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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and Technology Department of Electrical and Computer Engineering University of Alberta Systems Research Institute Polish Academy of Sciences Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University

Changes in the Atmospheric Electric Field Signal(AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information implicit in AEFS changes. In this paper, a Fuzzy C-Means(FCM) clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS. First, a time series dataset is created in the time domain using AEFS attributes. The AEFS-based weather is evaluated according to the time-series Membership Degree(MD) changes obtained by inputting this dataset into the FCM. Second, thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus. Thus, a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain. Finally, the rationality and reliability of the proposed method are verified by combining radar charts and expert experience. The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS, and a negative distance-MD correlation is obtained for the first time. The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field (AEF) Thunderstorm Fuzzy C-means (FCM) Attribute

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Deep learning and machine learning based highly accurate reflection prediction model for multi layers anti-reflection coatings

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Optical and Quantum electronics 2025年第1期57卷 1-22页

作者： Oktay, Semih Duru, İremnur Bakır, Halit Tabaru, Timuçin Emre Department of Electrical-Electronics Engineering Sivas University of Science and Technology Sivas Turkey Computer Engineering Department Sivas University of Science and Technology Sivas Turkey

This study focuses on creating an accurate reflection prediction model that will guide the design of filters with multilayer Anti-Reflection Coating (ARC) to optimize the thickness parameters using Machine Learning (ML) and Deep Learning (DL) techniques. This model aims to shed light on the design process of a multilayer optical filter, making it more cost-effective by providing faster and more precise production. In creating this model, a dataset containing data obtained from 3000 (1500 Ge–Al2O3, 1500 Ge–SiO2) simulations previously performed on a computer based on the thicknesses of multilayer structural materials was used. The data are generated using Computational Electromagnetic simulation software based on the Finite-Difference Time-Domain method. To understand the mechanism of the proposed model, two different two-layer coating simulations were studied. While Ge was used as the substrate in both coatings, Al2O3 and SiO2 were used as the second layers. The data set consists of the 3–5 µm and 8–12 µm bands typical for the mid-wave infrared (MWIR) and long-wave infrared (LWIR) bands and includes reflectance values for wavelengths ranging between these spectra. In the specified 2-layer data set, the average reflectance was obtained with a minimum of 0.36 at 515 nm Ge and 910 nm SiO2 thicknesses. This value can be increased by adapting the proposed model to more than 2 layers. Six ML algorithms and a DL model, including artificial neural networks and convolutional neural networks, are evaluated to determine the most effective approach for predicting reflectance properties. Furthermore, in the proposed model, a hyperparameter tuning phase is used in the study to compare the efficiency of ML and DL methods to generate dual-band ARC and maximize the prediction accuracy of the DL algorithm. To our knowledge, this is the first time this has been implemented in this field. The results show that ML models, particularly decision tree (MSE: 0.00000069, RMSE: 0.00083), rand

关键词： Antireflection coatings

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Reconfigurable Modular Antenna System With Null Steering and Circular Polarization

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IEEE Transactions on Antennas and Propagation 2025年第5期73卷 2823-2833页

作者： Younus, Karam Mudhafar Sultan, Kamel Fumeaux, Christophe Abbosh, Amin The University of Queensland School of Electrical Engineering and Computer Science BrisbaneQLD4072 Australia Ninevah University College of Electronics Engineering Mosul41002 Iraq Electronics Research Institute Cairo12622 Egypt

The design and analysis of a modular antenna system that can achieve both multidimensional null steering and dual-sense circular polarization (CP) is presented. The system is built using compact elementary units, each comprising a wideband tunable phase shifter and a printed dipole, allowing a modular assembly for various operational modes. The tunable phase shifter, designed with a broadside coupling structure and varactor-tuned stubs, provides a 145° tuning range with ±2° phase imbalance and less than 1 dB insertion loss across the band 5–7 GHz. Reconfiguration is achieved by tuning the phase shifters, enabling both 1-D and 2-D null steering as well as dual-sense CP. In its 1-D configuration, the system achieves a null steering range of ±40° from the broadside with more than 15 dB null depth. The 2-D configuration extends this steering capability to two planes. A different configuration of modules enables left-hand and right-hand CPs with high purity, maintaining an axial ratio below 2.3 dB across the operation band. Experimental results align closely with simulations, underscoring the potential of compact, tunable phase shifters for versatile reconfigurable antenna systems. © 1963-2012 IEEE.

关键词： Circular polarization

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Monitoring COVID-19 environment: a real-time facial mask detection using YOLO models

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Research on Biomedical engineering 2025年第1期41卷 1-18页

作者： Pandiyan, P. Thangaraj, Rajasekaran V, Balasubramanian V. K, Manavalasundaram S, Balakrishnan Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Information Technology Velalar College of Engineering and Technology Erode India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore India

Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective measure for themselves and others. Coronavirus protection guidelines have been published by the World Health Organization (WHO). According to WHO standards, COVID-19 can be prevented by wearing a mask in public places and congested regions. In these places, it is very difficult to personally check to see if people are wearing face masks or not. Methods: The objective of this research work is to build a powerful, efficient, and real-time approach for detecting people not wearing masks. Three cutting-edge object identification models, namely YOLOv4, Tiny-YOLOv4, and YOLOv5, are employed in this study for the identification of masked faces. Result: The proposed YOLOv5 model is evaluated using real-time images captured using a smartphone or tablet. The test images include both single and multiple people with and without masks. The YOLOv5 model achieved recognition accuracy of 88.90% with an average detection speed of 0.0316 s per image, whereas the YOLOv4 and Tiny-YOLOv4 produced recognition accuracy of 82.24% and 74.80% with an average detection speed of 0.0530 s and 0.0541 s per image, respectively. Conclusion: The comparative performance suggests that the YOLOv5 model has a maximum recognition accuracy of 88.90% in face mask identification tasks compared to other models such as the YOLOv4 and Tiny-YOLOv4. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： COVID-19

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A Framework for Flood Disaster Detection From Remote Sensing Images Using Spatiotemporal Fusion With Digital Twin Technology

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 11547-11560页

作者： Lim, Se-Jung Sankaran, K. Sakthidasan Haldorai, Anandakumar Chonnam National University School of Electrical and Computer Engineering Yeosu Campus Yeosu-si59626 Korea Republic of Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Chennai603103 India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore641202 India

Flood is regarded as common disaster which could cause serious devastation in any country. Typically, it is caused due to precipitation & river runoffs, specifically at the time of excessive rainfall season. The technology of sensor network has been used to monitor changes in landcovers and water level fluctuations. Moreover, effective disaster monitoring & notification system in real-time becomes a crucial part which needs to be overcome. For this reason, the proposed methodology is designed aiming at developing natural disaster prediction and monitoring system for alerting that aids in offering right decision at right time. At first, remote sensing image data are collected and preprocessed using Frequency Ratio and Multi-collinearity test (MCT) to ensure noise removal and image augmentation by enhancing their quality. A feature extraction process is carried with the use of Deep Convolution VGGNet-16 from which optimal features are selected using Improved Harris Hawks Optimization algorithm (IHHOA). Then, a Flexible Spatio-temporal image fusion (F-SPTF) approach is employed to fuse images. After this, Deep cascaded RNN classifier is employed for predicting flood occurrence and to map flood susceptibility areas. This, in turn, classifies the normal and abnormal condition of flood occurrence thus giving alerts in case of natural disaster occurrences which could be visualized through digital twin technologies. The suggested scheme offers an accuracy rate of about (99.89%), precision (99.37%), recall (99.82%), and F1-score (99.74%). The error rates estimated like RMSE (0.784), MAE (0.764), and MAPE (0.102) also seems to be lower than other existing models compared. © 2008-2012 IEEE.

关键词： Image enhancement

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Deep Reinforcement Learning-Based Ultra Reliable and Low Latency Vehicular OCC

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IEEE Transactions on Communications 2025年第5期73卷 3254-3267页

作者： Islam, Amirul Thomos, Nikolaos Musavian, Leila Department of Electrical and Electronics Engineering Dhaka1229 Bangladesh University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

In this paper, we present a deep reinforcement learning (DRL) framework for vehicular optical camera communication (OCC) systems that ensures ultra-reliable and low-latency communication (uRLLC). We first formulate a throughput maximization problem that aims at optimizing speed of vehicles, channel code rate, and modulation order while respecting the uRLLC requirements. We model reliability by satisfying a target bit error rate and latency as transmission latency. To improve the transmission rate and provide high reliability and low latency, our scheme uses low-density parity-check codes and adaptive modulation. We then solve the optimization problem using the actor-critic-based DRL scheme with Wolpertinger framework. We employ a deep deterministic policy gradient algorithm to operate over continuous action spaces. The evaluation confirms that our proposed DRL-based optimization scheme achieves superior performance compared to radio frequency-based communication systems as well as variants of the proposed scheme. Finally, we verify through simulations that our proposed solution can maximize the communication rate while meeting the uRLLC constraints. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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Single-Frequency Self-Alignment RF Resonant Beam for Information and Power Transfer

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IEEE Internet of Things Journal 2025年第11期12卷 16622-16636页

作者： Jiang, Qingwei Liu, Mingqing Xu, Mengyuan Fang, Wen Xiong, Mingliang Liu, Qingwen Zhou, Shengli Tongji University College of Computer Science and Technology Shanghai201804 China Tongji University College of Electronics and Information Engineering Shanghai201804 China University of Connecticut Department of Electrical and Computer Engineering CT06250 United States

Due to power attenuation, improving transmission efficiency in the radio-frequency (RF) band remains a significant challenge, which hinders advancements in various fields of the Internet of Things (IoT), such as wireless power transfer (WPT) and wireless communication. Array design and retro-directive beamforming (RD-BF) techniques offer simple and effective ways to enhance transmission efficiency. However, when the target is an array or in the near field, the RD-BF system (RD-BFS) cannot radiate more energy to the target due to phase irregularities in the target region, resulting in challenges in achieving higher efficiency. To address this issue, we propose the RF-based resonant beam system (RF-RBS), which adaptively optimizes phase and power distribution between transmitting and receiving arrays by leveraging the resonance mechanism to achieve higher transmission efficiency. We analyze the system structure and develop an analytical model to evaluate power flow and resonance establishment. Numerical analysis demonstrates that the proposed RF-RBS achieves self-alignment without beam control and provides higher transmission efficiency compared to RD-BFS, with improvements of up to 16%. This self-alignment capability allows the system to effectively transfer power and information across varying distances and offsets. The numerical results indicate the capability to transmit watt-level power and achieve 21 bps/Hz of downlink spectral efficiency in indoor settings, highlighting the advantages of RF-RBS in information and power transfer for mobile applications. © 2025 IEEE.

关键词： Inductive power transmission

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Aircraft Simulation in Game Development Using Reinforcement Learning 4

Aircraft Simulation in Game Development Using Reinforcement ...

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4th International Conference on Robotics, electrical and Signal Processing Techniques, ICREST 2025

作者： Redwan Talha, Md. Navid Hassan, Tonmoy Sabab, Al Mithila, Shaila Sharmin Ahmed, Syed Hasib Department of Electrical and Computer Engineering Plot # 15 Dhaka1229 Bangladesh Department of Electrical and Electronics Engineering Bangladesh Dhaka1229 Bangladesh Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798331530938

The progress in technology has provided opportunities for innovative solutions to intricate challenges. One possible method is employing reinforcement learning to model flying trajectories in intricate environments. Game development is a discipline that involves intricate reasoning and dynamic interplay between the user and the game environment. By employing several gaming engines, developers are now able to replicate real-life situations through the implementation of diverse machine learning methods. Aircraft simulation in game creation using reinforcement learning involves creating a visual depiction of real-life settings where aircraft may navigate complex environments without direct input from a human user. Currently, reinforcement learning is not widely applied in game development, particularly in simulation-based path finding techniques. This algorithm approaches possess the efficacy and capacity to generate sophisticated neural networks capable of directing an agent to do certain tasks. The aim of this project is to create aircraft simulations for game development by utilizing reinforcement-learning techniques, so that it can provide a foundational idea of the usage of this algorithm in path-detection based decision-making techniques. The goal is to demonstrate the effectiveness of reinforcement learning in a real-world scenario, where the aircraft independently assesses and selects its flying trajectory. The system will undergo testing in three distinct phases, involving the utilization of Blender3D, Unity 3D, and Anaconda prompts. The results will then be compared using TensorFlow. Several training sessions will be conducted in various environments using the Anaconda environment to optimize the outcomes. In the latter stages of development, a dynamic user interface will be implemented to enhance the user's experience. The method is anticipated to produce 152% improved AI-trained data, which can be utilized for constructing extensive simulation and game-proj

关键词： Flight simulators

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Harnessing Machine Learning in Dynamic Thermal Management in Embedded CPU-GPU Platforms

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ACM Transactions on Design Automation of Electronic Systems 2025年第2期30卷 1-32页

作者： Maity, Srijeeta Majumder, Anirban Roy, Rudrajyoti Hota, Ashish Dey, Soumyajit Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Electrical Engineering Indian Institute of Technology Kharagpur Kharagpur India Electronics And Communication Engineering Indian Institute of Technology Kharagpur Kharagpur India

With increasing transistor density, modern heterogeneous embedded processors often exhibit high temperature gradients due to complex application scheduling scenarios which may have missed design considerations. In many use cases, off-chip "active"cooling solutions are considered prohibitive in such reduced form factors. Core frequency throttling by existing dynamic thermal management techniques often compromises the Quality-of-Service (QoS) and violates real-time deadlines. This necessitates the adoption of intelligent resource management that simultaneously manages both thermal and latency performance. Coupled with the complexity of modern heterogeneous multi-cores, the periodic application updates that cater to ever-changing user requirements often render model-driven thermal-aware resource allocation approaches unsuitable for heterogeneous multi-core systems. For such application-architecture scenarios, we propose a novel self-learning based resource manager using Reinforcement Learning that intelligently manipulates core frequencies and task set mappings to fulfill thermal and latency objectives. Our framework employs a data-driven system modeling technique using Gaussian Process Regression to enable efficient offline training of this learning-based resource manager to avoid challenges associated with initial online training. We evaluate the approach on a heterogeneous embedded CPU-GPU platform with real workloads and observe a significant reduction in peak operating temperature when compared to the default onboard frequency governor as well as other learning-based state-of-the-art approaches. © 2025 Copyright held by the owner/author(s).

关键词： Quality of service

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Safety Critical Control of a Quadrupedal Robot Using MPC-CBF

Safety Critical Control of a Quadrupedal Robot Using MPC-CBF

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Mcconnellogue, Peter James Van, Mien Mcmullan, Rhyss Mcelroy, Ronan Naeem, Wasif School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast United Kingdom

ISBN: (纸本)9798331533892

Quadrupedal robots have been deployed in numerous challenging environments, such as nuclear facility inspections and industrial surveys. These unstructured and dynamic environments expose the robots to significant safety risks. Therefore, this paper proposes a novel design for a safety-critical control system for quadrupedal robots, integrating Model Predictive Control (MPC) with Control Barrier Functions (CBF) to enhance overall system safety. A comprehensive simulation environment has been established, and results demonstrate that the proposed MPCCBF controller outperforms the conventional MPC controller. Specifically, while the MPC controller alone provides adequate tracking of a desired trajectory in a trot gait, integrating CBF enhances obstacle avoidance and improves stability. This combination allows for navigation through more challenging environments while maintaining accurate trajectory tracking. © 2025 IEEE.

关键词： Predictive control systems

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