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A Spin Photonic Topological Metasurface Based On A Kagome Lattice For Use In Leaky-wave Antenna Application

IEEE Access 2025年 13卷 89811-89818页

作者： Abtahi, Sayyed Ahmad Maddahali, Mohsen Bakhtafrouz, Ahmad Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan84156-83111 Iran

The emerging field of topological metasurfaces offers unique advantages, particularly in robustness against backscattering in low-profile structures. The lattice configuration of these structures significantly influences the ability to achieve sharp turns in the propagation path. One of the most studied lattices in condensed matter physics is the kagome lattice, characterized by its hexagonal Brillouin zone, which displays a Dirac cone in its dispersion diagram. Previous research on kagome lattices in photonic topological insulators has primarily focused on valley types of insulators. This article introduces a spin topological metasurface based on the kagome lattice and its unit cell, enabling a broad range of sharp turns and propagation paths. The unit cell is compared to its hexagonal and 60-degree rhombic counterparts, and a parametric study of its dimensions is conducted. As a result of this research, a new X-band leaky-wave antenna designed in the kagome lattice with an armchair arrangement interface has been developed. This antenna provides two forward and two backward beams, each pair achieving an approximately 50-degree scan within the 8.8 to 11.1 GHz bandwidth. © 2013 IEEE.

关键词： Crystal lattices

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Uncertainty quantification in large language models through convex hull analysis

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Discover Artificial Intelligence 2024年第1期4卷 1-14页

作者： Catak, Ferhat Ozgur Kuzlu, Murat Department of Electrical Engineering and Computer Science University of Stavanger Rogaland Norway Department of Engineering Technology Old Dominion University NorfolkVA United States

Uncertainty quantification approaches have been more critical in large language models (LLMs), particularly high-risk applications requiring reliable outputs. However, traditional methods for uncertainty quantification, such as probabilistic models and ensemble techniques, face challenges when applied to the complex and high-dimensional nature of LLM-generated outputs. This study proposes a novel geometric approach to uncertainty quantification using convex hull analysis. The proposed method leverages the spatial properties of response embeddings to measure the dispersion and variability of model outputs. The prompts are categorized into three types, i.e., ’easy’, ’moderate’, and ’confusing’, to generate multiple responses using different LLMs at varying temperature settings. The responses are transformed into high-dimensional embeddings via a BERT model and subsequently projected into a two-dimensional space using Principal Component Analysis (PCA), Isomap, Multidimensional Scaling (MDS). The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to cluster the embeddings and compute the convex hull for each selected cluster. The experimental results indicate that the uncertainty of the model for LLMs depends on the prompt complexity, the model, and the temperature setting. © The Author(s) 2024.

关键词： Embeddings

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Efficient 2 × 2 multimode switch employing Sb2Se3 phase change material for enhanced mode division multiplexing systems

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Optical and Quantum Electronics 2025年第5期57卷 1-20页

作者： Atri, Ali Zarifkar, Abbas Department of Communication and Electronics School of Electrical and Computer Engineering Shiraz University Shiraz Iran

Mode division multiplexing (MDM) technology represents a significant advancement in high-capacity optical data transmission in photonics integrated circuits (PICs). Among the critical components in MDM architecture are multimode switches that enable signals to be routed simultaneously along different waveguides. In this study, we present a two-mode 2×2 optical switch utilizing an asymmetric directional coupler with Sb2Se3 as the phase change material which is designed at an ultra-compact footprint of 53 μm × 6 μm. This switch effectively routes the first two transverse magnetic modes simultaneously over a broad bandwidth within the third telecommunications window, specifically from 1535 to 1565 nm. Our 3D finite-difference time-domain simulation results indicate that the switch exhibits a low insertion loss of 0.78 dB and a crosstalk level of -7.98 dB in the "on" state across the specified bandwidth. Furthermore, in the "off" state, the switch maintains favorable performance characteristics, achieving insertion loss and crosstalk values of less than 0.41 dB and -15.69 dB throughout the operational bandwidth. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Directional couplers

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RelaxVR: Cybersickness Reduction in Immersive Virtual Reality through Explainable AI and Large Language Models

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IEEE Access 2025年 13卷 84689-84712页

作者： Kundu, Ripan Kumar Hoque, Khaza Anuarul University of Missouri Department of Electrical Engineering Computer Science ColumbiaMO United States

Virtual reality (VR) systems are susceptible to cybersickness, significantly hindering user immersion. Very recently, researchers introduced explainable artificial intelligence (XAI) enabled methods for detecting and explaining cybersickness features. Since XAI methods can identify the dominant features causing cybersickness, we argue that this knowledge can also guide the dynamic adoption of effective cybersickness reduction strategies, unlike state-of-the-art static approaches that rely on fixed methods. This paper introduces a new dataset MazeSick and RelaxVR, an interactive XAI-guided VR cybersickness reduction framework to predict, explain, and reduce cybersickness. Specifically, we propose an innovative XAI-guided cybersickness reduction engine, which selects the most appropriate reduction technique based on XAI-provided feature importance scores from a cybersickness reduction library of different reduction techniques. We also design an interactive dialogue system powered by large language models, enabling users to engage in the VR simulation via voice commands to understand the reasoning of cybersickness and select effective reduction strategies through natural language interaction. We deployed RelaxVR on a consumer-grade VR headset (e.g., HTC VIVE Pro) and validated it through a user study on cybersickness evaluation, where participants were immersed in a custom-built VR Maze simulation. Our results demonstrate that RelaxVR effectively reduces cybersickness with minimal impact on immersion, and 94% of the users agreed that RelaxVR is highly effective for reducing cybersickness and easier to use through the dialogue engine for selecting effective reduction strategies. © 2013 IEEE.

关键词： Virtual reality

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Encryption of medical data based on blockchain and multi-chaotic maps

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Multimedia Tools and Applications 2024年第17期84卷 1-22页

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India

In this work, the SHA-256 mapper of the blockchain has been utilized to secure medical data from brute-force attacks. The uniform distribution and lower correlation of the encrypted data are achieved using the multi-chaotic in the encryption approach. Also, the circular shift method is employed in the approach to avoid differential attacks. The encryption approach is applied to secure the patient information, medical signals (ECG and EEG), and medical images (Grayscale, RGB, and Hyperspectral). The performance of the proposed approach has been analyzed for twenty signals and fifty medical images. Performance metrics such as MSE, SNR, PSNR, entropy, histogram, variance, chi-square, NPCR, UACI, and SSIM have been considered to check the resilience of the proposed approach against various types of attacks. The proposed approach satisfies the requirements of performance metrics by achieving higher MSE values with lower SNR and PSNR values for the encrypted data. Furthermore, the proposed methodology attains an entropy approaching 8, signifying robust randomness and resistance against entropy attacks. The SSIM values for encrypted images are close to 0, indicating significant dissimilarity from the original images. The NPCR values surpassing 99.60% and UACI exceeding 33.40% demonstrate resilience against differential attacks in the proposed approach. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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Skin lesion classification using transfer learning

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Soft Computing 2024年第20期28卷 12337-12343页

作者： Nivedhitha, G. Kalpana, P. Sidthik, A. Sheik Rani, V. Anusha Singh, Ajith B. Rajagopal, R. Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering VSB College of Engineering and Technical Campus Tamilnadu Coimbatore642109 India Department of Electronics and Instrumentation Engineering Saveetha Engineering College Tamilnadu Chennai India Department of Electrical and Electronics Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering Francis Xavier Engineering College Tirunelveli India

This work presents an essential module for the Transfer Learning approach's classification of melanoma skin lesions. Melanoma, a highly lethal form of skin cancer, poses a significant health threat globally. Image analysis plays a crucial role in enhancing the accuracy of malignant skin lesion classification. Although neural networks trained on extensive datasets have emerged as the latest solution, their scalability remains a challenge. This study proposes an efficient method for classifying skin lesions utilizing labelled data from open sources, leveraging EfficientNet as the foundational model to robustly capture discriminative features from diverse visual perspectives. Validation of the proposed algorithms relies on the classifier's capacity to distinguish between classes is measured by the Area Under the Receiver Operating Characteristic (AUC-ROC) curve. AUC-ROC score greater than zero denotes better classification performance. Our proposed model achieves an impressive score of 98.65%. In contrast to existing approaches, our method demonstrates swift and accurate identification and segmentation of melanoma skin lesions, showcasing its efficacy in advancing the field of skin lesion classification. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Dermatology

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Extended Sequential Function Chart

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IEEE Access 2025年 13卷 85585-85596页

作者： Gniewek, L. Markiewicz, M. Rzeszow University of Technology Department of Electrical and Computer Engineering Rzeszow35-959 Poland

Sequential Function Chart (SFC) is one of the PLC programming languages defined in the IEC-61131 standard. SFC has been increasingly used in practical applications because even small controllers can now be programmed in this graphical language. Representing the sequential-parallel control algorithm in the form of a diagram makes the program transparent, and the possibility of using Petri net analysis tools allows for formal verification of the program’s correctness. Unfortunately, in SFC, the graph nodes take only binary values, which limits, for example, the possibility of modeling resources that are produced or consumed during the production process. It also makes the use of signals from analog sensors challenging, thanks to which it is possible to observe the progress of the supervised tasks. Hence, this paper proposes an extension of SFC (eSFC), which introduces nodes that model quantitative resources and variables storing analog signals. In addition, a formal model of eSFC and its algebraic interpretation are proposed, ensuring the unambiguity of the eSFC operation. © 2013 IEEE.

关键词： Photomapping

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A Deep Learning based Ultra-Lightweight Architecture for Atrial Fibrillation Detection Using Single-Lead ECG Recordings

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IEEE Access 2025年 13卷 86474-86486页

作者： Khan, Abbas Mughal, Muhammad Rizwan Irtaza, Syed Ali Khan, Mohsin Tahir, Madiha Ali, Ahtisham Saeed, Zoraize Ghulam Ishaq Khan Institute of Engineering Science and Technology Pakistan Sultan Qaboos University Electrical and Computer Engineering Department Muscat Oman Institute of Space Technology Electrical Engineering and Computer Science Department Islamabad Pakistan FAST National University of Computer and Emerging Sciences Islamabad Pakistan

In this paper, we have proposed a novel deep-learning model to process electrocardiogram (ECG) signals from single-lead ECG device. This is achieved by using a hybrid of CNN (convolutional neural network) and LSTM (long short-term memory) neural netwrok, where CNN extracts localized features from ECG signals, while the LSTM network extract temporal relationships. Moreover, a filtering-based method is proposed to generate synthetic data to overcome the data imbalance posed by the publicly available PhysioNet/CinC Challenge 2017 dataset. Specifically, three distinct ECG filters: Neurokit, BioSPPy, and EngZeeMod are used to perform data augmentation. As depicted by the simulation results, the proposed model exhibits superior performance with an F1 score of 99.56% providing a gain of 102.25 dB in terms of model size and performance gain of 1.06% outperforming existing state-of-the-art method enabling cost-effective real-time diagnosis with an inference time of 0.48×10-3 secs. Atrial fibrillation (A-Fib), a common cardiac arrhythmia, is linked to serious complications like heart failure and stroke, which early diagnosis could prevent. However, its paroxysmal and often asymptomatic nature complicates detection. In this paper, we propose a novel deep-learning model for single-lead ECG signal analysis, combining a convolutional neural network (CNN) to extract localized features and a long short-term memory (LSTM) network to capture temporal dependencies. To address data imbalance in the PhysioNet/CinC Challenge 2017 dataset, we employ a filtering-based approach with Neurokit, BioSPPy, and EngZeeMod for synthetic data generation. Simulation results show the model achieves a 99.56% F1 score, a 102.25 dB gain in efficiency, a 1.06% performance gain over the state-of-the-art method, and real-time diagnosis with a 0.48×10-3 second inference time. These simulation results confirm the superior performance of the proposed model. © 2013 IEEE.

关键词： Electrocardiography

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THE EFFICIENCY AND SECURITY OF MULTI-UNMANNED AERIAL VEHICLE COMMUNICATION NETWORKS: FEDERATED LEARNING BASED ON LIGHTWEIGHT BLOCKCHAIN

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International Journal of electrical engineering 2024年第1期31卷 23-31页

作者： Chung, Hao-Tse Wang, Li-Chun Cheng, Shao-Hung Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan Department of Electrical and Electronic Engineering Chung Cheng Institute of Technology NDU Taiwan

This study develops an innovative federated learning framework specifically designed for multi-unmanned aerial vehicles (UAVs), utilizing blockchain technology to achieve lightweight, secure, and efficient data processing. In this architecture, UAVs directly participate in federated learning without the need for a central server. Instead, a series of base stations execute blockchain nodes to process gradient information and maintain the system. We eliminate single points of failure by transferring gradient aggregation from the central server to blockchain nodes, thereby enhancing the system’s stability and reliability. Furthermore, the use of a private Inter Planetary File System (IPFS) reduces storage and communication costs. A Byzantine Fault Tolerance (BFT) consensus mechanism is adopted to improve system efficiency and security. Experiments confirm that our proposed architecture effectively prevents model poisoning attacks, even in scenarios where malicious blockchain nodes are in the majority, thus ensuring learning performance. Finally, a node reputation selection mechanism based on the Multi-Krum algorithm is introduced to increase the likelihood of nodes submitting high-quality gradients, thereby enhancing overall system efficiency. © 2024, Chinese Institute of electrical engineering. All rights reserved.

关键词： Aircraft communication

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A Method for Reducing High-Frequency Sidelobes of a Millimeter-Wave Double-Ridged Horn Antenna

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 4098-4103页

作者： Cross, Benjamin S. Filipovic, Dejan S. Electrical Computer and Energy Engineering Department University of Colorado at Boulder Boulder CO USA

A simple method for reducing sidelobes at high-frequencies and stabilizing the 10dB-beamwidth of a wideband, 36-95 GHz millimeter-wave ridged feed horn is proposed. Specifically, the modulation of the ridges enabled by additive manufacturing is shown to lower sidelobe levels by up to 10 dB within a 15 GHz SLL reduction bandwidth. Tradeoffs between sidelobe levels, phase center, and impedance match are studied using parameterized full-wave models;the near-field, far-field and port results are all used in characterizing our antennas. A theoretical analysis of the proposed design using the sampled aperture fields (simulated) indicates that the increased phase taper and flattened amplitude distribution directly cause the sidelobe reduction and beamwidth broadening. Measured far-field results agree well with theory, validating the robustness of the proposed approach. © 1963-2012 IEEE.

关键词： Horn antennas

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