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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Zou, Jiadao Zhang, Qingxue Purdue Univeristy School of Engineering and Technology Electrical and Computer Engineering IN United States Purdue Univeristy School of Engineering and Technology Electrical and Computer Engineering Biomedical Engineering IN United States

ISBN: (纸本)9798350324136

Electroencephalogram (EEG) reveals the brain dynamics that are of a great value in clinical applications, e.g., epilepsy diagnosis, cognitive status, and other disorders. Intracranial Electroencephalogram (iEEG) is encoding rich dynamics, with its invasive electrodes that go deep into the brain. Despite the high precision, the iEEG surgery may limit its use in daily lives due to the risk, setup time and cost. In this study, we propose a deep learning framework that can reconstruct iEEG from scalp EEG, thereby demonstrating the potential of convenient deep brain monitoring in daily lives. We leverage a long short-Term memory (LSTM) model for ultra-deep iEEG prediction from the non-invasive scalp EEG. Our simulation has demonstrated that, our deep learning model, with the nonlinear dynamics mining capability, facilitates the deep source signal estimation performance, compared with the linear model. Furthermore, we find that the estimation performance may be improved with selection of more EEG measurement sites. This study will greatly advance brain dynamics decoding and broadly benefit diverse brain-computer-interface and smart health applications. © 2024 IEEE.

关键词： Brain computer Interface Electroencephalogram Recurrent Learning Smart Health

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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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Cardiac Ischemia Detection Using Self-Organizing Map Feature Selection Method 31

Cardiac Ischemia Detection Using Self-Organizing Map Feature...

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31st National and 9th International Iranian Conference on biomedical engineering, ICBME 2024

作者： Karampour, Ramin Aghili Bakhtiar, Amir Arshia Hajizamani, Kiarash Fallahi, Alireza Zadeh, Hamid Soltanian Hamedan University of Technology Biomedical Engineering Department Hamedan Iran School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331529710

Cardiac ischemia, a prevalent cause of heart failure, remains the leading cause of death in Iran. Early diagnosis of this condition is crucial, and electrocardiogram (ECG) signal processing techniques offer valuable insights. By analyzing ECG signals from ischemic patients and comparing them to those of normal individuals, we can extract relevant features for classification. Our study extracted some features from ECG signals, including statistical, time-based, frequency, and time-frequency features. We employed the self-organizing maps method (SOM) to identify the most informative features. Subsequently, we evaluated the classification accuracy using several machine learning algorithms, including support vector machines (SVM), Naive Bayes, Linear Discriminant Analysis (LDA), and Logistic Regression. The results demonstrated the superiority of the SOM method in selecting the best features compared to PCA, with accuracies of 9 1.2%, 84.3%, 72.5%, and 88.9% for SVM, LDA, NB, and LR methods, respectively. Our findings contribute to the early detection and management of cardiac ischemia, potentially improving patient outcomes. © 2024 IEEE.

关键词： Conformal mapping

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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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ViT-LSTM synergy: a multi-feature approach for speaker identification and mask detection

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Neural Computing and Applications 2024年第35期36卷 22569-22586页

作者： Nassif, Ali Bou Shahin, Ismail Bader, Mohamed Ahmed, Abdelfatah Werghi, Naoufel Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering and Computer Sciences Khalifa University Abu Dhabi United Arab Emirates

The global health crisis caused by the COVID-19 pandemic has brought new challenges to speaker identification systems, particularly due to the acoustic alterations caused by the widespread use of face masks. Aiming to mitigate these distortions and improve the accuracy of speaker identification, this study introduces a novel two-level classification system, leveraging a unique integration of Vision Transformers (ViT) and Long Short-Term Memory (LSTM). This ViT-LSTM model was trained and tested on an extensive dataset composed of diverse speakers, both masked and unmasked, allowing a comprehensive evaluation of its capabilities. Our experimental results demonstrate remarkable improvements in speaker identification, with an accuracy score of 95.67%, significantly surpassing traditional and other deep learning-based methods. Moreover, our framework also shows considerable strength in detecting the presence of a mask, achieving an accuracy of 91.15% and outperforming existing state-of-the-art models. This study provides the first-ever benchmark for mask detection in the context of speaker identification, opening new pathways for research in this emerging area and presenting a robust solution for speaker identification in the era of face masks. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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Selection of Wind Turbine Systems for the Sultanate of Oman

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computer Systems Science & engineering 2023年第4期45卷 343-359页

作者： M.A.A.Younis Anas Quteishat Department of Electrical and Computer Engineering Sohar UniversitySultanate of Oman Electrical Engineering Department Faculty of Engineering TechnologyAl-Balqa Applied UniversityAl-SaltJordan

The Sultanate of Oman has been dealing with a severe renewable energy issue for the past few decades,and the government has struggled to find a *** addition,Oman’s strategy for converting power generation to sources of renewable energy includes a goal of 60 percent of national energy demands being met by renewables by 2040,including solar and wind ***,the use of small-scale energy from wind devices has been on the rise in recent *** upward trend is attributed to advancements in wind turbine technology,which have lowered the cost of energy from *** calculate the internal and external factors that affect the small-scale energy of wind technologies,the study used a fuzzy analytical hierarchy process technique for order of preference by similarity to an ideal *** a result,in the decision model,four criteria,seventeen sub-criteria,and three resources of renewable energy were calculated as options from the viewpoint of the Sultanate of *** research is based on an examination of statistics on energy produced by wind turbines at various locations in the Sultanate of ***,six distinct miniature wind turbines were investigated for four different *** outcomes of this study indicate that the tiny wind turbine has a lot of potential in the Sultanate of Oman for applications such as homes,schools,college campuses,irrigation,greenhouses,communities,and small *** government should also use renewable energy resources to help with the renewable energy issue and make sure that the country has enough renewable energy for its long-term growth.

关键词： Multi criteria decision making model fuzzy theory fuzzy sets renewable energy wind turbine supplier selection

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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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M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-Distortion

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IEEE Transactions on Communications 2024年第2期72卷 845-860页

作者： Liu, Yangyi Rini, Stefano Salehkalaibar, Sadaf Chen, Jun McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4L8 Canada Department of Electrical and Computer Engineering Hsinchu30010 Taiwan University of Toronto Department of Electrical and Computer Engineering TorontoONM5A 4T4 Canada

In federated learning (FL), the communication constraint between the remote clients and the Parameter Server (PS) is a crucial bottleneck. For this reason, model updates must be compressed so as to minimize the loss in accuracy resulting from the communication constraint. This paper proposes 'M-magnitude weighted L2 distortion + 2 degrees of freedom' (M22) algorithm, a rate-distortion inspired approach to gradient compression for federated training of deep neural networks (DNNs). In particular, we propose a family of distortion measures between the original gradient and the reconstruction we referred to as ' M-magnitude weighted L{2} ' distortion, and we assume that gradient updates follow an i.i.d. distribution-generalized normal or Weibull, which have two degrees of freedom. In both the distortion measure and the gradient distribution, there is one free parameter for each that can be fitted as a function of the iteration number. Given a choice of gradient distribution and distortion measure, we design the quantizer to minimize the expected distortion in gradient reconstruction. To measure the gradient compression performance under a communication constraint, we define the per-bit accuracy as the optimal improvement in accuracy that one bit of communication brings to the centralized model over the training period. Using this performance measure, we systematically benchmark the choice of gradient distribution and distortion measure. We provide substantial insights on the role of these choices and argue that significant performance improvements can be attained using such a rate-distortion inspired compressor. © 1972-2012 IEEE.

关键词： Deep neural networks

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Optimizing UPVC profile production using adaptive neuro-fuzzy inference system

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International Journal of Information Technology (Singapore) 2024年 1-18页

作者： Naghipour, Avaz Salehpour, Arash Iranag, Behnam Safiri Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

This paper explores the implementation of an Adaptive Neuro-Fuzzy Inference System to optimize Unplasticized Polyvinyl Chloride profile production. Given the intrinsic complexities of polymer extrusion, such as maintaining consistent quality amidst varying raw material properties and environmental conditions, traditional control methods like Proportional-Integral-Derivative (PID) controllers often need to catch up in adapting to non-linear production environments. This study proposes an ANFIS-based control system that leverages the adaptability of neural networks and the human-like reasoning of fuzzy logic to address these challenges effectively. The system uses real-world data from expert operators to refine its control parameters, ensuring accurate regulation of critical variables such as extrusion temperature, feed material composition, and ambient conditions. By testing the system against traditional control methods, the research demonstrates significant improvements in quality control, efficiency, and waste reduction. The ANFIS system’s adaptability allows it to minimize errors and adjust dynamically to variations in production conditions, outperforming traditional PID systems in maintaining consistent quality. Furthermore, the study outlines the implications of this research for intelligent manufacturing within Industry 4.0 frameworks, highlighting how ANFIS can integrate seamlessly with existing manufacturing systems to optimize production. This paper ultimately contributes to the growing literature advocating for intelligent control systems in polymer extrusion. It underscores their potential to transform manufacturing efficiency and quality in Unplasticized Polyvinyl Chloride production. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adaptive neuro-fuzzy inference system (ANFIS) Fuzzy logic controller Manufacturing optimization Neural networks Polymer extrusion process UPVC profile production

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