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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Johnson, Hans Zorzetti, Silvia Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States Fermi National Accelerator Laboratory BataviaIL60510 United States

ISBN: (纸本)9781665493765

This work explores avenues and target areas for optimizing FPGA-based control hardware for experiments conducted on superconducting quantum computing systems and serves as an introduction to some of the current research at the intersection of classical and quantum computing hardware. With the promise of building larger-scale error-corrected quantum computers based on superconducting qubit architecture, innovations to room-temperature control electronics are needed to bring these quantum realizations to fruition. The QICK (Quantum Instrumentation Control Kit) is one leading experimental FPGA-based implementations. However, its integration into other experimental quantum computing architectures, especially those using superconducting radiofrequency (SRF) cavities, is largely unexplored. We identify some key target areas for optimizing control electronics for superconducting qubit architectures and provide some preliminary results to the resolution of a control pulse waveform. With optimizations targeted at 3D superconducting qubit setups, we hope to bring to light some of the requirements in classical computational methodologies to bring out the full potential of this quantum computing architecture, and to convey the excitement of progress in this research. © 2023 IEEE.

关键词： Digital signal processing

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An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems

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npj Computational Materials 2023年第1期9卷 1217-1226页

作者： Yuwei Mao Mahmudul Hasan Arindam Paul Vishu Gupta Kamal Choudhary Francesca Tavazza Wei-keng Liao Alok Choudhary Pinar Acar Ankit Agrawal Department of Electrical and Computer Engineering Northwestern UniversityEvanstonILUSA Virginia Tech BlacksburgVAUSA Materials Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD 20899USA Theiss Research La JollaCA 92037USA

Materials design aims to identify the material features that provide optimal properties for various engineering applications,such as aerospace,automotive,and *** of the important but challenging problems for materials design is to discover multiple polycrystalline microstructures with optimal *** paper proposes an end-to-end artificial intelligence(AI)-driven microstructure optimization framework for elastic properties of *** this work,the microstructure is represented by the Orientation Distribution Function(ODF)that determines the volume densities of crystallographic *** framework was evaluated on two crystal systems,cubic and hexagonal,for Titanium(Ti)in Joint Automated Repository for Various Integrated Simulations(JARVIS)database and is expected to be widely applicable for materials with multiple crystal *** proposed framework can discover multiple polycrystalline microstructures without compromising the optimal property values and saving significant computational time.

关键词： microstructure cubic crystal

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Improving Signal-to-Noise Ratio (SNR) for Readout Signals Using Adaptive Filters on Reconfigurable Controls Hardware 4

Improving Signal-to-Noise Ratio (SNR) for Readout Signals Us...

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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

ISBN: (纸本)9798350343236

This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the noise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing. © 2023 IEEE.

关键词： Digital signal processing

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Predictive modeling of MXene-based solar absorbers using a deep neural network

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Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Time domain analysis

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Dynamic Modeling of Key Operating Parameters for Supercritical Circulating Fluidized Bed Units based on Data-Knowledge-Driven Method

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Journal of Thermal Science 2024年第3期33卷 1216-1230页

作者： YU Haoyang GAO Mingming ZHANG Hongfu CHEN Jiyu LIU Jizhen YUE Guangxi The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer EngineeringNorth China Electric Power UniversityBeijing102206China CHN Energy New Energy Technology Research Institute Co. Ltd.Beijing102209China Department of Energy and Power Engineering Tsinghua UniversityBeijing100084China

To address the pressing need for intelligent and efficient control of circulating fluidized bed(CFB)units,it is crucial to develop a dynamic model for the key operating parameters of supercritical circulating fluidized bed(SCFB)***,data-knowledge-driven dynamic model of bed temperature,load,and main steam pressure of the SCFB unit has been ***,a knowledge-driven method is employed to develop a dynamic model for key operating parameters of SCFB *** model parameters are determined based on the operating data of the unit and continuously optimized in real ***,Bidirectional Long Short-Term Memory combined with Convolutional Neural Network and Attention Mechanism is utilized to build the dynamic model of bed temperature,load,and main steam ***,a collaboration and integration method based on the critic weight method and the variation coefficient method is proposed to establish data-knowledge-driven model of key operating parameters for SCFB *** model displays great accuracy and fitting ability compared with other methods and effectively captures the dynamic characteristics,which can provide a research basis for the design of intelligent flexible control mode of SCFB unit.

关键词： circulating fluidized bed dynamic model data-knowledge-driven method deep learning power plant

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Managing natural disasters: An analysis of technological advancements, opportunities, and challenges

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 99-109页

作者： Krichen, Moez Abdalzaher, Mohamed S. Elwekeil, Mohamed Fouda, Mostafa M. Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3029 Tunisia Seismology Department National Research Institute of Astronomy and Geophysics Helwan Cairo11421 Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University. Menoufia Menouf32952 Egypt Department of Electrical and Computer Engineering College of Science and Engineering Idaho State University PocatelloID83209 United States

Natural disasters (NDs) have always been a major threat to human lives and infrastructure, causing immense damage and loss. In recent years, the increasing frequency and severity of natural disasters have highlighted the need for more effective and efficient disaster management strategies. In this context, the use of technology has emerged as a promising solution. In this survey paper, we explore the employment of recent technologies in order to relieve the impacts of various natural disasters. We provide an overview of how different technologies such as Remote Sensing, Radars and Satellite Imaging, internet-of-things (IoT), Smartphones, and Social Media can be utilized in the management of NDs. By utilizing these technologies, we can predict, respond, and recover from NDs more effectively, potentially saving human lives and minimizing infrastructure damage. The paper also highlights the potential benefits, limitations, and challenges associated with the implementation of these technologies for natural disaster management purposes. While the use of technology can significantly improve NDM, there are also various challenges that need to be addressed, such as the cost of implementation and the need for specialized knowledge and skills. Overall, this survey paper provides a comprehensive overview of the use of technology in managing NDs and sheds light on the important role such technologies can play in NDM. By exploring the potential applications of different technologies, this paper aims to contribute to the development of more effective and sustainable disaster management strategies. © 2023 The Authors

关键词： Internet of things

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La-substituted W-type barium-nickel ferrites for tunable and high-performance electromagnetic wave absorption

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International Journal of Minerals,Metallurgy and Materials 2025年第3期32卷 645-656页

作者： Long Wang Jiurong Liu Shenghui Xie Yanli Deng Zhou Wang Shanyue Hou Shengying Yue Gang Wang Na Wu Zhihui Zeng Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials(Ministry of Education) School of Materials Science and EngineeringShandong UniversityJinan 250061China Electronic Materials Research Laboratory Key Laboratory of the Ministry of Education and International Center for Dielectric ResearchSchool of Electronic Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China Army Engineering University of PLA College of Field Engineering Nanjing 210007China Laboratory for Multiscale Mechanics and Medical Science State Key Laboratory for Strength and Vibration of Mechanical StructuresSchool of AerospaceXi’an Jiaotong UniversityXi’an 710049China Department of Electrical and Computer Engineering Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbana 61801USA School of Chemistry and Chemical Engineering Shandong UniversityJinan 250100China

W-type barium-nickel ferrite(BaNi_(2)Fe_(16)O_(27))is a highly promising material for electromagnetic wave(EMW)absorption be-cause of its magnetic loss capability for EMW,low cost,large-scale production potential,high-temperature resistance,and excellent chemical ***,the poor dielectric loss of magnetic ferrites hampers their utilization,hindering enhancement in their EMW-absorption *** efficient strategies that improve the EMW-absorption performance of ferrite is highly desired but re-mains ***,an efficient strategy substituting Ba^(2+)with rare earth La^(3+)in W-type ferrite was proposed for the preparation of novel La-substituted ferrites(Ba_(1-x)LaxNi_(2)Fe_(15.4)O_(27)).The influences of La^(3+)substitution on ferrites’EMW-absorption performance and the dissipative mechanism toward EMW were systematically explored and ***^(3+)efficiently induced lattice defects,enhanced defect-induced polarization,and slightly reduced the ferrites’bandgap,enhancing the dielectric properties of the ***^(3+)also enhanced the ferromagnetic resonance loss and strengthened magnetic *** effects considerably improved the EMW-absorption perform-ance of Ba_(1-x)LaxNi_(2)Fe_(15.4)O_(27)compared with pure W-type *** x=0.2,the best EMW-absorption performance was achieved with a minimum reflection loss of-55.6 dB and effective absorption bandwidth(EAB)of 3.44 GHz.

关键词： electromagnetic wave absorption W-type barium ferrite rare earth dielectric

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Cluster-HSFL: A Cluster-Based Hybrid Split and Federated Learning

Cluster-HSFL: A Cluster-Based Hybrid Split and Federated Lea...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Zhang, Songge Tu, Haoyu Li, Zuguang Liu, Shengbo Li, Shaofeng Wu, Wen Shen, Xuemin Sherman Peking University School of Electronic and Computer Engineering Shenzhen China Frontier Research Center Peng Cheng Laboratory Shenzhen China Department of Electrical and Computer Engineering Sun Yat-Sen University Guangzhou China Shenzhen China Department of Electrical and Computer Engineering University of Waterloo ON Canada

ISBN: (纸本)9798350345384

Federated learning (FL) and split learning (SL) are two machine learning paradigms that can distributedly train artificial intelligence (AI) models while guaranteeing client privacy. FL can parallelly train AI models based on clients' dataset, but it suffers from considerable communication overhead for model uploading and imposes a significant on-device computation workload. SL can mitigate the computation workload of the devices by offloading part of the AI model training to an edge server, but it suffers from long training delay for a large number of clients. To address these challenges, in this paper we present a cluster-based hybrid split federated learning (Cluster-HSFL) scheme by integrating FL and SL paradigms. This scheme can reduce training latency and alleviate on-device computational constraints while preserving accuracy performance. We establish a testbed for the Cluster-HSFL scheme based on the NVIDIA Jetson series hardware. The experimental results demonstrate that the Cluster-HSFL surpasses FL and SL by 6.3× and 1.8×, respectively, in terms of delay performance. Our results provide insight into the practical performance of the Cluster-HSFL scheme and can help guide future experimental research on SL in resource-limited wireless networks. © 2023 IEEE.

关键词： Artificial intelligence

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Assessment of Neuromuscular Function in Patients with Long-Term Symptoms of COVID-19 9th

Assessment of Neuromuscular Function in Patients with Lon...

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9th Latin American Congress on Biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical engineering, CBEB 2022

作者： Cremasco, Emilia F. Zambalde, Ellen P. Germer, Carina M. Elias, Leonardo A. Department of Electronics and Biomedical Engineering School of Electrical and Computer Engineering University of Campinas Campinas Brazil Neural Engineering Research Laboratory Center for Biomedical Engineering University of Campinas Campinas Brazil Department of Biomedical Engineering Federal University of Pernambuco Recife Brazil

ISBN: (纸本)9783031494062

Part of individuals infected by SARS-CoV-2 experiences long-lasting effects that include a wide range of symptoms, with fatigue being one of the most common complaints. Self-reported fatigue can have different manifestations, and although chronic fatigue has been reported as a long-term effect of other viral infections, the etiology of this complaint is still unclear. In this work, we employed an experimental protocol to assess the neuromuscular function in participants who had COVID-19 (12 women, 5–30 weeks after COVID-19 diagnosis) and age-matched controls (9 women). The COVID participants presented a high degree of variability in their symptoms, and the majority (91%) had mild to moderate manifestations. Participants performed an abduction force of the index finger in three different motor tasks, namely isometric force steadiness, reaction time, and time-to-task failure (fatigue task). The experiment was performed in three stages: pre-fatigue, post-fatigue, and after a resting interval of 5 min. We found significant differences between groups for the maximum voluntary contraction, indicating the COVID group had a lower force generation capacity. Nonetheless, no significant difference was found in measures related to force control;however, a larger and more homogeneous number of participants is needed to confirm these observations. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Coronavirus

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Relative Permittivity of Natural Ester Oil-Based Nanofluids with Iron Oxide Nanoparticles 58

Relative Permittivity of Natural Ester Oil-Based Nanofluids ...

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58th International Universities Power engineering Conference, UPEC 2023

作者： Tsovilis, Thomas E. Staikos, Evangelos T. Hadjicostas, Alexandros Y. Datsios, Zacharias G. Litsardakis, George Pyrgioti, Eleftheria C. Bakandritsos, Aristeidis Peppas, George D. School of Electrical & Computer Engineering Aristotle University of Thessaloniki High Voltage Laboratory Greece School of Electrical & Computer Engineering Aristotle University of Thessaloniki Laboratory of Materials for Electrotechnics Greece University of Patras High Voltage Laboratory Electrical & Computer Engineering Department Greece Palacký University Olomouc Czech Republic Raycap Sa Industrial Area of Drama Research & Development Department Drama66100 Greece

ISBN: (纸本)9798350316834

The real part of the relative permittivity and the electrical conductivity of a natural ester oil, that serves as a liquid matrix for nanofluids, are measured for a wide frequency and electric field range. The focus of this work is the exploration of the modification of these basic electrical parameters of the liquid matrix after the integration of iron oxide nanoparticles. A series of experiments with the aid of an impedance analyzer, an LCR meter, and a variable-frequency power supply employing two different liquid test fixtures reveal an increase of both real and imaginary parts of relative permittivity with increasing nanoparticles concentration up to 0.5% w/w. © 2023 IEEE.

关键词： Iron oxides

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