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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Song, Yuanqing Liu, Yuhao Djurić, Petar M. Department of Electrical and Computer Engineering Stony Brook University Stony BrookNY11794 United States

ISBN: (纸本)9798350368741

This paper introduces a novel structure for deep Gaussian processes (DGPs) and a method for determining their depths. The proposed framework enables faster convergence of their parameters and reduces computational cost to optimize them while maintaining performance comparable to that of conventional DGP models. Furthermore, our approach presents a feasible solution to reduce the risk that the model becomes trapped in local minima during the simultaneous training of multiple layers, which ensures more efficient and reliable model training. Through experimental evaluation, we demonstrate the effectiveness of these models and the advantages of integrating transfer learning to reduce retraining costs. © 2025 IEEE.

关键词： Bayesian machine learning Deep Gaussian processes flexible depth random features transfer learning

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Synthesis of Dynamic Virtual-Inertia for Microgrid System Applications

Synthesis of Dynamic Virtual-Inertia for Microgrid System Ap...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Justo, Jackson J. Demidova, Galina Lukichev, Dmitry Poliakov, Nikolai Makolo, Peter Bansal, Ramesh C. Itmo University Faculty of Control System and Robotics Saint-Petersburg Russia University of Dar es Salaam Department of Electrical Engineering P.O. Box 33335 Dar es Salaam Tanzania United Republic of University of Sharjah Depatment of Electrical Engineering Sharjah United Arab Emirates University of Pretoria Department of Electric Electronic and Computer Engineering Pretoria South Africa

ISBN: (纸本)9798331527495

During the last two decades, interest in grid integrated renewable energy sources (RERs) into the electrical power grids has significantly grown. The motive being a promising strategy to reduce greenhouse gas emissions generated by conventional power plants (CPPs) and hence environmental consequences, decrease losses in distribution lines to improve power system supply efficiency and reliability to consumers. As a result, several CPPs are being replaced by RERs such as solar PV system, wind energy, micro hydro-turbine, fuel cell, and small gas power plants. These are installed close to customers on low-voltage and medium-voltage sites to reduce losses in the network and improve power quality, overall efficiency and reliability. However, grid-connected RERs via inverter decreases system inertia due to decrease in CPPs with rotating mass serving as an immediate natural response to frequency deviations following network disturbances. Therefore, this paper presents the design of dynamic virtual-inertia control (DVIC) scheme. The developed DVIC incorporates the mechanism to adjust active and reactive demand in response to any frequency and voltage variations to ensure system stability. To validate the suitability of the presented technique, MATLAB/Simulink model is developed and simulated under various operating scenarios. Using the results obtained simulation study, it is evidently the proposed DVIC shows improved performance is achieved than when no inertia control system applied. © 2025 IEEE.

关键词： Solar fuels

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Multi-Radar Near-Field System Employing Multi-Band Non-Interleaved Metasurface for Enhanced Bio-Sensing 19

Multi-Radar Near-Field System Employing Multi-Band Non-Inter...

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19th European Conference on Antennas and Propagation, EuCAP 2025

作者： Bagheri, Mohammad Omid Ramahi, Omar M. Shaker, George University of Waterloo Department of Electrical and Computer Engineering Waterloo ON Canada

ISBN: (纸本)9788831299107

Multi-radar sensing significantly enhances accuracy and sensitivity in non-invasive, real-time wearable health monitoring. This paper introduces a system that employs near-field focusing of radar interactions on human skin at various locations, with the multi-radar setup gathering data from multiple points to enhance detection capabilities. A multi-band non-interleaved metasurface is integrated into this system, improving the detection of physiological parameters, including the identification of human blood abnormalities. The methodology utilizes discrete zones of a transmissive metasurface, each composed of phase-synthesized arrays. These zones serve as low-profile impedance matching networks, optimized for specific frequencies within the 58 to 63 GHz millimeter-wave radar range, thereby increasing efficiency through higher absorbed power density at different frequencies, which also enables varied penetration depths into human skin. Analysis using a customized phantom, closely resembling human skin, demonstrates significant increases in near-field absorbed power density: over 11 dB at 60 GHz and 2 mm penetration depth, over 17 dB at 58 GHz, and 9 dB at 61 GHz at the same depth. © 2025 European Association on Antennas and Propagation.

关键词： Biomedical Sensing Glucose Monitoring Metasurface Multi-Band Antenna Multi-Radar Near-Field

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A Practical Light Field Representation and Coding Scheme with an Emphasis on Refocusing

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IEIE Transactions on Smart Processing and Computing 2022年第5期11卷 305-315页

作者： Zhao, Chun Jeon, Byeungwoo Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Light field images have drawn a great deal of interest owing to their flexible post-processing and versatile functionalities. However, the tremendous data volume has imposed a practical limit in applications, which requires a better way to represent and compress the data. In this paper, we propose a practical representation and coding scheme for light field data with a special emphasis on retaining the refocusing functionality as much as possible. Under the proposed method, light field data are represented and compressed in the form of a rendered all-in-focus image and a depth map. Before encoding, the all-in-focus image is rendered accurately based on the depth map using the proposed difference focus measure. At the decoder, a focal stack comprising multiple images having different focus levels can be reconstructed by applying a defocusing function to the compressed all-in-focus image where the defocusing function parameter depends on the selected depth level of the compressed depth map. Compared with well-known state-of-the-art methods that compress the Fourier Disparity Layer or the focal stack, the proposed coding scheme shows much smaller loss in the refocusing capability (16.2% and 17.8% less than two state-of-the-art methods) and provides PSNR improvements of 1.60dB to 2.38dB at the same compression ratio. © 2022 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Image reconstruction

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Optimization of Load Frequency Control using Zebra Optimization Algorithm in Renewable Energy Integrated Power System 33

Optimization of Load Frequency Control using Zebra Optimizat...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Tshinavhe, Ntanganedzeni Ratshitanga, Mukovhe Tshemese-Mvandaba, Nomzamo Mditshwa, Mkhutazi Cape Peninsula University of Technology Department of Electrical Electronic and Computer Engineering Cape Town South Africa

ISBN: (纸本)9798331535162

Integrating renewable energy sources, particularly photovoltaic (PV) systems, into modern power systems poses major challenges to maintaining supply-demand balance due to their inherent variability and intermittency. This instability compromises the reliability and efficiency of power system operation. Traditional load frequency control (LFC) methods struggle to effectively manage these fluctuations, leading to frequency deviations beyond acceptable limits. This paper presents a Zebra Optimization Algorithm (ZOA) for fine-tuning the parameters of a proportional-integral-derivative (PID) controller in the LFC of a power system integrating renewable energy sources (RES) and Battery Energy Storage Systems (BESS). The study addresses the stability challenges posed by the variability and intermittency of renewable energy and proposes ZOA to enhance the PID controller's adaptability to system fluctuations. Simulation results demonstrate that the ZOA-based PID controller significantly improves recovery speed, reduces overshoot, and shortens settling time compared to conventional PID controllers. A comparative analysis with Particle Swarm Optimization (PSO) based PID control shows that the ZOA-PID controller provides greater stability and faster response times when the algorithm has large areas for searching. In scenarios with high penetration of renewable sources, ZOA's ability to balance exploration and exploitation requires improvements for a more flexible and effective optimization of PID parameters, outperforming PSO-based PID control in overall system performance. © 2025 IEEE.

关键词： Three term control systems

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ML Based Light-Weight Security Protocol for IoFT Device 6

ML Based Light-Weight Security Protocol for IoFT Device

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6th International Conference on robotics and Automation in Industry, ICRAI 2024

作者： Khan, Awais Aziz, Ayesha Tariq, Muhamad Kamal, Mohsin National Univeristy of Computer and Emerging Sciences Department of Electrical Engineering Islamabad Pakistan School of Computer Science National Univeristy of Computer and Emerging Sciences Islamabad Pakistan School of Electrical Engineering and Computer Science National Univeristy Science and Technology Islamabad Pakistan

ISBN: (纸本)9798350355703

Forest plays a vital role in environmental change and the protection of forests is the utmost important thing. The study attempts to suggest a solution for a smart forest (Internet of Forest Things). As nowadays, the Internet of Things is emerging very vastly and a large number of researchers are working towards the field of Internet of Forest Things (loFT). The protection of smart forests is of utmost importance because the infrastructure is physically unprotected and they generate huge data about the forest. Due to scalability, smart forest controllers are small-sized and low-cost devices with less computational capabilities. Keeping it in mind, the designed algorithms should be lightweight so that they can meet the requirements. To address this issue, this paper proposes a machine learning approach to develop a lightweight security solution to address data altering and man-in-the-middle attacks. The algorithm generates a link fingerprint of the received signal strength indicator (RSSI) value of the communicating IoFT node. The packet is sent to the server unit and it is decoded and matched with the table to identify the path. A trained machine learning model at the server gets the RSSI value from the packet and successfully classify about the presence of an attack. © 2024 IEEE.

关键词： k-nearest neighbors

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Parameter Dependent Chen-Fliess Series: Convergence and Continuity 59

Parameter Dependent Chen-Fliess Series: Convergence and Cont...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Pham, Natalie Gray, W. Steven Old Dominion University Department of Electrical and Computer Engineering NorfolkVA23529-0246 United States

ISBN: (纸本)9798331513269

A class of parameter dependent Chen-Fliess series is introduced where the series coefficients are taken from a noncommutative ring of multivariable differential operators. Such series can represent solutions to Cauchy initial value problems for nonhomogeneous nonlinear PDEs and thus are useful for characterizing the input-output maps of distributed control systems. Sufficient conditions for convergence of these series and absolute continuity of the output are given for a number of special cases. Specific examples involving transport equations are presented. © 2025 IEEE.

关键词： Distributed parameter control systems

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Machine Learning–Based Pre-Stroke Detection System

Machine Learning–Based Pre-Stroke Detection System

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2024 International Conference on Science, Technology, engineering and Management, ICSTEM 2024

作者： Maheshwaran, M. Devi, Kiruthiga V. Gunapriya, D. Pushpalatha, N. Sam Karthik, S. Selvi, A. Department of Mechatronics Engineering Nehru Institute of Engineering and Technology Thirumalayampalayam Tamilnadu Coimbatore641105 India Department of Electrical and Electronics Engineering K.S.R College of Engineering Tamilnadu India Department of Electrical and Electronics Engineering Sri Eshwar College of Engineering Tamilnadu Coimbatore641 202 India Department of Electrical and Electronics Engineering Dhanalakshmi Srinivasan College of Engineering Tamilnadu Coimbatore India Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur639113 India

ISBN: (纸本)9798350376913

Stroke remains a significant health concern worldwide, often leading to severe disabilities and mortality. Early detection and intervention are crucial for preventing strokes and mitigating their impacts. In this study, the proposed machine learning-based pre-stroke detection system aimed at identifying individuals at risk of experiencing a stroke before the onset of symptoms. The proposed system utilizes a diverse dataset containing demographic information, medical history, lifestyle factors, and biomarkers relevant to stroke risk. Various machine learning algorithms, including Decision Tree Algorithm, Random Forest Algorithm, Naïve Bayes Algorithm, Multilayer Perceptron Algorithm, and J Rip Algorithm, were trained, and their evaluation was conducted separately using the dataset. The proposed system uses a cloud-based stroke warning system that utilizes machine learning technology to predict impending strokes. The peculiar scrutiny of many machine learning methods led to the development of an effective machine learning strategy for the precise detection of strokes. A microcontroller-based system was used to establish and exhibit an actual patient monitoring system that can sense a variety of real-time indicators, including core temperature, blood pressure, blood flow, heart rhythm, and oxygen level, to let caregivers and doctors keep an eye on stroke patients around the clock. Decisions can be made quickly and easily with the aid of various decision-making algorithms, and anyone can access the database according to their needs. © 2024 IEEE.

关键词： Blood pressure

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Diagnosis of Lung Diseases from Chest X-Ray Images Using Different Fusion Techniques 11

Diagnosis of Lung Diseases from Chest X-Ray Images Using Dif...

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11th International Conference on Information and Communication Technology, ICoICT 2023

作者： Mostafa, Fatma A. Elrefaei, Lamiaa A. Fouda, Mostafa M. Hossam, Aya Shoubra Benha University Faculty of Engineering Department of Electrical Engineering Cairo Egypt Idaho State University Department of Electrical and Computer Engineering PocatelloID United States

ISBN: (纸本)9798350321982

Lung diseases refer to a group of disorders that affect the lungs and respiratory system. Several factors, such as genetics, environmental pollution, infections, and smoking can these. Lung diseases include coronavirus (COVID-19), pneumonia, chronic obstructive pulmonary disease (COPD), and asthma. Lung diseases cause significant damage to lung function and lead to respiratory failure or even death. The symptoms of lung diseases can range from mild difficulty breathing to severe ones, including chest pain, bloody coughing, and shortness of breath. Early detection can increase the chances of successful treatment and improve the overall outcome for affected individuals. Artificial intelligence (AI) has demonstrated considerable potential for detecting and diagnosing lung diseases through machine learning algorithms and deep learning models. The detection of lung diseases using chest X-rays (CXRs) is demonstrated in this paper by applying feature-level fusion (FLF) and decision-level fusion (DLF) techniques. FLF involves concatenating the features from two models before the classification process. In comparison, DLF is executed after training the two models and then concatenating the results to make a single decision. The two models are DenseNet-169 and Vision Transformer (ViT-L32). On the COVID-19 Radiography database, the proposed models have been tested and trained. The data has been preprocessed using data augmentation and a blurring method. An 'Adam' optimizer is used while compiling the model. The accuracy of the DLF is 93.3%, while the FLF achieved an accuracy of 94.54%, which is better than the accuracy of the models without fusion. © 2023 IEEE.

关键词： COVID-19

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Literature Survey on Power System Resilience Against Ice Storms

Literature Survey on Power System Resilience Against Ice Sto...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Badawi, Rawand Overbye, Thomas Texas A&M University Department of Electrical and Computer Engineering College StationTX United States

ISBN: (纸本)9798331541125

Ice accumulation on power lines during an ice storm can result in permanent damage and widespread power outages if left unaddressed. This paper presents a literature survey on the resilience of power systems against ice storms, focusing on three pillars of resilience: modelling, situational awareness, and response. Physical and data driven approaches for modelling ice formation around power lines are examined. Situational awareness as achieved through various methods for tracking spatiotemporal changes in ice storms, fragility modelling of power system components, and the assessment of power system resilience in different studies is also discussed. Finally, mitigation strategies are explored, including mechanical techniques that rely on force to break ice off a power line and electrical methods that use heat from power flow to melt it. © 2025 IEEE.

关键词： Power distribution lines

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