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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kim, Seongwook Byeon, Gwangeun Lee, Seungeui Bae, Youngseok Hong, Seokin Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Advanced R&D Center Hanwha Systems Co. Ltd. Korea Republic of Agency for Defense Development Emerging Science and Technology Directorate Korea Republic of

ISBN: (纸本)9798331510756

Since deep learning inference involves a significant amount of computations, there have been a lot of efforts to accelerate the inference process by eliminating ineffectual compu-tations. As a solution to this problem, several sparsity-aware deep learning accelerators have been proposed. These accelerators perform the inference on compressed data (filters and feature maps) and dynamically remove the ineffectual multiplications with zero operands. For efficiently handling the compressed data on the accelerators, a simple and effective compression scheme, which encodes the whole data with its nonzero values and their positions, is necessary. However, prior approaches have several shortcomings, such as low compression efficiency and high complexity in compression and decompression hardware. This paper proposes a low-cost compression scheme that can be applied to sparsity-aware deep-learning accelerators. The compression scheme synergistically combines two low-cost compression schemes: Zero-Value Compression (ZVC) and Base-Delta-Immediate Compression (BDI). Our experimental evaluation shows that the proposed compression scheme achieves an average compression ratio of 2.26, resulting in 2.33x performance improvement, © 2025 IEEE.

关键词： Data handling

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Promoting effect of d-band center and in-situ precipitation strategy on sulfur evolution reaction

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Journal of Energy Chemistry 2025年第5期104卷 740-748页

作者： Jun Pu Yun Tan Jiasen Yin Zihan Shen Shoujie Liu Kai Zhang Lei Zhou Yagang Yao Key Laboratory of Functional Molecular Solids Ministry of EducationAnhui Provincial Engineering Laboratory for New-Energy Vehicle Battery Energy-Storage MaterialsCollege of Chemistry and Materials ScienceAnhui Normal UniversityWuhu 241002AnhuiChina National Laboratory of Solid State Microstructures College of Engineering and Applied SciencesJiangsu Key Laboratory of Artificial Functional MaterialsCollaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093JiangsuChina State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China School of Materials Science and Engineering Anhui UniversityHefei 230601AnhuiChina School of Energy and Power Engineering Jiangsu UniversityZhenjiang 212013JiangsuChina Department of Electrical Engineering Eindhoven University of TechnologyEindhoven MB 5600the Netherlands

The slow conversion of polyphase in lithium-sulfur(Li-S)batteries not only intensifies the shuttle effect of lithium polysulfides(LiPSs),but also causes the continuous accumulation of inactive sulfur species,resulting in rapid capacity attenuation and sluggish dynamic ***,the promoting effect of atomic interface stress on sulfur reaction was investigated via CoFe-CoFe_(2)O_(4)heterogeneous nanosheets with a cavity *** strain force induced by the in-situ precipitation of Co Fe bimetallic alloy in oxide matrix increased the d-band center,which was conducive to the interaction between catalyst and Li *** sulfur cathode based on this two-dimensional(2D)nanosheet design showed an extremely high capacity of 751 mAh g^(-1)at 4 *** with a sulfur loading of 5.55 mg cm^(-2),its area capacity was still as high as 7.15 mAh cm^(-2).Meanwhile,the enhanced stability greatly improved the practical potential of Li-S batteries.

关键词： 2D nanosheets In-situ precipitation Strain effect D-band center Li-S batteries

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Design and Development of a Fixed-Wing Tail-Sitter Tethered VTOL Drone with Custom Autopilot and Robust Control Techniques 16th

Design and Development of a Fixed-Wing Tail-Sitter Tether...

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16th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Safaee, Ahmad Aguiar, A. Pedro Santos, Filipe Neves Moreira, Antonio Paulo SYSTEC-ARISE Research Center for Systems and Technologies Faculty of Engineering University of Porto Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering Technology and Science CRIIS-Centre for Robotics in Industry and Intelligent Systems Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering and DEEC/FEUP - Department of Electrical and Computer Engineering Faculty of Engineering of the University of Porto Porto4200-465 Portugal

ISBN: (纸本)9783031817236

The need for aerial platforms capable of sustained operation is critical in fields such as surveillance, agricultural monitoring, disaster response, and temporary telecommunication systems. Traditional unmanned aerial vehicles (UAVs) are hampered by limited battery capacity and inefficient energy utilization. This paper introduces an autonomous vertical flight drone tethered to a ground-based platform, which overcomes these limitations. The drone is designed to optimize its position and energy usage by harnessing wind currents, functioning similarly to a kite when conditions permit. This is enabled by a custom-designed autopilot system comprising both hardware and software tailored for this application. We detail the model identification process for three vertical take-off and landing (VTOL) drone systems, employing authentic data that measures lateral positional errors from the tether. Robust mixed sensitivity controllers are developed and implemented. The effectiveness of these controllers is validated through simulations showing their capability to handle uncertainties. Furthermore, the practical application and robust performance of the system are confirmed by a real-world case study, highlighting its potential in real operational environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： VTOL/STOL aircraft

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Multi-modal Multitask Learning Model for Simultaneous Classification of Two Epilepsy Biomarkers 27th

Multi-modal Multitask Learning Model for Simultaneous Classi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Broti, Nawara Mahmood Sawada, Masaki Takayama, Yutaro Iijima, Keiya Iwasaki, Masaki Ono, Yumie Electrical Engineering Program Graduate School of Science and Technology Meiji University Kanagawa Kawasaki Japan Department of Neurosurgery Yokohama City University Hospital Kanagawa Yokohama Japan Department of Neurosurgery National Center Hospital National Center of Neurology and Psychiatry Tokyo Kodaira Japan Department of Electronics and Bioinformatics School of Science and Technology Meiji University Kanagawa Kawasaki Japan

ISBN: (纸本)9783031781971

Accurate estimation of the epileptogenic zone is critical for surgical treatment of drug-resistant epilepsy. While epilepsy biomarkers detection for epileptogenic zone localization has traditionally relied on expert clinician knowledge, the use of deep learning in this context has recently gained appeal for objective diagnosis and reducing clinician burden. However, previously proposed classifiers for electrocorticogram data focused on only single biomarkers among many types, requiring separate models and large datasets for each. To minimize clinicians’ workload and patients’ sufferings, innovative methods to classify multiple biomarkers using small training datasets and minimal computation time is required. This research marks the first implementation of multi-modal multitask learning in epilepsy biomarker classification, proposing a model to automatically classify two biomarkers—high-frequency oscillations (HFO) and interictal epileptiform discharges (IED)—from separate datasets simultaneously. We validated the proposed model on 1500 annotated HFO and IED candidate signals each, obtained from 5 patients. Our proposed model can perform both HFO and IED classification simultaneously, with cross-validation accuracies of 90.99% and 93.99%;F1 scores were reported as 0.80 and 0.82, respectively. Our leave-one-patient-out experiments achieved 78.03% and 81.19% accuracy for HFO and IED classification, respectively. Compared to existing state-of-the-art classification architectures, our model is shown to be efficient and robust, yet simple and lightweight. The usefulness of sharing parameters between the two classifiers was confirmed in this practical clinical setting of epileptic biomarker detection, potentially contributing to minimizing the number of annotated datasets and realizing robust, accurate, and simultaneous identification of two biomarkers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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High-Lift Prediction Workshop 5: High-Order Discretization Technology Focus Group Summary

High-Lift Prediction Workshop 5: High-Order Discretization T...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Galbraith, Marshall C. Ruiz-Gironés, Eloi Roca, Xevi Wang, Z.J. Holst, Kevin R. Skaperdas, Vangelis Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States Applications in Science and Engineering Barcelona Supercomputing Center - BSC Barcelona Spain Department of Aerospace Engineering University of Kansas LawrenceKS66045 United States Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Customer Service BETA CAE Systems Thessaloniki Greece

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper summarizes the High-Order Technical Focus Group (HO-TFG) submissions forthe 5th AIAA High-Lift Prediction Workshop (HLPW-5). The goal of the workshop was to assess the state-of-the-art in mesh generation and computational fluid dynamics software for predicting peak lift of transport aircraft with flaps and slats deployed. Numerous geometric build ups of the Common Research Model High-Lift (CRM-HL) variant and Reynolds number considerations served as the primary focus of the workshop, and a simple wing-body variant of the CRM-HL was used as a verification test case. The HO-TFG was tasked with generating high-order curved meshes for these complex geometries, and computing high-order solutions using both Reynolds Averaged Navier-Stokes (RANS) and scale resolving Large Eddy Simulation(LES) formulations. While participation in the HO-TFG was limited, the effort produced curved meshes on highly-complex geometric configurations and computational results from two participants. The LES results demonstrate the potential for producing results comparable to 2nd-order accurate methods, but with fewer degrees of freedom. The RANS submission for this workshop does not demonstrate a benefit over 2nd-order schemes. However, the authors will argue a lack of understanding for optimal mesh distributions for high-order methods is the cause for the limited benefits observed for the RANS results. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Reynolds number

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Deep learning framework for wind speed prediction in Saudi Arabia

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Neural Computing and Applications 2025年第5期37卷 3685-3701页

作者： Alabdulhadi, Arwa Ahmed Rehman, Shafiqur Ali, Amjad Shafiullah, Md Physics Department College of Science and Humanities Imam Abdulrahman Bin Faisal University Jubail3065 Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Increased utilization of conventional energy sources severely impacts the environment by increasing the global temperature and contributing to global warming. Sustainable energy sources could contribute to handling the increment while also providing cheap, clean, and abundant energy. Wind energy is considered one of the most reliable sources of sustainable energy due to its abundance and availability during the day and night compared to other renewable resources. However, effective forecasting of such intermittent resources is considered a key challenge for power system operators. This paper develops a novel deep learning framework for forecasting the wind speed in Dhahran City, Saudi Arabia, using two years of data acquired at different heights from the light detection and ranging device, an active remote sensing wind monitoring system. It also segments the data and removes wrong measurements through data pre-processing. The article identifies the best configuration for deep learning models, such as long short-term memory, through a systematic approach. The presented results confirm the efficacy of the developed models against the selected statistical performance measures. The developed model performs better with large data volumes than with lower volumes. Finally, the comparative analysis with the literature-reported results provides confidence in the competency of the proposed model in predicting wind speed for various periods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Clean energy

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Molecular architecture of mammalian pyruvate dehydrogenase complex

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Protein & Cell 2025年第1期16卷 72-78页

作者： Maofei Chen Yutong Song Sensen Zhang Yitang Zhang Xudong Chen Minghui Zhang Meng Han Xin Gao Sai Li Maojun Yang Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological StructureSchool of Life SciencesTsinghua UniversityBeijing 100084China Ministry of Education Key Laboratory of Protein Science Tsinghua UniversityBeijing 100084China State Key Laboratory of Membrane Biology Tsinghua UniversityBeijing 100084China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen University Health Science CenterShenzhen 518060China Protein Research Technology Center Protein Chemistry and Omics PlatformSchool of Life SciencesTsinghua UniversityBeijing 100084China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Cryo-EM Facility Center Southern University of Science and TechnologyShenzhen 518055China Beijing Life Science Academy Beijing 102209China

Dear Editor,Pyruvate dehydrogenase complex(PDHc) is a large multienzyme assembly(Mr = 4–10 million Daltons) consisting of three essential components: pyruvate dehydrogenase(E1p), dihydrolipoyl transacetylase(E2p), and dihydrolipoyl dehydrogenase(E3). These three enzymes perform distinct functions sequentially to catalyze the oxidative decarboxylation of pyruvate with formation of nicotinamide adenine dinucleotide(NADH) and acetyl-coenzyme A(Patel and Roche, 1990).

关键词： sequentially oxidative Dalton

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Label-Free organelle image prediction using deep learning and large-scale hyperspectral stimulated Raman imaging

Label-Free organelle image prediction using deep learning an...

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Advanced Chemical Microscopy for Life science and Translational Medicine 2025

作者： Ma, Yang Mizuguchi, Takaha Spratt, Spencer John Ozeki, Yasuyuki Department of Electrical Engineering and Information Systems The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan

ISBN: (纸本)9781510684126

Stimulated Raman scattering (SRS) microscopy is regarded as a powerful technique of vibrational spectroscopic imaging. Nevertheless, it is still challenging to discriminate various organelles only with spectroscopic signatures. Recently, a deep learning model was employed to predict molecular-specific fluorescence images from SRS images, paving the way for label-free organelle imaging. Here we demonstrate fluorescence prediction with an SRS/fluorescence image dataset with a larger data size (∼300 image pairs) and a larger number of hyperspectral points compared to the previous report. Taking advantage of the larger dataset, we realize accurate fluorescence prediction. © 2025 SPIE.

关键词： Fluorescence imaging

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Precision Multiband Terahertz Metamaterial Biosensor With Targeted Spectral Selectivity for Early Detection of MCF-7 Breast Cancer Cells

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IEEE Sensors Journal 2025年第9期25卷 14970-14987页

作者： Hamza, Musa N. Alibakhshikenari, Mohammad Virdee, Bal S. Lavadiya, Sunil Ud Din, Iftikhar Cavalcante De Souza Sanches, Bruno Koziel, Slawomir Iffat Naqvi, Syeda Panda, Abinash Farmani, Ali Rashid, Nasr Kouhalvandi, Lida Shariatifar, Milad University of Raparin College of Science Department of Physics Sulaymaniyah46012 Iraq University of Rome Tor Vergata Department of Electronics Engineering Rome00133 Italy London Metropolitan University Department of Center for Communications Technology LondonN7 8DB United Kingdom Marwadi University Department of Information and Communication Technology Gujarat Rajkot360003 India University of Engineering and Technology Department of Telecommunication Engineering Mardan23200 Pakistan Escola Politécnica da Universidade de São Paulo Department of Electronic Systems Engineering São Paulo05508 Brazil Reykjavik University Engineering Optimization and Modeling Center Reykjavik102 Iceland Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gdansk80-233 Poland University of Engineering and Technology Department of Telecommunication Engineering Taxila34109 Pakistan CMR Institute of Technology Department of Electronics and Communication Engineering Bengaluru560037 India Lorestan University Department of Electronics engineering Khorramabad68151-44316 Iran Jouf University College of Engineering Department of Electrical Engineering Sakaka72388 Saudi Arabia Al-Azhar University Faculty of Engineering Department of Electrical Engineering Cairo11884 Egypt Dogus University Department of Electrical and Electronics Engineering Istanbul34775 Turkey

Cancer remains one of the leading causes of mortality worldwide, with breast cancer (BC) being a particularly prevalent form. Projections estimate nearly 20 million new cases globally over the next two decades. Early detection is critical for effective treatment;however, conventional diagnostic techniques often lack the necessary sensitivity and specificity, with some methods being invasive and labor-intensive. Recent advancements in microwave imaging (MWI) have shown significant potential as efficient, noninvasive tools for monitoring various cancer types. MWI operating in the terahertz (THz) range has emerged as a promising approach for biosensing, offering the precision needed to differentiate between healthy and cancerous tissues by analyzing small-scale biological features. Among the methods for BC detection, the identification and analysis of MCF-7 BC cells are particularly significant. THz waves interact uniquely with the intrinsic properties of MCF-7 cells, making THz-based biosensors ideal candidates for diagnostic tools. However, many existing sensors are limited in key performance areas, including operating bandwidth and absorption efficiency. This study introduces a novel multiband metamaterial (MTM)-based biosensor specifically designed for the detection of MCF-7 BC cells. The sensor features a compact geometry composed of multiple resonators made from 200-nm-thick aluminum (Al) layers on a 50-$\mu $ m-thick polyethylene terephthalate (PET) substrate. With dimensions of only $198\times 198μ $ m2, the proposed device is exceptionally compact. It operates in the 0.5-1.6-THz frequency range and achieves near-perfect absorption rates (>99%) across multiple bandwidths. These results are achieved through precise tuning of the sensor's geometry and architectural optimization, significantly enhancing its sensitivity for cancer detection. Comprehensive validation of the sensor is performed using full-wave electromagnetic analysis, which includes evaluating elect

关键词： Terahertz waves

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Distributed Prescribed-Time Observer for Nonlinear systems in Block-Triangular Form

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IEEE Control systems Letters 2025年 9卷 222-227页

作者： Vincent de Heij M. Umar B. Niazi Karl H. Johansson Saeed Ahmed Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen The Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

This letter proposes a design of a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite the limited observability of local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that monotonically increase and approach infinity at the prescribed time. The theoretical convergence is rigorously proven and validated through numerical simulations, where some implementation issues due to increasing gains have also been clarified.

关键词： Observers Convergence Nonlinear systems Symmetric matrices Observability Directed graphs Control systems Vectors Linear systems Electronic mail

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