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IoT Cloud Computing Middleware for crowd monitoring and evacuation

International Journal of Circuits, systems and Signal Processing 2021年 15卷 1790-1802页

作者： Gazis, Alexandros Katsiri, Eleftheria Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi67100 Greece Institute for the Management of Information Systems Athena Research & Innovation Center in Information Communication & Knowledge Technologies Marousi15125 Greece

Map-Reduce is a programming model and an associated implementation for processing and generating large data sets. This model has a single point of failure: the master, who coordinates the work in a cluster. On the contrary, wireless sensor networks (WSNs) are distributed systems that scale and feature large numbers of small, computationally limited, low-power, unreliable nodes. In this article, we provide a top-down approach explaining the architecture, implementation and rationale of a distributed fault-tolerant IoT middleware. Specifically, this middleware consists of multiple mini-computing devices (Raspberry Pi) connected in a WSN which implement the Map-Reduce algorithm. First, we explain the tools used to develop this system. Second, we focus on the Map-Reduce algorithm implemented to overcome common network connectivity issues, as well as to enhance operation availability and reliability. Lastly, we provide benchmarks for our middleware as a crowd tracking application for a preserved building in Greece (i.e., M. Hatzidakis’ residence). The results of this study show that IoT middleware with low-power and low-cost components are viable solutions for medium-sized cloud computing distributed and parallel computing centres. Potential uses of this middleware apply for monitoring buildings and indoor structures, in addition to crowd tracking to prevent the spread of COVID-19. © 2021, North Atlantic University Union NAUN. All rights reserved.

关键词： Middleware

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Study on Gait Stabilization Method Using Wearable Cyborg HAL Trunk-Unit for Parkinson's Disease and Parkinsonism with Freezing of Gait

Study on Gait Stabilization Method Using Wearable Cyborg HAL...

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IEEE International Conference on systems, Man and Cybernetics

作者： K. Ikeda A. Uehara H. Kawamoto Y. Sankai Department of Intelligent and Mechanical Interaction Systems University of Tsukuba Tsukuba Japan Faculty of Engineering Information and Systems Center of Cybernics Research University of Tsukuba Tsukuba Japan

Freezing of Gait (FOG) is one of the typical parkinsonian gait disturbances of progressive neurological disorders such as Parkinson's disease (PD) and progressive supranuclear palsy. Previous studies showed that the wearable cyborg HAL trunk-unit improved their gait disturbances through only an autonomous sway control that provided lateral swing with constant frequency. To promote the improvement by establishing interactive Bio-Feedback loop, it is necessary to realize synchronization between the wearer's intention and gait states for stabilizing gait, i.e. reducing variation of gait cycle. In this study, we developed a voluntary control method that responded to an intentional change in gait cycle and a method that switched 2 kinds of controls including the voluntary control method and the autonomous sway control method for stabilizing gait. The voluntary swing control method synchronized the HAL's force with wearer's gait in a stable gait state less prone to FOG. The autonomous sway control method provided lateral swing with constant frequency as feedback to the patients to achieve a gait state less prone to FOG. These controls were switched according to the gait stability calculated by wearer's gait cycle. Through the gait experiments with an abled-body participant, we confirmed that the HAL's lateral cyclic sway synchronized with the participant's gait and each control switched based on a gait stability. These results showed that the proposed methods had the feasibility of stabilizing gait.

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systems Modeling of Trust in AI-Enabled Medical Diagnosis

Systems Modeling of Trust in AI-Enabled Medical Diagnosis

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Annual IEEE systems Conference

作者： Negin Moghadasi Misagh Piran Rupa S. Valdez Negar Moghaddasi Davis C. Loose Thomas L. Polmateer James H. Lambert Dept. of Systems and Information Engineering University of Virginia Johnson & Johnson Medtech Charlottesville USA Dept. of Radiology Nuclear Medicine and Molecular Imaging Heart and Diabetes Center North-Rhine Westphalia Ruhr University of Bochum Bad Oeyhausen Germany Dept. of Systems and Information Engineering School of Data Science University of Virginia Charlottesville USA Dept. of Dentistry Western University of Health Sciences Pomona USA Dept. of Engineering Systems and Environment University of Virginia Charlottesville USA Dept. of Engineering Systems and Environment CCALS University of Virginia Charlottesville USA Dept. of Systems and Information Engineering University of Virginia Charlottesville USA

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

The progression of artificial intelligence, particularly in healthcare domain, has brought significant progress to treatment, drug discovery, disease diagnosis and more. The main contribution of this paper is the development of a scenario-based preferences risk register framework for quantifying AI related risks in disease diagnosis. Two case studies, one involving physician and the other one with involving patients as the main stakeholders, are compared to evaluate the effectiveness of the framework and to evaluate how the initiatives ranking orders will evolve based on the most and least disruptive scenarios for each cases. The framework is applied to realistic case studies on cardiac sarcoidosis, identifying success criteria, initiatives, emergent conditions, and the most and least disruptive scenarios. The success criteria align with the National Institute of Standards and Technology AI Risk Management Framework (NIST AI RMF) seven trustworthy AI characteristics. Finally, the framework identifies the most and least disruptive scenarios and the most important initiatives for both cases. Results show that Non-Interpretable AI and Lack of Human-AI Communications, Privacy Attacks, and Human Errors in Design, Develop, Measurement and Implementation are the most disruptive scenarios in both cases, however, scenario Cyber Security Threats also counted as other most disruptive scenarios to the initiatives ranked by patients. This adaptable framework has the potential not only to transfer findings to global healthcare systems but also to extend its effectiveness to various other domains, such as transportation, finance, environment, and design.

关键词： Privacy Measurement uncertainty Transportation NIST systems modeling Registers Medical diagnosis

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Event-Driven Receding Horizon Control of Energy-Aware Dynamic Agents for Distributed Persistent Monitoring

Event-Driven Receding Horizon Control of Energy-Aware Dynami...

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IEEE Conference on Decision and Control

作者： Shirantha Welikala Christos G. Cassandras Division of Systems Engineering and Center for Information and Systems Engineering Boston University Brookline MA

ISBN: (纸本)9781665436601

This paper addresses the persistent monitoring problem defined on a network where a set of nodes (targets) needs to be monitored by a team of dynamic energy-aware agents. The objective is to control the agents’ motion to jointly optimize the overall agent energy consumption and a measure of overall node state uncertainty, evaluated over a finite period of interest. To achieve these objectives, we extend an established event-driven Receding Horizon Control (RHC) solution by adding an optimal controller to account for agent motion dynamics and associated energy consumption. The resulting RHC solution is computationally efficient, distributed and on-line. Finally, numerical results are provided highlighting improvements compared to an existing RHC solution that uses energy-agnostic first-order agents.

关键词： Energy consumption Uncertainty Conferences Dynamics Measurement uncertainty Optimal control Energy measurement

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Development of a Universal RNA Dual-Terminal Labeling Method for Sensing RNA-Ligand Interactions

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CCS Chemistry 2023年第1期5卷 221-233页

作者： Longhuai Cheng Dejun Ma Jie Zhang Xueying Kang Yang Wu Yi Zhao Long Yi Zhen Xi State Key Laboratory of Elemento-Organic Chemistry Department of Chemical BiologyCollege of ChemistryNational Pesticide Engineering Research CenterCollaborative Innovation Center of Chemical Science and EngineeringNankai UniversityTianjin 300071 State Key Laboratory of Organic-Inorganic Composites and Beijing Key Laboratory of Bioprocess Beijing University of Chemical Technology(BUCT)Beijing 100029 Key Laboratory of Intelligent Information Processing Research Center for Ubiquitous Computing SystemsInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190

Dual labeling of an RNA can provide Förster resonance energy transfer(FRET)sensors for studying RNA folding,miRNA maturation,and RNA-protein ***,we report the development of a highly efficient strategy for direct dual-terminal labeling of any RNA of *** explored new Michael cycloaddition for facile labeling of 5′-terminal RNA with improved *** chemical tetrazinylation of RNA at the 3′-terminus was achieved with the highly efficient and catalysis-free tetrazine-cycloalkyne *** single-terminal labeling methods were combined for dual-terminal labeling of an RNA including short hairpin RNA,pre-miRNA,riboswitch,and noncoding ***,these dual-labeled RNA-based FRET sensors were used to monitor RNA-ligand interactions in vitro and in live *** is anticipated that these universal RNA labeling strategies will be useful to study RNA structures and functions.

关键词： dual labeling Förster resonance energy transfer sensor Michael cycloaddition tetrazinecycloalkyne ligation RNA-ligand interaction

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Driver Identification Using Accelerometers Data and Optimized Dataset Construction: A Naturalistic Driving Study 27

Driver Identification Using Accelerometers Data and Optimize...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Zhou, Hongmei Cao, Longtian Tian, Kai Zhang, Hui Huang, Yan Huang, Zhen Wang, Xu School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan430063 China Intelligent Transportation Systems Research Center Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan University of Technology Wuhan430063 China Dongfeng Usharing Technology Company Limited Wuhan430100 China School of Automation Wuhan University of Technology Wuhan430070 China School of Qilu Transportation Shandong University Jinan250002 China

ISBN: (纸本)9798331505929

The development of intelligent connected vehicles provides refined multidimensional data and connected conditions for driving behavior research. Relevant literature suggests that drivers can be distinguished by analyzing their driving behavior disparities. However, existing studies encounter challenges, such as diminished classification accuracy, obstacles in practical implementation, and ambiguity in defining the boundaries of feature extraction methods. This study aims to establish a high-accuracy driver identity identification model with readily accessible data. Driving behavior data, including vehicle speed and three-axis acceleration, were acquired from a field driving experiment involving 20 participants, each driving for a minimum of 6 hours. Driver identification accuracy was assessed by constructing random and continuous datasets and adjusting feature extraction parameters. The K-nearest Neighbor and Convolutional Neural Network models were employed to develop the driver identification model. Evaluate results show that the test accuracy using a random dataset is significantly better than a continuous dataset. When employing the random dataset, the K-Nearest Neighbors algorithm achieved the highest identification accuracy, reaching 93.4%. Notably, within a specific range, reducing the interval of acceleration sampling frequency and incorporating data from multiple axes substantially enhances driver identification accuracy. The research outcomes may facilitate the practical application of the driver identification model. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimal Control of Connected Automated Vehicles with Event/Self-Triggered Control Barrier Functions

arXiv

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arXiv 2022年

作者： Sabouni, Ehsan Cassandras, Christos G. Xiao, Wei Meskin, Nader The Division of Systems Engineering Center for Information and Systems Engineering Boston University BrooklineMA02446 United States CSAIL MIT United States Electrical Engineering Department Qatar University Doha Qatar

We address the problem of controlling Connected and Automated Vehicles (CAVs) in conflict areas of a traffic network subject to hard safety constraints. It has been shown that such problems can be solved through a combination of tractable optimal control problem formulations and the use of Control Barrier Functions (CBFs) that guarantee the satisfaction of all constraints. These solutions can be reduced to a sequence of Quadratic Programs (QPs) which are efficiently solved on-line over discrete time steps. However, the feasibility of each such QP cannot be guaranteed over every time step. To overcome this limitation, we develop both an event-triggered approach and a self-triggered approach such that the next QP is triggered by properly defined events. We show that both approaches, each in a different way, eliminate infeasible cases due to time-driven inter-sampling effects, thus also eliminating the need for selecting the size of time steps. Simulation examples are included to compare the two new schemes and to illustrate how overall infeasibilities can be significantly reduced while at the same time reducing the need for communication among CAVs without compromising performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Quadratic programming

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Sensor Fault Detection and Isolation in Autonomous Nonlinear systems Using Neural Network-Based Observers

Sensor Fault Detection and Isolation in Autonomous Nonlinear...

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European Control Conference (ECC)

作者： John Cao M. Umar B. Niazi Matthieu Barreau Karl Henrik Johansson Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper presents a novel observer-based approach to detect and isolate faulty sensors in nonlinear systems. The proposed sensor fault detection and isolation (s-FDI) method applies to a general class of nonlinear systems. Our focus is on s-FDI for two types of faults: complete failure and sensor degradation. The key aspect of this approach lies in the utilization of a neural network-based Kazantzis-Kravaris/Luenberger (KKL) observer. The neural network is trained to learn the dynamics of the observer, enabling accurate output predictions of the system. Sensor faults are detected by comparing the actual output measurements with the predicted values. If the difference surpasses a theoretical threshold, a sensor fault is detected. To identify and isolate which sensor is faulty, we compare the numerical difference of each sensor measurement with an empirically derived threshold. We derive both theoretical and empirical thresholds for detection and isolation, respectively. Notably, the proposed approach is robust to measurement noise and system uncertainties. Its effectiveness is demonstrated through numerical simulations of sensor faults in a network of Kuramoto oscillators.

关键词： Uncertainty Fault detection Noise Observers Numerical simulation Sensor systems Sensors

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Development of an Anomaly Detection System for Rugby Match Based on Majority Rule Using Deep Neural Networks

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IEEJ Transactions on Industry Applications 2025年第4期145卷 238-246页

作者： Tamaki, Kanta Uza, Hirotomo Kudoh, Reo Hirose, Kiyoshi Nojiri, Shuko K., Nakajima T., Kawasaki M., Ishijima I., Nagayama Department of Information Engineering University of the Ryukyus 1 Senbaru Okinawa Nishihara903-0213 Japan Graduate School of Science and Engineering University of the Ryukyus 1 Senbaru Okinawa Nishihara903-0213 Japan Department of Production Systems Engineering and Science Komatsu University 4-1-3 Ishikawa Komatsu923-8511 Japan Medical Technology Innovation Center Juntendo University 2-1-1 Hongo Bunkyo-ku Tokyo113-8421 Japan National Institute of Technology Sasebo College 1-1 Okishinmachi Nagasaki Sasebo857-1193 Japan Juntendo University School of Medicine 3-1-3 Hongo Bunkyo-ku Tokyo113-8431 Japan

This paper describes the development of an anomaly detection system for players in a rugby match to provide appropriate aid and avoiding severe injury. In the developed system, deep neural networks with fine tuning and its combination of different types of construction are key techniques for anomaly detection in rugby match footage. Certain appearance-based characteristics are captured from movie streams, and the system uses deep neural networks to automatically classify concerned scenes, especially, lying down scenes. The proposed system performs very well by recognizing many types of appearances of players within occlusion from a free viewpoint. Experimental results show that the system can effectively detect concerned players with high accuracy. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Deep neural networks

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A Sub-mother UAV System Capable of Autonomous Aerial Docking

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年

作者： Li, Gang Lu, Yi He, Bin Pang, Tao Cheng, Bin Zhang, Pengpeng College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai200120 China Shanghai Sunshine Rehabilitation Center Shanghai201613 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai200120 China East China Institute of Computing Technology Shanghai201822 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai China College of Electronic and Information Engineering Tongji University Shanghai China

Completing the docking between two multi-rotor unmanned aerial vehicles (UAVs) in the air has great significance for expanding the application scenarios of multiple UAVs and improving their endurance capability. In this regard, this paper first designs a safe and autonomous docking scheme and constructs a sub-mother UAV *** address the issue of unstable flight in downwash airflow, this paper designs a high-performance control method suitable for the sub-mother UAV system based on Incremental Nonlinear Dynamic Inversion (INDI) and achieves robust attitude control with strong disturbance rejection capability. A UWB-based Ortho-UWB 3D positioning method is proposed in this paper with orthogonal installation, which, after being integrated with visual methods, enables high-precision 3D relative positioning during the docking process of the sub-mother UAV ***, the paper designs a complete docking process for the sub-mother UAVs and incorporates safety constraints into the docking process to ensure the safety of the docking process for both UAVs. Experiments of successfully docking the sub UAV to the mother UAV from bottom to top prove the feasibility and effectiveness of this scheme. © 2024 IEEE.

关键词： Disturbance rejection

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