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Correcting for sub-resolution tissue motion in localization optoacoustic tomography

Optics Letters 2025年第9期50卷 2966-2969页

作者： Zhao, Jim Nozdriukhin, Daniil Razansky, Daniel Dean-Ben, Xose Luis Institute of Pharmacology and Toxicology Institute for Biomedical Engineering Faculty of Medicine University of Zurich Zurich Switzerland Institute for Biomedical Engineering Department of Information Technology and Electrical Engineering ETH Zurich Zurich Switzerland

Localization optoacoustic tomography (LOT) has recently been suggested for enhancing spatial resolution in optoacoustic imaging beyond the acoustic diffraction barrier and further enabling measuring blood flow velocity. LOT relies on tracking highly absorbing particles in a sequence of images acquired following intravenous injection. Much like for other high-resolution imaging methods, the achievable resolution in vivo is afflicted by physiological motion. Inter-frame displacements further hamper particle tracking and accurate velocity measurements. Herein, we propose a geometric-transformation-based approach to align motion-affected frames to a reference frame. This is achieved with a singular value decomposition (SVD) clutter filter, clustering the acquired sequence into low-order singular vectors representing blood-vessel background and high-order singular values related to flowing particles. Motion estimation is then performed in the background sequence, and localization and tracking are subsequently done in the flowing-particle sequence after motion correction. The enhanced performance achieved with the suggested approach is demonstrated in phantom experiments and LOT images of the mouse brain. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Blood vessels

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Combined Architecture of Destination Sequence Distance Vector (DSDV) Routing with Software Defined Networking (SDN) and Blockchain in Cyber-Physical Systems

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computers, Materials & Continua 2025年第2期82卷 2311-2330页

作者： Jawad Ahmad Ansari Mohamad Khairi Ishak Khalid Ammar School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains MalaysiaNibong TebalPulau Pinang11800Malaysia Department of Electrical and Computer Engineering College of Engineering and Information TechnologyAjman UniversityAjman346United Arab Emirates

Cyber-Physical System (CPS) devices are increasing exponentially. Lacking confidentiality creates a vulnerable network. Thus, demanding the overall system with the latest and robust solutions for the defence mechanisms with low computation cost, increased integrity, and surveillance. The proposal of a mechanism that utilizes the features of authenticity measures using the Destination Sequence Distance Vector (DSDV) routing protocol which applies to the multi-WSN (Wireless Sensor Network) of IoT devices in CPS which is developed for the Device-to-Device (D2D) authentication developed from the local-chain and public chain respectively combined with the Software Defined Networking (SDN) control and monitoring system using switches and controllers that will route the packets through the network, identify any false nodes, take preventive measures against them and preventing them for any future problems. Next, the system is powered by Blockchain cryptographic features by utilizing the TrustChain features to create a private, secure, and temper-free ledger of the transactions performed inside the network. Results are achieved in the legitimate devices connecting to the network, transferring their packets to their destination under supervision, reporting whenever a false node is causing hurdles, and recording the transactions for temper-proof records. Evaluation results based on 1000+ transactions illustrate that the proposed mechanism not only outshines most aspects of Cyber-Physical systems but also consumes less computation power with a low latency of 0.1 seconds only.

关键词： DSDV intelligent authentication SDN control&monitoring blockchain recording of transactions

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Utilizing Machine Learning and SHAP Values for Improved and Transparent Energy Usage Predictions

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computers, Materials & Continua 2025年第5期83卷 3553-3583页

作者： Faisal Ghazi Beshaw Thamir Hassan Atyia Mohd Fadzli Mohd Salleh Mohamad Khairi Ishak Abdul Sattar Din School of Electrical and Electronic Engineering Universiti Sains MalaysiaSeberang Perai14300Malaysia Electrical Engineering Department Tikrit UniversityTikrit34001Iraq Department of Electrical and Computer Engineering College of Engineering and Information TechnologyAjman UniversityAjmanP.O.Box 346United Arab Emirates

The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of *** order to improve the precision and openness of energy consumption projections,this study investigates the combination of machine learning(ML)methods with Shapley additive explanations(SHAP)*** study evaluates three distinct models:the first is a Linear Regressor,the second is a Support Vector Regressor,and the third is a Decision Tree Regressor,which was scaled up to a Random Forest Regressor/Additions made were the third one which was Regressor which was extended to a Random Forest *** models were deployed with the use of Shareable,Plot-interpretable Explainable Artificial Intelligence techniques,to improve trust in the *** findings suggest that our developedmodels are superior to the conventional models discussed in prior studies;with high Mean Absolute Error(MAE)and Root Mean Squared Error(RMSE)values being close to *** detail,the Random Forest Regressor shows the MAE of 0.001 for predicting the house prices whereas the SVR gives 0.21 of MAE and 0.24 *** outcomes reflect the possibility of optimizing the use of the promoted advanced AI models with the use of Explainable AI for more accurate prediction of energy consumption and at the same time for the models’decision-making procedures’*** addition to increasing prediction accuracy,this strategy gives stakeholders comprehensible insights,which facilitates improved decision-making and fosters confidence in AI-powered energy *** outcomes show how well ML and SHAP work together to enhance prediction performance and guarantee transparency in energy usage projections.

关键词： Renewable energy consumption machine learning explainable AI random forest support vector machine decision trees forecasting energy modeling

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Accurate Representation of Distribution System Dynamics With DERs for Bulk System Studies

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IEEE Transactions on Industry Applications 2025年第3期61卷 5187-5197页

作者： Challa, Kiran Kumar Matavalam, Amarsagar Reddy Ramapuram Venkatraman, Ramakrishnan Ajjarapu, Venkataramana Iowa State University Department of Electrical and Computer Engineering Ames50011 United States Arizona State University School of Electrical Computer and Energy Engineering Tempe85282 United States

While there has beensignificant progress in recent years to incorporate the dynamics of distribution systems and industrial loads into the transmission using aggregated composite load models (CLM);there is no study that evaluates the accuracy of a well-tuned CLM model versus the true dynamics of a distribution system (DS) with 3-φ, 1-φ motors, ZIP loads, and distributed energy resources (DERs) connected to a transmission system (TS). This paper aims to address this gap by analyzing the limitations of the CLM and proposing physics-based approaches to address these drawbacks. We show that a single CLM is not flexible enough to represent the aggregated DS dynamics in situations when only a few motors in the DS stall or a few DERs trip. To address the drawback of the single CLM, we propose a DS structure preserving reduced order model (DS-ROM) that clusters nodes in the DS and represents each cluster using a CLM. We then present a Python-based automated approach to determine the parameters of the DS-ROM. Results on IEEE-14-bus TS connected to IEEE-37-node DS and a practical 240-node DS demonstrate that DS-ROM can capture the DS dynamics for various kinds of TS faults with an accuracy of ∼98%. © 2025 IEEE.

关键词： Problem oriented languages

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Binary feature mask optimization for feature selection

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Neural Computing and Applications 2025年第6期37卷 5155-5167页

作者： Lorasdagi, Mehmet E. Turali, Mehmet Y. Kozat, Suleyman S. Department of Electrical and Electronics Engineering Bilkent University Ankara06800 Turkey Department of Electrical and Computer Engineering University of California Los Angeles Los Angeles United States

We investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framework introduces a novel feature masking approach to eliminate the features during the selection process, instead of completely removing them from the dataset. This allows us to use the same machine learning model during feature selection, unlike other feature selection methods where we need to train the machine learning model again as the dataset has different dimensions on each iteration. We obtain the mask operator using the predictions of the machine learning model, which offers a comprehensive view on the subsets of the features essential for the predictive performance of the model. A variety of approaches exist in the feature selection literature. However, to our knowledge, no study has introduced a training-free framework for a generic machine learning model to select features while considering the importance of the feature subsets as a whole, instead of focusing on the individual features. We demonstrate significant performance improvements on the real-life datasets under different settings using LightGBM and multilayer perceptron as our machine learning models. Our results show that our methods outperform traditional feature selection techniques. Specifically, in experiments with the residential building dataset, our general binary mask optimization algorithm has reduced the mean squared error by up to 49% compared to conventional methods, achieving a mean squared error of 0.0044. The high performance of our general binary mask optimization algorithm stems from its feature masking approach to select features and its flexibility in the number of selected features. The algorithm selects features based on the validation performance of the machine learning model. Hence, the number of selected features is not predetermined and adjusts dynamically to the dataset. Additionally, we openly s

关键词： Mean square error

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Power System Reliability Evaluation Based on Sequential Monte Carlo Simulation Considering Multiple Failure Modes of Components

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Journal of Modern Power Systems and Clean Energy 2025年第1期13卷 202-214页

作者： Wei Huang Bo Hu Changzheng Shao Wei Li Xiaozhe Wang Kaigui Xie C.Y.Chung State Key Laboratory of Power Transmission Equipment Technology School of Electrical EngineeringChongqing UniversityChongqingChina China Southern Power Grid GuangzhouChina Department of Electrical and Computer Engineering McGill UniversityMontrealCanada Department of Electrical and Electronic Engineering Hong Kong Polytechnic UniversityHong KongChina

The component aging has become a significant concern worldwide,and the frequent failures pose a serious threat to the reliability of modern power *** light of this issue,this paper presents a power system reliability evaluation method based on sequential Monte Carlo simulation(SMCS)to quantify system reliability considering multiple failure modes of ***,a three-state component reliability model is established to explicitly describe the state transition process of the component subject to both aging failure and random failure *** this model,the impact of each failure mode is decoupled and characterized as the combination of two state duration variables,which are separately modeled using specific probability ***,SMCS is used to integrate the three-state component reliability model for state transition sequence generation and system reliability ***,various reliability metrics,including the probability of load curtailment(PLC),expected frequency of load curtailment(EFLC),and expected energy not supplied(EENS),can be *** ensure the applicability of the proposed method,Hash table grouping and the maximum feasible load level judgment techniques are jointly adopted to enhance its computational *** studies are conducted on different aging scenarios to illustrate and validate the effectiveness and practicality of the proposed method.

关键词： Power system reliability evaluation aging failure sequential Monte Carlo simulation

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QoS-Aware Application Assignment and Resource Utilisation Maximization Using AHP in Edge Computing

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IEEE Internet of Things Journal 2025年第11期12卷 17717-17728页

作者： Koganti, Yasasvitha Sridhar, Vidhyuth Yadav, Ram Narayan Pratap, Ajay Iitram Department of Electrical and Computer Science Engineering Ahmedabad380026 India Indiana University Department of Computer Science and Engineering Bloomington United States Banaras Hindu University Indian Institute of Technology Department of Computer Science and Engineering Varanasi India

Edge computing has emerged as a promising technology to satisfy the demand for data computational resources in Internet of Things (IoT) networks. With edge computing, processing of the massive data-intensive tasks can be done in the proximity of IoT users. Thus, the required constraints related to tasks such as latency requirements, and the Quality Of Service (QoS) can be guaranteed. However, the question that how to determine the task offloading strategy under various constraints of resources, distance, and cost is still an open issue. In this paper, we study the task offloading problem from a matching perspective and propose an Edge-User Assignment Algorithm (EUAA) that aims to maximize the resource utilization of edge servers and maximize the number of assigned IoT users. The main concern, in any matching algorithm, is how to generate the preference order of either side. To generate preference orders for edge servers, we use the concept of the Analytical Hierarchy Process (AHP). We have considered the following criteria: distance from users to the server, latency, available resources, and pricing. This generates the priority of the users for matching to edge servers. From IoT users' perspective, we use cost, and QoS parameters to improve their satisfaction. We have used state-of-the-art schemes to compare the performance of the proposed algorithm under different simulation scenarios. We compare the performance using the number of assigned users, servers' profit, number of satisfied users, edge server resources used, and execution time. The simulation results confirm that significant profit and satisfied user can be achieved by the proposed algorithm. © 2014 IEEE.

关键词： Computation offloading

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Permanent Magnet Brushless DC Motor Sensorless Control for Solar-Powered and Grid-Integrated Water-Pumping Systems

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International Journal of engineering, Transactions B: Applications 2025年第11期38卷 2760-2775页

作者： Sen, A. Singh, B. Moradzadeh, A. Mahtani, K. Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical and Electronic Engineering Universidad Politécnica de Madrid Madrid Spain

Permanent magnet brushless DC motors (PMBLDCM) are at the core of most water-pumping systems, which are increasingly moving towards the integration of solar photovoltaic power in domestic, agricultural and industrial applications. However, these systems often face power intermittency due to variable environmental conditions and nighttime outages, leading to underperformance. This study tackles the inherent challenges in ensuring efficient, stable, and cost-effective operation of PMBLDCMs in hybrid solar-grid water-pumping systems without position sensors. The proposed control method integrates an adaptive proportional resonant controller-based phase-locked loop (PLL) to enhance system reliability and performance, ensuring smooth grid interaction with minimal power fluctuations, along with a novel sensorless commutation algorithm for precise rotor position estimation. The proposed control system requires only one line voltage for position estimation, and one phase current for estimation refinement under varying dynamic conditions. The proposed system is validated through an experimental approach, demonstrating its potential to significantly improve the efficiency of solar-powered grid-integrated water-pumping systems, by allowing for a 17% load capacity increase, and 13 % reduction in system costs, making it a significant improvement over existing methods. ©2025 The author(s).

关键词： Brushless DC motors

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Highly sensitive optical biosensor based on electromagnetically induced transparency in a graphene-dielectric metasurface

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Optical and Quantum Electronics 2025年第4期57卷 1-17页

作者： Bagheritabar, Mohsen Molaei Zarasvand, Mahdi Yazdani, Afsoon Molaei Zarasvand, Melika Salemizadeh Parizi, Mohammad Bahadori-Haghighi, Shahram Department of Electrical Engineering University of Cincinnati CincinnatiOH45221-0030 United States Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States Department of Electrical and Computer Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30332 United States Department of Communication and Electronics Engineering Shiraz University Shiraz Iran

In this paper, a graphene-dielectric metasurface is proposed for effective optical biosensing. The structure is composed of a periodic array of double-slit split-ring resonators (SRRs) adjacent to silicon bars over a sheet of graphene and silicon dioxide substrate. Interactions between electric dipoles (bright modes) in silicon bars and magnetic dipoles (dark modes) excited in SRRs lead to a high quality factor resonance based on the phenomenon of electromagnetically induced transparency (EIT). High electric field confinement is achieved within the two gaps of SRRs at the resonance frequency, which result in strong light-analyte interaction. The structural parameters of the proposed metasurface are optimized to achieve the best performance. The biosensor is analyzed using the finite-element method (FEM) and the results are presented. Owing to the high Q-factor EIT resonance and the enhanced light-matter interaction inside the gaps, a high sensitivity of 496 nm/RIU and figure-of-merit (FOM) of as high as 741 RIU−1 are achieved that are higher than those reported in recently published works. The resolution and the linearity R2 value of the proposed biosensor are calculated to be 2.02e-4 and 0.999, respectively. The reported characteristics could be promising for sensing of various biomolecules such as hemoglobin. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Q factor measurement

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