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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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Exploring the Impact of Robotics in STEM Education Activities and Competitive Challenges

Exploring the Impact of Robotics in STEM Education Activitie...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Adil Farooq Syeda Zillay Nain Zukhraf Hafsa Maryam Conrad Attard Mohsin Kamal Dept. of Electrical and Computer Engineering University of Cyprus Cyprus Dept. of Electrical Engineering National University of Computer and Emerging Sciences Pakistan Dept. of Computer Information Systems University of Malta Msida Malta

As robotics and technology become increasingly integrated into education and daily life, it is important to recognize their essential role. Even young children have access to electronic toys and gadgets, which can facilitate their learning and interaction. This study highlights the significance of STEM (science, technology, engineering, and mathematics) education at the elementary school level, emphasizing how university students from a variety of disciplines can design and construct robots capable of carrying out challenging tasks for humanitarian purposes. The study also emphasizes the need to encourage women's participation in robotics to showcase their skills and innovative ideas. Furthermore, the study demonstrates how robotics can effectively be used in both STEM education and competitive challenges, showcasing its effectiveness in teaching and promoting technological advancement. By encouraging early engagement in STEM education and promoting diversity in the field of robotics, we can empower future generations to lead the way in creating innovative solutions to global challenges.

关键词： Technological innovation Education Toy manufacturing industry Task analysis Robots Cybernetics STEM

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Performance Comparison of Various YOLO Architectures on Object Detection of UAV Images 8

Performance Comparison of Various YOLO Architectures on Obje...

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8th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2022

作者： Gunawan, Teddy Surya Ismail, Islam Mohamed Mahmoud Kartiwi, Mira Ismail, Nanang International Islamic University Malaysia Electrical and Computer Engineering Department Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Uin Sunan Gunung Djati Department of Electrical Engineering Bandung40614 Indonesia

ISBN: (数字)9781665488006

ISBN: (纸本)9781665488006

Today, the rapid development of deep learning offers an extraordinary opportunity to enhance the performance and efficiency of various industries, including business, the military, medicine, and transportation. Using deep learning algorithms in the transportation industry, for instance, makes UAVs vital and efficient in this industry. Current Unmanned Aerial Vehicles (UAVs) applications in transportation systems encourage the development of object detection methods to collect real-time traffic data using UAVs. Due to the versatility and portability of UAVs, particularly drones, individuals require systems that operate with UAVs to identify objects in real-time for military, safety observation, and protection. The culmination of the evolution of computer vision technology is the development of sophisticated algorithms centered on extensive training and testing datasets. This research aims to compare the performance of object detection of UAV images using various YOLO architectures. Tiny YOLOv3 and YOLOv5s models were implemented to extract the object's features and classify them into the dataset's multiple classes. This paper selected the VisDrone2019 dataset for its various object classes: pedestrian, person, bicycle, car, van, truck, tricycle, awning-tricycle, bus, and motor. Results demonstrated that YOLOv5s have acceptable precision and processing speed. © 2022 IEEE.

关键词： Drones

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Transient states to control the electromagnetic response of space-time dispersive media

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Physical Review B 2024年第23期110卷 235125-235125页

作者： Pablo H. Zapata-Cano Salvador Moreno-Rodríguez Stamatios Amanatiadis Antonio Alex-Amor Zaharias D. Zaharis Carlos Molero School of Electrical and Computer Engineering Aristotle University of Thessaloniki 54124 Thessaloniki Greece Department of Signal Theory Telematics and Communications Research Centre for Information and Communication Technologies (CITIC-UGR) Universidad de Granada 18071 Granada Spain Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia Pennsylvania 19104 USA

In this paper, we study the dynamic formation of transients when plane waves impinge on a dispersive slab that abruptly changes its electrical properties in time. The time-varying slab alternates between air and metal-like states, whose frequency dispersion is described by the Drude model. It is shown how the physics of this complex system can be well described with the joint combination of two terms: one associated with temporal refractions and the other associated with spatial refractions. To test the validity of the approach, some analytical results are compared with a self-implemented finite-difference time-domain method. Results show how the transients that occur after the abrupt temporal changes can shape the overall steady-state response of the space-time system. In fact, far from always being detrimental, these transient states can be conveniently used to perform frequency conversion or to amplify or attenuate the electromagnetic fields.

关键词： Electromagnetic transients

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Swarm Learning IRS in 6G-Metaverse: Secure Configurable Resources Trading for Reliable XR Communications

Swarm Learning IRS in 6G-Metaverse: Secure Configurable Reso...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Qianqian Pan Jun Wu Xinping Guan M. Jamal Deen Graduate School of Information Production and Systems Waseda University Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Department of Electrical and Computer Engineering McMaster University Canada

The emerging Metaverse has challenging requirements for the reliability of extended reality (XR) data transmission. Configurable communication is a promising technology to improve the XR communication performance, where the intelligent reflecting surface (IRS) is representative of the ability to control transmission channels. However, because of the absence of incentives and untrust among IRS and Metaverse users, there is no easy way to establish the configuration resource scheduling for XR communication. Existing trusted third party-based methods face single-point/collusion attacks, inefficiency in arbitration, and low intelligence problems. To solve these problems, we propose a swarm learning (SL)-based secure configurable resource trading mechanism for reliable 6G-Metaverse XR communication. First, an SL-based configurable resource trading framework is established, which includes two designed subchains for decentralized IRS resource management and intelligent allocation. Second, a smart contract-enabled configurable resource trading scheme is designed, where decentralized trust is built among IRS devices, Metaverse users, and base stations. Third, we propose a decentralized federated learning (FL)-driven IRS allocation scheme, which consists of XR communication-related data collection, model training, and resource configuration. Finally, experimental results demonstrate the effectiveness of the proposed SL-based configurable resource trading for reliable XR communication.

关键词：

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Towards AI-driven Next Generation Personalized Healthcare and Well-being

Towards AI-driven Next Generation Personalized Healthcare an...

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2024 Ital-IA Intelligenza Artificiale - Thematic Workshops, Ital-IA 2024

作者： Aksu, Fatih Bria, Alessandro Caragliano, Alice Natalina Caruso, Camillo Maria Chen, Wenting Cordelli, Ermanno Coser, Omar Francesconi, Arianna Furia, Leonardo Guarrasi, Valerio Iannello, Giulio Lauretti, Clemente Manni, Guido Marino, Giustino Paolo, Domenico Ruffini, Filippo Shen, Linlin Sicilia, Rosa Soda, Paolo Tamantini, Christian Tortora, Matteo Wu, Zhuoru Zollo, Loredana Department of Biomedical Sciences Humanitas University Milan Italy Department of Electrical and Information Engineering University of Cassino and Southern Latium Cassino Italy Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Italy City University of Hong Kong Hong Kong Unit of Advanced Robotics and Human-Centered Technologies Department of Engineering University Campus Bio-Medico of Rome Italy Shenzhen University China Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Sweden

In the last few years Artificial Intelligence (AI) is emerging as a game changer in many areas of society and, in particular, its integration in medicine heralds a transformative approach towards personalized healthcare and well-being, promising significant improvements in diagnostic precision, therapeutic outcomes, and patient care. Our research explores the cutting-edge realms of multimodal AI, resilient AI, and healthcare robotics, aiming to harness the synergy of diverse data modalities and advanced computational models to redefine healthcare paradigms. This multidisciplinary effort seeks to bridge technology and clinical practice, advancing AI-driven next generation personalized healthcare and well-being. © 2024 Copyright for this paper by its authors.

关键词： Personalized medicine

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Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

arXiv

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arXiv 2024年

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

This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design’s innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis’ residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy. © 2024, CC BY.

关键词： MapReduce

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IoT Cloud Computing Middleware for crowd monitoring and evacuation

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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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Automatic Watering System for Smart Agriculture using ESP32 Platform 8

Automatic Watering System for Smart Agriculture using ESP32 ...

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8th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2022

作者： Gunawan, Teddy Surya Kamarudin, Nur Najiehah Kartiwi, Mira Effendi, Mufid Ridlo International Islamic University Malaysia Electrical and Computer Engineering Department Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Uin Sunan Gunung Djati Department of Electrical Engineering Bandung40614 Indonesia

ISBN: (数字)9781665488006

ISBN: (纸本)9781665488006

Agriculture can be transformed into smart agriculture due to the advanced wireless network and the Internet of Things (IoT). As the human population increases, so does the demand for crops. With real-time monitoring, a smart agriculture system's awareness of soil moisture measurement, humidity, and environmental temperature will increase food security. In addition, smart agriculture can increase crop yield, reduce water waste, and boost productivity. In light of this, this study proposes smart agriculture, which employs sensor-based monitoring in real-time without human intervention. Users have direct access to real-time data, and all sensor data is recorded and monitored at all times and from anywhere within the internet's coverage area. The development of a mobile application facilitates growth monitoring and user access to data. This research measures yield-affecting agricultural factors such as soil moisture, humidity, and temperature. Nonetheless, monitoring environmental factors is crucial to increasing crop yields. The experiment was conducted on two chili plants, one with an automation system and one without, which were manually watered once daily at 7:00 a.m. It is estimated that the plant with the system can maintain the optimal moisture level, whereas the other plant's moisture measurement continues to decrease. Compared to manual watering, automatic watering also results in accelerated plant growth. Consequently, a smart agriculture system is anticipated to improve plant growth and system efficiency. © 2022 IEEE.

关键词： Internet of things

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Take Your Best Shot: Sampling-Based Next-Best-View Planning for Autonomous Photography & Inspection

arXiv

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arXiv 2024年

作者： Gao, Shijie Bramblett, Lauren Bezzo, Nicola De art-ments of Electrical & Computer Engineering University of Virginia CharlottesvilleVA22904 United States Systems & Information Engineering University of Virginia CharlottesvilleVA22904 United States

Autonomous mobile robots (AMRs) equipped with high-quality cameras have revolutionized the field of inspections by providing efficient and cost-effective means of conducting surveys. The use of autonomous inspection is becoming more widespread in a variety of contexts, yet it is still challenging to acquire the best inspection information autonomously. In situations where objects may block a robot’s view, it is necessary to use reasoning to determine the optimal points for collecting data. Although researchers have explored cloud-based applications to store inspection data, these applications may not operate optimally under network constraints, and parsing these datasets can be manually intensive. Instead, there is an emerging requirement for AMRs to autonomously capture the most informative views efficiently. To address this challenge, we present an autonomous Next-Best-View (NBV) framework that maximizes the inspection information while reducing the number of pictures needed during operations. The framework consists of a formalized evaluation metric using ray-tracing and Gaussian process interpolation to estimate information reward based on the current understanding of the partially-known environment. A derivative-free optimization (DFO) method is used to sample candidate views in the environment and identify the NBV point. The proposed approach’s effectiveness is shown by comparing it with existing methods and further validated through simulations and experiments with various vehicles. Copyright © 2024, The Authors. All rights reserved.

关键词： Inspection

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