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Hybrid surface waves in twisted anisotropic heterometasurfaces

Physical Review Applied 2024年第6期21卷 064034-064034页

作者： Xinyan Zhang Chenxu Bian Zheng Gong Ruoxi Chen Tony Low Hongsheng Chen Xiao Lin Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation International Joint Innovation Center The Electromagnetics Academy at Zhejiang University Department of Electrical and Computer Engineering Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Shaoxing Institute of Zhejiang University

Recent advances in twisted photonic structures (e.g., twisted bilayer graphene and twisted anisotropic metasurfaces) have enabled many exotic phenomena of light-matter interactions, such as topological transition of the isofrequency contour of surface waves at the magic angle, photonic flatbands, and localization-to-delocalization transition of light. In general, the optical features of these twisted photonic structures are sensitive to the interlayer twist angle. Here we investigate the mode hybridization of surface waves supported by twisted bilayer anisotropic heterometasurfaces, whose two constituent metasurfaces are nonidentical and support surface waves with similar spatial confinements but distinct polarizations. Counterintuitively, we find that the shape of the isofrequency contour of these hybrid surface waves—eigenmodes with both transverse magnetic and transverse electric wave components—can be insensitive to the twist angle, due to the weak interlayer electromagnetic coupling. Moreover, the interlayer distance provides a unique route to reshape the dispersion of one specific kind of hybrid surface wave, while it has negligible influence on the other kinds. Our finding enriches the physics of light-matter interactions in twisted photonic structures and may open new possibilities to mold the flow of light at the subwavelength scale.

关键词： Classical optics Light-matter interaction Metamaterials Metasurfaces Near-field optics Photonics Plasmonics Van der Waals interaction

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Hundredfold increase of stimulated Brillouin-scattering bandwidth in whispering-gallery mode resonators

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Photonics Research 2023年第6期11卷 917-924页

作者： GUOPING LIN JINGYI TIAN TANG SUN QINGHAI SONG YANNE K.CHEMBO Ministry of Industry and Information Technology Key Laboratory of Micro-Nano Optoelectronic Information System School of ScienceHarbin Institute of TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Shenzhen 518055China Department of Electrical and Computer Engineering&Institute for Research in Electronics and Applied Physics(IREAP) University of MarylandCollege ParkMaryland 20742USA

Backward stimulated Brillouin scattering(SBS)is widely exploited for various applications in optics and *** typically features a narrow gain bandwidth of a few tens of megahertz in fluoride *** we report a hundredfold increase of SBS bandwidth in whispering-gallery mode *** crystalline orientation results in a large variation of the acoustic phase velocity upon propagation along the periphery,from which a broad Brillouin gain is *** 2.5 GHz wide Brillouin gain profile is theoretically found and experimentally *** phenomena with Brillouin shift frequencies ranging from 11.73 to 14.47 GHz in ultrahigh Q Z-cut magnesium fluoride cavities pumped at the telecommunication wavelength are ***,the Brillouin-Kerr comb in this device is *** 400 comb lines spanning across a spectral window of 120 nm are *** finding paves a new way for tailoring and harnessing the Brillouin gain in crystals.

关键词： scattering pumped Brillouin

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Exact solution of the longitudinally-graded rectangular plate under mixed instantaneous shock loading

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Waves in Random and Complex Media 2023年

作者： Amari, Abdelfattah Salman, Hayder Mahmood Department of Chemical Engineering College of Engineering King Khalid University Abha Saudi Arabia Research Laboratory of Processes Energetics Environment and Electrical Systems National School of Engineers of Gabes Gabes University Gabes Tunisia Department of Computer Science Al-Turath University College Baghdad Iraq

This study follows a novel path toward analysis of the thermoelastic reaction of the longitudinally-graded graphene-nanoplates enriched composite (LG-GNPEC) rectangular-shaped plates exposed to the mixed-form instantaneous shock. Three-dimensional form of the linear thermoelasticity theory is picked as the base for establishing the governing equations. Lord-Shulman theorem and energy balance relations are employed to determine the time-dependency of the system’s behavior. Due to simply-supported boundary conditions, solving the governing equations through an analytical solution on the bases of Navier’s approach is performed. Accuracy of the applied methods is verified by comparing the obtained results with those presented in the published studies. Results of this research can be directly considered in the analysis of the thermoelastic behavior of the advanced composite materials exposed to the mixed-form thermomechanical shocks. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Graphene

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TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support

arXiv

引用

arXiv 2023年

作者： Wang, Jie Yan, Zheng Lan, Jiahe Bertino, Elisa Pedrycz, Witold State Key Laboratory on Integrated Services Networks School of Cyber Engineering Xidian University Xi’an710071 China Department of Computer Science Purdue University West LafayetteIN47907 United States Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Electrical and Computer Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Engineering Istinye University Sariyer Istanbul34010 Turkey

Trust evaluation assesses trust relationships between entities and facilitates decision-making. Machine Learning (ML) shows great potential for trust evaluation owing to its learning capabilities. In recent years, Graph Neural Networks (GNNs), as a new ML paradigm, have demonstrated superiority in dealing with graph data. This has motivated researchers to explore their use in trust evaluation, as trust relationships among entities can be modeled as a graph. However, current trust evaluation methods that employ GNNs fail to fully satisfy the dynamic nature of trust, overlook the adverse effects of trust-related attacks, and cannot provide convincing explanations on evaluation results. To address these problems, we propose TrustGuard, a GNN-based accurate trust evaluation model that supports trust dynamicity, is robust against typical attacks, and provides explanations through visualization. Specifically, TrustGuard is designed with a layered architecture that contains a snapshot input layer, a spatial aggregation layer, a temporal aggregation layer, and a prediction layer. Among them, the spatial aggregation layer adopts a defense mechanism to robustly aggregate local trust, and the temporal aggregation layer applies an attention mechanism for effective learning of temporal patterns. Extensive experiments on two real-world datasets show that TrustGuard outperforms state-of-the-art GNN-based trust evaluation models with respect to trust prediction across single-timeslot and multi-timeslot, even in the presence of attacks. In addition, TrustGuard can explain its evaluation results by visualizing both spatial and temporal views. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Achieving Enhanced Bi-Linear Attention Network for Teaching Manner Analysis over Edge Cloud-assisted AIoT: Voice-Body Coordination Perspective

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IEEE Transactions on Cloud Computing 2025年

作者： Zhou, Yu Zou, Sai Wu, Bochun Ni, Wei Du, Xiaojiang Guizhou University Micro-nano and Intelligent Manufacturing Engineering Research Centre of Ministry of Education College of Big Data and Information Engineering China Fudan University State Key Laboratory of Integrated Chips and Systems Shanghai200433 China Data61 CSIRO Australia Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

Edge computing, an advanced extension of cloud computing, provides superior computational capabilities and lowlatency processing at the network edge, facilitating its availability for real-time data analysis in resource-limited settings. When applied to the analysis of teaching methodologies, edge computing enables the seamless integration of vocal and physical cues, facilitating collaborative, dynamic, and real-time evaluations of teaching quality. However, the inherent complexity of human perception and multimodal interactions impose great challenges to the analysis of these aspects in Artificial Intelligence of Things (AIoT). This paper introduces an innovative mathematical model and a measurement index specifically designed to assess changes in voice-body coordination over time. To achieve this, we propose a cloud-enabled enhanced Bi-Linear Attention Network incorporating entropy and Fourier transforms (BAN-E-FT), which leverages both temporal and frequencydomain features. Specifically, by harnessing the computational and storage capabilities of edge computing, BAN-E-FT facilitates distributed training, expedites large-scale data processing, and enhances model scalability, where entropy measures and Fourier transforms capture modality dynamics, enhancing BAN's fusion capabilities. Moreover, a conditional domain adversarial network is embedded to address regional teaching variations, improving model generalizability. We also verify the robustness of BAN-EFT with accuracy and convergence through convex optimization analysis. Experiments on the eNTERFACE'05 dataset demonstrate 81% accuracy in assessing teaching adaptability, while real-world test at Guizhou University confirms 78% accuracy when using BAN-E-FT, matching human expert assessments. © 2013 IEEE.

关键词： Data integration

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A Multi-Layer Software Architecture for Aerial Cognitive Multi-Robot systems in Power Line Inspection Tasks

A Multi-Layer Software Architecture for Aerial Cognitive Mul...

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2021 International Conference on Unmanned Aircraft systems, ICUAS 2021

作者： Silano, Giuseppe Bednar, Jan Nascimento, Tiago Capitan, Jesus Saska, Martin Ollero, Anibal Czech Technical University Faculty of Electrical Engineering Prague Czech Republic Universidade Federal da Paraiba Department of Computer Systems Brazil Grvc Robotics Laboratory University of Seville Spain

ISBN: (纸本)9780738131153

This paper presents a multi-layer software architecture to perform cooperative missions with a fleet of quad-rotors providing support in electrical power line inspection operations. The proposed software framework guarantees the compliance with safety requirements between drones and human workers while ensuring that the mission is carried out successfully. Besides, cognitive capabilities are integrated in the multi-vehicle system in order to reply to unforeseen events and external disturbances. The feasibility and effectiveness of the proposed architecture are demonstrated by means of realistic simulations. © 2021 IEEE.

关键词： Inspection

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SubPipe: A Submarine Pipeline Inspection Dataset for Segmentation and Visual-inertial Localization

SubPipe: A Submarine Pipeline Inspection Dataset for Segment...

引用

OCEANS

作者： Olaya Álvarez-Tuñón Luiza Ribeiro Marnet Martin Aubard László Antal Maria Costa Yury Brodskiy Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR-Lab) Aarhus University Aarhus C Denmark EIVA a/s Skanderborg Denmark OceanScan Marine Systems & Technology Matosinhos Portugal RWTH Aachen University Aachen Germany

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

This paper presents SubPipe, an underwater dataset for SLAM, object detection, and image segmentation. SubPipe has been recorded using a lightweight autonomous underwater vehicle (LAUV), operated by OceanScan MST, and carrying a sensor suite including two cameras, a side-scan sonar, and an inertial navigation system, among other sensors. The AUV has been deployed in a pipeline inspection environment with a submarine pipe partially covered by sand. The AUV's pose ground truth is estimated from the navigation sensors. The side-scan sonar and RGB images include object detection and segmentation annotations, respectively. State-of-the-art segmentation, object detection, and SLAM methods are benchmarked on SubPipe to demonstrate the dataset's challenges and opportunities for leveraging computer vision algorithms. To the authors' knowledge, this is the first anno-tated underwater dataset providing a real pipeline inspection scenario. The dataset and experiments are publicly avail-able online at https://***/remaro-network/SubPipe-dataset.

关键词： Location awareness Image segmentation Simultaneous localization and mapping Oceans Pipelines Object detection Inertial navigation

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Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments

arXiv

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arXiv 2024年

作者： Wang, Jiacheng Du, Hongyang Niyato, Dusit Xiong, Zehui Kang, Jiawen Ai, Bo Han, Zhu Kim, Dong In School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore China Ministry of Education Guangzhou510006 China State Key Laboratory of Rail Traffic Control and Safety Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Groundbreaking applications such as ChatGPT have heightened research interest in generative artificial intelligence (GAI). Essentially, GAI excels not only in content generation but also signal processing, offering support for wireless sensing. Hence, we introduce a novel GAI-assisted human flow detection system (G-HFD). Rigorously, G-HFD first uses the channel state information (CSI) to estimate the velocity and acceleration of propagation path length change of the human induced reflection (HIR). Then, given the strong inference ability of the diffusion model, we propose a unified weighted conditional diffusion model (UW-CDM) to denoise the estimation results, enabling detection of the number of targets. Next, we use the CSI obtained by a uniform linear array with wavelength spacing to estimate the HIR’s time of flight and direction of arrival (DoA). In this process, UW-CDM solves the problem of ambiguous DoA spectrum, ensuring accurate DoA estimation. Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i.e., the subflow size. The evaluation based on practical downlink communication signals shows G-HFD’s accuracy of subflow size detection can reach 91%. This validates its effectiveness and underscores the significant potential of GAI in the context of wireless sensing. © 2024, CC BY.

关键词： Artificial intelligence

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Autonomous control management of synchronous generators in microgrid system

Autonomous control management of synchronous generators in m...

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International Conference on Energy, Power, Environment, Control and Computing (ICEPECC 2025)

作者： Hafiz Ghulam Murtza Qamar Adil Ashraf Fareed Ahmad Ayesha Riaz Muhammad Imran Muhammad Zain Yousaf Imran Khan School of Electrical Engineering Yanshan University Qinhuangdao 066004 People's Republic of China Department of Electrical Electronics and Computer Systems University of Sargodha People's Republic of China Center for Renewable Energy and Microgrids Huanjiang Laboratory Zhejiang University Zhuji Zhejiang 311816 People's Republic of China

ISBN: (数字)9781837243150

Decentralized excitation control based on local asynchronous clock feedback measurement leads to the problem of decentralized incompatibility of excitation controllers, which reduces the ability of excitation control design to improve the stability of power systems. Therefore, the problem of decentralized compatible excitation control is proposed, and the effect of excitation control on the dynamic characteristics of the system under compatible and incompatible modes is compared and analyzed, and the shortcomings of decentralized incompatible excitation control and the advantages of decentralized compatible excitation control are given.

关键词：

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Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

arXiv

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arXiv 2024年

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Wanna, Selma Solovyev, Nicholas Rasmussen, Kim Alexandrov, Boian S. Nicholas, Charles Matuszek, Cynthia Theoretical Division Los Alamos National Laboratory Los Alamos United States Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos United States Department of Computer Science and Electrical Engineering University of Maryland Baltimore CountyMD United States

Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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