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Development of Flexible Sendust Composite Sheets with Enhanced Magnetic Permeability for Microwave Absorbers and Antennas

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IEEE Transactions on Magnetics 2025年

作者： Whalen, Matthew P. Plouff, David T. Shevchenko, Nicholas B. Mirotznik, Mark Xiao, John Q. University of Delaware Quantum Science and Engineering Program NewarkDE19716 United States University of Delaware Department of Physics and Astronomy NewarkDE19716 United States University of Delaware Center for Composite Materials NewarkDE19716 United States University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States

The demand for high-frequency soft magnetic composites with elevated permeability and flexibility is growing for applications in microwave absorbers and magnetodielectric antennas. However, balancing high permeability with mechanical flexibility remains challenging. This study presents flexible Sendust (FeSiAl) composite sheets, fabricated with a hot-pressing technique that aligns flakes within an acrylic rubber and epoxy resin matrix. The alignment significantly enhances relative permeability at high frequencies, achieving single-layer film properties of μr' = 14.8 and μr"= 20.4 at 1 GHz, alongside high saturation magnetization (118 eμg) and high hesitivity (44.7 kΩ m-1), outperforming commonly used materials such as NiZn ferrite. Our method supports customizable thickness without sacrificing permeability, establishing a versatile platform for advanced conformal microwave absorber and antenna applications. © 2025 IEEE.

关键词： Saturation magnetization

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Twin-Array Quasi FSS Reflector with Dual Band Cross-Dipole for 5G Base Station Antenna Application

Twin-Array Quasi FSS Reflector with Dual Band Cross-Dipole f...

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2023 International Conference on Power, Energy and Innovations, ICPEI 2023

作者： Santos, Ian Jerome Q. Tangjitjetsada, Maleeya Rakluea, Paitoon Wongsin, Norakamon Rakluea, Chawalit Mahatthanajatuphat, Chatree Chudpooti, Nonchanutt Thianthong, Tawan Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Thanyaburi Pathum Thani 12110 Thailand Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Department of Electronics and Telecommunication Engineering Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Wongsawang Bangsue Bangkok10800 Thailand King Mongkut's University of Technology North Bnagkok Faculty of Applied Science Department of Industrial Physics and Medical Instrumentation Wongsawang Bangsue Bangkok10800 Thailand Siam Airport Ground Service Co. Ltd. Don Mueang Bangkok10210 Thailand

ISBN: (纸本)9798350344585

This article provides an explanation of the use of a Twin-Array Quasi Frequency Selective Surface (FSS) Reflector with Dual Band Cross-Dipole for antenna applications in 5G base stations. The base material employed in this study is FR4 sheet, which has a dielectric constant (ϵr) of 4.3. The antenna obtained has dimensions of 100x100 mm2 and functions throughout the frequency spectrum spanning from 730 to 860 MHz, 1700 to 2130 MHz. The primary benefits of this antenna design are its lightweight structural composition, compatibility with the frequency range of 730-860 MHz, and discernible performance attributes. The CST Studio Suite software was used to conduct a simulation of the antenna's performance. The antenna has an operating frequency range of 730-860 MHz and 1700-2130 MHz, enabling it to support a unidirectional wave propagation pattern that is suitable for the implementation of a 5G mobile communication base station. © 2023 IEEE.

关键词： Dipole antennas

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Ultrastrong Spin-Photon Coupling in Gadolinium Gallium Garnet

Ultrastrong Spin-Photon Coupling in Gadolinium Gallium Garne...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Kritzell, T. Elijah Bao, Junzhe Doumani, Jacques Lee, Jae Joon Xu, Hongjing Tay, Fuyang Nojiri, Hiroyuki Bamba, Motoaki Baydin, Andrey Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Institute for Materials Research Tohoku University Sendai980-8577 Japan The Hakubi Center for Advanced Research Kyoto University Kyoto606-8502 Japan Department of Physics I Kyoto University Kyoto606-8502 Japan Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Material Science and NanoEngineering Rice University HoustonTX77005 United States

We examine ultrastrong coupling of an ensemble of paramagnetic spins in Gd3Ga5O12 with terahertz photons in the presence of a high external magnetic field both in the bulk and thin-film limits. © 2023 The Author(s)

ISBN: (纸本)9781957171258

关键词： Photons

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Tuning the activation function to optimize the forecast horizon of a reservoir computer

arXiv

引用

arXiv 2023年

作者： Hurley, Lauren A. Restrepo, J.G. Shaheen, S.E. Department of Electrical Computer and Energy Engineering University of Colorado Boulder United States Department of Applied Mathematics University of Colorado Boulder United States United States Department of Physics University of Colorado Boulder United States

Reservoir computing is a machine learning framework where the readouts from a nonlinear system (the reservoir) are trained so that the output from the reservoir, when forced with an input signal, reproduces a desired output signal. A common implementation of reservoir computers is to use a recurrent neural network as the reservoir. The design of this network can have significant effects on the performance of the reservoir computer. In this paper we study the effect of the node activation function on the ability of reservoir computers to learn and predict chaotic time series. We find that the Forecast Horizon (FH), the time during which the reservoir’s predictions remain accurate, can vary by an order of magnitude across a set of 16 activation functions used in machine learning. By using different functions from this set, and by modifying their parameters, we explore whether the entropy of node activation levels or the curvature of the activation functions determine the predictive ability of the reservoirs. We find that the FH is low when the activation function is used in a region where it has low curvature, and a positive correlation between curvature and FH. For the activation functions studied we find that the largest FH generally occurs at intermediate levels of the entropy of node activation levels. Our results show that the performance of reservoir computers is very sensitive to the activation function shape. Therefore, modifying this shape in hyperparameter optimization algorithms can lead to improvements in reservoir computer performance. © 2023, CC BY.

关键词： Forecasting

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Challenges and Opportunities of Machine Learning on Neutron and X-ray Scattering

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Synchrotron Radiation News 2022年第4期35卷 16-20页

作者： Drucker, Nathan C. Liu, Tongtong Chen, Zhantao Okabe, Ryotaro Chotrattanapituk, Abhijatmedhi Nguyen, Thanh Wang, Yao Li, Mingda Quantum Measurement Group MIT Cambridge MA United States School of Engineering and Applied Sciences Harvard University Cambridge MA United States Department of Physics MIT Cambridge MA United States Department of Mechanical Engineering MIT Cambridge MA United States Department of Chemistry MIT Cambridge MA United States Department of Electrical Engineering and Computer Science MIT Cambridge MA United States Department of Nuclear Science and Engineering MIT Cambridge MA United States Department of Physics and Astronomy Clemson University Clemson SC United States

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Nonlinear Conversion of Orbital Angular Momentum States of light

Nonlinear Conversion of Orbital Angular Momentum States of l...

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Laser Science, LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Bassène, Pascal Buldt, Finn Rumman, Nazifa Wang, Tianhong Heitert, Phillip N'Gom, Moussa Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute 110 8th Street TroyNY12180 United States Electrical Computer and Systems Engineering Department Rensselaer Polytechnic Institute United States

ISBN: (纸本)9781557528209

We investigate the second harmonic generation of light field carrying orbital angular momentum in bulk χ(2) material. We show that due to conservation of energy and momentum, the frequency doubled beam light has a modified spatial distribution and mode characteristics. Through rigorous phase matching conditions, we demonstrate efficient mode and frequency conversion based on three-wave nonlinear optical mixing. © Optica Publishing Group 2021, © 2021 The Author(s)

关键词： Phase matching

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Microfabrication of dielectric resonators with Q-factor exceeding 5 billion

Microfabrication of dielectric resonators with Q-factor exce...

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Frontiers in Optics, FiO 2022

作者： Jin, Naijun Mason, David Luo, Yizhi Kharel, Prashanta Isichenko, Andrei Blumenthal, Daniel J. Papp, Scott B. Rakich, Peter T. Department of Applied Physics Yale University New HavenCT06520 United States Department of Electrical and Computer Engineering University of California Santa Barbara Santa BarbaraCA United States Department of Physics University of Colorado Boulder BoulderCO80309 United States Time and Frequency Division 688 National Institute of Standards and Technology BoulderCO80305 United States

We demonstrate a new method to fabricate arrays of micro-Fabry-Pérot resonators supporting Q-factors as high as 5 Billion and highly tailorable resonator geometries to serve applications ranging from frequency me...

ISBN: (纸本)9781557528209

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Robust AI-Enabled Electronic Components Authentication and Anti -Counterfeiting

Robust AI-Enabled Electronic Components Authentication and A...

引用

International Conference on Science Technology engineering and Management (ICONSTEM)

作者： E. Afreen Banu R. Priyanka P. Thiruramanathan T. Senthilnathan Vithya V T K. Vinoth Department of Computer Science Engineering SIMATS Saveetha School of Engineering Chennai Tamil Nadu India Department of Computer Science Engineering S.A Engineering College Chennai Tamil Nadu India Department of Physics Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Applied Physics Sri Venkateswara College of Engineering Sriperumbudur Tamil Nadu India Department of Computer Science Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In consumer electronics, aircraft, health care, and defense, fake electronic parts compromise system security and reliability. This study presents an AI-powered electrical component authentication and anti-counterfeiting system for efficiency and robustness. To prevent counterfeit electronic components, a multi-layered defense uses cutting-edge authentication and AI algorithms. Deep learning models for picture identification, spectroscopic analysis, and other cutting-edge component physical attribute evaluation. A decentralized blockchain platform can record the whole supply chain from producers to consumers. Smart contracts automate and enforce quality standards, eliminating many counterfeiting layers. An Integrated sensors and triggers detect physical tampering, alarm, and authenticate. Tracking and reporting component movements in real time permits quick response to suspicious activity. Stakeholders can easily validate components with AI algorithms via web or mobile apps. NFC, RFID, and QR code authentication is simple. To prevent counterfeiting, the framework specifies a stringent electrical component validation method. Advanced AI and physical and digital security measures provide comprehensive electrical component security throughout their lifespan.

关键词： Tracking Supply chains Authentication Electronic components Aerospace electronics Security Stakeholders

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Sensing with shallow recurrent decoder networks

arXiv

引用

arXiv 2023年

作者： Williams, Jan P. Zahn, Olivia Nathan Kutz, J. Department of Mechanical Engineering University of Washington SeattleWA United States Department of Physics University of Washington SeattleWA United States Departments of Applied Mathematics and Electrical and Computer Engineering University of Washington SeattleWA United States

Sensing is a universal task in science and engineering. Downstream tasks from sensing include inferring full state estimates of a system (system identification), control decisions, and forecasting. We propose a SHallow REcurrent Decoder (SHRED) neural network structure for sensing which incorporates (i) a recurrent neural network to learn a latent representation of the temporal dynamics of the sensors, and (ii) a shallow decoder that learns a mapping between this latent representation and the high-dimensional state space. SHRED enables accurate reconstructions with far fewer sensors, outperforms existing techniques when more measurements are available, and is more robust to random sensor placements. In the example cases explored, complex spatio-temporal dynamics are characterized with exceedingly limited sensors that can be randomly placed with minimal loss of performance. © 2023, CC BY.

关键词： Recurrent neural networks

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Deep learning improved autofocus for motion artifact reduction and its application in quantitative susceptibility mapping

arXiv

引用

arXiv 2024年

作者： Li, Chao Zhang, Jinwei Zhang, Hang Li, Jiahao Spincemaille, Pascal Nguyen, Thanh D. Wang, Yi Department of Applied and Engineering Physics Cornell University IthacaNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States Meinig School of Biomedical Engineering Cornell University IthacaNY United States Department of Electrical and Computer Engineering Cornell University IthacaNY United States

Purpose: To develop a pipeline for motion artifact correction in mGRE and quantitative susceptibility mapping (QSM). Methods: Deep learning is integrated with autofocus to improve motion artifact suppression, which is applied QSM of patients with Parkinson's disease (PD). The estimation of affine motion parameters in the autofocus method depends on signal-to-noise ratio and lacks accuracy when data sampling occurs outside the k-space center. A deep learning strategy is employed to remove the residual motion artifacts in autofocus. Results: Results obtained in simulated brain data (n =15) with reference truth show that the proposed autofocus deep learning method significantly improves the image quality of mGRE and QSM (p = 0.001 for SSIM, p © 2024, CC BY.

关键词： Image quality

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