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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1017-1028页

作者： Huang, Keke Liu, Xinyi Li, Fanbiao Yang, Chunhua Kaynak, Okyay Huang, Tingwen Central South University School of Automation Changsha410083 China Peng Cheng Laboratory Shenzhen518055 China Bogazici University Department of Electrical and Electronics Engineering Istanbul34342 Turkey University of Science and Technology Beijing Beijing100083 China Texas A&m University at Qatar Science Program Doha23874 Qatar

Edge computing is an indispensable technology in the Industry 4.0 era. The data are stored at the edge and transmission is prohibited due to concerns about data privacy. Thus, the traditional centralized data-driven modeling methods will face many difficulties and can hardly be used in practice. Federated learning aims to let local nodes learn a global model cooperatively while keeping their data localized. Motivated by the pioneering framework of federated learning, a federated dictionary learning method is proposed for process monitoring. In detail, local nodes train local data by the K-SVD method and learn local dictionaries. Then, local dictionaries, instead of local data, are transferred to the fusion center for calculation of the global dictionary. In order to guarantee an optimal global dictionary, a novel federated dictionary average strategy is introduced. Finally, the reconstruction errors of local data and the control limit are calculated based on the global dictionary for process monitoring. The performance of the proposed method is verified on a numerical simulation, the continuous stirred tank heater benchmark process, and the industrial aluminum electrolysis process. Process monitoring can help industrial enterprises monitor the operating status of the industrial process in real time based on the collected process data. However, industrial data are stored at the edge and enterprises pay more attention to the privacy of industrial data. The prohibition of external transmission of raw data leads to challenges for traditional centralized process monitoring methods. The federated dictionary learning method proposed in this article can have the good process monitoring performance under the premise of protecting data privacy, which is superior to some state-of-the-art methods. For example, in the aluminum electrolysis industrial process, the false alarm rate is only 1.91%, and the false discovery rate is 86.23%. The method has high data security and low mod

关键词： Process monitoring

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FeRAM Recovery up to 200 Periods with Accumulated Endurance 1012 Cycles and an Applicable Array Circuit toward Unlimited eNVM Operations

FeRAM Recovery up to 200 Periods with Accumulated Endurance ...

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2023 IEEE Symposium on VLSI technology and Circuits, VLSI technology and Circuits 2023

作者： Hsiang, K.-Y. Lee, J.-Y. Chang, F.-S. Lou, Z.-F. Li, Z.-X. Li, Z.-H. Chen, J.-H. Liu, C.W. Hou, T.-H. Lee, M.H. Institute and Undergraduate Program of Electro-Optical Engineering National Taiwan Normal University Taiwan Institute of Electronics National Yang Ming Chiao Tung University Taiwan National Taiwan University Graduate School of Advance Technology Taiwan

ISBN: (纸本)9784863488069

Asymmetric Field Cycling Recovery (AFCR) with a low E-field is proposed for the first time to extend the endurance cycles of a ferroelectric (FE) capacitor and is experimentally demonstrated for 200 periods and accumulated to 1012 switching cycles. Positive and negative Asymmetric minor loops (AmL) with AFCR achieve the nondegradation and complete restoration of Δ 2 Pr toward the prospect of unlimited operation. Furthermore, an FeRAM array circuit with an inverting amplifier is designed to execute the Write/Read and Recovery procedures simultaneously by AFCR scheme. © 2023 JSAP.

关键词： Timing circuits

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Study on Electromagnetic Resonance Antennas in the Vicinity of Human Body

Study on Electromagnetic Resonance Antennas in the Vicinity ...

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2022 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2022

作者： Hiramoto, Toshiya Hirabayashi, Koichi Noguchi, Keisuke Kanazawa Institute of Technology Graduate Program in Electrical Engineering and Electronics Ishikawa Nonoichi Japan Fukuvi Chemical Industry Co Ltd Equipment Engineering Section Fukui Fukui City Japan Kanazawa Institute of Technology Depertment of Electrical and Electronic Engineering Ishikawa Nonoichi Japan

ISBN: (数字)9781665432382

ISBN: (纸本)9781665432382

In this study, we investigate a directional electromagnetic resonance antenna (EMRA) for application to wearable antennas. We designed an EMRA in free space and confirmed its unidirectional pattern through simulations and prototype experiments. We also analyze the characteristics of EMRA when it is in close to the surface of a human phantom. As a result, EMRA in the vicinity of human body resulted in a gain reduction of up to 2 dB compared to the free-space condition. However, when the distance from the surface of the human phantom was more than 10 mm, the gain was confirmed to be close to that in free space. © 2022 IEEE.

关键词： Phantoms

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Exploring Regression and Classification in Big Data with Deep Learning

Exploring Regression and Classification in Big Data with Dee...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Pandey, Ravindra Rongali, Aruna Sri Gopinath, S. Anute, Nilesh Nama, Forda Pund, Sachin Hult International Business School United States University of the Cumberlands Doctoral Information Technology Information Technology Emphasis Information Technology United States Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India ASM's Institute of Business Management and Research Pune India University of Lampung Doctoral Program of Environtmental Science Department of Informatics Bandar Lampung Indonesia Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350357769

Deep gaining knowledge is a synthetic intelligence approach used to recognize complex facts. It uses many facts to recognize the complicated relationships between various factors. With deep learning, models can be built which might be capable of understanding the complicated patterns within big datasets. In this paper, we can explore using deep mastering in regression and type tasks on large datasets. In particular, we can use deep learning strategies to build a regression model that predicts a target value primarily based on the functions in a massive dataset and a classification version that can classify an object into one of the many classes of an extensive dataset. We can then discuss the overall performance of the fashions in comparison to standard models and talk about capability areas for development. Moreover, we will analyze the scalability of deep mastering strategies used on large datasets and discuss exceptional practices for building deep mastering fashions on massive datasets. Finally, we will summarize our findings and offer our conclusions approximately the use of deep studying in regression and type obligations on large datasets. © 2023 IEEE.

关键词： approximately gaining knowledge mastering performance regression

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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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First Demonstration of HZO-LNOI Integrated Ferroelectric Electro-Optic Modulator and Memory to Enable Reconfigurable Photonic Systems

First Demonstration of HZO-LNOI Integrated Ferroelectric Ele...

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2023 International Electron Devices Meeting, IEDM 2023

作者： Xu, Zefeng Chen, Chun-Kuei Lin, Hong-Lin Gao, Yuan Ke, Wei Xu, Baochang Dmitriev, Pavel Arbiz, Carlan Zamburg, Evgeny Touzard, Steven Cai, Xinlun Lee, James Venkatesan, Suresh Danner, Aaron Thean, Aaron Voon-Yew Department of Electrical and Computer Engineering Singapore Nus Graduate School Integrative Sciences and Engineering Program Singapore 117583 Singapore School of Electronics and Information Technology 117583 Singapore Centre for Quantum Technologies Nus Singapore Poet Technologies Canada Sun Yat-Sen University 510275 China

ISBN: (纸本)9798350327670

We have successfully demonstrated, for the first time, an innovative back-end-of-line (BEOL) compatible electro-optic modulator and memory (EOMM) based on Lithium Niobate on Insulator (LNOI) micro-ring resonator (MRR) integrated with Ferroelectric Hafnium Zirconate Hf0.5Zr0.5O (HZO) non-volatile analog memory. High non-volatile memory and modulation performances are both achieved in a single compact device, exhibiting high extinction ratio of 13.3 dB, excellent efficiency of 66pm/V, stable nine-state switching, record-high endurance exceeding 109 cycles. This is accomplished by utilizing Pockels effect in LNOI, induced by electric-field effect from remnant HZO ferroelectric polarization. We studied the system implementation of reconfigurable chiplet-interposer photonic interconnect, enabled by the EOMM and EOMM with hybrid thermal-optical modulation. Our model shows a potential 70% energy efficiency improvement over conventional electrical interposer interconnect. We have also tested the integration of the EOMM with POET technologies' 400G Tx/Rx optical interposer chip and studied a limited scale demonstration of the EOMM device. © 2023 IEEE.

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Numerical analysis of Ga gradient effects in ACIGS absorbers for high-efficiency tandem solar cells

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Optical and Quantum electronics 2025年第3期57卷 1-19页

作者： Boukortt, Nour El I. Loureiro, Antonio Garcia Almutairi, Ohood A. Centro Singular de Investigación en Tecnoloxías Intelixentes University of Santiago de Compostela Santiago de Compostela Spain Department of Electronics and Information Systems Ghent University Technology Park 126 Zwijnaarde Belgium Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom Energy Systems Program College of Engineering & Energy Abdullah Al Salem University Khaldiya Kuwait

Ag-alloyed Cu(In,Ga)Se₂ (ACIGS) is a promising material for the bottom subcell in tandem solar cell structures, offering a tunable bandgap that spans the optimal range for maximum efficiency;however, the bandgap widening of ACIGS thin films poses a significant challenge in enhancing their photocurrent. This paper investigates the impact of different grading profiles, specifically focusing on the 'hockey stick'/V-like Ga gradient–induced (GGI) profiles, on the performance of thin-film ACIGS cells. To enhance the physical understanding of these grading profiles we have used Silvaco TCAD tools to analyze how these profiles' depths, notch position, and slope affect the power conversion efficiency (PCE). The optimized ACIGS cell achieves a PCE of 25.80%, compared to 23.60% for the reference device. For the tandem configuration, we pair the optimized ACIGS bottom cell (double grading profile) with a perovskite top cell (band gap ~ 1.66 eV), achieving an overall efficiency of 30.26% under AM1.5G illumination. The obtained results highlight the critical role of Ga gradient profiles in enhancing the bottom cell's performance and stability. Additionally, we compare our results with existing studies to evaluate the broader impact of Ga compositional optimization on ACIGS-based solar cells. This work provides valuable insights into advanced grading strategies and helps us understand the physical behaviour of these high-performance tandem solar cells. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Silver alloys

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A review of hybrid EEG-based multimodal human–computer interfaces using deep learning: applications, advances, and challenges

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Biomedical engineering Letters 2025年 1-32页

作者： Lee, Hyung-Tak Shim, Miseon Liu, Xianghong Cheon, Hye-Ran Kim, Sang-Gyu Han, Chang-Hee Hwang, Han-Jeong Department of Electronics and Information Engineering Korea University 2511 Sejong-ro Jochiwon-eup Sejong-si30019 Korea Republic of Department of Artificial Intelligence Tech University of Korea Siheung Korea Republic of Department of Computer Information Korea University 2511 Sejong-ro Jochiwon-eup Sejong-si30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Korea Republic of

Human–computer interaction (HCI) focuses on designing efficient and intuitive interactions between humans and computer systems. Recent advancements have utilized multimodal approaches, such as electroencephalography (EEG)-based systems combined with other biosignals, along with deep learning to enhance performance and reliability. However, no systematic review has consolidated findings on EEG-based multimodal HCI systems. This review examined 124 studies published from 2016 to 2024, retrieved from the Web of Science database, focusing on hybrid EEG-based multimodal HCI systems employing deep learning. The keywords used for evaluation were as follows: ‘Deep Learning’ AND ‘EEG’ AND (‘fNIRS’ OR ‘NIRS’ OR ‘MEG’ OR ‘fMRI’ OR ‘EOG’ OR ‘EMG’ OR ‘ECG’ OR ‘PPG’ OR ‘GSR’). Main topics explored are: (1) types of biosignals used with EEG, (2) neural network architectures, (3) fusion strategies, (4) system performance, and (5) target applications. Frequently paired signals, such as EOG, EMG, and fNIRS, effectively complement EEG by addressing its limitations. Convolutional neural networks are extensively employed for spatio-temporal-spectral feature extraction, with early and intermediate fusion strategies being the most commonly used. Applications, such as sleep stage classification, emotion recognition, and mental state decoding, have shown notable performance improvements. Despite these advancements, challenges remain, including the lack of real-time online systems, difficulties in signal synchronization, limited data availability, and insufficient explainable AI (XAI) methods to interpret signal interactions. Emerging solutions, such as portable systems, lightweight deep learning models, and data augmentation techniques, offer promising pathways to address these issues. This review highlights the potential of EEG-based multimodal HCI systems and emphasizes the need for advancements in real-time interaction, fusion algorithms, and XAI to enhance their adaptability, interpreta

关键词： Convolutional neural networks

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Single-pixel super-resolution with a space–time modulated computational metasurface imager

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Photonics Research 2024年第10期12卷 2311-2322页

作者： WENZHI LI JIARAN QI ANDREA ALU Department of Microwave Engineering School of Electronics and Information EngineeringHarbin Institute of TechnologyHarbin 150001China Photonics Initiative Advanced Science Research CenterCity University of New YorkNew YorkNew York 10031USA Physics Program Graduate Center of the City University of New YorkNew YorkNew York 10016USA

Single-pixel imaging is a burgeoning computational imaging technique that utilizes a single detector devoid of spatial resolution to capture an image,offering great potential for creating cost-effective and simplified imaging ***,achieving super-resolution with a single pixel remains a formidable ***,we introduce a single-pixel super-resolution imaging technique based on space–time *** modulation parametrically mixes the incoming signals,enabling the space–time scattered signals of the object carrying finer details to be captured by the single-pixel imaging *** validate our proposed technique,we designed and fabricated a computational metasurface imager that needs only a single transmitting port and a single receiving *** achieved resolution surpasses the Abbe resolution *** principle of our proposed technique is well-suited for low-cost and compact imaging systems.

关键词： resolution utilize details

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Mini-YOLOX: A Lightweight Network for Real-Time Embedded Applications

Mini-YOLOX: A Lightweight Network for Real-Time Embedded App...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Ahmed N. El-Zeiny Adham Hassan Hassan Mostafa Ahmed H. Khalil Electronics and Communications Engineering Department Cairo University Zewail City of Science and Technology University of Science and technology Nanotechnology and Nanoelectronics Program Giza Egypt

In this paper, we present an efficient real-time object detector designed for embedded system applications. Although increased memory and power consumption pose significant challenges, our network adopts techniques to overcome them. Depthwise and pointwise separable convolutions are used across the backbone, neck, and head of the network to reduce the number of parameters. To compensate for accuracy loss, we employ several techniques including starting with a larger baseline, using depthwise separable convolution mixed with blueprint separable convolution in the backbone, replacing the SiLU activation function with the Mish activation function, and switching from Adam to SGD optimization algorithms. These techniques help to achieve a state-of-the-art object detection network called Mini-YOLOX, which optimizes YOLOX-S from the YOLOX series and reaches only ∽57% of its parameter size and ∽52% of its floating-point operations with better accuracy. Mini-YOLOX achieves impressive results with only 5.08M parameters and 13.95 GFLOPs, resulting in a 40.9 AP score in just 5.71 ms.

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