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Color-conversion displays:current status and future outlook

Light(Science & Applications) 2024年第12期13卷 2941-2961页

作者： Guijun Li Man-Chun Tseng Yu Chen Fion Sze-Yan Yeung Hangyu He Yuechu Cheng Junhu Cai Enguo Chen Hoi-Sing Kwok Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer EngineeringThe Hong Kong University of Science and TechnologyHong KongChina National and Local United Engineering Laboratory of Flat Panel Display Technology College of Physics and Information EngineeringFuzhou UniversityCollege of Physics and Information Engineering 350108FuzhouChina Fujian Science&Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou 350108China

The growing focus on enhancing color quality in liquid crystal displays(LCDs)and organic light-emitting diodes(OLEDs)has spurred significant advancements in color-conversion ***,color conversion is also important for the development and commercialization of *** article provides a comprehensive review of different types of color conversion methods as well as different types of color conversion *** summarize the current status of patterning process,and discuss key strategies to enhance display ***,we speculate on the future prospects and roles that color conversion will play in ultra-high-definition micro-and projection displays.

关键词： materials projection outlook

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Short-Term Dynamic Voltage Stability Status Estimation Using Multilayer Neural Networks

Short-Term Dynamic Voltage Stability Status Estimation Using...

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2023 IEEE Texas Power and Energy Conference, TPEC 2023

作者： Massaoudi, Mohamed Refaat, Shady S. Ghrayeb, Ali Abu-Rub, Haitham Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States University of Hertfordshire School of Physics Engineering and Computer Science Hatfield United Kingdom

ISBN: (纸本)9781665490719

The power grid stability is significantly impacted by the exponentially growing electrical demand and the complex electrical systems modernization projects. This intensifies the urgent need and yet challenging Dynamic Security Assessment (DSA) to withstand high-probability severe contingencies. This paper proposes an effective machine-learning solution for Short-Term Voltage Stability (STVS) detection and classification. This work also addresses fault detection and classification into line faults or bus faults under different operating conditions as a supplementary warning system to boost power system protection and resiliency with fast remedial actions. The proposed approach combines three necessary steps for high accuracy: feature subset selection, hyperparameter optimization, and critical bus identification. The efficiency of the proposed forecasting model is assessed using the IEEE New England 39-bus test case with the CLOD composite model. The generated N-1 contingency test cases data from dynamic Power System Simulator/engineering (PSS/E) time domain simulations for fault-induced voltage events include the measured post-disturbance voltage magnitude, angle, frequency, and active and reactive power trajectories of the system buses. Numerical results from the proposed classifier confirm a high classification accuracy of 94.24% in identifying the post-disturbance stability state. The proposed method will be outperforming traditional shallow learning-based approaches. Further, the robustness of classifiers is demonstrated by evaluating the computational time, accuracy, precision, recall, and F-measure. © 2023 IEEE.

关键词： Machine learning

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Nanoplasmonics for Purcell-enhanced Spontaneous Emission in Diamond Silicon Vacancy Centers

Nanoplasmonics for Purcell-enhanced Spontaneous Emission in ...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Li, Hengming Acil, Deniz Boyce, Andrew M. Wilson, Nathaniel C. Shen, Qixin Mikkelsen, Maiken H. Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Physics Duke University DurhamNC27708 United States

ISBN: (纸本)9781957171395

Slow spontaneous emission limits photonic-based quantum information applications. Here, we discuss our recent progress in realizing ultrafast emission in silicon vacancy centers through integrating diamond membranes with sub-diffraction limited plasmonic cavities through Purcell enhancement. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Silicon photonics

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Leveraging Deep Learning for Fault Detection and Classification of Induction Machines: A Review 50

Leveraging Deep Learning for Fault Detection and Classificat...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： AlShaikh Saleh, Mohammad Refaat, Shady S. Kammermann, Jörg Department of Electrical and Computer Engineering Texas A&M University College Station United States Technical University of Munich Garching Germany Department of Physics Engineering and Computer Science University of Hertfordshire Hatfield Hertfordshire United Kingdom

ISBN: (纸本)9781665464543

The management of incipient faults in induction machines (IMs) is crucial for ensuring reliability and efficiency in diverse industrial applications, including power grids, electric vehicles, and manufacturing processes. This review explores advanced fault detection and diagnosis (FDD) strategies, emphasizing deep learning (DL) methods such as convolutional neural networks (CNN), recurrent neural networks (RNN), and autoencoders for fault detection and classification. Traditional machine learning (ML) approaches are also discussed, highlighting their integration with signal processing techniques like wavelet transforms and Fourier transforms to enhance FDD accuracy. Additionally, the potential of physics-informed neural networks (PINNs) is examined, demonstrating how incorporating physical knowledge into data-driven models can improve diagnostic precision. The paper presents an analysis of recent publications, identifies current research gaps, and proposes future directions, including the development of robust AI-based FDD systems and the consideration of stochastic industrial data for more accurate predictive maintenance. By offering a comprehensive overview of FDD techniques and highlighting key research areas, this review aims to advance the reliability and performance of IMs. © 2024 IEEE.

关键词： Wavelet transforms

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A 4-bit 28-GS/s 4×Time-Interleaved Current-Steering DAC for High-Speed Interfacing

A 4-bit 28-GS/s 4×Time-Interleaved Current-Steering DAC for...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Michailidis, A. Noulis, T. Pavlidis, V.F. Hatzopoulos, A. Aristotle University of Thessaloniki Electronics Laboratory Department of Physics 54124 Greece Aristotle University of Thessaloniki Electronics Laboratory Department of Electrical and Computer Engineering 54124 Greece

ISBN: (纸本)9798350318845

In this work, a fully integrated 4-bit current-steering DAC was implemented into silicon using a 22nm SoI CMOS process node. By exploiting the time-interleaved design approach, a sampling frequency of 28-GS/s was achieved, enabling high-speed interfacing capabilities between analog and digital circuitry. The time-interleaved design approach, proposed in this work, uses an insightful current superposition technique, extending the time-interleaved factor barrier while maintaining high signal integrity and high conversion linearity, without compromising the spectral content of the output signal. For validation purposes of the proposed design approach, RLCk-aware parasitic extraction of the physical layout using Ansys RaptorX was employed and RLCk-aware simulations of the full design were performed. The performance degradation due to physical layout parasitics was compared with the schematic-view simulation scheme of the full design and it was quantified by monitoring the ENOB, SINAD and SFDR dynamic metrics of the design. © 2024 IEEE.

关键词： current superposition current-steering DAC time-interleaved transconductance amplifiers

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INFusion: Diffusion Regularized Implicit Neural Representations for 2D and 3D Accelerated MRI Reconstruction 58

INFusion: Diffusion Regularized Implicit Neural Representati...

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58th Asilomar Conference on Signals, systems and computers, ACSSC 2024

作者： Arefeen, Yamin Levac, Brett Stoebner, Zach Tamir, Jonathan I. The University of Texas at Austin Chandra Family Department of Electrical and Computer Engineering United States Md Anderson Cancer Center Department of Imaging Physics United States

ISBN: (纸本)9798350354058

Implicit Neural Representations (INRs) are a learning-based approach to accelerate Magnetic Resonance Imaging (MRI) acquisitions, particularly in scan-specific settings when only data from the under-sampled scan itself are available. Previous work demonstrates that INRs improve rapid MRI through inherent regularization imposed by neural network architectures. Typically parameterized by fully-connected neural networks, INRs support continuous image representations by taking a physical coordinate location as input and outputting the intensity at that coordinate. Previous work has applied unlearned regularization priors during INR training and have been limited to 2D or low-resolution 3D acquisitions. Meanwhile, diffusion-based generative models have received recent attention as they learn powerful image priors decoupled from the measurement model. This work proposes INFusion, a technique that regularizes the optimization of INRs from under-sampled MR measurements with pre-trained diffusion models for improved image reconstruction. In addition, we propose a hybrid 3D approach with our diffusion regularization that enables INR application on large-scale 3D MR datasets. 2D experiments demonstrate improved INR training with our proposed diffusion regularization, and 3D experiments demonstrate feasibility of INR training with diffusion regularization on 3D matrix sizes of 256× 256× 80. © 2024 IEEE.

关键词： Image reconstruction

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Bidirectional Gated Recurrent Unit Based-Grey Wolf Optimizer for Interval Prediction of Voltage Margin Stability Index in Power systems

Bidirectional Gated Recurrent Unit Based-Grey Wolf Optimizer...

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2023 IEEE Texas Power and Energy Conference, TPEC 2023

ISBN: (纸本)9781665490719

Despite the importance of Photovoltaic (PV) generation systems for smart grids, the uncertainty of PV power outputs due to weather variations can cause power stability problems leading up to voltage collapse in power systems. Governed by load dynamics, data-driven short-term Voltage Stability Margin (VSM) assessment is a critical technique for power system design and operation. This paper addresses improving the VSM accuracy and quantifying the uncertainties associated with the predicted values using deep learning. Interval estimation of VSM is significant for monitoring short-term voltage stability in real-time against fast voltage collapse and sustained low voltage without recovery. The proposed solution lies in the use of a Bidirectional Gated Recurrent Unit (GRU) optimized by a multi-objective Grey Wolf optimizer (GWO) algorithm. Time domain simulation results are conducted using IEEE59 and IEEE Nordic-44 bus systems to provide the training samples and maintain a high level of grid observability for the grid stability evaluation. The generated N-l contingency test cases data were conducted using Power System Simulator for engineering (PSS/E)-based time domain simulations. The generated features from fault-induced voltage events include the post-disturbance voltage magnitude, angles, frequencies, and active and reactive power trajectories of the system buses. The obtained results show that the proposed method can enable timely stability assessment with higher effectiveness. © 2023 IEEE.

关键词： Smart power grids

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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A high-speed autozeroing comparator with reduced current consumption

A high-speed autozeroing comparator with reduced current con...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Kontelis, Spyros Tsirmpas, George Laoudias, Costas Souliotis, George University of Patras Electronics Laboratory Physics Department Patras Greece Nanozeta Technologies Larnaca Cyprus University of Peloponnese Department of Electrical and Computer Engineering Patras Greece

ISBN: (纸本)9798350318845

A comparator based on autozeroing technique with reduced power consumption for high-speed and high-resolution applications is proposed in this paper. The overall comparator topology is based on a cascade connection of differential amplifiers and one latch comparator. Due to its high sensitivity, it is suitable to be employed in high-resolution analog to digital converters and sampling rates up to 50Msps. The differential input voltage resolution is equal to 400 μVpp. The circuit is designed using a CMOS 65 nm technology node. Following a power switching technique, the current consumption of amplification stages can be reduced up to 30%, without affecting the overall performance of the comparator. © 2024 IEEE.

关键词： Comparators (optical)

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Developing a Smart Drip Infusion System to Elevate Precision in Healthcare 4

Developing a Smart Drip Infusion System to Elevate Precision...

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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Molefinyane, Sefemo Yahya, Abid Hamsadhwani, V. Rohini, R. Samikannu, Ravi Begam, K.Maruliya Botswana International University of Science and Technology Department of Electrical and Communications Systems Engineering Palapye Botswana Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tiruchengode India Saveetha School of Engineering Saveetha Institute of Medical and Technology Science Department of Computer Science and Communications Engineering Tiruchengode India

ISBN: (纸本)9798331529635

Intravenous (IV) therapy is a critical component of modern healthcare, yet traditional IV systems are prone to human errors, manual adjustments, and limited monitoring capabilities, which can jeopardize patient safety and hinder healthcare efficiency. This paper presents the development of the Smartdrip IV Infusion System, a sophisticated and automated solution that integrates advanced sensors, control algorithms, and intuitive user interfaces to address these limitations. The Smartdrip system minimizes human intervention, provides real-time monitoring, and automates fluid administration with enhanced precision and safety features. The system demonstrates high reliability through simulation and prototype testing in detecting anomalies, issuing alerts, and ensuring accurate infusion rates, thereby improving patient outcomes and reducing healthcare provider workload. The results validate the Smartdrip system as a transformative approach to optimizing IV therapy in diverse clinical settings. © 2024 IEEE.

关键词： Automated Infusion System Drip Rate Monitoring Healthcare Automation Intravenous Therapy Patient

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