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arXiv 2019年

作者： Xia, Weiguo Ye, Mengbin Liu, Ji Cao, Ming Sun, Xi-Ming School of Control Science and Engineering Dalian University of Technology China Faculty of Science and Engineering ENTEG University of Groningen Netherlands Department of Electrical and Computer Engineering Stony Brook University United States

—This paper analyzes a nonlinear opinion dynamics model which generalizes the DeGroot model by introducing a bias parameter for each individual. The original DeGroot model is recovered when the bias parameter is equal to zero. The magnitude of this parameter reflects an individual’s degree of bias when assimilating new opinions, and depending on the magnitude, an individual is said to have weak, intermediate, and strong bias. The opinions of the individuals lie between 0 and 1. It is shown that for strongly connected networks, the equilibria with all elements equal identically to the extreme value 0 or 1 is locally exponentially stable, while the equilibrium with all elements equal to the neutral consensus value of 21 is unstable. Regions of attraction for the extreme consensus equilibria are given. For the equilibrium consisting of both extreme values 0 and 1, which corresponds to opinion polarization according to the model, it is shown that the equilibrium is unstable for all strongly connected networks if individuals all have weak bias, becomes locally exponentially stable for complete and two-island networks if individuals all have strong bias, and its stability heavily depends on the network topology when individuals have intermediate bias. Analysis on star graphs and simulations show that additional equilibria may exist where individuals form clusters. Copyright © 2019, The Authors. All rights reserved.

关键词： Topology

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Media and non-media WebRTC communication between a terrestrial station and a drone: the case of a flying IoT system to monitor parking

Media and non-media WebRTC communication between a terrestri...

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International Workshop/Symposium on Distributed Simulation and Real-Time Applications (DS-RT)

作者： Agnieszka Chodorek Robert R. Chodorek Krzysztof Wajda Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology Kielce Poland Department of Telecommunications The AGH University of Science and Technology Kraków Poland

In this paper we propose a flying IoT system designed to monitor parking lot. The system is composed of a flying mobile station and a terrestrial station. The mobile station consists of visual monitoring camera associated with air quality monitoring sensors, mounted on a Turnigy SK450 quad copter controlled with Pixhawk autopilot hardware. Data gathered from the monitoring system are transmitted to an IoT broker running on a Raspberry Pi 3+ microcontroller, also mounted on a drone, that aggregates data into a common flow and retransmits them to the terrestrial station. The transmission system was build according to WebRTC architecture. In order to assure separation of the production network (for transmission of application data) and the management network (for management and control purposes), two different transmission channels are used: one for the WebRTC transmission and the other for controlling the drone. The paper includes a description of the system and the results of field trials carried out on the parking lot of the AGH University of technology.

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An Adaptive Strategy via Reinforcement Learning for the Prisoner＇s Dilemma Game

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 301-310页

作者： Lei Xue Changyin Sun Donald Wunsch Yingjiang Zhou Fang Yu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing 210096 China Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO 65409 USA College of Automation Nanjing University of Posts and Telecommunications Nanjing 210023 China Institute of Logistics Science and Engineering Shanghai Maritime University Shanghai 210096 China IEEE

The iterated prisoner's dilemma(IPD) is an ideal model for analyzing interactions between agents in complex networks. It has attracted wide interest in the development of novel strategies since the success of tit-for-tat in Axelrod's tournament. This paper studies a new adaptive strategy of IPD in different complex networks, where agents can learn and adapt their strategies through reinforcement learning method. A temporal difference learning method is applied for designing the adaptive strategy to optimize the decision making process of the agents. Previous studies indicated that mutual cooperation is hard to emerge in the IPD. Therefore, three examples which based on square lattice network and scale-free network are provided to show two features of the adaptive strategy. First, the mutual cooperation can be achieved by the group with adaptive agents under scale-free network, and once evolution has converged mutual cooperation, it is unlikely to shift. Secondly, the adaptive strategy can earn a better payoff compared with other strategies in the square network. The analytical properties are discussed for verifying evolutionary stability of the adaptive strategy.

关键词： Complex network prisoner’s dilemma reinforcement learning temporal differences learning

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Findings from the Tushirikiane-4-MH (Supporting Each Other for Mental Health) Mobile Health-Supported Virtual Reality Randomized controlled Trial Among Urban Refugee Youth in Kampala, Uganda

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Global Mental Health 2025年 12卷 e12-e12页

作者： Logie, Carmen H. Okumu, Moses Admassu, Zerihun MacKenzie, Frannie Gittings, Lesley Kortenaar, Jean-Luc Khan, Naimul Hakiza, Robert Musoke, Daniel Kibuuka Nakitende, Aidah Katisi, Brenda Kyambadde, Peter Lester, Richard Mbuagbaw, Lawrence Factor-Inwentash Faculty of Social Work University of Toronto Toronto ON Canada Women's College Research Institute Women's College Hospital Toronto ON Canada United Nations University Institute for Water Environment & Health Hamilton ON Canada Centre for Gender and Sexuality Health Equity Vancouver BC Canada School of Social Work University of Illinois Urbana-Champaign United States Uganda Christian University Mukono Uganda School of Health Studies Western University London ON Canada Centre for Social Science Research University of Cape Town South Africa Dalla Lana School of Public Health University of Toronto Toronto ON Canada Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada Young African Refugees for Integral Development (YARID) Kampala Uganda International Research Consortium Kampala Uganda National AIDS and STI Control Programme Ministry of Health Kampala Uganda Most At Risk Population Initiative Mulago Hospital Kampala Uganda Department of Medicine University of British Columbia Vancouver BC Canada Department of Health Research Methods Evidence and Impact McMaster University Hamilton ON Canada Department of Anesthesia McMaster University Hamilton ON Canada

Background: Virtual reality (VR) for mental health promotion remains understudied in low income humanitarian settings. We examined the effectiveness of VR in reducing depression with urban refugee youth in Kampala, Uganda. Methods: This randomized controlled trial assessed VR alone (Arm 1), VR followed by group problem management plus (GPM+) (Arm 2), and a control group (Arm 3), with a peer-driven and convenience sample of refugee youth aged 16-25 in Kampala. The primary outcome, depression, was measured with the Patient Health Questionnaire-9. Secondary outcomes included: mental health literacy, mental health stigma, self-compassion, mental wellbeing, and adaptive coping. Analyses were conducted at three timepoints (baseline, 8-weeks, 16-weeks) using generalized estimating equations (GEE). Results: Among participants (n=335, mean age: 20.77, standard deviation: 3.01;cisgender women: n=158, cisgender men: n=173, transgender women: n=4) we found no depression reductions for Arms 1 or 2 at 16-weeks compared with Arm 3. At 16-weeks, mental health literacy was significantly higher for Arm 2 compared with Arm 3, and self compassion was significantly higher in Arms 1 and Arm 2 compared with Arm 3. Discussion: VR alongside GPM+ may benefit self-compassion and MHL among urban refugee youth in Kampala, but these interventions were not effective in reducing depression. © The Author(s), 2025. Published by Cambridge University Press.

关键词： depression refugees self-compassion Uganda virtual reality youth

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Preface

Lecture Notes in Control and Information Sciences

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Lecture Notes in control and Information sciences 2021年 485卷 v-viii页

作者： Faulwasser, Timm Müller, Matthias A. Worthmann, Karl Department of Electrical Engineering and Information Technology Institute of Energy Systems Energy Efficiency and Energy Economics TU Dortmund University Dortmund Germany Faculty of Electrical Engineering and Computer Science Institute of Automatic Control Leibniz University Hannover Hannover Germany Faculty of Mathematics and Natural Sciences Institute for Mathematics Optimization-based Control Technische Universität Ilmenau Ilmenau Germany

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Scoring the terabit/s goal: Broadband connectivity in 6G

arXiv

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arXiv 2020年

作者： Rajatheva, Nandana Atzeni, Italo Bicaïs, Simon Björnson, Emil Bourdoux, André Buzzi, Stefano D'Andrea, Carmen Doré, Jean-Baptiste Erkucuk, Serhat Fuentes, Manuel Guan, Ke Hu, Yuzhou Huang, Xiaojing Hulkkonen, Jari Jornet, Josep Miquel Katz, Marcos Makki, Behrooz Nilsson, Rickard Panayirci, Erdal Rabie, Khaled Rajapaksha, Nuwanthika Salehi, Mohammadjavad Sarieddeen, Hadi Shahabuddin, Shahriar Svensson, Tommy Tervo, Oskari Tölli, Antti Wu, Qingqing Xu, Wen Centre for Wireless Communications University of Oulu Finland CEA-Leti France KTH Royal Institute of Technology Sweden Linköping University Sweden IMEC Belgium University of Cassino and Southern Latium Italy Italy Department of Electrical and Electronics Engineering Kadir Has University Turkey Fivecomm Valencia Spain State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University China The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications China Algorithm Department ZTE Corporation China School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology Sydney Australia Nokia Bell Labs Finland Institute for the Wireless Internet of Things Department of Electrical and Computer Engineering Northeastern University United States Ericsson Research Gothenburg Sweden Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Department of Engineering Manchester Metropolitan University United Kingdom Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Saudi Arabia Nokia Finland Department of Electrical Engineering Chalmers University of Technology Sweden State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering University of Macau China Huawei Technologies Germany

This paper explores the road to vastly improving the broadband connectivity in future 6G wireless systems. Different categories of use cases are considered, with peak data rates up to 1 Tbps. Several categories of enablers at the infrastructure, spectrum, and protocol/algorithmic levels are required to realize the intended broadband connectivity goals in 6G. At the infrastructure level, we consider ultra-massive MIMO technology (possibly implemented using holographic radio), intelligent reflecting surfaces, user-centric cell-free networking, integrated access and backhaul, and integrated space and terrestrial networks. At the spectrum level, the network must seamlessly utilize sub-6 GHz bands for coverage and spatial multiplexing of many devices, while higher bands will be mainly used for pushing the peak rates of point-to-point links. Finally, at the protocol/algorithmic level, the enablers include improved coding, modulation, and waveforms to achieve lower latency, higher reliability, and reduced complexity. © 2020, CC BY.

关键词： MIMO systems

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Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

arXiv

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arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

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Applicability of magnetic hysteresis models for predicting the behaviour of MR dampers 9

Applicability of magnetic hysteresis models for predicting t...

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9th ECCOMAS Thematic Conference on Smart Structures and Materials, SMART 2019

作者： Goldasz, J. Sapinski, B. Jastrzebski, L. Kubik, M. Faculty of Electrical Engineering and Computer Science Cracow University of Technology ul. Warszawska 24 Krakow31-155 Poland BWI Technical Center BWI Group ul. Podgorki Tynieckie 2 Krakow30-399 Poland Department of Process Control AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland Faculty of Mechanical Engineering Brno University of Technology Technicka 2869/2 Brno616 69 Czech Republic

ISBN: (纸本)9788494919466

This study concerns modeling the magnetic hysteretic behaviour of magnetorheological (MR) dampers. Magnetic hysteresis is one of factors influencing the output of such actuators. The origin of magnetic hysteresis in the actuator’s output is obvious - it is the inherent property of ferromagnetic materials which form the electromagnet’s assembly of the actuator. Prior work on solenoid actuators indicates that hysteresis plays a major role in the flux generation mechanism, and it should be accounted for in the controls development process. So far less attention has been paid to studying the contribution of magnetic hysteresis on the output of MR dampers. Majority of studies on the hysteretic behaviour of MR dampers have virtually neglected that specific contributor. Instead, the hysteretic behaviour of MR actuators was studied by observing the relationships between the output (force/torque) and the inputs (current, velocity, position). Instead, in the present study we separate the magnetic hysteresis from the mechanical/hydraulic one by investigating the magnetic flux vs exciting current relationship of an exemplary MR damper subjected to sinusoidal current (voltage) loading. The resulting behaviour of the electromagnets is then examined using the nonlinear inductor approach with hysteresis. The performance of several hysteretic operators is then evaluated based on their ability to reproduce the magnetic flux output of the MR actuator. © 2019 9th ECCOMAS Thematic Conference on Smart Structures and Materials, SMART 2019. All rights reserved.

关键词： Damping

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Remote real-time wastewater surveillance by the optical sensor supported by machine learning

Sensors and Actuators Reports

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Sensors and Actuators Reports 2025年 10卷

作者： Małgorzata Szczerska Kacper Cierpiak Monika Kosowska Paweł Wityk Sebastián García-Galán Patryk Sokołowski Sylwia Fudala-Książek Michał T. Tomczak Bei Ye Aneta Łuczkiewicz Department of Metrology and Optoelectronics Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology 11/12 Narutowicza Street 80-233 Gdansk Poland Faculty of Telecommunications Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Al. Prof. S. Kaliskiego 7 85-796 Bydgoszcz Poland Department of Biotechnology and Microbiology Faculty of Chemistry Gdańsk University of Technology 11/12 Narutowicza Street 80-233 Gdansk Poland Department of Biopharmaceutics and Pharmacodynamics Medical University of Gdańsk Hallera 107 Pharmacy Faculty Gdańsk Poland Telecommunications Engineering Department Linares Higher Polytechnic School Jaén University Avenida de la Universidad S/N 23700 Linares Jaén Spain Department of Water and Wastewater Technology Faculty of Civil and Environmental Engineering Gdańsk University of Technology 11/12 Narutowicza Street 80-233 Gdańsk Poland Faculty of Management and Economics Gdańsk University of Technology 11/12 Narutowicza Street 80-233 Gdańsk Poland Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Environmental Protection Key Laboratory of Microorganism Application and Risk Control Institute of Environment and Ecology Shenzhen International Graduate School Tsinghua University Shenzhen 518055 PR China

In this study, a remote monitoring sewage system based on optical method is presented for the first time. The built-in wastewater surveillance system can perform autonomous monitoring with no requirement of sample collection and its transport to the laboratory. The measurement results can be obtained in real time, the work operation allows continuous or on-demand mode. The study includes design, development and application of a biofunctionalized fiber-optic sensor, as well as engaging machine learning algorithms for measured signals classification. The validated SARS-CoV-2 antibodies sensor has a measurement range of 10 −12 mg/mL to 10 −1 mg/mL. Known concentrations of Immunoglobulin G (IgG) from 10 −6 mg/mL to 10 −1 mg/mL were added to the tested wastewater samples and then detected by the prepared optical probe. The data obtained were then processed and classified by traditional method and selected machine learning algorithms; the results obtained for the KNeighbors algorithm are Balanced Accuracy of 92.97 % and F1-score of 94.19 %. This study is of significance to establish a system that can effectively monitor the outbreak of potential infections in society.

关键词： Remote wastewater surveillance Real-time monitoring SARS-CoV-2 Optical sensors Fiber optic sensor Machine learning

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