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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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Microcontroller-based Automated Body Fat Percentage Calculating System Using U.S. Navy Method 24

Microcontroller-based Automated Body Fat Percentage Calculat...

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7th International Conference on Information Science and Systems, ICISS 2024

作者： Gopole, Khyle Matthew Pablo Andaya, Kyle Hanz Pagsanjan Linsangan, Noel Bautista School of Electrical Electronics and Computer Engineering Mapua University Philippines

ISBN: (纸本)9798400717567

Limited outdoor activities and inadequate exercise pose health risks that can contribute to cardiovascular diseases. Monitoring one’s body composition can be done by determining the body fat percentage (BF%), a good indicator of overall body fat composition as it separates fat excess from weight excess compared to Body Mass Index. The U.S. Navy Method formula uses body circumference measurements as parameters to estimate BF% conveniently. Thus, this study aims to develop a microcontroller-based calculating system that automatically calculates BF% using the U.S. Navy Method formula using Arduino UNO R3 and Arduino Nano. A main system containing an ultrasonic sensor is used to extract the height of an individual. In contrast, a secondary system uses a rotary encoder to read body circumference measurements (neck, waist, and hip). Transceiver modules are used to transfer measurements between systems wirelessly. The overall system’s performance was assessed using the Wilcoxon signed-rank test by comparing the estimated BF% of the system and the skinfold method. The calculated test statistic 120 and p-value 0.06 are greater than the critical value 116 and the significance level 0.05, respectively. Hence, no significant difference exists between the calculated BF% using the system and the skinfold method. © 2024 Copyright held by the owner/author(s).

关键词： Ultrasonic sensors

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Microstrip patch antenna modeling using neural networks with knowledge-based regularization

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Neural Computing and Applications 2025年第5期37卷 3827-3837页

作者： Saçın, Ekin Su Durgun, Ahmet Cemal Department of Electrical Electronics Engineering Middle East Technical University Ankara Çankaya06800 Turkey Department of Electrical and Computer Engineering University of Massachusetts AmherstMA01003 United States

Neural networks (NNs) have proven a useful surrogate model for the design and optimization of high frequency structures including antennas. Black-box NNs are known to have scalability and accuracy problems as the dimension of the problem increases. This study proposes knowledge-based regularization methods, referred to as derivative, spectral, and magnitude regularization to address these issues. The proposed methods utilize the functional properties of S-parameters to improve the accuracy and prevent unphysical predictions. The NNs are trained and tested using a data set of 5000 samples generated by Latin Hypercube Sampling and simulated by Ansys HFSS. The goodness of fit is evaluated using Relative Squared Error. Derivative and spectral regularizations reduce the RSE loss from 0.052 to 0.046 and 0.043, respectively. When combined with magnitude regularization, up to 17% and 88% reduction in loss and passivity violations can be achieved, at the expense of a 37% increase in training time. Moreover, 25% less data is required, to maintain a similar loss to the reference NN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neural network models

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Design and usability assessment of touchscreen interface in medical devices: a case study of a neonatal incubator

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Research on Biomedical engineering 2025年第1期41卷 1-6页

作者： Viviani, Carlos Alessandro Bassi Elias, Leonardo Abdala Calil, Saide Jorge Department of Electronics and Biomedical Engineering School of Electrical and Computer Engineering University of Campinas SP Campinas Brazil Center for Biomedical Engineering University of Campinas Campinas Brazil

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Secured IIoT against trust deficit - A flexi cryptic approach

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Multimedia Tools and Applications 2025年第9期84卷 5625-5652页

作者： Padmapriya, V.M. Thenmozhi, K. Hemalatha, M. Thanikaiselvan, V. Lakshmi, C. Chidambaram, Nithya Rengarajan, Amirtharajan Department of Computer Sciences Marquette University MilwaukeeWI53233 United States School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur613401 India Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah22254 Saudi Arabia School of Electronics Engineering Vellore Institute of Technology Vellore632014 India

This research allows the secure surveillance approach for the Internet of Things (IoT) methodology to be developed by integrating wireless signalling and image encryption strategy. Since the Cloud Service Telco (CST) is a semi-trusted body in cloud services, user data is encrypted before uploading to a cloud server for data protection from disclosure. The flexibility of encrypted data sharing is essential for cloud storage users. This study investigates the Discrete Wavelet Transform (DWT) technique with modified Huffman compression and Elliptic Curve Cryptography (ECC). It encrypts and decrypts the data and enhances industrial security surveillance in transmission. It uses the wireless network’s next generation (5G or 6G) as uplink Single Carrier Frequency Division Multiple Access (SC-FDMA) strategies via the IoT. This study presented a novel approach to proposing hardware architecture for a secure web camera integrated with the Atmel in the mega AVR family (ATMEGA) microcontroller, suitable for IoT applications. The experimental results confirm the proposed model’s efficacy compared with existing robustness and security analysis algorithms. These systems are also used by implementing industry-standard protocols using IoTs to monitor industrial applications. The proposed framework can also minimise bandwidth, transmission cost, storage space, tracking data, and decisions about abnormal events such as potential fraud and extinguisher detection in surveilling applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： 5G mobile communication systems

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Data-driven Component-level Design of Switching Power Converters Using Predictive Modeling

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IEEE Transactions on Power electronics 2025年第8期40卷 11126-11142页

作者： Reese, Skye Sauter, Bailey Maksimovic, Dragan University of Colorado Colorado Power Electronics Center Department of ElectricalComputer and Energy Engineering BoulderCO80309 United States

This paper shows how the design of switching power converters at the component level can be assisted by data-driven, automated, systematic methods. This work uses regression machine learning (ML) techniques, where predictive component models are trained using large amounts of component data and design-oriented parameters derived from the data. For a given converter topology and specification set, the ML-based component models are used to predict converter power loss, bill-of-material (BOM) cost, and footprint area. This allows for an algorithm to iterate over design parameters to optimize the objective function in terms of efficiency, size, cost, or a combination of these performance metrics while also meeting all design constraints. Once the algorithm converges, the optimized parameters are applied to filter the original dataset, identifying components that are most likely to be chosen for the specific design. The developed design automation approach allows for comparisons between topologies, device technologies, and operating modes, and reduces the design time by quickly providing component recommendations. To illustrate an application of the ML-based design approach, the tool is applied to the design of 48-to-12 V, 5 A synchronous buck dc-dc converters using Si or GaN power semiconductors, where the design objective is to minimize loss while meeting BOM cost and area constraints. © 1986-2012 IEEE.

关键词： Gallium nitride

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Classification of Common Stem-based Dragon Fruit Diseases using Convolutional Neural Network 19

Classification of Common Stem-based Dragon Fruit Diseases us...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Magahis, Carlos Andrei F. Ugale, Rionne Angelo Villaverde, Jocelyn F. School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines

ISBN: (纸本)9798331507817

Dragon Fruit stem diseases significantly threaten crop yield and agricultural productivity. While multiple capable dragon fruit disease detection systems already exist, there is a scarcity of research investigating the application of ResNet-50 in classifying dragon fruit stem diseases. This study presents a portable device that can capture pictures of dragon fruit stems for early disease detection using a ResNet-50 convolutional neural network (CNN) model based on Raspberry Pi 3B+. Images of healthy and infected dragon fruit stems with visual symptoms of anthracnose, cactus virus x, and stem canker were collected using a standard webcam and preprocessed to train the model. The device was tested in a controlled environment and achieved a 91.67% accuracy in classifying between the three common stem diseases. The results demonstrate the potential of ResNet-50 for accurate and efficient dragon fruit stem disease identification. © 2025 IEEE.

关键词： Convolutional neural networks

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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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Two-dimensional confined topological modes in all dielectric guided mode resonance structure through surface features

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Optical and Quantum electronics 2025年第4期57卷 1-17页

作者： Ramachandra, Swathy Menezes, Maclean Paul Tumuluru, Vamsi Krishna Kulkarni, Raghavendra G. Sinha, Rajat K. Bhowmick, Kaustav Department of Electronics and Communication Engineering PES University Karnataka Bangalore India Department of Electrical and Computer Engineering University of Toronto Toronto Canada

Guided mode resonance (GMR) structures offer simplicity and have spectral resonance capabilities such as narrowband resonance, high Q-factor, whereas topological surface features offer spatial light control. In this paper, we explore the integration of the GMR structures with topological photonics to achieve extreme two-dimensional (2D) spatial confinement of light. With this integration, we propose a novel design that leverages discontinuous surface features (crosscut) to excite Jackiw—Rebbi (JR) solution, which describes relationship between Dirac equation and topological insulators. Finite element modelling, considering various structural and material parameters, demonstrate a 40% improvement in light confinement compared to benchmark GMR structures, with a calculated full width half maximum (FWHM) of the confined optical mode measured to be approximately ~ 18 ± 3 nm. This FWHM value represent the spatial extent of the confined optical field, indicating the achieved field localization in the proposed structures. This significant enhancement showcases the potential of our approach for applications demanding ultra- precise spatial resolution. Furthermore, tolerance studies reveal good robustness to variations in the dimensions of the crosscut and angle of polarization, ensuring practical feasibility. The proposed structure exhibited good tolerance of up to ~ ± 10%. This work paves the way for highly confined 2D light manipulation within all- dielectric platforms, opening exciting avenues for nanophotonic devices and applications requiring light localization. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Topology

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Securing MIMO Wiretap Channel With Learning-Based Friendly Jamming Under Imperfect CSI

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IEEE Internet of Things Journal 2025年第11期12卷 16009-16022页

作者： Minh Tuan, Bui Nguyen, Diep N. Linh Trung, Nguyen Nguyen, Van-Dinh Van Huynh, Nguyen Thai Hoang, Dinh Krunz, Marwan Dutkiewicz, Eryk University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia University of Engineering and Technology Vietnam National University Faculty of Electronics and Telecommunications Hanoi100000 Viet Nam College of Engineering and Computer Science VinUniversity Hanoi100000 Viet Nam University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom The University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States

Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g., for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g., cryptography-based solutions, which unfortunately may not be feasible in practice. This challenge is even more acute in low-end IoT devices. We thus introduce a novel deep learning-based FJ framework that can effectively defeat eavesdropping attacks with imperfect CSI and even without CSI of legitimate channels. In particular, we first develop an autoencoder-based communication architecture with FJ, namely, AEFJ, to jointly maximize the secrecy rate and minimize the block error rate (BLER) at the receiver without requiring perfect CSI of the legitimate channels. In addition, to deal with the case without CSI, we leverage the mutual information neural estimation (MINE) concept and design a MINE-based FJ scheme that can achieve comparable security performance to the conventional FJ methods that require perfect CSI. Extensive simulations in a multiple-input-multiple-output (MIMO) system demonstrate that our proposed solution can effectively deal with eavesdropping attacks in various settings. Moreover, the proposed framework can seamlessly integrate MIMO security and detection tasks into a unified end-to-end learning process. This integrated approach can significantly maximize the throughput and minimize the BLER, offering a good solution for enhancing communication security in wireless communication systems. © 2014 IEEE.

关键词： Jamming

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