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Polarization-induced photocurrent switching effect in heterojunction photodiodes

Nano Research 2023年第4期16卷 5503-5510页

作者： Dingbo Chen Yu-Chang Chen Guang Zeng Yu-Chun Li Xiao-Xi Li Dong Li Chao Shen Nan Chi Boon S.Ooi David Wei Zhang Hong-Liang Lu State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics&SystemsSchool of MicroelectronicsFudan UniversityShanghai 200433China Key Laboratory for Information Science of Electromagnetic Waves Department of Communication Science and EngineeringFudan UniversityShanghai 200433China Photonics Laboratory Division of ComputerElectricaland Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia

The unipolar photocurrent in conventional photodiodes(PDs)based on photovoltaic effect limits the output modes and potential versatility of these devices in *** photodiodes with photocurrent switching are emerging as a promising solution for obtaining photoelectric devices with unique and attractive functions,such as optical logic ***,we design an all-solid-state chip-scale ultraviolet(UV)PD based on a hybrid GaN heterojunction with engineered bipolar polarized electric *** introducing the polarization-induced photocurrent switching effect,the photocurrent direction can be switched in response to the wavelength of incident light at 0 V *** particular,the photocurrent direction exhibits negative when the irradiation wavelength is less than 315 nm,but positive when the wavelength is longer than 315 *** device shows a responsivity of up to−6.7 mA/W at 300 nm and 5.3 mA/W at 340 nm,*** particular,three special logic gates in response to different dual UV light inputs are demonstrated via a single bipolar PD,which may be beneficial for future multifunctional UV photonic integrated devices and systems.

关键词： GaN heterostructure ultraviolet(UV)photodiodes bipolar photocurrent optical logic device

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Employing Feature engineering for River Stage Forecasting to Improve Hybrid Model Performance 32

Employing Feature Engineering for River Stage Forecasting to...

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32nd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Mihel, Anna Maria Pecnik, Spela Vrbancic, Grega Lerga, Jonatan Krvavica, Nino University of Rijeka Faculty of Engineering Department of Computer Engineering Rijeka Croatia University of Maribor Intelligent Systems Laboratory Faculty of Electrical Engineering and Computer Science Maribor Slovenia University of Rijeka Faculty of Civil Engineering Department of Hydrotechnics and Geotechnics Rijeka Croatia

ISBN: (纸本)9789532901382

Establishing early warning systems and efficient management of water resources in tidal reaches is crucial for achieving adequate flood protection. In tidal reaches, the river stage interacts non-linearly with tides (downstream) and discharge (upstream), making the modeling of river processes extremely complex. Data-driven methods have proven helpful for predicting the river stage parameter. This contrasts with existing numerical approaches, which suffer from several limitations. The aim of this study was to predict the river stage of the upstream part of the tidal river, where the influence of salinity is present during the summer months. Using the CNN-LSTM model trained on historical records of the discharge (station of the variable to be predicted), the river stage of three downstream stations, and the point of interest, the prediction was carried out up to 24 hours in advance. By incorporating feature engineering, we achieved an improvement in model results for the longest horizon, confirmed by standard performance metrics, and considered it efficient and effective for risk mitigation. Introducing feature engineering into the data preprocessing resulted in a predictive performance improvement of 0.98% for the Nash-Sutcliffe Efficiency (NSE) metric, 7.0% for the root mean squared error (RMSE) metric, and 7.25% for the mean absolute error (MAE) metric over the second and third-best scenarios using a spectrogram and time-series data. © 2024 University of Split, FESB.

关键词： Mean square error

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USR: Unrolled Super-Resolution with Deep Priors for Structured Illumination Microscopy

USR: Unrolled Super-Resolution with Deep Priors for Structur...

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3D Image Acquisition and Display: Technology, Perception and Applications, 3D, COSI, DH, FLatOptics, IS, pcAOP 2023 - Part of Imaging and Applied Optics Congress 2023

作者： Dajkhosh, S. Parisa Hossain, Mazharul Preza, Chrysanthe Computational Imaging Research Laboratory Electrical and Computer Engineering Department United States Computer Science Department The University of Memphis MemphisTN United States

We introduce a method for enhancing the resolution of Structured Illumination Microscopy images. It uses a deep prior network, unrolled with a model-based analytical process to improve performance, maintain structural... 详细信息

ISBN: (纸本)9781957171289

关键词： Image enhancement

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OblivTime: Oblivious and Efficient Interval Skyline Query Processing Over Encrypted Time-Series Data

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IEEE Transactions on Services Computing 2025年第03期18卷 1602-1617页

作者： Ouyang, Huajie Zheng, Yifeng Wang, Songlei Hua, Zhongyun Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518055 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518055 China

Time-series data is prevalent in many applications like smart homes, smart grids, and healthcare. And it is now increasingly common to store and query time-series data in the cloud. Despite the benefits, data privacy concerns in such outsourced services are pressing, making it imperative to embed privacy assurance mechanisms from the outset. Most existing related works have been focused on querying for different types of aggregate statistics. In this paper, we instead focus on the secure support for advanced interval skyline queries, which allow to identify time series that are not dominated by any other time series within a query time interval. This is valuable for time-series data analytics in applications like remote health monitoring (e.g., identifying patients with high heart rates in a certain week). We present OblivTime, a new system framework for oblivious and efficient interval skyline query processing over encrypted time-series data. OblivTime is built from a synergy of time-series data analytics, lightweight cryptography, and GPU parallel computing, achieving stronger security guarantees and lower online query latency over the state-of-the-art prior work. Extensive experiments demonstrate that OblivTime can achieve up to 666\times speedup in online query latency over the state-of-the-art prior work. © 2008-2012 IEEE.

关键词： Structured Query Language

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Attack Detection for Spoofed Synchrophasor Measurements Using Segmentation Network

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CSEE Journal of Power and Energy Systems 2022年第5期8卷 1327-1337页

作者： Wei Qiu Chengcheng Li Qiu Tang Kaiqi Sun Yilu Liu Wenxuan Yao College of Electrical and Information Engineering Hunan UniversityChangsha 410082China Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN37996USA Oak Ridge National Laboratory Oak RidgeTN37831USA

Synchrophasor measurements are essential to realtime situational awareness of the smart grid but vulnerable to cyber-attacks during the process of transmission and *** ensure data security and mitigate the impact of spoofed synchrophasor measurements,this work proposes a novel object detection method using a Weight-based One-dimensional Convolutional Segmentation Network(WOCSN)with the ability of attack behavior identification and time *** WOCSN,automatic data feature extraction can be achieved by onedimensional convolution from the input signal,thereby reducing the impact of handcrafted features.A weight loss function is designed to distribute the contribution for normal and attack ***,attack time is located via the proposed binary method based on pixel ***,the actual synchrophasor data collected from four locations are used for the performance evaluation of the ***,combined with designed evaluation metrics,the time localization ability of WOCSN is validated in the scenarios of composite attacks with different spoofed intensities and time-sensitivities.

关键词： Data security spoofed synchrophasor measurements weight-based one-dimensional convolutional segmentation network(WOCSN)

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Optical Microsphere Nano-Imaging:Progress and Challenges

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engineering 2024年第5期36卷 102-123页

作者： Guangxing Wu Minghui Hong Department of Electrical and Computer Engineering National University of SingaporeSingapore 117576Singapore Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen UniversityXiamen 361005China Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province(IKKEM) Xiamen 361005China

The optical diffraction effect imposes a radical obstacle preventing conventional optical microscopes from achieving an imaging resolution beyond the Abbe diffraction limit and thereby restricting their usage in a multitude of nanoscale *** the past decade,the optical microsphere nanoimaging technique has been demonstrated to be a cost-effective solution for overcoming the diffraction limit and has achieved an imaging resolution of up to about k6k8 in a real-time and label-free manner,making it highly competitive among numerous super-resolution imaging *** this review,we summarize the underlying nano-imaging mechanisms of the microsphere nanoscope and key advancements aimed at imaging performance enhancement:first,to change the working environment or modify the peripheral hardware of a single microsphere nanoscope at the system level;second,to compose the microsphere compound lens;and third,to engineer the geometry or ingredients of *** also analyze challenges yet to be overcome in optical microsphere nano-imaging,followed by an outlook of this technique.

关键词： Microsphere Nano-imaging Virtual imaging Microsphere compound lens Engineered microsphere

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An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

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computer Modeling in engineering & sciences 2023年第5期135卷 1571-1587页

作者： Jun Liu Geng Yuan Changdi Yang Houbing Song Liang Luo Robotics Institute School of Computer ScienceCarnegie Mellon UniversityPittsburghPA15217USA Department of Electrical&Computer Engineering College of EngineeringNortheastern UniversityBostonMA02115USA Security and Optimization for Networked Globe Laboratory(SONG Lab) Embry-Riddle Aeronautical UniversityDaytona BeachFL32114USA School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China

The interpretability of deep learning models has emerged as a compelling area in artificial intelligence *** safety criteria for medical imaging are highly stringent,and models are required for an ***,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and ***,the interpretability of CNNs has come into the *** medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning ***,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these *** this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac *** enhanced GPU training system implemented interpretable global average pooling for graphics using deep *** deep learning tasks were *** included data management,neural network architecture,and *** system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment *** results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning *** model is lightweight and more convenient to deploy on mobile devices than transfer learning models.

关键词： Interpretable graphics training visualization image segmentation left ventricle CNNs global average pooling

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Effective short text classification via the fusion of hybrid features for IoT social data

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Digital Communications and Networks 2022年第6期8卷 942-954页

作者： Xiong Luo Zhijian Yu Zhigang Zhao Wenbing Zhao Jenq-Haur Wang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Shunde Innovation School University of Science and Technology BeijingFoshan528399China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Department of Electrical Engineering and Computer Science Cleveland State UniversityClevelandOH44115USA Department of Computer Science and Information Engineering National Taipei University of TechnologyTaipei10608China

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.

关键词： Information fusion Short text classi fication BERT Bidirectional encoder representations fr 0om transformers Deep learning Social data

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A GPGPU-Based Algorithm Acceleration System for ECG Signal Processing 7

A GPGPU-Based Algorithm Acceleration System for ECG Signal P...

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7th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2024

作者： Huang, Tianyu Wang, Zhijiong Hu, Guannan Pun, Sio Hang Li, Hung Chun Vai, Mang I Science and Technology Research Institute Lingyange Semiconductor Incorporated University of Macau Joint Laboratory of Zhuhai UM Department of Electrical and Computer Engineering China Science and Technology Research Institute Lingyange Semiconductor Incorporated University of Macau Joint Laboratory of Zhuhai UM Lingyange Semiconductor Incorporated Department of Electrical and Computer Engineering China Science and Technology Research Institute Lingyange Semiconductor Incorporated Joint Laboratory of Zhuhai UM Lingyange Semiconductor Incorporated Zhuhai China Institute of Microelectronics Science and Technology Research Institute Lingyange Semiconductor Incorporated University of Macau Joint Laboratory of Zhuhai UM Department of Electrical and Computer Engineering China

ISBN: (纸本)9798331529314

Electrocardiogram (ECG) is an important non-invasive technique for diagnosing cardiovascular diseases (CVD). After acquiring the patients' raw ECG signal data, signal processing is essential for the diagnosis. Conventional ECG signal processing is usually performed serially, which takes a long time to process signals with large amounts of data. In this study, we have proposed a GPGPU-based algorithm acceleration system to improve the efficiency of ECG signal processing. This system utilizes OpenCL for parallel algorithm development on GPGPU. GPGPU executes certain steps in the QRS complex detection algorithm via parallel computing, thereby accelerating the algorithmic process. Experimental results showed that the system achieved a speedup of 1.279 compared to serial computing when processing 39 ECG signal sequences in parallel. In addition, our system has been equipped with a progressive transmission mechanism and a function that intelligently determines whether an algorithm can be accelerated. These features make this system practical not only in biomedical signal processing but also in engineering and research fields with large amounts of data. © 2024 IEEE.

关键词： Noninvasive medical procedures

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A Composite Decomposition Method for Large-Scale Global Optimization

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6304-6319页

作者： Tian, Maojiang Chen, Minyang Du, Wei Tang, Yang Jin, Yaochu Yen, Gary G. Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China East China University of Science and Technology Engineering Research Center of Process System Engineering Ministry of Education Shanghai200237 China Westlake University School of Engineering Hangzhou310030 China Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74048 United States

Cooperative coevolution (CC) algorithms, based on the divide-and-conquer strategy, have emerged as the predominant approach to solving large-scale global optimization (LSGO) problems. The efficiency and accuracy of the grouping stage significantly impact the performance of the optimization process. While the general separability grouping (GSG) method has overcome the limitation of previous differential grouping (DG) methods by enabling the decomposition of nonadditively separable functions, it suffers from high computational complexity. To address this challenge, this article proposes a composite separability grouping (CSG) method, seamlessly integrating DG and GSG into a problem decomposition framework to utilize the strengths of both approaches. CSG introduces a step-by-step decomposition framework that accurately decomposes various problem types using fewer computational resources. By sequentially identifying additively, multiplicatively and generally separable variables, CSG progressively groups nonseparable variables by recursively considering the interactions between each nonseparable variable and the formed nonseparable groups. Furthermore, to enhance the efficiency and accuracy of CSG, we introduce two innovative methods: a multiplicatively separable variable detection method and a nonseparable variable grouping method. These two methods are designed to effectively detect multiplicatively separable variables and efficiently group nonseparable variables, respectively. Extensive experimental results demonstrate that CSG achieves more accurate variable grouping with lower computational complexity compared to GSG and state-of-the-art DG-series designs. © 2024 IEEE.

关键词： Computational efficiency

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