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Wideband Circularly-Polarized Hexagonal SIW Cavity-Backed Slot Antenna With Enhanced Bandwidth and Compact Design

IEEE Access 2025年 13卷 80407-80415页

作者： Sarani, Vahid Mousa Ali, Esraa Shariatifar, Milad Virdee, Bal S. Mariyanayagam, Dion Rashid, Nasr Alibakhshikenari, Mohammad Dalarsson, Mariana Ferdowsi University of Mashhad Electrical Engineering Department Mashhad*** Iran Al-Ahliyya Amman University Communications and Computer Engineering Department Amman19111 Jordan University of Rome "Tor Vergata " Electronics Engineering Department Rome00133 Italy London Metropolitan University Center for Communications Technology LondonN7 8DB United Kingdom Jouf University College of Engineering Electrical Engineering Department Al Jowf Sakaka72388 Saudi Arabia Al-Azhar University Faculty of Engineering Department of Electrical Engineering Nasr City Cairo11884 Egypt Dogus University Department of Electrical and Electronics Engineering Istanbul Umraniye34775 Turkey KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper presents a novel wideband circularly polarized (CP) cavity-backed slot antenna based on Substrate Integrated Waveguide (SIW) technology, designed for compact and high-efficiency performance. The proposed antenna utilizes a hexagonal SIW cavity to simultaneously excite two closely spaced resonant modes (TM110 and TM210), resulting in enhanced bandwidth for linear polarization (LP). To achieve circular polarization, a passive, single-layer linear-to-circular polarization converter is integrated above the cavity, offering a structurally simple and PCB-compatible solution. Unlike conventional CP designs that rely on complex feeding networks or multilayered structures, this configuration maintains a planar profile and efficient performance. A fabricated prototype demonstrates strong agreement between simulation and measurement, achieving a peak gain of 9.2 dBic and a 14% axial ratio (AR) bandwidth. These results highlight the antenna’s suitability for modern wireless systems requiring wideband CP functionality, including satellite communications, 5G modules, and compact embedded devices. © 2013 IEEE.

关键词： Circular polarization

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PerfCam: digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models

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IEEE Access 2025年 13卷 85390-85406页

作者： Khan, Michel Gokan Guarese, Renan Johnson, Fabian Wang, Xi Vincent Bergman, Anders Edvinsson, Benjamin Romero, Mario Vachier, Jeremy Kronqvist, Jan KTH Royal Institute of Technology School of Engineering Sciences Stockholm Sweden KTH Royal Institute of Technology Digital Futures Stockholm Sweden KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden AstraZeneca Slovakia KTH Royal Institute of Technology School of Industrial Engineering and Management Stockholm Sweden Linköping University Department of Science and Technology Sweden

We introduce PerfCam, an open source Proof-of-Concept (PoC) digital twinning framework that combines camera and sensory data with 3D Gaussian Splatting and computer vision models for digital twinning, object tracking, and Key Performance Indicators (KPIs) extraction in industrial production lines. By utilizing 3D reconstruction and Convolutional Neural Networks (CNNs), PerfCam offers a semi-automated approach to object tracking and spatial mapping, enabling highly accurate digital twins that capture real-time KPIs such as availability, performance, Overall Equipment Effectiveness (OEE), and rate of conveyor belts in the production line. We validate the effectiveness of PerfCam through a practical deployment within realistic test production lines in the pharmaceutical industry and contribute an openly published dataset to support further research and development in the field. The results demonstrate PerfCam’s ability to deliver actionable insights through its precise digital twin capabilities, underscoring its value as an effective tool for developing usable digital twins in smart manufacturing environments and extracting operational analytics. © 2013 IEEE.

关键词： Convolutional neural networks

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Safe Model Predictive Control of a Non-Holonomic Mobile Manipulator Under Multiple Constraints Using Control Barrier Functions

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IEEE Access 2025年 13卷 76638-76653页

作者： Kashi, Zahra Yadegar, Meysam Meskin, Nader Cassandras, Christos G. Qom University of Technology Electrical and Computer Engineering Department Qom*** Iran Qatar University Electrical Engineering Department Doha Qatar Boston University Division of Systems Engineering Center for Information and Systems Engineering BostonMA02446 United States

This paper introduces a novel control strategy to ensure safety in the navigation of a mobile manipulator comprising a fixed-base manipulator mounted on a mobile platform. The approach initially addresses the trajectory tracking problem of a non-holonomic mobile manipulator (NH-MM) by employing decoupling dynamic control through model predictive control-optimizable control barrier function (MPC-OCBF). This allows for independent control of the end-effector and mobile platform trajectories, obstacle avoidance, and simultaneously adjusting the joint and control input limitations. The objective is to leverage the system’s redundancy to enable the mobile platform to effectively navigate feasible obstacle avoidance scenarios without affecting the primary task performance of the end-effector. Additionally, the method aims to perform obstacle avoidance when the redundancy of the manipulator is insufficient, addressing non-feasible obstacle avoidance scenarios. Through this approach, the mobile manipulator effectively avoids obstacles, allowing the end-effector to autonomously carry out its intended task. The effectiveness of the proposed method is validated through simulations and comparison with existing approaches. Additionally, a quantitative analysis is provided to evaluate and compare the performance of the controllers. © 2013 IEEE.

关键词： Predictive control systems

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24.4 THz·V fT×BV figure-of-merit AlN/GaN/AlN MISHEMTs with thin AlN buffer layer

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Science China(Information Sciences) 2025年第3期68卷 403-404页

作者： Chaoqun ZHANG Hong ZHOU Sami ALGHAMDI Kun ZHANG Zhihong LIU Yue HAO Jingcheng ZHANG State Key Laboratory of Wide-Bandgap Semiconductor Devices and Integrated Technology Xidian University Guangzhou Wide Bandgap Semiconductor Innovation Center Guangzhou Institute of TechnologyXidian University Electrical and Computer Engineering Department King Abdulaziz University

Gallium nitride-based high-electron-mobility-transistors(HEMTs) have gained widespread interest and become primary candidates for next-generation high-frequency and high-power RF electronics, due to their wide bandgap,high breakdown field, and strong polarization-induced high-density 2-dimensional electron gas(2DEG) at the heterojunction interface.

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Field of view and angular-resolution enhancement in microlens array type virtual reality near-eye display using polarization grating

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Optics Express 2025年第1期33卷 263-278页

作者： Shin, Kwang-Soo Hong, Jisoo Han, Woongseob Park, Jae-Hyeung Department of Electrical and Computer Engineering Inha University 100 Inha-ro Michuhol-gu Incheon22212 Korea Republic of Hologram Research Center Korea Electronics Technology Institute Seoul03924 Korea Republic of Department of Electrical and Computer Engineering Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of

In this paper, we propose what we believe to be a novel method to enhance both the field of view and angular resolution of a microlens array-based thin virtual reality near-eye display using a polarization grating. The proposed system employs a polarization grating to steer the field angle according to the polarization states, simultaneously enhancing the field of view and angular resolution. Optical experiments and Zemax simulations validate the effectiveness of our approach, with the field of view in the experimental setup expanding from 58.8° to 95.17°. Additionally, the improvement in angular resolution was demonstrated experimentally by positioning two images with right-handed and left-handed circular polarization states at a half-pixel diagonal offset. The proposed method is expected to contribute to the advancement of virtual reality technology significantly by addressing the trade-off between field of view and resolution. © 2025 Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Virtual environments

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Cognitive Radio Receiver Design Employing Pipeline Successive Approximation ADC 22

Cognitive Radio Receiver Design Employing Pipeline Successiv...

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22nd International Learning and technology Conference, L and T 2025

作者： Baig, Muhammad Samee Hussein, Aziza I. Effat University Electrical and Computer Engineering Department Saudi Arabia Effat University College of Engineering Energy and Technology Research Center Saudi Arabia

ISBN: (纸本)9798331532215

Smart cities require better cellular communication, incorporating a high data rate that satisfies the Internet, cloud computing, and the Internet of Things (IoT) requirements. A high data rate demands higher bandwidth with low latency. However, the already saturated or underutilized frequency spectrum hinders this realization. 5G and cognitive radio technology can enhance spectrum utilization to meet the high data demands of future cellular communication. Cognitive radio uses spectrum sensing to identify underutilized frequencies without interfering with licensed users, addressing the scarcity of the electromagnetic spectrum. In this paper, a cognitive radio receiver design with a SAR analog-to-digital converter, which is recognized for having a modest resolution for high bandwidth signals, is implemented. Moreover, the pipeline technique in the SAR design is proposed to increase the resolution to be suitable for 5G applications. The high-level model of the system is simulated using MATLAB Simulink software. The quantization error observed in the regular SAR ADC design is compared to that in the proposed pipeline SAR ADC, highlighting the enhanced accuracy and efficiency of the pipeline SAR ADC, which makes it suitable for spectrum sensing in next-generation cognitive radio systems. © 2025 IEEE.

关键词： Bandwidth

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Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using Machine Learning

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IEEE Access 2025年 13卷 86768-86780页

作者： Saleem, Muhammad Asim Javeed, Ashir Benjapolakul, Watit Srisiri, Wattanasak Chaitusaney, Surachai Kaewplung, Pasu Chulalongkorn University Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Faculty of Engineering Bangkok 10330 Thailand Blekinge Institute of Technology Department of Computer Science Karlskrona Sweden

Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction model (N-XGB), a hybrid model that combines neural networks (NN) for feature extraction with XGBoost for classification. The NN component extracts high-level features, while XGBoost uses gradient-boosted decision trees for accurate predictions, combining the strengths of deep learning and boosting techniques for improved accuracy. The N-XGB model achieves an accuracy of 94.8% and an average F1 score of 0.95 on a real-world dataset that includes wildfires, floods and earthquakes, significantly outperforming baseline models such as random forest, Support vector machine and logistic regression 85% accuracy. The balanced F1 scores for wildfires 0.96, floods 0.93, and earthquakes 0.96 demonstrate the model's robustness in multi-class classification. The Synthetic Minority Oversampling Technique (SMOTE) balances datasets and improves model efficiency and capability. The proposed N-XGB model provides a reliable and accurate solution for predicting disasters and contributes to improving preparedness, resource allocation and risk management strategies. © 2013 IEEE.

关键词： Disaster Prediction Feature Extraction Machine Learning SMOTE XGBoost

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Unveiling anomalies: transformative insights from transformer-based autoencoder models

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International Journal of computers and Applications 2025年第1期47卷 29-44页

作者： Dehkordi, Sajad Balali Nasri, Saeed Dami, Sina Department of Electrical Engineering Najaf Abad Branch Islamic Azad University Najaf Abad Iran Digital Processing and Machine Vision Research Center Najaf Abad Branch Islamic Azad University Najaf Abad Iran Department of Computer Engineering West Tehran Branch Islamic Azad University Tehran Iran

Nowadays, Intrusion Detection Systems (IDS) play a critical role in safeguarding networks by identifying and mitigating unauthorized access and malicious activities, yet face challenges in accurately detecting sophisticated threats while minimizing false positives. In this paper, we present a comprehensive analysis of a Transformer-based Autoencoder model for network intrusion detection using the NSL-KDD dataset. We evaluate the model's anomaly detection performance based on key metrics such as Mean Squared Error (MSE), Precision-Recall Area Under Curve (AUC), and Root Mean Squared Error (RMSE) on the test set. Our findings reveal promising results, with the model achieving low MSE, high AUC, and relatively low RMSE values, indicating its effectiveness in detecting anomalies. Additionally, we provide an in-depth examination of the model architecture, highlighting the role of encoder and decoder layers, dropout regularization, and optimization techniques such as Adam optimizer. Our analysis sheds light on the efficacy of Transformer-based Autoencoder models for network intrusion detection tasks, offering insights into their architectural design and performance evaluation. The novelty of this study lies in the implementation of a Transformer-based Autoencoder architecture for network intrusion detection, showcasing its superior performance in anomaly detection compared to traditional methods. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Network intrusion

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MSRFormer: Hybrid Scale Self-Attention and Local Fast Convolution Transformer for Facial Expression Recognition

MSRFormer: Hybrid Scale Self-Attention and Local Fast Convol...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shen, Zi-Qiang Tang, Yu-Yi Yan, Jun-Feng Li, Yang Zhao, Guo-Ying Hunan University of Chinese Medicine China Hunan Digital Chinese Medicine Engineering Technology Research Center China Center for Machine Vision Research Department of Computer Science and Engineering University of Oulu Finland

ISBN: (纸本)9798350368741

With the emergence of the Transformer architecture, the accuracy of deep learning within the domain of facial emotion recognition has seen further enhancement. However, Transformer comes with increased training complexity and time due to the large parameter count. Additionally, the global receptive field in Transformer's attention leads to unnecessary computations for features with limited spatial extent in image sentiment analysis. In this paper we presents a MSRFormer model, which combines Hybrid-scale self-attention and local fast convolution to address existing issues. The Hybrid-scale self-attention enables precise focus on salient regions of the image, where key features are located. A Local Fast Convolution module was integrated into the MLP head of the Transformer model, enhancing training speed and reducing parameters while maintaining feature learning. we conducted experiments on FERPlus, RAF-DB, and KDEF datasets and confirming its strong performance and broad applicability in facial expression recognition. © 2025 IEEE.

关键词： facial expression recognition hybrid scale self attention local fast convolution transformer

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Dzyaloshinskii-Moriya-type spin-spin interaction from mixed-parity superconductivity

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Physical Review B 2025年第6期111卷 L060504-L060504页

作者： Jabir Ali Ouassou Takehito Yokoyama Jacob Linder Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences NO-5528 Haugesund Norway Department of Physics Tokyo Institute of Technology Meguro Tokyo 152-8551 Japan Center for Quantum Spintronics Department of Physics Norwegian University of Science and Technology NO-7491 Trondheim Norway

Interacting impurity spins adsorbed on surfaces have been suggested as basic components for applications in quantum computation and spintronics. Such spins usually prefer a parallel or antiparallel configuration, but weakly noncollinear alignments are possible due to the Dzyaloshinskii-Moriya interaction (DMI) that arises in the presence of relativistic spin-orbit coupling. Here, we show that an effective Dzyaloshinskii-Moriya-type interaction (DMTI) can emerge purely from superconducting correlations without any spin-orbit interaction. We give an analytical proof and provide a numerical study which shows that DMTI arises in mixed-parity superconductors solely from the superconducting pairing. Moreover, we show that the same effect can be realized in Josephson junctions between s-wave and p-wave superconductors, where a phase bias toggles the DMTI entirely on and off. These results enable a way to engineer spin textures using superconducting order.

关键词： Shear waves

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