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IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE)

作者： Anatolijs Zabasta Nadezda Kunicina Kristaps Vitols Ilmars Duritis Uldis Grunde Janis Judvaitis Modris Greitans Ilga Sematovica Aija Malniece Ilja Galkins Institute of Electrical Engineering and Electronics EEF Riga Technical University Riga Latvia Faculty of Veterinary Medicine Latvia University of Life Sciences and Technologies Jelgava Latvia Robotics and Machine Perception laboratory Institute of Electronics and Computer Science Riga Latvia Cyber-Physical Systems laboratory Institute of Electronics and Computer Science Riga Latvia

High-yielding cow rations under intensive production conditions contribute to the development of subacute rumen acidosis (SARA), leading to pathologies such as rumenitis, laminitis, reproductive disorders, loss of productivity and reduced longevity. The aim of this work is to develop a reticulo-ruminal long-acting cyber-physical system for monitoring rumen parameters. Three scientific institutions in cooperation with two farmers conduct the research in order to create a prototype of a low-power wireless sensor network system for early diagnostic of subacute rumen acidosis of dairy cows. The new system architecture includes, reticulo-ruminal bolus with pH and temperature sensors, a microcontroller, a radio transmitter and a power supply module. The system includes a base station for data collection from boluses, an MQTT broker, a web server and a database. Data communication solution has been developed and tested in the laboratory, and micro-controllers have been selected and adapted for data processing. In addition, research is under way to create an autonomous long-term power supply system. Work shall be conducted in two directions: (a) stand-alone battery-powered electricity supply system; (b) an autonomous power supply system based on the generation of an electrostatic generator. The results of the initial stage of the research are discussed in this paper.

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An Efficient Scheme for Path Planning in Internet of Drones

An Efficient Scheme for Path Planning in Internet of Drones

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IEEE Global Communications Conference

作者： Aditya Goyal Nikhil Kumar Amit Dua Neeraj Kumar Joel J. P. C. Rodrigues Dushantha Nalin K. Jayakody Computer Science and Information Systems Department Birla Institute of Technology and Science Pilani Computer science and Engineering Department Thapar Institute of Engineering and Technology Punjab National Institute of Telecommunications (INATEL) Brazil Instituto de Telecomunicações Portugal Federal University of Piauí (UFPI) Brazil Faculty of Engineering Sri Lanka Technological Campus Sri Lanka School of Computer Science & Robotics National Research Tomsk Polytechnic University Russia

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

The Internet of Drones (IoD) is a multi-layered, control architecture to regulate and coordinate the navigation of Unmanned Aerial Vehicles (UAV) in a shared public airspace. UAVs have the potential to be employed in public space for purposes like surveillance, monitoring, package delivery, emergency services, etc. For proper operation, an efficient path planning among IoD is required so that they can adaptively decide their path for data dissemination. Most of the existing solutions for this problem have made unreasonable assumptions and do not offer scalability. The scheme proposed in the paper provides a network architecture for the scalable solution of UAVs in an urban environment addressing issues of path planning, safety, privacy, and network connectivity. The scheme has been tested using exhaustive simulation and results prove that the proposed scheme is efficient in terms of reducing the overall cost and delivery time with the increasing weight of payload in the drones.

关键词： Drones Path planning Internet Navigation Atmospheric modeling Scalability Companies

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Bearing-only formation control under persistence of excitation

arXiv

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arXiv 2020年

作者： Tang, Zhiqi Cunha, Rita Hamel, Tarek Silvestre, Carlos Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Portugal I3S-CNRS Université Côte d'Azur Nice-Sophia Antipolis France I3S-CNRS Institut Universitaire de France Nice-Sophia Antipolis France Department of Electrical and Computer Engineering The Faculty of Science and Technology The University of Macau China

This paper addresses the problem of bearing-only formation control in d (d ≥ 2)-dimensional space by exploring persistence of excitation (PE) of the desired bearing reference. By defining a desired formation that is bearing PE, distributed bearing-only control laws are proposed, which guarantee exponential stabilization of the desired formation only up to a translation vector. The key outcome of this approach relies in exploiting the bearing PE to significantly relax the conditions imposed on the graph topology to ensure exponential stabilization, when compared to the bearing rigidity conditions, and to remove the scale ambiguity introduced by bearing vectors. Simulation results are provided to illustrate the performance of the proposed control method. Copyright © 2020, The Authors. All rights reserved.

关键词： Stabilization

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Assessing Trustworthy AI in times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Predictive Inverse Kinematics: optimizing Future Trajectory through Implicit time Integration and Future Jacobian Estimation

Predictive Inverse Kinematics: optimizing Future Trajectory ...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Ko Ayusawa Wael Suleiman Eiichi Yoshida CNRS-AIST JRL (Joint Robotics Laboratory) UMI13218/RL Intelligent Systems Research Institute National Institute of Advanced Industrial Science and Technology (IS-AIST) Japan Electrical and Computer Engineering Department Faculty of Engineering University of Sherbrooke Canada

This paper presents an inverse kinematics (IK) method which can control future velocities and accelerations for multi-body systems. The proposed IK method is formulated as a quadratic programing (QP) that optimizes future joint trajectories. The features of the proposed IK are: (1) the evaluation of accelerations at future time instances, (2) the trajectory representation that can implicitly integrate the time integral formula into QP, (3) the computation of future Jacobian matrices based on the comprehensive theory of differential kinematics proposed in our previous work. Those features enable a stable and fast IK computation while evaluating the future accelerations. We also conducted thorough numerical studies to show the efficiency of the proposed method.

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Online multi-object tracking and segmentation with GMPHD filter and mask-based affinity fusion

arXiv

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arXiv 2020年

作者： Song, Young-Min Yoon, Young-Chul Yoon, Kwangjin Jeon, Moongu Lee, Seong-Whan Pedrycz, Witold The School of Electrical Engineering and Computer Science Gwangju Institute of Technology Gwangju61005 Korea Republic of The Robotics Laboratory Hyundai Motor Company Gyeonggi-do 16082 Korea Republic of SI Analytics Co. Ltd. Daejeon34051 Korea Republic of The Department of Artificial Intelligence Korea University Seoul02841 Korea Republic of The Department of Brain and Cognitive Engineering Korea University Seoul02841 Korea Republic of The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2H5 Canada The Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia The Systems Research Institute Polish Academy of Sciences Warsaw01-447 Poland

In this paper, we propose a highly practical fully online multi-object tracking and segmentation (MOTS) method that uses instance segmentation results as an input. The proposed method is based on the Gaussian mixture probability hypothesis density (GMPHD) filter, a hierarchical data association (HDA), and a mask-based affinity fusion (MAF) model to achieve high-performance online tracking. The HDA consists of two associations: segment-to-track and track-to-track associations. One affinity, for position and motion, is computed by using the GMPHD filter, and the other affinity, for appearance is computed by using the responses from a single object tracker such as a kernalized correlation filter. These two affinities are simply fused by using a score-level fusion method such as min-max normalization referred to as MAF. In addition, to reduce the number of false positive segments, we adopt mask IoU-based merging (mask merging). The proposed MOTS framework with the key modules: HDA, MAF, and mask merging, is easily extensible to simultaneously track multiple types of objects with CPU-only execution in parallel processing. In addition, the developed framework only requires simple parameter tuning unlike many existing MOTS methods that need intensive hyperparameter optimization. In the experiments on the two popular MOTS datasets, the key modules show some improvements. For instance, ID-switch decreases by more than half compared to a baseline method in the training sets. In conclusion, our tracker achieves state-of-the-art MOTS performance in the test sets. Copyright © 2020, The Authors. All rights reserved.

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Quantitative Evaluation of Endoscopic SLAM Methods: EndoSLAM Dataset

arXiv

引用

arXiv 2020年

作者： Ozyoruk, Kutsev Bengisu Incetan, Kagan Coskun, Gulfize Gokceler, Guliz Irem Almalioglu, Yasin Mahmood, Faisal Durr, Nicholas J. Curto, Eva Perdigoto, Luis Oliveira, Marina Araujo, Helder Alexandrino, Henrique Gilbert, Hunter B. Turan, Mehmet Institute of Biomedical Engineering Bogazici University Computer Science Department University of Oxford Oxford United Kingdom Department of Pathology Harvard Medical School BostonMA United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Institute for Systems and Robotics University of Coimbra Portugal Faculty of Medicine Clinical Academic Center of Coimbra University of Coimbra Coimbra Portugal Department of Mechanical and Industrial Engineering Louisiana State University Baton RougeLA United States

Deep learning techniques hold promise to improve dense topography reconstruction and pose estimation, as well as simultaneous localization and mapping (SLAM). However, currently available datasets do not support effective quantitative benchmarking. With this paper, we introduce a comprehensive endoscopic SLAM dataset containing both capsule and standard endoscopy recordings. A Panda robotic arm, two different commercially available high precision 3D scanners, two different commercially available capsule endoscopes with different camera properties and two different conventional endoscopy cameras were employed to collect data from eight ex-vivo porcine gastrointestinal (GI)-tract organs. In total, 35 sub-datasets are provided: 18 sub-datasets for colon, 12 sub-datasets for stomach and five sub-datasets for small intestine, while four of these contain polyp-mimicking elevations carried out by an expert gastroenterologist. To exemplify the use-case, SC-SfMLearner was comprehensively benchmarked. The codes and the link for the dataset are publicly available at https://***/CapsuleEndoscope/EndoSLAM. A video demonstrating the experimental setup and procedure is available at https://***/watch?v=G_LCe0aWWdQ. Copyright © 2020, The Authors. All rights reserved.

关键词： computer vision

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Port-Hamiltonian Approach to Neural Network Training

Port-Hamiltonian Approach to Neural Network Training

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IEEE Annual Conference on Decision and Control

作者： Stefano Massaroli Michael Poli Federico Califano Angela Faragasso Jinkyoo Park Atsushi Yamashita Hajime Asama Department of Precision Engineering The University of Tokyo Tokyo Japan Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea Faculty of Electrical Engineering Mathematics & Computer Science (EWI) Robotics and Mechatronics (RAM) University of Twente Enschede The Netherlands

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

Neural networks are discrete entities: subdivided into discrete layers and parametrized by weights which are iteratively optimized via difference equations. Recent work proposes networks with layer outputs which are no longer quantized but are solutions of an ordinary differential equation (ODE);however, these networks are still optimized via discrete methods (e.g. gradient descent). In this paper, we explore a different direction: namely, we propose a novel framework for learning in which the parameters themselves are solutions of ODEs. By viewing the optimization process as the evolution of a port-Hamiltonian system, we can ensure convergence to a minimum of the objective function. Numerical experiments have been performed to show the validity and effectiveness of the proposed methods.

关键词： Training Biological neural networks Convergence Mathematical model Optimization Linear programming

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Adaptive Backstepping of Synergistic Hybrid Feedbacks with Application to Obstacle Avoidance

Adaptive Backstepping of Synergistic Hybrid Feedbacks with A...

引用

American Control Conference

作者： Pedro Casau Ricardo G. Sanfelice Carlos Silvestre Institute for Systems and Robotics (ISR) Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal University of California Santa Cruz CA 95064 USA Department of Electrical and Computer Engineering of the Faculty of Science and Technology of the University of Macau Macau China

ISBN: (纸本)9781538679012;9781538679265

In this paper, we show that the hybrid controller that is induced by a Synergistic Lyapunov Function and Feedback (SLFF) pair relative to a compact set, can be extended to the case where the original affine control system is subject to a class of additive disturbances known as matched uncertainties, provided that the estimator dynamics do not add new equilibria to the closed-loop system. We also show that the proposed adaptive hybrid controller is amenable to backstepping. Finally, we apply the proposed hybrid control strategy to the problem of global asymptotic stabilization of a compact set in the presence of an obstacle and we illustrate this application by means of simulation results.

关键词： Obstacle avoidance Backstepping Feedback Collision avoidance Liapunov function control systems Closed loop systems Self tuning Controllers

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