbana-Champaign United States The Department of Electrical and Computer Engineering The University of Illinois Urbana-Champaign United States The Department of Computer Science The University of Illinois Urbana-Champaign United States The Robotics Institute Carnegie Mellon University United States The Department of Aerospace Engineering The University of Illinois Urbana-Champaign United States Foxconn Interconnect Technology Taiwan

Significant progress in robotics reveals new opportunities to advance manufacturing. Next-generation industrial automation will require both integration of distinct robotic technologies and their application to challenging industrial environments. This paper presents lessons from a collaborative assembly project between three academic research groups and an industry partner. The goal of the project is to develop a flexible, safe, and productive manufacturing cell for sub-centimeter precision assembly. Solving this problem in a high-mix, low-volume production line motivates multiple research thrusts in robotics. This work identifies new directions in collaborative robotics for industrial applications and offers insight toward strengthening collaborations between institutions in academia and industry on the development of new technologies. © 2022, CC BY.

关键词： Collaborative robots

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NR-RRT: Neural Risk-Aware Near-Optimal Path Planning in Uncertain Nonconvex Environments

arXiv

引用

arXiv 2022年

作者： Meng, Fei Chen, Liangliang Ma, Han Wang, Jiankun Meng, Max Q.-H. The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China The Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen518057 China

Balancing the trade-off between safety and efficiency is of significant importance for path planning under uncertainty. Many risk-aware path planners have been developed to explicitly limit the probability of collision to an acceptable bound in uncertain environments. However, convex obstacles or Gaussian uncertainties are usually assumed to make the problem tractable in the existing method. These assumptions limit the generalization and application of path planners in real-world implementations. In this article, we propose to apply deep learning methods to the sampling-based planner, developing a novel risk bounded near-optimal path planning algorithm named neural risk-aware RRT (NR-RRT). Specifically, a deterministic risk contours map is maintained by perceiving the probabilistic nonconvex obstacles, and a neural network sampler is proposed to predict the next most-promising safe state. Furthermore, the recursive divide-andconquer planning and bidirectional search strategies are used to accelerate the convergence to a near-optimal solution with guaranteed bounded risk. Worst-case theoretical guarantees can also be proven flowing to a standby safety guaranteed planner utilizing a uniform sampling distribution. Simulation experiments demonstrate that the proposed algorithm outperforms the stateof-the-art remarkably for finding risk bounded low-cost paths in seen and unseen environments with uncertainty and nonconvex constraints. Note to Practitioners-This article is motivated by developing an efficient risk-aware path planner that can quickly find risk bounded solutions in uncertain nonconvex environments for practical applications, such as surgical navigation and delivery in crowded squares. Sampling-based planning approaches such as rapidly-exploring random tree (RRT) and its variants are popular for their good performance in exploring the state space. However, it is quite time-consuming to look for risk bounded paths in uncertain environments, especially under no

关键词： Motion planning

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Grand Challenges in the Verification of Autonomous Systems

arXiv

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arXiv 2024年

作者： Leahy, Kevin Asgari, Hamid Dennis, Louise A. Feather, Martin S. Fisher, Michael Ibanez-Guzman, Javier Logan, Brian Olszewska, Joanna I. Redfield, Signe Robotics Engineering Department Worcester Polytechnic Institute WorcesterMA United States Complex and Autonomous Systems Research Group Thales UK Research Reading United Kingdom Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Department of Computer Science University of Manchester Manchester United Kingdom Renault Guyancourt France Department of Computing Science University of Aberdeen Aberdeen United Kingdom Department of Information and Computing Sciences Utrecht University Utrecht Netherlands School of Computing and Engineering University of the West of Scotland Glasgow United Kingdom U.S. Naval Research Laboratory WashingtonDC United States

Autonomous systems use independent decisionmaking with only limited human intervention to accomplish goals in complex and unpredictable environments. As the autonomy technologies that underpin them continue to advance, these systems will find their way into an increasing number of applications in an ever wider range of settings. If we are to deploy them to perform safety-critical or mission-critical roles, it is imperative that we have justified confidence in their safe and correct operation. Verification is the process by which such confidence is established. However, autonomous systems pose challenges to existing verification practices. This paper highlights viewpoints of the Roadmap Working Group of the IEEE robotics and Automation Society Technical Committee for Verification of Autonomous Systems, identifying these grand challenges, and providing a vision for future research efforts that will be needed to address them. © 2024, CC BY.

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Enhancing Knee Contact Force Prediction Using Transformer Networks: A Comparative Study with Lstm and Mlp

SSRN

引用

SSRN 2025年

作者： Xu, Fashu Wang, Junqing Tong, Bingkui Fang, Xuyi Zhang, Hui Deng, Tao Wei, Qu Nie, Yong Li, Kang Department of Orthopedic Surgery West China Biomedical Big Data Center West China Hospital Sichuan University Sichuan Chengdu China Department of Orthopedic Surgery Orthopedic Research Institute West China Hospital Sichuan University Sichuan Chengdu China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang China College of Computer Science Sichuan University Sichuan Chengdu China College of Electrical Engineering Sichuan University Sichuan Chengdu China School of Mechanical Engineering Sichuan University Sichuan Chengdu China

Background and objectiveKnee contact force (KCF) is pivotal in both the study and degenerative pathologies like Knee Osteoarthritis. Traditionally, they are primarily estimated using musculoskeletal modeling and machine learning predictive algorithms. However, musculoskeletal modeling is limited by intricate experimental protocols, prohibitive equipment cost, and scalability challenges. While machine learning methods, though computationally efficient, exhibit suboptimal predictive accuracy. This study innovatively developed a Transformer model for predicting KCF, benchmarked against long short-term memory (LSTM) and multilayer perceptron (MLP) *** comprehensive database was constructed with gait data from 513 participants, incorporating three degrees of freedom of hip, knee flexion/extension, and ankle dorsiflexion/plantarflexion, constituting four input combinations for the models. Contact forces derived from OpenSim served as the ground truth. Root mean square error (RMSE) and coefficient of determination (R2) were used to evaluate the performance of *** results indicated that the Transformer model demonstrated superior performance, achieving a significantly lower mean root mean square error of 0.02 body weight (BW) compared to MLP (0.54 BW) and LSTM (0.45 BW). Furthermore, the mean R2 of the Transformer network (0.99) was markedly superior to that of MLP (0.45) and LSTM (0.58).ConclusionsThe application of Transformer networks significantly improved the accuracy of KCF prediction, addressing the limitations of traditional musculoskeletal modeling and existing machine learning approaches, which was of paramount importance in both clinical practice and biomechanical research. © 2025, The Authors. All rights reserved.

关键词： Mean square error

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Machine Learning and computer vision Techniques in Continuous Beehive Monitoring Applications: A Survey

arXiv

引用

arXiv 2022年

作者： Bilik, Simon Zemcik, Tomas Kratochvila, Lukas Ricanek, Dominik Richter, Miloslav Zambanini, Sebastian Horak, Karel Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 Brno61600 Czech Republic Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Yliopistonkatu 34 Lappeenranta53850 Finland Computer Vision Lab Institute of Visual Computing & Human-Centered Technology Faculty of Informatics TU Wien Favoritenstr. 9/193-1 ViennaA-1040 Austria

Wide use and availability of machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new applications appears also in biology and agriculture, where they may be used to detect infections, parasites and weeds, but also for automated monitoring and early warning systems. This goes in concordance with the introduction of the easily accessible hardware and development kits such as the Arduino, or RaspberryPi families. In this paper, we survey 50 papers focusing on the methods of automated beehive monitoring using computer vision techniques. Particularly on the pollen and Varroa mite detection together with the bee traffic monitoring. Such systems could also be used for monitoring of honeybee colonies and for the inspection of their health state, which could potentially identify dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Further on, we also include analysis of the research trends in this application field and we outline the possible directions of new development. Our paper is also aimed at veterinary and apidology professionals and experts, who may not be familiar with machine learning to introduce them to its capabilities, hence each family of techniques is prefaced by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring. © 2022, CC BY.

关键词： computer vision

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Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects

arXiv

引用

arXiv 2022年

作者： Islam, Md Milon Nooruddin, Sheikh Karray, Fakhri Muhammad, Ghulam Centre for Pattern Analysis and Machine Intelligence Department of Electrical and Computer Engineering University of Waterloo ONN2L 3G1 Canada Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Center of Smart Robotics Research King Saud University Riyadh Saudi Arabia

Human Activity Recognition (HAR) plays a significant role in the everyday life of people because of its ability to learn extensive high-level information about human activity from wearable or stationary devices. A substantial amount of research has been conducted on HAR and numerous approaches based on deep learning and machine learning have been exploited by the research community to classify human activities. The main goal of this review is to summarize recent works based on a wide range of deep neural networks architecture, namely convolutional neural networks (CNNs) for human activity recognition. The reviewed systems are clustered into four categories depending on the use of input devices like multimodal sensing devices, smartphones, radar, and vision devices. This review describes the performances, strengths, weaknesses, and the used hyperparameters of CNN architectures for each reviewed system with an overview of available public data sources. In addition, a discussion with the current challenges to CNN-based HAR systems is presented. Finally, this review is concluded with some potential future directions that would be of great assistance for the researchers who would like to contribute to this field. © 2022, CC BY.

关键词： Smartphones

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Expanding the Reach of Cryo-EM through Open Design robotics and Remote Screening

引用

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1019-1019页

作者： Zhang, Steven Peele, Wyatt Bouvette, Jonathan Huang, Qinwen Bartesaghi, Alberto Dandey, Venkata Borgnia, Mario J. Genome Integrity and Structural Biology Laboratory National Institute of Environmental Health Sciences Research Triangle Park NC United States Department of Computer Science Duke University Durham NC United States Department of Electrical and Computer Engineering Duke University Durham NC United States Department of Biochemistry Duke University School of Medicine Durham NC United States

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