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Online Menu-Based Scheduling of Electric Vehicle Charging

IEEE Transactions on Smart Grid 2025年第3期16卷 2478-2491页

作者： Mathioudaki, Angeliki Tsaousoglou, Georgios Varvarigos, Emmanouel Fotakis, Dimitris National Technical University of Athens School of Electrical and Computer Engineering Zografou15772 Greece Technical University of Denmark Department of Applied Mathematics and Computer Science Section for Dynamical Systems Kongens Lyngby2800 Denmark Archimedes Research Unit Athena Research Center Marousi15125 Greece

This paper addresses the problem of efficiently scheduling of EV charging requests in a single Charging Station (CS), also taking into consideration the inability of EV users to express their preferences in closed-form utility functions. We consider a paradigm where a menu of possible charging intervals, each associated with an urgency price, is generated online. By letting the EV users pick their most preferable menu option, the proposed algorithm commits on each EV’s charging completion time upon its arrival, achieves a near optimal total weighted charging completion time, and prevents the users from strategically misreporting their preferences, while offering a practical and implementable solution to the problem of EVs - charging station interaction. Our experimental evaluation demonstrates that our algorithmic approach performs nearly optimal in a variety of practically relevant scenarios. © 2010-2012 IEEE.

关键词： Charging stations

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Mixed RF/FSO Communication systems With Rate Splitting

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1621-1634页

作者： Zhang, Yiran Xiao, Zhichen Yang, Liang Bithas, Petros S. Hasna, Mazen O. Karagiannidis, George K. Hunan University College of Computer Science and Electronic Engineering Changsha410082 China National and Kapodistrian University of Athens Department of Digital Industry Technologies Athens157 72 Greece Qatar University Department of Electrical Engineering Doha2713 Qatar Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki541 24 Greece Artificial Intelligence and Cyber Systems Research Center Beirut0379-7751 Lebanon

In this paper, rate splitting (RS) has been applied in a mixed dual-hop radio frequency/free space optical (RF/FSO) relay system. In particular, assuming fixed-gain amplify-and-forward (AF) and decode-and-forward (DF) relay protocols, the performance of both heterodyne detection and intensity-modulated direct detection (IM-DD) has been analytically evaluated. In this framework, it is assumed that the RF channel suffers from Rayleigh fading, while the FSO channel is affected by the atmospheric turbulence-induced fading with Málaga (M) distribution and pointing errors. For the considered system, we derive expressions for the outage probability, the average bit error rate (BER), and the average channel capacity. In addition, the asymptotic performance of the system at high signal-to-noise ratios regime is analyzed, special cases are discussed, and useful insights have been extracted. Finally, numerical results are presented to verify the accuracy of the analytical and asymptotic expressions. © 1983-2012 IEEE.

关键词： Heterodyne detection

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Robust Error State Sage-Husa Adaptive Kalman Filter for UWB Localization

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IEEE Sensors Journal 2025年第9期25卷 16034-16049页

作者： Juston, Marius Gupta, Soumil Mathur, Shrey Norris, William R. Nottage, Dustin Soylemezoglu, Ahmet University of Illinois Urbana-Champaign The Grainger College of Engineering Industrial and Enterprise Systems Engineering Department UrbanaIL61801 United States University of Illinois Urbana-Champaign The Grainger College of Engineering Electrical and Computer Engineering Department UrbanaIL61801 United States Construction Engineering Research Laboratory U.S. Army Corps of Engineers Engineering Research and Development Center ChampaignIL61822 United States

Given the sensors’ path and interference mitigation capabilities, ultra-wideband (UWB)-based positioning systems have demonstrated high accuracy and reliability. This work aims to improve the Sage-Husa fuzzy adaptive filter (SHFAF) proposed in previous works by modifying the motion model to a 3-D ground-based differential drive robot using IMU and wheel encoder kinematic fused control inputs. In addition to the changed motion model kinematics, this article improved the positive definite constraint on P and R during dynamic estimations, thus making the filter more robust to outliers. An improvement to the computation and derivation of the fuzzy logic system for the SHFAF based on the adaptive neuro-fuzzy inference system (ANFIS) structure was developed, and training the fuzzy system using gradient descent was applied to improve the system’s accuracy. Experimental validation was conducted using real-world data from a Clearpath Jackal robot equipped with Qorvo UWB sensors and static nodes. Regarding localization accuracy, the proposed velocity-based SHFAF (VelSHFAF) system outperformed the previous SHFAF implementation by approximately 30%–25% across two test courses, demonstrating its enhanced performance and reliability. © 2001-2012 IEEE.

关键词： Adaptive filters

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Monocular vision navigation system for UAV autonomous mission: a real-time window-based obstacle avoidance approach

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Neural Computing and Applications 2025年 1-18页

作者： Javaid, Abdulrahman Alnaser, Mustafa Baroudi, Uthman Alfaraj, Amjad Research and Development Department Yokogawa Saudi Arabia Company Al Khobar Saudi Arabia Department of Computer Engineering King Fahd University of Petroleum Minerals Dhahran Saudi Arabia Center for Intelligent Secure Systems King Fahd University of Petroleum Minerals Dhahran Saudi Arabia Department of Electrical Engineering King Fahd University of Petroleum Minerals Dhahran Saudi Arabia

The need for autonomous unmanned aerial vehicles (UAVs) is rapidly increasing for various industrial applications today. However, the realization of UAV autonomy requires a careful mix of Artificial Intelligence (AI) and computer vision (CV) techniques to be implemented to address end-user challenges and satisfy field requirements. This study aims to develop an intelligent system for UAV to achieve autonomy with standard low-cost hardware components. We have infused state-of-the-art AI and CV technologies into our developed system architecture to improve autonomous navigational capabilities of UAVs. The proposed architecture has been implemented and tested on the DJI Tello UAV equipped only with a monocular camera on-board and an inertial measurement unit (IMU). We focused on solving multiple practical technical problems that have arisen during this study due to the hardware limitations of the UAV. The study investigated methods for representing obstacles within an environmental map, which are crucial for effective avoidance and path planning, such as their edges, width, and shape, to ensure accurate detection and navigation. The experiments were carried out in an environment that included dynamic obstacles that were not considered in the map. The experimental results showed outstanding performance in terms of successful autonomous navigation for a wide range of light intensity. These results show that the proposed approach has a high potential to achieve autonomous UAV operation in the field with low hardware and energy requirements. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

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Integrated Non-Terrestrial and Terrestrial Quantum Anonymous Networks

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IEEE Network 2025年第3期39卷 196-206页

作者： Khan, Awais Bui, Tinh Thanh Rehman, Junaid Ur Chatzinotas, Symeon Cotton, Simon L. Dobre, Octavia A. Duong, Trung Q. Shin, Hyundong Kyung Hee University Department of Electronics and Information Convergence Engineering Gyeonggi-do Yongin-si17104 Korea Republic of King Fahd University of Petroleum and Minerals Department of Electrical Engineering Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia University of Luxembourg Luxembourg City1855 Luxembourg Memorial University Department of Electrical and Computer Engineering St. John'sNLA1B 3X5 Canada Queen's University Belfast Department of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

Due to optical fiber limitations for quantum communication, global-scale quantum networks are possible only by integrating non-terrestrial components in the overall network architecture. Quantum networks are expected to support distributed tasks for quantum information processing, such as quantum sensing, control, communication, and computing. These networks enable emerging applications that are uniquely quantum or augmented by quantum mechanics. Leveraging quantum resources, anonymous networking can be enhanced to such an extent that even an adversary with control over all network resources cannot trace the message source, achieving perfect untraceability. To provide this untraceable global connectivity, we demonstrate the integration of non-terrestrial networks (NTNs) and terrestrial networks (TNs) for quantum anonymous communication (QAC), highlighting possible architectures and key challenges in these integrated NTN-TN quantum anonymous networks (QANs). To illustrate and benchmark the design of QAC protocols within integrated networks, we present essential quantum protocols such as anonymous conference key agreement (CKA) and anonymous broadcast. In the first case study, we propose a satellite-to-ground quantum anonymous CKA (QA-CKA) protocol and assess the anonymous key exchange rate. This QA-CKA protocol utilizes low Earth orbit satellites to generate anonymous keys among two distinct TN nodes. In the second case study, we develop an air-to-ground quantum anonymous broadcast (QAB) protocol and examine the anonymous broadcast announcement rate. This QAB protocol exploits unmanned aerial vehicles to enable a broadcasting party to transmit classical information anonymously across two distinct TNs. These QAC protocols are simulated with realistic integrated network parameters to provide practical estimates of achievable performance. Furthermore, we discuss future research directions for enabling integrated NTN-TN QANs. © 1986-2012 IEEE.

关键词： Broadcasting antennas

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Study of orography–MJO interaction and its impact on rainfall variability in West Sumatra

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Journal of Earth System Science 2025年第2期134卷 1-27页

作者： Satiadi, Didi Purwaningsih, Anis Harjupa, Wendi Saufina, Elfira Fathrio, Ibnu Trismidianto Rahmatia, Fahmi Pratama, Ridho Hashiguchi, Hiroyuki Shimomai, Toyoshi Research Center for Climate and Atmosphere National Research and Innovation Agency (BRIN) Bandung Indonesia Department of Computer Engineering School of Electrical Engineering Telkom University Bandung Indonesia Disaster Prevention Research Institute Kyoto University Kyoto Japan Research Institute for Sustainable Humanosphere Kyoto University Kyoto Japan Department of Electronic and Control Systems Engineering Shimane University Shimane Japan

Abstract: This study examines the impact of orography and the Madden–Julian Oscillation (MJO) on rainfall patterns in West Sumatra, Indonesia, using ERA5 reanalysis data. We analyzed variables such as precipitation, zonal and vertical winds, vertically integrated moisture convergence (VIMC), Froude number, lifting condensation level (LCL), level of free convection (LFC), level of non-buoyancy (LNB), convective available potential energy (CAPE), and convective inhibition (CINH), focusing on MJO phases 3 (MJO-3) and 4 (MJO-4). The ERA5 data was validated with observations from the Agam Observation Station, including data from the equatorial atmosphere radar, X-band Doppler Radar, optical rain gauge, microwave radiometer, and radiosonde. Our analysis, covering 22 years (2001–2022) of hourly data from five locations at varying elevations, reveals that topography significantly influences rainfall distribution, with windward slopes and mountain peaks receiving the most rainfall. This pattern closely aligns with VIMC, indicating that moisture convergence is a key driver of rainfall variability. MJO-3 significantly enhances rainfall by reducing zonal wind speeds, thereby increasing moisture convergence, while MJO-4 has a minimal impact, with increased zonal wind speeds limiting moisture convergence. The study concludes that VIMC patterns, influenced by wind dynamics, are central to understanding rainfall variability in West Sumatra’s mountainous terrain under different MJO phases. research highlights: Orography strongly influences rainfall in West Sumatra, with the most precipitation on windward slopes and peaks, linked to vertically integrated moisture convergence (VIMC). MJO-3 boosts rainfall by reducing zonal wind speeds, which enhances moisture convergence and emphasizes wind dynamics in rainfall variability. MJO-4 has minimal impact on rainfall, as increased zonal winds restrict moisture convergence, leading to slight changes in precipitation. VIMC is crucial for under

关键词： MJO Orography rainfall topography West Sumatra

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From Simulation to Reality with Random Noise

From Simulation to Reality with Random Noise

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Luo, Steven Lipkis, Rory López-Francos, Ignacio Vlastos, Pavlo Agogino, Adrian Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Intelligent Systems Division NASA Ames Research Center Moffett FieldCA94035 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The challenging environment of autonomous vehicle (AV) navigation necessitates certain functions be performed by deep neural networks. Optimizing these models involves collecting vast quantities of domain-specific training data and ensuring that the dataset is representative of expected conditions. High-fidelity simulation plays a vital role in making this process feasible, allowing a wide range of scenarios to be explored at relatively low cost. However, learning from simulation introduces subtle biases into models, which can degrade real-world performance in unpredictable ways. This effect can be mitigated with learning schemes specialized to bridge distributional shifts (transfer learning). Given the complex nature of these methods, the underlying models, and their environments, meaningfully evaluating performance is not straightforward. Many unrelated factors can effect an improvement in generalization accuracy, but a full ablation analysis is often difficult to perform. To tease out signal from noise, it is necessary to understand how transfer learning performance is affected by noise itself. The goals of this paper are (i) to establish a domain randomization baseline for a simple classification transfer learning task and (ii) to validate the RRAV testbed as a platform for further research in sim-to-real learning. We generate imagery from a simulation of NASA Ames research center and train a small convolutional neural network to classify camera position of an ego vehicle relative to a center line. Further models are trained with different types of noise progressively added to the data. In our experiments, we find that naive domain randomization raises sim-to-real accuracy from 64% to 79%, while training directly on real data yields an 89% accuracy ceiling. These results suggest that the isolated mechanism of domain randomization can significantly improve generalization. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： NASA

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Adaptive Robust Control Integrated With Gaussian Processes for Quadrotors: Enhanced Accuracy, Fault Tolerance and Anti-Disturbance

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3235-3248页

作者： Liang, Weisheng Amer, Abdelhakim Mehndiratta, Mohit Chen, Zheng Yao, Bin Kayacan, Erdal Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310027 China Aarhus University Artificial Intelligence in Robotics Laboratory Department of Electrical and Computer Engineering Aarhus8000 Denmark GIM Robotics Espoo02650 Finland Zhejiang University Ocean College Ocean Research Center of Zhoushan Zhoushan316021 China Purdue University School of Mechanical Engineering West Lafayette47907 United States Department of Electrical Engineering and Information Technology Paderborn33098 Germany

With increasingly challenging applications for quadrotors, higher requirements are emerging for tracking accuracy and safety. While high accuracy is a prerequisite for complex tasks, safety is ensured through tolerance to actuator faults and resistance to external disturbances. In this article, adaptive robust control (ARC) integrated with Gaussian processes (GPs), i.e., ARC-GP, is proposed to achieve enhanced accuracy, fault tolerance, and anti-disturbance. These three requirements are interrelated and affected by uncertainties. The primary idea of this article is to categorize uncertainties into parametric and nonparametric types, which are then addressed through parameter adaptation and GP, respectively. First, a detailed dynamic model is established, including actuator models that reflect different types of faults corresponding to changes in different physical parameters. Then, parameter adaptation is designed, with direct and indirect methods adopted for different parameters. In particular, the actuator parameters are effectively estimated to achieve targeted fault compensation. Regarding GP for nonparametric uncertainties, its model parameters are also updated via parameter adaptation. The GP thereby also learns parameter estimation errors along with external disturbances. Accordingly, ARC controllers are designed, for which robust feedback terms are constructed to further mitigate uncertainties on the basis of the covariances predicted by GP. The experiments demonstrate that the proposed ARC-GP can actively tolerate various types of actuator faults and better resist wind disturbances. © 2013 IEEE.

关键词： Robust control

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Machine learning innovations in CPR: a comprehensive survey on enhanced resuscitation techniques

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Artificial Intelligence Review 2025年第8期58卷 1-41页

作者： Islam, Saidul Rjoub, Gaith Elmekki, Hanae Bentahar, Jamal Pedrycz, Witold Cohen, Robin Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Department of Computer Science 6 G Research Center Khalifa University Abu Dhabi United Arab Emirates Gina Cody School of Engineering and Computer Science Concordia University Montreal Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Research Center of Performance and Productivity Analysis Istinye University Sariyer/Istanbul Turkey David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada

This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR), marking a paradigm shift from conventional, manually driven resuscitation practices to intelligent, data-driven interventions. It examines the evolution of CPR through the lens of predictive modeling, AI-enhanced devices, and real-time decision-making tools that collectively aim to improve resuscitation outcomes and survival rates. Unlike prior surveys that either focus solely on traditional CPR methods or offer general insights into ML applications in healthcare, this work provides a novel interdisciplinary synthesis tailored specifically to the domain of CPR. It presents a comprehensive taxonomy that classifies ML techniques into four key CPR-related tasks: rhythm analysis, outcome prediction, non-invasive blood pressure and chest compression modeling, and real-time detection of pulse and Return of Spontaneous Circulation (ROSC). The paper critically evaluates emerging ML approaches-including Reinforcement Learning (RL) and transformer-based models-while also addressing real-world implementation barriers such as model interpretability, data limitations, and deployment in high-stakes clinical settings. Furthermore, it highlights the role of eXplainable AI (XAI) in fostering clinical trust and adoption. By bridging the gap between resuscitation science and advanced ML techniques, this survey establishes a structured foundation for future research and practical innovation in ML-enhanced CPR. It offers clear insights, identifies unexplored opportunities, and sets a forward-looking research agenda identifying emerging trends and practical implementation challenges aiming to improve both the reliability and effectiveness of CPR in real-world emergencies. © The Author(s) 2025.

关键词： Reinforcement learning

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An improved and decentralized/distributed healthcare framework for disabled people through AI models

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Alexandria engineering Journal 2025年 125卷 441-448页

作者： Rathee, Geetanjali Garg, Sahil Kaddoum, Georges Alzanin, Samah M. Hassan, Mohammad Mehedi Department of Computer Science and Engineering Netaji Subhas University of Technology Dwarka Sector-3 New Delhi110078 India Electrical Engineering Department École de technologie supérieure MontréalQC H3C 1K3 Canada Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia King Salman Center for Disability Research Riyadh11614 Saudi Arabia

Access to adequate healthcare is critical for everyone, but people with disabilities often face considerable challenges in receiving reliable and timely medical treatment. The Vision 2030 plan in Saudi Arabia intends to change the healthcare system by incorporating new technologies that increase accessibility, efficiency, and service delivery. However, current healthcare systems continue to suffer from delays, inefficient data processing, and accessibility concerns, especially for the visually impaired. This study proposes a more decentralized healthcare system that uses artificial intelligence (AI) and machine learning (ML) models to improve healthcare services for individuals with disabilities. The system achieves real-time data processing, reduces latency, and enhances decision-making accuracy by combining federated learning and zero-shot architectures. Furthermore, smart technologies such as the Internet of Things (IoT) and natural language processing (NLP) provide seamless data collection and analysis, allowing healthcare practitioners to provide prompt and personalized treatment. The suggested solution solves crucial issues such as inefficiencies in data processing, delays in obtaining medical information, and limits in current healthcare processes. This platform improves impaired people's freedom and mobility by delivering remote healthcare solutions using AI-powered diagnostics and real-time monitoring. This study contributes to a more inclusive and efficient healthcare system in Saudi Arabia by bridging the gap between technology and accessibility, which aligns with the Vision 2030 objective of providing fair healthcare services to everyone. © 2025 The Authors

关键词： Electronic health record

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