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International Electron Devices Meeting (IEDM)

作者： Kyuree Kim Sangwook Youn Jinwoo Park Hyungjin Kim Department of Electrical and Computer Engineering Inha University Incheon Korea Department of Semiconductor Engineering Division of Materials Science and Engineering Hanyang University Seoul Korea

ISBN: (数字)9798350365429

ISBN: (纸本)9798350365436

In this work, a weight profile design is presented for efficient Ising solver system based on a Hopfield neural network (HNN) using $32\times 32$ memristor crossbar array. It utilizes device noise in the probabilistic decision process of simulated annealing for binary current inputs. By implementing 0 and 1 weight matrix across various conductance states in the crossbar, we experimentally solve an unweighted max-cut problem. It is confirmed that higher noise levels, concentrated in high resistance states, enable more efficient convergence to the minimum point of the HNN energy function. This approach effectively exploits intrinsic noise, reducing external hardware overhead and demonstrating feasibility for optimization problems.

关键词： Resistance Noise Memristors Hopfield neural networks Simulated annealing Probabilistic logic Hardware Electron devices Optimization Noise level

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Meta-optic accelerators for object classifier

Meta-optic accelerators for object classifier

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Zheng, Hanyu Liu, Quan Zhou, You Kravchenko, Ivan I. Huo, Yuankai Valentine, Jason Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37212 United States Department of Computer Science Vanderbilt University NashvilleTN37212 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37212 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in a high-speed and robust classifier with 93.1% accurac...

ISBN: (纸本)9781957171258

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Demonstration of P-side Down Green Light Emitting Diodes with High External Quantum Efficiencies 27

Demonstration of P-side Down Green Light Emitting Diodes wit...

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Light-Emitting Devices, materials, and Applications XXVII 2023

作者： Rahman, Sheikh Ifatur Xavier, Agnes Maneesha Dominic Armitage, Robert Rajan, Siddharth Department of Electrical and Computer Engineering The Ohio State University ColumbusOH43210 United States Lumileds LLC San JoseCA United States Department of Materials Science and Engineering The Ohio State University ColumbusOH43210 United States

ISBN: (纸本)9781510659872

GaN/(In,Ga)N heterostructure based visible light emitting diodes (LED) have enabled a wide range of solid-state lighting applications through excellent efficiency and power output in the shorter wavelengths (≤ 475nm) of violet/blue emission. However, the efficiency of emitters in the longer wavelength range (≥ 500nm) drops drastically due to the need to include higher Indium-content in the InGaN quantum wells. Large average polarization fields for high Indium-content quantum wells for conventional P-up structure, opposes the depletion field leading to large electrostatic barriers for both electrons and holes injection. LEDs fabricated along the N-polar direction with a p-up orientation or Ga-polar direction with p-down orientation lower such electrostatic barriers to carrier injection due to alignment of the polarization dipole field and depletion region field. This can therefore theoretically improve the electrical injection efficiency and reduce the forward voltage of operation. Such a Ga-polar p-down LED requires a bottom buried tunnel junction to avoid current spreading issues for a buried p-GaN layer. In this report, we demonstrate for the first time Ga-polar p-down green emitting LEDs using bottom tunnel junctions and having external quantum efficiencies comparable to those of equivalent p-up LEDs grown by metal organic chemical vapor deposition (MOCVD). © 2023 SPIE. All rights reserved.

关键词： III-V semiconductors

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Online Classification of Alarm Floods Using a Word2vec Algorithm 49

Online Classification of Alarm Floods Using a Word2vec Algor...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Tamascelli, Nicola Rao, Harikrishna Rao Mohan Cozzani, Valerio Paltrinieri, Nicola Chen, Tongwen Norwegian University of Science and Technology Department of Mechanical and Industrial Engineering Trondheim Norway University of Bologna Department of Civil Chemical Environmental and Materials Engineering Bologna Italy University of Alberta Department of Electrical & Computer Engineering EdmontonABT6G 1H9 Canada

ISBN: (纸本)9798350331820

Alarm floods are periods of intense alarm activity that may hinder control room operators' ability to diagnose and respond to process abnormalities. In this context, a method to guide and assist operators during alarm floods would provide critical support in preventing abnormalities from escalating into serious accidents. Therefore, this study introduces a novel approach for the online classification of alarm floods based on their fault categories. Historical alarm data are used to train an ensemble of Natural Language Processing models, specifically word2vec, which learn contextual relationships between alarms under different fault conditions. As a new alarm flood appears, the models predict the most probable context alarms by exploiting the knowledge gained during training. Finally, a scoring system is proposed to reward the models that make correct predictions and eventually identify the most probable fault category. The efficacy of the method has been tested on simulated alarm data from the Tennessee Eastman Process benchmark. The results are encouraging, as the models achieved relatively high accuracy in most fault categories. © 2023 IEEE.

关键词： Natural language processing systems

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The Deep Learning Approach For American Sign Language Detection

The Deep Learning Approach For American Sign Language Detect...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Pangestu, Gusti Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Bina Nusantara University School of Computer Science Computer Science Department Binus Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9781665468763

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising. © 2022 IEEE.

关键词： Convolutional neural networks

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Brain Tumor Detection and Segmentation Using Deep Learning Models 6

Brain Tumor Detection and Segmentation Using Deep Learning M...

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Abiodun, Moses Kazeem Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Aworinde, Halleluyah Tinuoye, George Imoize, Agbotiname Lucky Li, Chun-Ta Lee, Cheng-Chi Landmark University Department of Computer Science Kwara State Omu-Aran Nigeria Bowen University Department of Computer Science Osun State Iwo Nigeria University of Ilorin Department of Computer Science Kwara State Ilorin Nigeria University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Nigeria Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City Taiwan Fu Jen Catholic University Department of Library and Information Science New Taipei City Taiwan

ISBN: (纸本)9798350374162

This study focuses on brain tumor detection and segmentation using Convolutional Neural Networks (CNN) with architectures of Fully Convolutional Net-work (FCN) and VGG16. The dataset imported for this study consists of brain MRI images, which were preprocessed to improve the overall image resolution. Data augmentation techniques were applied to heighten the variability and volume of the training data. For feature extraction, CNN models were utilized, specifically the VGG16 architectures. This model was trained on a preprocessed dataset to learn the important features associated with the presence of brain tumor. Image segmentation was performed to precisely outline and identify the tumor region within the brain images. FCN architecture was employed for this task, which allowed pixel-level classification of tumor regions. Finally, image classification was carried out to group brain images based on the absence or presence of a tumor. The trained models were evaluated using accuracy, precision, recall and F1score as the performance metrics. The results of the study demonstrated promising outcomes, achieving an accuracy score of 93% and a precision, recall and f1score ranging from 75% to 100% based on the specific type of tumor enlisted. This indicates that the developed CNN models, with the FCN and VGG16 architectures, effectively detect and segment brain tumors from MRI images. The findings of this study contribute to the field of medical image analysis and gives a foundation for further research and development in brain tumor diagnosis and treatment. The study recommends the use of more deep learning algorithms for the process of detecting brain tumors. © 2024 IEEE.

关键词： Convolutional neural networks

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Vectorial liquid‑crystal holography

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eLight 2024年第1期4卷 251-261页

作者： Ze‑Yu Wang Zhou Zhou Han Zhang Yang Wei Hong‑Guan Yu Wei Hu Wei Chen Hai‑Tao Dai Ling‑Ling Ma Cheng‑Wei Qiu Yan‑Qing Lu National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and ManipulationCollaborative Innovation Center of Advanced MicrostructuresCollege of Engineering and Applied SciencesNanjing UniversityNanjing 210023China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Tianjin Key Laboratory of Low Dimensional Materials Physics and Preparing Technology School of ScienceTianjin UniversityTianjin 300072China

Vectorial optics with fine inhomogeneous polarization control are highly *** have been captivated a promising candidate,but their static post-fabrication geometry largely limits the dynamic *** crystal(LC)is usually employed as an additional index-changing layer together with ***,most of the reported LCs only impart a varying but uniform phase on top of that from the metasurface,which we term“scalar”LC ***,we pixelate a single-layer LC to display versatile and tunable vectorial holography,in which the polarization and amplitude could be arbitrarily and independently controlled at varying spatial ***,the subtle and vectorial LC-holography highlights the broadband and electrically-switchable *** vectorial LC holography reveals significant opportunities for advanced cryptography,super-resolution imaging,and many other applications.

关键词： Liquid crystals Vectorial optics Holography

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Meta-optic accelerators for object classification

Meta-optic accelerators for object classification

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2023 Flat Optics: Components to Systems, FLatOptics 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in high-speed classifiers that are demonstrated for hand... 详细信息

ISBN: (纸本)9781957171289

关键词： Imaging systems Machine vision Neural networks Optical components Optical neural systems Optical systems

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Analysis of Partial Discharges in Oil-Impregnated Transformer Paper Insulation and PET-G Insulation

IEEE Open Journal of Instrumentation and Measurement

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IEEE Open Journal of Instrumentation and Measurement 2025年 4卷

作者： Kameli, Sayed Mohammad Abuelrub, Abdelaziz AlShaikh Saleh, Mohammad Refaat, Shady S. Ghrayeb, Ali Abu-Rub, Haitham Olesz, Marek Texas A&M University at Qatar Electrical and Computer Engineering Department Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College StationTX77843 United States University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering HatfieldAL10 9EU United Kingdom Hamad Bin Khalifa University Qatar Foundation Electrical and Computer Engineering Program in College of Science and Engineering Doha Qatar Gdańsk University of Technology Department of Electrical Power Engineering Gdańsk80-233 Poland

Partial discharge (PD) is a widespread phenomenon instigated in power transformer (PT) insulation systems. PDs are triggered by voids that vary in size and position within the PT insulation. The electrical characteristics of those damaging, PD-causing cavities must be well understood, to accelerate the development of advanced PD detection techniques. Thus, the impact of varying the radius and position of spherical air voids on the characteristics of PDs in PTs is examined using a 3-D finite element analysis (FEA) model designed in COMSOL Multiphysics. The spherical voids are positioned between two windings of a 512 kV, three-phase (3φ ) PT. The peak electric field (EF) and aggregate energy in the FEA model are used in conjunction with laboratory measurements of the apparent discharge magnitude, for detailed analysis of Polyethylene Terephthalate Glycol (PET-G)-based cylindrical voids with different heights. Simulations demonstrate that the inception of PD activity in the PT model occurs for spherical voids with a radius exceeding 1 mm. Furthermore, the most severe PDs occur within the press-board insulation, adjacent to the uppermost part of the innermost windings. Experiments demonstrate that a significant increase in PD activity was observed for PET-G-based cylindrical voids with heights exceeding 1 mm. © 2022 IEEE.

关键词： Partial discharges

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Understanding wide field-of-view flat lenses:an analytical solution[Invited]

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Chinese Optics Letters 2023年第2期21卷 111-118页

作者：杨帆安森松 Mikhail Y.Shalaginov 张华亮胡崛隽古田 Department of Materials Science&Engineering Massachusetts Institute of TechnologyCambridgeMassachusetts 02139USA Department of Electrical&Computer Engineering University of Massachusetts LowellLowellMassachusetts 01854USA Materials Research Laboratory Massachusetts Institute of TechnologyCambridgeMassachusetts 02139USA

Wide field-of-view(FOV)optics are widely used in various imaging,display,and sensing *** wide FOV optics rely on complicated lens assembly comprising multiple elements to suppress coma and other Seidel *** emergence of flat optics exemplified by metasurfaces and diffractive optical elements(DOEs)offers a promising route to expand the FOV without escalating complexity of optical *** date,design of large FOV flat lenses has been relying upon iterative numerical ***,we derive,for the first time,to the best of our knowledge,an analytical solution to enable computationally efficient design of flat lenses with an ultra-wide FOV approaching 180°.This analytical theory further provides critical insights into working principles and otherwise non-intuitive design trade-offs of wide FOV optics.

关键词： metasurface metalens field-of-view

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