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Hot‑electron dynamics in plasmonic nanostructures:fundamentals,applications and overlooked aspects

eLight 2024年第1期4卷 107-128页

作者： Jacob Khurgin Anton Yu.Bykov Anatoly V.Zayats Electrical and Computer Engineering Department Johns Hopkins UniversityBaltimoreMDUSA Department of Physics and London Centre for Nanotechnology King’s College LondonStrandLondon WC2R 2LSUK

Light absorption near a surface of conductive materials and nanostructures leads to the excitation of nonequilibrium,high-energy charge carriers:electrons above the Fermi level or holes below *** remaining inside a material,these so-called hot carriers result in nonlinear,Kerr-type,optical effects important for controlling light with *** can also transfer into the surroundings of the nanostructures,resulting in photocurrent,or they can interact with adjacent molecules and media,inducing photochemical *** the dynamics of hot carriers and related effects in plasmonic nanostructures is important for the development of ultrafast detectors and nonlinear optical components,broadband photocatalysis,enhanced nanoscale optoelectronic devices,nanoscale and ultrafast temperature control,and other technologies of *** this review,we will discuss the fundamentals of plasmonically-engendered hot electrons,focusing on the overlooked aspects,theoretical descriptions and experimental methods to study them,and describe prototypical processes and examples of most promising applications of hotelectron processes at the metal interfaces.

关键词： dynamics nonlinear excitation

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Intelligent IoT-Driven Advanced Predictive Maintenance System for Industrial Applications

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SN computer Science 2025年第2期6卷 1-24页

作者： Dhinakaran, D. Edwin Raja, S. Velselvi, R. Purushotham, N. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of CSE School of Computing Mohan Babu University Tirupati India

Predictive Maintenance (PdM) aims to ensure the continuous operation of high-risk industrial systems. This challenge is especially critical in environments where equipment failure can cause major financial losses and disrupt operations. Traditional PdM approaches, while fairly effective, often fall short in accurately predicting failures due to their reliance on simple statistical methods and predefined rules. The proposed work introduces an Intelligent IoT-Driven Advanced Predictive Maintenance System (APdM) that addresses these limitations. By integrating advanced machine learning techniques, including supervised and reinforcement learning, with IoT technologies to enhance predictive accuracy as well as the operational efficiency. The system leverages Explainable AI (XAI) to ensure transparency in decision-making and employs federated learning for distributed model training, preserving data privacy across multiple edge devices. The proposed APdM system was evaluated using the Kaggle "Air Compressor Predictive Maintenance" Dataset, which provided extensive sensor data from heavy vehicle operations. Comparative analysis against four prevailing PdM approaches—Traditional PdM, Decision Support System-PdM, Genetic Algorithm-based PdM, and AI-based PdM—demonstrated the superior performance of our system. The results show that the APdM system, powered by intelligent IoT integration, diminishes the Mean Error Percentage (MEP) by up to 38.4% compared to traditional methods and decreases the Mean Absolute Error (MAE) by 36.7% relative to DSS-PdM. Additionally, the system achieves a 78% improvement in Symmetric Mean Absolute Percentage Error (SMAPE) over GA-PdM, underscoring its robust performance in real-world industrial scenarios. These outcomes validate the effectiveness of the proposed APdM system, marking a significant improvement in the field of predictive maintenance through the intelligent integration of IoT technologies. © The Author(s), under exclusive licence to S

关键词： Explainable AI IoT Machine learning Predictive maintenance Supervised and reinforcement learning

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Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction

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IEEE Access 2025年 13卷 92468-92482页

作者： Cheema, Muhammad Omar Din, Zia Mohy Ud Imran, Azhar Khan, Fawad Salam Al-Rawi, Mahmood Basil A. El-Meligy, Mohammed Gul, Jahan Zeb Air University Department of Biomedical Engineering Islamabad44000 Pakistan Air University Faculty of Computing & AI Department of Creative Technologies Islamabad44000 Pakistan King Saud University College of Applied Medical Sciences Department of Optometry Riyadh Saudi Arabia Amman Arab University College of Computer Science and Informatics Amman Jordan Maynooth University Department of Electronic Engineering Ireland

There are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term and preterm pregnancies. However, the effectiveness of machine learning classifiers is hampered by the inherent class imbalance in datasets. In order to overcome this constraint, this study uses a Generative Adversarial Network to produce high-quality synthetic EHG data while maintaining the statistical properties of actual signals. By successfully reducing class imbalance, the enhanced dataset makes it possible for machine learning models to be trained with resilience. Real and synthetic features did not differ significantly, according to statistical validation using a t-test (p > 0.05). With this method, the Random Forest classifier obtained a ROC-AUC of 0.99 and a 10-fold cross-validation accuracy of 98%. The suggested GAN-based method’s simplicity and efficacy demonstrate its promise for resolving class imbalance and developing reliable classification systems for diagnostic applications. © 2013 IEEE.

关键词： Generative adversarial networks

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Evaluation of Deep Learning Models for Person Authentication Based on Touch Gesture

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computer Systems Science & engineering 2022年第8期42卷 465-481页

作者： Asrar Bajaber Mai Fadel Lamiaa Elrefaei Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt

Touch gesture biometrics authentication system is the study of user's touching behavior on his touch device to identify *** features traditionally used in touch gesture authentication systems are extracted using hand-crafted feature extraction *** this work,we investigate the ability of Deep Learning(DL)to automatically discover useful features of touch gesture and use them to authenticate the *** different models are investigated Long-Short Term Memory(LSTM),Gated Recurrent Unit(GRU),Convolutional Neural Network(CNN)combined with LSTM(CNN-LSTM),and CNN combined with GRU(CNN-GRU).In addition,different regularization techniques are investigated such as Activity Regularizer,Batch Normalization(BN),Dropout,and *** deep networks were trained from scratch and tested using TouchAlytics and BioIdent datasets for dynamic touch *** result reported in terms of authentication accuracy,False Acceptance Rate(FAR),False Rejection Rate(FRR).The best result we have been obtained was 96.73%,96.07%and 96.08%for training,validation and testing accuracy respectively with dynamic touch authentication system on TouchAlytics dataset with CNN-GRU DL model,while the best result of FAR and FRR obtained on TouchAlytics dataset was with CNN-LSTM were FAR was 0.0009 and FRR was *** BioIdent dataset the best results have been obtained was 84.87%,78.28%and 78.35%for Training,validation and testing accuracy respectively with CNN-LSTM *** use of a learning based approach in touch authentication system has shown good results comparing with other state-of-the-art using TouchAlytics dataset.

关键词： Touch authentication system touch gestures behavioral biometric deep learning classification CNN RNN LSTM

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Model Free Reinforcement Learning Based Controller For Grid-tied 9-Level Packed-E-Cell Multi-level Inverter

Model Free Reinforcement Learning Based Controller For Grid-...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Alquennah, Alamera Nouran Krama, Abdelbasset Abu-Rub, Haitham Ghrayeb, Ali Bayhan, Sertac Texas A&m University Department of Electrical and Computer Engineering College StationTX United States Texas A&m University at Qatar Department of Electrical and Computer Engineering Doha Qatar Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Doha34110 Qatar College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Department of Electrical and Computer Engineering Doha Qatar

ISBN: (纸本)9798350376067

This paper proposes a model-free reinforcement learning-based controller (RLC) for single phase grid connected 9-level packed-E-Cell (PEC9) multilevel inverter (MLI). The RLC design consists of actor-critic architecture which combines the value-based and policy-based learning methods using the stable learning algorithm, proximal policy optimization (PPO). In the system under study, the control objectives are regulating the two capacitors' voltages around their reference values and generating a sinusoidal current with reduced total harmonic distortion (THD). The training environment is designed in MATLAB/Simulink in which different variations in the voltage and current references are included. The testing results showed the capability of the designed RLC to generate 5A and 10A current signals with 1.9% and 1.3%, respectively, while the two capacitors voltage error were kept below 1.5 V. The dynamic response is also investigated in the case of having a variation in the reference phase shift or the DC voltage source. The robustness of the proposed control is tested in the case of voltage grid sag and swell scenarios. Furthermore, the performance of the RLC is compared with the results of finite control set - model predictive control (FCS-MPC) to show the RLC competitiveness in fulfilling the control objectives simultaneously. © 2024 IEEE.

关键词： Model predictive control

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Hand-Powered Point-of-Care: Centrifugal Microfluidic Platform for Urine Routine Examination (μCUREX)

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Langmuir 2023年第5期39卷 1897-1904页

作者： Kuo, Shao-Hsuan Peraro, Alberto Lin, Hsiu-Pen Chang, Chun-Hao Li, Bor-Ran Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of Biomedical Engineering University of Padua Padova35122 Italy Center for Emergent Functional Matter Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of Electrical and Computer Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan

Urinalysis is one of the simplest and most common medical tests in modern cities. With the assistance of professional technicians and equipment, people in metropolitan areas can effortlessly acquire information about their physiological conditions from traditional clinical laboratories. However, the threshold, including precise benchtop equipment and well-trained personnel, still remains a considerable dilemma for residents in healthcare-poor areas. Hence, it is a crucial and urgent topic to develop a smart and affordable widget to address this challenge. To improve the healthcare rights of residents, we proposed a disposable centrifugal microfluidic urine routine examination platform (named μCUREX) actuated with a modified hand-powered fan. Two parts of urinalysis (sediment test and chemical strip test) were integrated into the μCUREX disc. The influence on sedimentation by variant hand-powered manipulation was simulated using COMSOL. As a result, more than 70% of the sediment can be collected. Moreover, the color change of chemical strip papers (indicators for glucose, pH, protein, and occult blood) was recorded with a 3D-printed studio and analyzed after reaction with chemical-spiked and pH-adjusted artificial and human urine specimens. The whole process can be completed within 10 min, with only 200 μL of urine needed. In conclusion, we successfully constructed an ultra-low-cost point-of-care platform for urinalysis in extremely resource-poor settings. The handy size, high affordability, and user-friendliness of the μCUREX disc provide strong potential and feasibility in solving problems in resource-poor settings. Furthermore, we highly expect the μCUREX platform to improve the level of healthcare in resource-limited areas. © 2023 American Chemical Society.

关键词： Microfluidics

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A Fast Iterative Method for Dynamic State Estimation with Unknown Noise Statistics 7

A Fast Iterative Method for Dynamic State Estimation with Un...

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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Hou, Dongchen Sun, Yonghui Dinavahi, Venkata Wang, Sen College of Energy and Electrical Engineering Hohai University Nanjing China University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798350345094

The extensive application of phasor measurement units (PMUs) provides a solid information foundation for real-time monitoring of power system dynamic processes. To improve the robustness of unscented Kalman filter (UKF), a spherical simplex unscented transform-based iterated unscented Kalman filter (SSUT-IUKF) is proposed. By introducing spherical simplex unscented transform and iterative correction technology into UKF, the computing efficiency and estimation accuracy have been effectively improved. Finally, simulation experiments are conducted on an IEEE 39-bus system. Compared with traditional dynamic state estimation methods for synchronous generators, SSUT- IUKF has potential advantages in algorithm robustness, computational efficiency and estimation accuracy. © 2023 IEEE.

关键词： Synchronous generators

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Robust beamforming for IRS-aided SWIPT in cognitive radio networks

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Digital Communications and Networks 2023年第3期9卷 645-654页

作者： Zining Wang Min Lin Shupei Huang Ming Cheng Wei-Ping Zhu Yan Guo College of Telecommunications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical and Computer Engineering Concordia UniversityMontrealQCH3G 1M8Canada

Intelligent reflecting surface(IRS)is widely recognized as a promising technique to enhance the system perfor-mance,and thus is a hot research topic in future wireless *** this context,this paper proposes a robust BF scheme to improve the spectrum and energy harvesting efficiencies for the IRS-aided simultaneous wireless information and power transfer(SWIPT)in a cognitive radio network(CRN).Here,the base station(BS)utilizes spectrum assigned to the primary users(PUs)to simultaneously serve multiple energy receivers(ERs)and information receivers(IRs)through IRS-aided multicast *** particular,by assuming that only the imperfect channel state information(CSI)is available,we first formulate a constrained problem to maximize the minimal achievable rate of IRs,while satisfying the harvesting energy threshold of ERs,the quality-of-service requirement of IRs,the interference threshold of PUs and transmit power budget of *** address the non-convex problem,we then adopt triangle inequality to deal with the channel uncertainty,and propose a low-complexity algorithm combining alternating direction method of multipliers(ADMM)with alternating optimi-zation(AO)to jointly optimize the active and passive beamformers for the BS and IRS,***,our simulation results confirm the effectiveness of the proposed BF scheme and also provide useful insights into the importance of introducing IRS into the CRN with SWIPT.

关键词： Cognitive radio networks Intelligent reflecting surface Simultaneous wireless information and power transfer Robust beamforming

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A Comparative Analysis of Denoising PCG Signals using Adaptive Wiener-Kalman Filtering 8

A Comparative Analysis of Denoising PCG Signals using Adapti...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Pandiaraj, A. Ramshankar, N. Gnanasekar, A. Arunchandran, R. Azariah John, B.S. Febin, C. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India R.M D. Engineering College Department of Computer Science and Engineering India R.M D. Engineering College Department of Computer Science and Engineering kavaraipettai India R.M.D Engineering College Chennai India

ISBN: (纸本)9798331505462

Cardiovascular disease (CVD) is the group of disorder occurs on heart and blood vessel, and they are coronary heart disease, cerebrovascular disease, rheumatic heart disease, etc. Particularly people under the age of 70 are mostly affected by this disease. Earlier cardiologist analyse phonocardiography (PCG) signals to know the condition of heart. Because of the undesirable noise present in PCG signal makes complication in diagnosis by experts. This study analyse and denoising heart sound using five different techniques namely Short-Time Fourier Transform (STFT), Empirical Mode Decomposition (EMD), Recurrent Neural Network (RNN), weiner filter (WF), adaptive Wiener-Kalman filtering (AWKF). There are Lot of noise removal techniques available as discussed in the literature, failed to achieve higher performance in denoising audio signal. The developed denoising techniques were applied on various pathological heart sounds and on heart sound recorded in a noisy environment. The AWKF filtering has great impact on removing distortion and interference present on the signal. This method reserves the major characteristics of heart beat sound. The experimental results showed that AWKF acquired higher SNR, PSNR, lower RMSE and great denoising effect. © 2024 IEEE.

关键词： Wiener filtering

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Comparative study of IoT-and AI-based computing disease detection approaches

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Data Science and Management 2025年第1期8卷 94-106页

作者： Wasiur Rhmann Jalaluddin Khan Ghufran Ahmad Khan Zubair Ashraf Babita Pandey Mohammad Ahmar Khan Ashraf Ali Amaan Ishrat Abdulrahman Abdullah Alghamdi Bilal Ahamad Mohammad Khaja Shaik Department of Computer Application Lovely Professional UniversityPhagwaraPunjab144411India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationGunturAndhra Pradesh522502India School of Computing Science and Engineering VIT Bhopal UniversityKothrikalanSehore466114India Babasaheb Bhimrao Ambedkar University Lucknow226025India College of Commerce and Business Administration Dhofar UniversitySalalah211Oman Faculty of Computer Studies Arab Open University18211Bahrain Department of Computer Application Shri Ramswaroop Memorial UniversityLucknow225003India College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia

The emergence of different computing methods such as cloud-,fog-,and edge-based Internet of Things(IoT)systems has provided the opportunity to develop intelligent systems for disease *** to other machine learning models,deep learning models have gained more attention from the research community,as they have shown better results with a large volume of data compared to shallow ***,no comprehensive survey has been conducted on integrated IoT-and computing-based systems that deploy deep learning for disease *** study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection,using the most recent papers on IoT-based disease detection systems that include computing approaches,such as cloud,edge,and *** analysis focused on an IoT deep learning architecture suitable for disease *** also recognizes the different factors that require the attention of researchers to develop better IoT disease detection *** study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms.

关键词： Deep learning Internet of Things(IoT) Cloud computing Fog computing Edge computing

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