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IEEE International Symposium on Biomedical Imaging

作者： Jiarui Xing Shuo Wang Amit R. Petal Kenneth C. Bilchick Frederick H. Epstein Miaomiao Zhang Department of Electrical and Computer Engineering University of Virginia USA School of Medicine University of Virginia Health System USA Department of Biomedical Engineering University of Virginia Health System USA Department of Computer Science University of Virginia USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Accurate identification of late mechanical activation (LMA) regions is crucial for optimal cardiac resynchronization therapy (CRT) lead implantation. However, existing approaches using cardiac magnetic resonance (CMR) imaging often over-look myocardial scar information, which may be mistakenly identified as delayed activation regions. To address this issue, we propose a scar-aware LMA detection network that simultaneously detects myocardial scar and prevents LMA localization in these scarred regions. More specifically, our model integrates a pre-trained scar segmentation network using late gadolinium enhancement (LGE) CMRs into a LMA detection network based on highly accurate strain derived from displacement encoding with stimulated echoes (DENSE) CMRs. We introduce a novel scar-aware loss function that utilizes the segmented scar information to discourage false-positive detections of late activated areas. Our model can be trained with or without paired LGE data. During inference, our model does not require the input of LGE images, leveraging learned patterns from strain data alone to mitigate false-positive LMA detection in potential scar regions. We evaluate our model on subjects with and without myocardial scar, demonstrating significantly improved LMA detection accuracy in both scenarios. Our work paves the way for improved CRT planning, potentially leading to better patient outcomes.

关键词： Image segmentation Accuracy Motion segmentation Medical treatment Magnetic resonance Myocardium Lead Data models Planning Strain

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In-Cache Processing with Power-of-Two Quantization for Fast CNN Inference on CPUs

In-Cache Processing with Power-of-Two Quantization for Fast ...

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International Technical Conference o Circuits/systems, computers and Communications (ITC-CSCC)

作者： Joseph Woo Seungtae Lee Seongwook Kim Gwangeun Byeon Seokin Hong Department of Electrical and Computer Engineering Sungkyunkwan University Department of AI System Engineering Sungkyunkwan University

Convolutional Neural Networks (CNN) demand high computational capabilities, motivating researchers to leverage Processing-In-Memory (PIM) technology to achieve significant performance improvements. However, implementing complex arithmetic operations such as multiplication within memory is a significant challenge in the PIM architecture. To address this challenge, this paper proposes a PIM-enabled cache (PEC) architecture that utilizes shifters for performing multiplication operations at a low cost. We also introduce a filter-wise hardware-friendly Power-of-Two (POT) quantization scheme that quantizes weights into power-of-two values for specific filters to accelerate convolution operations with the PEC. Our experimental results demonstrate that the proposed PEC, together with the POT quantization, achieves 2.28x performance improvement on average with an accuracy degradation of 0.784%.

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Evaluation of an Intelligent Controller for Electric Vehicle Charging with Vienna Rectifier Topology

Evaluation of an Intelligent Controller for Electric Vehicle...

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International Conference on Power Electronic Drives

作者： Adithya Lingadahalli Nima Tashakor Hans-Joerg Wiehoff Hui Wang Stefan Goetz System Engineering Power Electronics Vitesco Technologies Regensburg Germany Electrical and Computer Engineering Department Duke University Durham USA Electrical and Computer Engineering Duke University Durham USA

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

The Vienna rectifier is a widely used topology in a variety of industrial applications, including electric vehicle (EV) charging. Although conventional control approaches exist, the nonlinear and highly dynamic nature of the Vienna rectifier as well as many existing uncertainties within the system hinder the development of a performant controller based on classic control theory. This paper proposes a model-free controller based on reinforcement learning (RL) to optimize the performance of the DC-link voltage control loop with concurrent regulation of the power factor. We developed a state-space model of the Vienna rectifier in MATLAB/Simulink and a classical proportional-integral (PI) controller for quantitative comparison. The RL controller's ability to handle system nonlinearities and component uncertainties, such as inductor and capacitor tolerances, enables it to outperform classical control strategies. Furthermore, this paper benchmarks multiple RL-based algorithms including deep deterministic policy gradient (DDPG) as well as twin delayed DDPG (TD3) in the voltage control loop. The results demonstrate that TD3 can reduce 2.5 percent voltage ripple and has a shorter settling time of 0.75 s at the cost of higher computational requirements.

关键词： Reactive power PI control Uncertainty Rectifiers Reinforcement learning Electric vehicle charging Regulation Topology Voltage control Vehicle dynamics

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Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

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Understanding the Impact of Unobservable Variables on the Performance of Predictive Models: The Need for Feature Space Partitioning and Fusion

Understanding the Impact of Unobservable Variables on the Pe...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Juarez Garcia, Ezequiel Stephens, Chad L. Napoli, Nicholas J. Laboratory Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Crew Systems & Aviation Operations Branch and System-Wide Safety Project NASA Langley Research Center HamptonVA23681 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

When developing predictive models over a dataset, the model is globally optimized across the entire feature space to learn a decision boundary. However, when unobservable variables—which cannot be measured or estimated—interact with the observable variables, this can negatively impact the optimization applied to the decision boundary since the data samples introduced by unobservable variables may have little to no association with the applied global optimization. This, consequently, penalizes the entire decision boundary and model performance. This paper examines some of the detrimental effects of unobservable variables, particularly their role in creating new modes in the distribution of observable variables and reducing the separ ability of class distributions. Such challenges result in unrepresentative training data, complex skewedor warped decision boundaries, and decreased accuracy of model predictions, particularly for interpretable models like logistic regression and decision trees. Through two illustrative case examples, we highlight the need to address the challenges imposed by unobservable variables. We propose a strategy to mitigate these challenges by creating local regions with in the feature space through partitioning. This enables the optimization of local models with inthe regions to overcome the impact of un observ ability and complex decision boundaries indifferent feature space localities. Research into a more sophisticated partitioning strategy and where the partition should be relative to the sample of interest is left as future work. Through the analysis of the impact of un observability and the development of a partitioning method, we demonstrate the clear need for a partitioning strategy that integrates knowledge from multiple local models to estimate risk factors using information fusion. Thus, we establish the foundation and motivation for using partitioning and information fusion to overcome the effects of unobserv ability in predictive mo

关键词： Information fusion

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Deep Conditional Generative Adversarial Networks for Efficient Channel Estimation in AmBC systems

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 805-822页

作者： Zargari, Shayan Tellambura, Chintha Maaref, Amine Li, Geoffrey Ye University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Huawei Canada Ottawa Wireless Advanced System Competency Centre OttawaONK2K 3J1 Canada Imperial College London School of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom

In ambient backscatter communication (AmBC), battery-free devices (tags) harvest energy from ambient radio frequency (RF) signals and communicate with readers. Although reliable channel estimation (CE) is critical, classical pilot-based estimators tend to perform poorly. To address this challenge, we treat CE as a denoising problem using conditional generative adversarial networks (CGANs). A three-dimensional (3D) denoising block leverages spatial and temporal characteristics of pilot signals, considering both real and imaginary components of channel matrices. The proposed CGAN estimator is extensively evaluated against traditional estimators like minimum mean-squared error (MMSE), least squares (LS), convolutional neural network (CNN), CNN-based deep residual learning denoiser (CRLD), and blind estimation. Simulation results show 82% gain of the proposed estimator over CRLD and MMSE estimators at an SNR of 5 dB. Moreover, it has advanced learning capabilities and accurately replicates complex channel characteristics. © 2023 CCBY.

关键词： Channel estimation

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A robust multi-objective optimization algorithm for accurate parameter estimation for solar cell models

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Soft Computing 2024年第19期28卷 11265-11277页

作者： Singla, Manish Kumar Gupta, Jyoti Alsharif, Mohammed H. Kim, Mun-Kyeom Aljaidi, Mohammad Safaraliev, Murodbek Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering & ampTechnology Chitkara University Punjab India Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology K. R. Mangalam University Gurgaon Haryana122003 India Department of Electrical Engineering College of Electronics and Information Engineering Sejong University 209 Neungdong-Ro Gwangjin-Gu Seoul05006 Korea Republic of School of Energy System Engineering Chung-Ang University 84 Heukseok-Ro Dongjak-Gu Seoul06974 Korea Republic of Department of Computer Science Zarqa University Zarqa13110 Jordan Department of Automated Electrical Systems Ural Federal University Yekaterinburg620002 Russia

The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic (PV) systems. However, existing solar cell models lack precise parameters, and the manufacturer's datasheet does not provide the required information for reliable modeling. Consequently, accurate parameter estimation becomes necessary. This paper presents a simple multi-objective optimization algorithm (Hybrid Particle Swarm Optimization and Rat Search Algorithm (PSORSA)) designed to estimate cell parameters based on this observation. Unlike other optimization algorithms addressing this issue, the proposed algorithm aims to overcome challenges related to local minima and premature convergence, which often lead to suboptimal results. The paper focuses on assessing the reliability of the proposed algorithm by comparing its performance with other well-known optimization algorithms. The proposed optimizing algorithm is tested on the CEC 2019 benchmark function. Experimental results (RMSE), including statistical analysis, validate the algorithm's effectiveness by comparing them with other algorithms. At the end, non-parametric test is performed to justify the outcomes, vouching for the better performance of the proposed algorithm. The findings demonstrate that the proposed algorithms are particularly well-suited for estimating solar PV models. With its simple structure and high accuracy, the proposed algorithm exhibits great potential for various applications in the field of solar energy. Moreover, its computational efficiency and ease of implementation further contribute to its practicality. © The Author(s) 2024.

关键词： Solar cells

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Customer Segmentation in the Retail Sector: A Data Analytics Approach 14

Customer Segmentation in the Retail Sector: A Data Analytics...

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14th International Conference on Intelligent Human-Machine systems and Cybernetics, IHMSC 2022

作者： Sahinbas, Kevser Catak, Ferhat Ozgur Istanbul Medipol University Department of Managament Information System Istanbul Turkey University of Stavanger Department of Electrical Engineering and Computer Science Rogaland Norway

ISBN: (数字)9781665461696

ISBN: (纸本)9781665461696

Data analytics techniques are widely used in customer segmentation, which groups objects according to the similarity difference on each object and provides a high level of homogeneity in the same cluster or a high level of heterogeneity between each group. In this study, the behavior of customers in the retail sector was analyzed using customer segmentation data mining methods such as OPTICS, BIRCH, Agglomerative Clustuering, K-Means and DBSCAN algithms. The aim of the study is to investigate different data analytics algorithms using a private textile and retail company that has an agreement with e-commerce sites and marketplaces. OPTICS, BIRCH, Agglomerative Clustuering, K-Means have shown almost same clustering results, DBSCAN has outperformed with 0.206086 Silhouette value. The purpose of this paper is to provide a proof of concept of how e-commerce data analytics can be used in customer segmentation. © 2022 IEEE.

关键词： Data Analytics

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Enhanced Energy Efficiency in Wireless Sensor Networks through Harvesting Technologies 2

Enhanced Energy Efficiency in Wireless Sensor Networks throu...

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2nd International engineering Conference on electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Saad, Mohammed Ayad Ali, Adnan H. Rashid, Ahmed Hashim Thabet, Hassaan Th. H. Al_Essa, Mohammed Alani, Sameer Faculty of Engineering and Built Environment Dep of Electrical Electronics and System Engineering Selangor Bangi43600 Malaysia Al-Kitab University Dep of Medical Instrumentations Technique Engineering Kirkuk36001 Iraq Middle Technical University Electrical Engineering Technical College Iraq Faculty of Engineering and Built Environment Department of Electrical Electronics and System Engineering Selangor Bangi43600 Malaysia Al-Hadba University College Department of Medical Instruments Techniques Engineering Mosul Iraq Jannat Iraq College Department of Medical Instrumentations Technique Engineering Ramadi Iraq Computer Center University of Anbar Iraq

ISBN: (纸本)9798350373363

In sensor networks, a wireless sub-system enabling the transfer of digitised data facilitates communication between sensors. Deploying sensor networks involves considering factors such as sensor quantity, topology (fixed or dynamic), data collection needs, and energy supply. Energy poses a significant challenge in wireless nodes, prompting the exploration of suitable power sources for wireless sensor networks (WSNs). While fixed utilities like power lines or manual battery recharging may suffice for a few nodes, deploying hundreds or thousands of nodes in challenging terrain demands alternative solutions. Solar cells and other energy harvesting techniques like wind and RF emerge as ideal options, rendering WSNs self-sustaining. Efficient energy-saving mechanisms are crucial to address energy constraints in small sensors. This review examines leveraging solar cells for energy harvesting, proposing a system incorporating an energy management technique based on node sleep and wake-up strategies. The results reveal a trade-off between dropped communication packets and blocked transmissions during node sleep. The proposed Nash equilibrium resolves this, optimising energy conservation against packet blocking probability in the envisioned scenario. © 2023 IEEE.

关键词： Energy harvesting

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Coil Optimization using Metaheuristic Techniques for Wireless Charging of Electric Vehicles - A Comparative Analysis. 5

Coil Optimization using Metaheuristic Techniques for Wireles...

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5th International Conference on Power and Energy Technology, ICPET 2023

作者： Imtiaz, Tabish Elsanabary, Ahmed Mekhilef, Saad Mubin, Marizan Binti Soon, Tey Kok Aziz, Nur Fadilah Ab Universiti Malaya Department of Electrical Engineering Kuala Lumpur Malaysia Universiti Tenaga Nasional Institute of Power Engineering Selangor Kajang Malaysia Swinburne University of Technology School of Science Computing and Engineering Technologies Melbourne Australia Universiti Malaya Department of Computer System & Technology Kuala Lumpur Malaysia Universiti Tenaga Nasional Department of Electrical & Electronic Engineering Selangor Malaysia

ISBN: (纸本)9798350339673

Wireless Power Transfer (WPT) used for charging of electric vehicles (EVs), suffers from the problem of misalignment. In this paper metaheuristic optimizing techniques are used to increase the misalignment tolerance of the receiver coil. This is done by optimizing the length of each turn of the transmitter coil such that a uniform magnetic field density is produced over the charging area, which is kept 26% higher than the transmitting coil area which further aids to accommodate misalignment of the receiver coil. Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) optimization algorithms are used to obtain the lengths of all the turns of the transmitter coil. These algorithms are also compared by varying the number of turns while keeping the number of iterations to a fixed value. © 2023 IEEE.

关键词： ABC Coil DE Misalignment Opti-mization PSO Wireless Power Transfer

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