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2021 Research, Invention, and Innovation Congress: Innovation Electricals and electronics, RI2C 2021

作者： Malik, Bilal Tariq Doychinov, Viktor Raza Zaidi, Syed Ali Chudpooti, Nonchanutt Duangrit, Nattapong Akkaraekthalin, Prayoot Somjit, Nutapong Robertson, Ian Comsats University Islamabad Department of Electrical and Computer Engineering Islamabad Pakistan School of Electronic and Electrical Engineering University of Leeds Leeds United Kingdom Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Faculty of Engineering Rajamangala University of Technology Lanna Chiangmai Thailand Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9781665403009

In this research article, the design and fabrication of a miniaturized flexible rectifier at 24 GHz for wireless power transfer to wearables is presented. The proposed circuit consists of a shunt topology rectifier including a single stub impedance matching network. Conventional PCB fabrication technique is used for the prototyping of the proposed flexible rectifier. It has a total footprint of 5.29 cm2. The proposed rectifier exhibits a good trade-off between compactness and RF-to-DC conversion efficiency. The implementation on a very thin substrate (130 μm) makes it durable for wearables applications. Experimental results of the proposed rectifier show an output DC voltage of 2.0 V across an optimal load resistance of 250 and measured RF-DC conversion efficiency of 23% for an input power of 17 dBm at 24 GHz. © 2021 IEEE.

关键词： Millimeter waves

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Terahertz Free-Space Measurement System Using Low-Cost 3D-Printed Hemispherical Lens Antenna

Terahertz Free-Space Measurement System Using Low-Cost 3D-Pr...

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2021 Research, Invention, and Innovation Congress: Innovation Electricals and electronics, RI2C 2021

作者： Chudpooti, Nonchanutt Duangrit, Nattapong Akkaraekthalin, Prayoot Robertson, Ian D. Somjit, Nutapong King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Industrial Physics and Medical Instrumentation Bangkok Thailand Rajamangala University of Technology Lanna Faculty of Engineering Chiangmai Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand University of Leeds School of Electronic and Electrical Engineering Leeds United Kingdom

ISBN: (纸本)9781665403009

This paper presents a terahertz free-space (THz) measurement technique using a photopolymer-based additive manufactured hemispherical lens antenna directly fed by a WR-3 rectangular waveguide with an operating frequency range of 220 GHz to 320 GHz. The hemispherical lens antenna is fabricated by in-house custom-made low-cost photopolymer 3D printing using a digital light processing (DLP) technique to fabricate layer-by-layer using Monocure 3DR3582C printing material. An asymptotically single-mode all-dielectric Bragg fiber is used to investigate the performance of the developed free-space measurement setup. From the measurement results, the propagation loss in this work has an accuracy compared with an analytical calculation and conventional free-space measurement setup of 83.36% and 88.08%, respectively. Moreover, the use of a 3D-printed hemispherical lens antenna for the THz free-space measurement reduces the complexity of the measurement setup. © 2021 IEEE.

关键词： Lens antennas

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Power Allocation and Client Selection For Privacy-Preserving Federated Learning in IoMT

Power Allocation and Client Selection For Privacy-Preserving...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jingyuan Liu Zheng Chang Kai Wang Geyong Min School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Department of Acute Care Surgery Sichuan Provincial People’s Hospital Chengdu China Department of Computer Science University of Exeter Exeter U.K.

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In recent years, the Internet of Medical Things (IoMT) has significantly boosted the healthcare industry. In the IoMT, federated learning (FL) can be applied, which can increase the utilization of patient data while protecting patient privacy. This work proposes a cutting-edge framework that combines differential privacy (DP) with FL and utilizes game theory to optimize power allocation and client selection in IoMT environments. Utilizing a Stackelberg game model, we orchestrate power allocation strategies among IoMT devices to enhance communication efficiency while meeting stringent privacy standards. We propose non-uniform and uniform pricing strategies based on the availability of network state information. Then, we derive the optimal interference price and power for the IoMT devices using nonlinear programming and convex optimization. In addition, our approach integrates DP to protect patients’ data, carefully balancing between privacy and the accuracy of the learning model. The conducted simulations show that our proposed method is effective in terms of communication efficiency, privacy preservation and FL performance.

关键词： Privacy Differential privacy Accuracy Federated learning Games Pricing Internet of Medical Things Interference Resource management Standards

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A Novel Algorithm for Digital Lithological Mapping: Case Studies in Sri Lanka’s Mineral Exploration

arXiv

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arXiv 2024年

作者： Ramanayake, R.M.L.S. Dammage, D.C. Zumri, I.Z.M. Rodrigo, K.A.R.S. Perera, A.A.P. Fernando, D. Godaliyadda, G.M.R.I. Herath, H.M.V.R. Ekanayake, M.P.B. Senaratne, A. Kizel, Fadi Department of Electrical and Computer Engineering Rutgers University New BrunswickNJ08901 United States Department of Electrical and Electronic Engineering Faculty of Engineering University of Peradeniya Peradeniya20400 Sri Lanka Department of Geology Faculty of Science University of Peradeniya Peradeniya20400 Sri Lanka Department of Mapping and Geoinformation Engineering Civil and Environmental Engineering Technion-Israel Institute of Technology Haifa3200003 Israel

Conventional manual lithological mapping (MLM) through field surveys are resource-extensive and time-consuming. Digital lithological mapping (DLM), harnessing remotely sensed spectral imaging techniques, provides an effective strategy to streamline target locations for MLM or an efficient alternative to MLM. DLM relies on laboratory-generated generic end-member signatures of minerals for spectral analysis. Thus, the accuracy of DLM may be limited due to the presence of site-specific impurities. A strategy, based on a hybrid machine-learning and signal-processing algorithm, is proposed in this paper to tackle this problem of site-specific impurities. In addition, a soil pixel alignment strategy is proposed here to visualize the relative purity of the target minerals. The proposed methodologies are validated via case studies for mapping of Limestone deposits in Jaffna, Ilmenite deposits in Pulmoddai and Mannar, and Montmorillonite deposits in Murunkan, Sri Lanka. The results of satellite-based spectral imaging analysis were corroborated with X-ray diffraction (XRD) and Magnetic Separation (MS) analysis of soil samples collected from those sites via field surveys. There exists a good correspondence between the relative availability of the minerals with the XRD and MS results. In particular, correlation coefficients of 0.8115 and 0.9853 were found for the sites in Pulmoddai and Jaffna respectively. © 2024, CC BY-NC-ND.

关键词： Remote sensing

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Cross Modal Sentiment Classification of Social Media Based on Meta Heuristic Algorithm

Cross Modal Sentiment Classification of Social Media Based o...

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International Conference on computer Supported Cooperative Work in Design

作者： Wantong Du Wanli Min Yushui Geng Hu Liang Hehu Zhou Ministry of Education Shandong Computer Science Center Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Synthesis Electronic Technology Co. Ltd

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The evolution of online social media has reshaped public behaviors, and social data infused with emotions provides crucial decision support for sentiment analysis tasks. Conventional multimodal approaches, influenced by redundant information in feature extraction during sentiment analysis, often focus solely on modality interactions without considering the unique content of each modality. This paper introduces the HGB-HFN model, addressing feature redundancy by optimizing extracted features using metaheuristic algorithm techniques, achieving a higher accuracy optimal feature dataset at lower computational costs. To counteract the neglect of modal-independent information, a hybrid fusion approach is adopted. It prioritizes visual features to extract precise semantic and emotional information from textual content, training multiple base classifiers to learn independent and diverse discriminative information. Comparative experiments on streaming datasets MOSI and MOSEI demonstrate the computational approach’s superiority in both accuracy and efficiency over existing methods.

关键词： Training Sentiment analysis Visualization Accuracy Social networking (online) Metaheuristics Semantics

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Triply Laplacian Scale Mixture Modeling for Seismic Data Noise Suppression

arXiv

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arXiv 2025年

作者： Pan, Sirui Zha, Zhiyuan Wang, Shigang Li, Yue Fan, Zipei Yan, Gang Nguyen, Binh T. Wen, Bihan Zhu, Ce College of Communication Engineering Jilin University Changchun130012 China School of Artificial Intelligence Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China Department of Computer Science Faculty of Mathematics and Computer Science University of Science Vietnam National University Ho Chi Minh City Ho Chi Minh City700000 Viet Nam School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore Glasgow College University of Electronic Science and Technology of China Chengdu611731 China

Sparsity-based tensor recovery methods have shown great potential in suppressing seismic data noise. These methods exploit tensor sparsity measures capturing the low-dimensional structures inherent in seismic data tensors to remove noise by applying sparsity constraints through soft-thresholding or hard-thresholding operators. However, in these methods, considering that real seismic data are non-stationary and affected by noise, the variances of tensor coefficients are unknown and may be difficult to accurately estimate from the degraded seismic data, leading to undesirable noise suppression performance. In this paper, we propose a novel triply Laplacian scale mixture (TLSM) approach for seismic data noise suppression, which significantly improves the estimation accuracy of both the sparse tensor coefficients and hidden scalar parameters. To make the optimization problem manageable, an alternating direction method of multipliers (ADMM) algorithm is employed to solve the proposed TLSM-based seismic data noise suppression problem. Extensive experimental results on synthetic and field seismic data demonstrate that the proposed TLSM algorithm outperforms many state-of-the-art seismic data noise suppression methods in both quantitative and qualitative evaluations while providing exceptional computational efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Tensors

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Cancelable Biometrics Based on Quaternion Adaptive Filter Optimization for Security Applications

Cancelable Biometrics Based on Quaternion Adaptive Filter Op...

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Computing and Informatics (ICCI), International Conference on

作者： Mahmoud Nasr Adam Piórkowski Pascal Muam Mah Fathi E. Abd El-Samie Department of Biocybernetics and Biomedical Engineering AGH University of Krakow Cracow Poland Faculty of Engineering and Technology Future University in Egypt Cairo Egypt Department of Technical Informatics and Telecommunications AGH University of Krakow Cracow Poland Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

This study introduces a unique cancelable face recognition technique that depends on adaptive filters and quaternion mathematics. The whale optimization is also applied to determine the optimal parameter settings for best performance. The primary concept of the suggested model revolves around the concealment of facial characteristics before their utilization in the face recognition process. The primary aim of this procedure is to guarantee the confidentiality of users' personal information throughout the enrollment and verification stages of the biometric system, particularly when the database is compromised and the stored biometric templates are altered. In this context, the color facial image is utilized to construct a quaternion. After that, a mask image is employed in a separate quaternion. The cancelable templates are obtained by using the input and mask images represented in quaternion format and applying an adaptive filter on both of them. This process involves preserving the weights of the adaptive filter. Investigation of the suggested tehnique performance demonstrates that the obtained templates lead to low (EER) and a large Area under the ROC curve.

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Digital Twin Data Management: A Comprehensive Review

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IEEE Transactions on Big Data 2025年

作者： Ouedraogo, Ezekiel B. Hawbani, Ammar Wang, Xingfu Liu, Zhi Zhao, Liang Al-Qaness, Mohammed A. A. Alsamhi, Saeed Hamood University of Science and Technology of China School of Computer Science and Technology Hefei230027 China Shenyang Aerospace University School of Computer Science Shenyang110136 China The University of Electro-Communications Tokyo Japan Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China University of Galway Insight centre for data analytics Ireland IBB University Ibb Faculty of Engineering Yemen Yemen

Digital Twins are virtual representations of physical assets and systems that rely on effective Data Management to integrate, process, and analyze diverse data sources. This article comprehensively examines Data Management challenges, architectures, techniques, and applications in the context of Digital Twins. It explores key issues such as data heterogeneity, quality assurance, scalability, security, and interoperability. The paper outlines architectural approaches like centralized, distributed, cloud-based, and blockchain solutions and Data Management techniques for modeling, integration, fusion, quality management, and visualization. Domain-specific considerations across manufacturing, smart cities, healthcare, and other sectors are discussed. Finally, open research challenges related to standards, real-time data processing, intelligent Data Management, and ethical aspects are highlighted. By synthesizing the state-of-the-art, this review serves as a valuable reference for developing robust Data Management strategies that enable Digital Twin deployments. © 2015 IEEE.

关键词： Data quality

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Chatbots and Customer Service: Enhancing Marketing Management with AI Technologies

Chatbots and Customer Service: Enhancing Marketing Managemen...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Rajchandar, K. Hari Krishna, Somanchi Khalil Wani, Showkat Pathak, Supriya Jain, Amit Kumar Naveen Kumar, C.M. Sr University School of Cs & Ai Telangana Warangal India Vignana Bharathi Institute of Technology Department of Business Management Telangana State 501301 India Saudi Electronic University College of Administrative and Financial Sciences Department of Business Administration Riyadh Saudi Arabia Oriental University Faculty of Management and Commerce Madhya Pradesh Indore India Poornima University Department of Electrical & Electronics Engineering Rajasthan JaipurIS-2027-2031 India Malnad College of Engineering Department of Computer Science & Business Systems Karnataka Hassan573202 India

ISBN: (纸本)9798331508685

By increasing engagement, expediting client contacts, and boosting satisfaction through AI-driven solutions, chatbot integration in customer service has revolutionised marketing management. With an emphasis on how model assessment techniques - such as accuracy, precision, recall, and F1 score - can be applied to enhance chatbot efficacy in customer service scenarios, this paper investigates the role of machine learning models in optimising chatbot performance. In order to appropriately meet a variety of consumer demands, this research offers perspectives into the selection, training, and evaluation of chatbot models by analysing a range of AI technologies, such as natural language processing (NLP) and predictive analytics. The influence of chatbots on customer loyalty and retention is highlighted in this article, which also explores the benefits and limitations of utilising AI in real-time consumer interactions. According to research, well-considered ML models can produce more precise and sympathetic answers, which enhances brand perception and opens up new channels for CRM in marketing plans. © 2025 IEEE.

关键词： Sales

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Heart Disease Risk Assessment and Prediction: A Robust Ensemble Approach with Extra Tree Classifier

Heart Disease Risk Assessment and Prediction: A Robust Ensem...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Simul Hasan Talukder Sohag Kumar Mondal Mohammad Aljaidi Rejwan Bin Sulaiman Taminul Islam Bangladesh Atomic Energy Regulatory Authority Dhaka Bangladesh Electrical and Electronic Engineering Khulna University of Engineering and Technology Khulna Bangladesh Khulna Bangladesh Department of computer Science Faculty of Information Technology Zarqa University Zarqa Jordan School of Computer Science & Technology Northumbria University Tayne United Kingdom Department of Computer Science Southern Illinois University Carbondale IL USA

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Heart disease remains a formidable global health challenge, affecting countless individuals and placing a substantial strain on healthcare systems worldwide. Various factors, including exercise, diabetes, age, gender, dietary habits, weight, height, and even emotional well-being, influence an individual's cardiac health. Detecting heart disease early and making precise predictions regarding its risk factors are pivotal in assessing, managing, and preventing its occurrence, ultimately leading to improved patient outcomes. In this research, a range of promising preprocessing techniques and a meticulously tuned ensemble of ten machine learning models, including Extra Trees (ET), Decision Trees (DT), Random Forest (RF), Logistic Regression (LR), Extremely Gradient Boosting (EGB), AdaBoost (AB), Support Vector Machine (SVM), Ridge Classifier (RC), Light Gradient Boosting (LGB), and Gradient Boosting (GB), were employed to analyze a publicly available heart assessment dataset. The models' robustness was assessed through a rigorous 10 k-fold validation process. Seven comprehensive evaluation metrics, encompassing accuracy, AUC, precision, recall, F1 Score, kappa, MCC, were used to gauge the models' performance and reliability. Impressively, ET model outshone the rest, achieving the highest accuracy (0.9559 ± 0.0006), AUC (0.9870 ± 0.0003), precision (0.9262 ± 0.0012), recall (0.9847 ± 0.0012), F1 Score (0.9546 ± 0.0006), kappa (0.9119 ± 0.0012), MCC (0.9135 ± 0.0012), and showcasing the lowest standard deviation.

关键词： Heart Support vector machines Accuracy Boosting Robustness Risk management Random forests

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