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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Amirali Molaei Amirhossein Aminimehr Armin Tavakoli Amirhossein Kazerouni Bobby Azad Reza Azad Dorit Merhof School of Computer Engineering Iran University of Science and Technology Iran School of Electrical Engineering Iran University of Science and Technology Iran Department of Electrical Engineering and Computer Science South Dakota State University USA Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Faculty of Informatics and Data Science University of Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing neural networks to parameterize data through implicit continuous functions, INRs offer several benefits. Recognizing the potential of INRs beyond these domains, this survey aims to provide a comprehensive overview of INR models in the field of medical imaging. In medical settings, numerous challenging and ill-posed problems exist, making INRs an attractive solution. The survey explores the application of INRs in various medical imaging tasks, such as image reconstruction, segmentation, registration, novel view synthesis, and compression. It discusses the advantages and limitations of INRs, highlighting their resolution-agnostic nature, memory efficiency, ability to avoid locality biases, and differentiability, enabling adaptation to different tasks. Furthermore, the survey addresses the challenges and considerations specific to medical imaging data, such as data availability, computational complexity, and dynamic clinical scene analysis. It also identifies future research directions and opportunities, including integration with multi-modal imaging, real-time and interactive systems, and domain adaptation for clinical decision support. To facilitate further exploration and implementation of INRs in medical image analysis, we have provided a compilation of cited studies along with their available open-source implementations on ${\color{Magenta}GitHub}$. Finally, we aim to consistently incorporate the most recent and relevant papers regularly.

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ProBLeSS: A Proactive Blockchain Based Spectrum Sharing Protocol Against SSDF Attacks in Cognitive Radio IoBT Networks

IEEE Networking Letters

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IEEE Networking Letters 2020年第2期2卷 67-70页

作者： Patnaik, Milan Prabhu, G. Rebeiro, Chester Matyas, Vashek Veezhinathan, Kamakoti Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai600036 India Faculty of Informatics Masaryk University Brno Czech Republic

Spectrum Sharing Data Falsification (SSDF) attacks can cause heavy performance degradation to Cognitive Radio (CR) based Internet of Battlefield Things (IoBT) networks. The challenge in such networks is to handle this security problem real time, in addition to spectrum sharing. This requires a robust CR architecture and protocol that can provide integrity of the spectrum sensing data being shared between Secondary Users (SUs) for collaborative spectrum decisions in protocols like PROLEMus. We propose one such protocol called Proactive Blockchain based Spectrum Sharing (ProBLeSS) protocol which leverages a blockchain to provide security against SSDF attacks in CR-IoBT networks. © 2019 IEEE.

关键词： Blockchain

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Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Mondal, Abhishek Mishra, Deepak Alexandropoulos, George C. Al-Nahari, Azzam Jantti, Riku National Institute of Technology Calicut Department of Electronics and Communication Engineering Kerala 673601 India University of New South Wales School of Electrical Engineering and Telecommunications Sydney2052 Australia National and Kapodistrian University of Athens Department of Informatics and Telecommunications Athens15784 Greece University of Illinois Department of Electrical and Computer Engineering ChicagoIL60601 United States Aalto University Department of Information and Communications Engineering Espoo02150 Finland Ibb University Department of Electrical Engineering Ibb70270 Yemen

Effective solutions for intelligent data collection in terrestrial cellular networks are crucial, especially in the context of Internet of Things (IoT) applications. The limited spectrum and coverage area of terrestrial base stations (BSs) pose challenges in meeting the escalating data rate demands of network users. Unmanned aerial vehicles (UAVs), known for their high agility, mobility, and flexibility, present an alternative means to offload data traffic from terrestrial BSs, serving as additional access points. In this paper, we aim to efficiently maximize the utilization of multiple UAVs for data traffic offloading from terrestrial BSs. Specifically, the focus is on maximizing user association with UAVs by jointly optimizing UAV trajectories and users' association indicators under quality-of-service (QoS) constraints. Since the formulated practical UAV control problem is non-convex and combinatorial, this study leverages the multi-agent reinforcement learning (MARL) framework. Employing MARL, each UAV acts as an independent agent, aiming to maintain inter-UAV cooperative behaviour. The proposed approach utilizes the finite-state Markov decision process (MDP) to account for UAVs' velocity constraints and the relationship between their trajectories and state space. A low-complexity distributed state-action-reward-state-action (SARSA) algorithm is presented to determine UAVs' optimal sequential decision-making policies over training episodes. The extensive simulation results validate the proposed analysis and offer valuable insights into the optimal UAV trajectories. The derived trajectories demonstrate superior average UAV association performance compared to benchmark techniques, such as Q-learning and particle swarm optimization. © 1965-2011 IEEE.

关键词： Markov processes

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Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity Applications

Automated Detection of Poultry Farms from Aerial Images for ...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Yu Li Rana Das Cuong Duong Teng-Teeh Lim Timothy Haithcoat Ilker Ersoy Chi-Ren Shyu Department of Electrical Engineering and Computer Science University of Missouri Columbia MO US Biological Engineering University of Missouri Columbia MO US Plant Science and Technology Extension University of Missouri Columbia MO US Institute for Data Science and Informatics University of Missouri Columbia US Institute for Data Science and Informatics University of Missouri Columbia MO US Electrical Eng. and Computer Science Dept. Institute for Data Science and Informatics University of Missouri Columbia MO US

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making.

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Flicker Visibility With Different Spectra of White Light

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IEEE Transactions on Industry Applications 2025年

作者： Kukacka, Leos Hergesel, Jan Necasek, Jakub Bilek, Petr Vik, Michal Stiegler, Robert Pourarab, Morteza Meyer, Jan Drapela, Jiri Technical University of Liberec Faculty of Mechatronics Informatics and Interdisciplinary Studies Institute-of Mechatronics and Computer Engineering Liberec Czech Republic Technical University of Liberec Faculty of Textile Engineering Department of Material Engineering Czech Republic Technische Universität Dresden Institute of Electrical Power Systems and High Voltage Engineering Chair of Electrical Power Supply Dresden01069 Germany Brno University of Technology Faculty of Electrical Engineering and Communications Brno61600 Czech Republic

The standard flickermeter (voltage flickermeter) algorithm commonly used for the evaluation of flicker severity in current electromagnetic compatibility standards originates from the 1980 s. It was developed to mimic irritation of an average human observer by flickering incandescent lamp as a reference light source and optimized to voltage fluctuations caused by arc furnaces. However, the algorithm is questionable as the situation in the grid changed since the 1980 s;many modern devices are being connected to the grid, creating voltage distortion patterns which are not properly recognized by the voltage flickermeter. In addition, incandescent lamps have been largely replaced by discharge lamps and later by LEDs. Transitioning the algorithm from incandescent lamp light to LED light requires detailed revision of every part of the algorithm. This paper is concerned with verifying the frequency weighting function of the average human observer which is one of the bases of the voltage flickermeter, as well as of the derived light flickermeter. Flicker sensitivity of 34 human observers is measured with two different light sources (LED warm and neutral white, incandescent lamp) and compared with the frequency weighting function used in the current specification of the flickermeters in standards. Finally, an optimization of the weighting function is proposed based on the findings. © 1972-2012 IEEE.

关键词： Flickering

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Automated Fire Hydrant Monitoring and Maintenance using IoT Data Analytics and Naive Bayes Classifier

Automated Fire Hydrant Monitoring and Maintenance using IoT ...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： R. Sarasu V. Nandagopal N V Shibu Suresh Lakshminarayanan T Saju Raj M. Rajmohan Department of Spatial Informatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Electrical and Electronics Engineering School of Engineering Mohan Babu University Tirupathi India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Engineering & Electronics University of Technology and Applied Sciences South Al Batina Oman Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This paper introduces a new method for managing fire hydrants that uses a monitoring system based on the Internet of Things (IoT) and a Naive Bayes Classifier (NBC) for predictive maintenance. Water pressure, flow rate, and the physical state of fire hydrants are among the essential factors for the proposed system continually collecting data using an IoT sensor network. The system analyzes real-time data using NBC. These predictive capabilities reduce the potential of hydrant breakdowns during emergencies through preventive maintenance potential. It introduces a reliable IoT infrastructure for fire hydrant monitoring, improving the acquired data's granularity and timeliness. NBC provides a powerful instrument for early problem detection, significantly improving maintenance effectiveness. Since the technology is compatible with existing fire department procedures, it can easily integrate into current operational processes. Better public safety, more efficient use of resources, and more dependable fire hydrants are potential outcomes of responsive and proactive data-driven maintenance techniques. The findings improve overall fire safety and operational efficiency, with a 25% decrease in maintenance expenditures and a 40% increase in issue detection accuracy.

关键词： Accuracy Scalability Urban areas Real-time systems Maintenance Naive Bayes methods Internet of Things Stakeholders Predictive maintenance Risk mitigation

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A Bibliometric Analysis and Research Landscape of Artificial Intelligence in Education

A Bibliometric Analysis and Research Landscape of Artificial...

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IEEE International Conference on Control System, Computing and engineering (ICCSCE)

作者： Samuel-Soma M. Ajibade Bayan Issa Muhammed Basheer Jasser Farrukh Hassan Ghassan Saleh ALDharhani Ismail Ahmed Al-Qasem Al-Hadi Kayode Akinlekan Akintoye Almighty Cortezo Tabuena Department of Computing and Information Systems School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkiye Faculty of Informatics Engineering University of Aleppo Syria Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia Faculty of Information Science and Technology UKM University Bangi Selangor Malaysia Department of Computer Science The Federal Polytechnic Ado Ekiti Ekiti State Nigeria College of Arts and Sciences University of the City of Valenzuela Philippines

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

This study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. The Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. Conference papers are the most often utilized document type, including 6757 publications or $64.5 \%$ of the overall amount. The ACM International Conference Proceeding Series is the primary source for published materials, accounting for 675 publications or $6.44 \%$ of the total publications on the topic. The most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). The output of the affiliations active in the topic is primarily attributed to the funders, specifically the National Science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students’ learning patterns.

关键词： Deep learning Education Bibliometrics Neural networks Market research Prediction algorithms Stakeholders Artificial intelligence Socioeconomics Meteorology

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Optimal Multi-Level ASK Modulations for RIS-Assisted Communications with Energy-Based Noncoherent Reception

arXiv

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arXiv 2024年

作者： Mishra, Sambit Dash, Soumya P. Alexandropoulos, George C. School of Electrical Sciences Indian Institute of Technology Bhubaneswar Khordha Argul India Department of Informatics and Telecommunications National and Kapodistrian University of Athens Panepistimiopolis Ilissia Athens15784 Greece Department of Electrical and Computer Engineering University of Illinois ChicagoIL60601 United States

This paper investigates the performance of one- and two-sided amplitude shift keying (ASK) modulations in noncoherent single-input single-output (SISO) wireless communication systems assisted by a reconfigurable intelligent surface (RIS). Novel noncoherent receiver structures are proposed based on the energy of the received symbol and the choice of the modulation scheme for data transmission. The system’s performance is assessed in terms of the symbol error rate (SER) and an optimization framework is proposed to determine the most effective oneand two-sided ASKs to minimize the SER, while adhering to average a transmit power constraint. Two scenarios based on the availability of the statistical characteristics of the wireless channel are explored: a) the transceiver pair has complete knowledge of the channel statistics, and b) both end nodes possess knowledge of the statistics of the channel gain up to its fourth moment, and novel algorithms are developed to obtain optimal ASKs for both of them. Extensive numerical evaluations are presented showcasing that there exists a threshold signal-to-noise ratio (SNR) above which the optimal ASKs outperform the traditional equispaced ASKs. The dependencies of the SER performance and the SNR threshold on various system parameters are assessed, providing design guidelines for RIS-assisted noncoherent wireless communication systems with multi-level ASK modulations © 2024, CC BY.

关键词： Amplitude shift keying

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Development of a novel visual servoing probe test method for fault diagnosis of printed circuit boards

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IFAC-PapersOnLine 2023年第2期56卷 5248-5254页

作者： B. Tipary Á. Juniki F.G. Erdős E. Takács K. Németh Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Eötvös Loránd Research Network Budapest Hungary Department of Manufacturing Science and Engineering Faculty of Mechanical Engineering Budapest University of Technology and Economics Budapest Hungary University of Pannonia Veszprém Hungary

Printed circuit board (PCB) measurement and repair is a challenging task that requires experience and expertise to perform. PCB diagnosis and repair shops employ skilled operators to carry out the corresponding measurement tasks using measuring instruments (e.g., oscilloscopes, multimeters) in order to uncover the condition of a particular product. However, these tasks are often repetitive and meticulous, and additionally, the results need to be collected and carefully documented so that the gathered experience regarding the product can be re-used when the next product of the same type arrives into the shop. Nevertheless, the diagnosis of used PCBs is less researched and current flexible automation possibilities are limited. In this paper, a novel visual servoing probe test method and measurement tool are proposed to provide a flexible solution for PCB diagnosis with a higher level of automation. The aim of the approach is to reduce the burden on the operators by carrying out the repetitive measurement tasks and automatically storing the results while leaving the responsibility of measurement profile setup to the human expert. The proposed visual servo system uses manually teached-in measurement points, where template patterns are recorded using cameras, and it is capable of compensating positioning errors in the range of a couple of millimeters. The proof of concept of the proposed method is presented through motherboard measuring experiments, with a 99.7% success rate.

关键词： Flexible and reconfigurable manufacturing systems Quality assurance and maintenance Visual servo control Printed circuit board measurement Fault diagnosis

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Secure IoT-Based Health Monitoring with Cloud-Based Machine Learning Analytics

Secure IoT-Based Health Monitoring with Cloud-Based Machine ...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Ram Deshmukh Arshini Gubbala B Pravallika Amit Dutt Ahmed Sabah Ahmed AL-Jumaili Manjunatha School of Engineering SR University Warangal Telangana India Department of Electronics and Computer Engineering Vignan's Institute of Information Technology (Autonomous) Visakhapatnam Institute of Aeronautical Engineering Dundigal Hyderabad Lovely Professional University Phagwara Department of Business Information Technology (BIT) College of Business informatics University of information Technology and Communications Baghdad Iraq Department of Mechanical Engineering New Horizon College of Engineering Bangalore

The development and deployment of an efficient and trustworthy Internet of Things-based health monitoring system are presented in this study. The system uses biometric authentication, a PKI framework, TLS, and MQTT for secure communication, as well as identification of users to guarantee data confidentiality. Real-time health data analysis with predictive analytics as well as anomaly detection is made possible by cloud-based machine learning analytics on platforms such as AWS. Evaluation in a range of healthcare environments demonstrates just how flexible and resilient the system is. Despite the achievements, there are still obstacles to overcome, such as problems with the current healthcare infrastructure's interoperability in addition to worries about the scalability of resource-intensive machine learning algorithms. It is imperative that these issues be resolved in order to continue enhancing and enhancing the system and ensuring that it has the capacity to completely transform healthcare procedures.

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