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Optimized Millimeter Wave AoA Localization in Indoor Parking Environments: A Precise Approach with Beam Forming and Antenna Analysis in Line of Sight Scenarios 4

Optimized Millimeter Wave AoA Localization in Indoor Parking...

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4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Cai, Lingxiang Lai, Hanyi Liu, Yongliang University of Illinois Department of Electrical and Computer Engineering Urbana-ChampaignIL United States Beijing University of Technology Faculty of Science Beijing China Hebei University of Technology Department of Electrical Engineering Tianjin China

ISBN: (纸本)9798350394375

In recent years, the demand for indoor localization techniques has grown rapidly. In order to meet such demand, this article focuses on indoor localization and proposes a novel application of AoA technology-based localization system. The main application scenario is inside an underground garage with line-of-sight, and a beam forming AoA positioning system is simulated using Matlab. Finally, an outlook of applications based on this system is presented. © 2024 IEEE.

关键词： Beamforming

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Enhancing Microgrid Energy Security Through Optimal Power Sharing in Multi-Modal Energy Storage Systems 4

Enhancing Microgrid Energy Security Through Optimal Power Sh...

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4th International Conference on Robotics, electrical and Signal Processing Techniques, ICREST 2025

作者： Anik, Shaikh Tawhidul Islam Mithil, Md Salman Khan Ahmad, Shameem Hazari, Md. Rifat Islam, Mahmoodul Ahmed, Tofael Parvez, Md. Shahariar Hassan, Mahamudul American International University-Bangladesh Faculty of Engineering Department of Electrical and Electronic Engineering Dhaka1229 Bangladesh Department of Electrical Engineering and Computer Science Rapid CitySD57701 United States Chittagong University of Engineering & Technology Department of Electrical and Electronic Engineering Chattogram4349 Bangladesh American International University-Bangladesh Faculty of Engineering Department of Industrial and Production Engineering Dhaka1229 Bangladesh

ISBN: (纸本)9798331530938

Microgrids (MGs), which combine distributed energy resources (DERs) with energy storage systems (ESS), offer improved energy security, reliability and sustainability by utilizing renewable energy sources for subsequent use. However, single ESS options often face challenges such as short life cycles, low power density etc. More than one ESS can resolve the mentioned issues though it increases complexity and maintaining stability becomes an issue. This paper proposes an effective energy management strategy between two ESS namely battery and supercapacitor to increase energy security in MG. The proposed strategy is designed in such a way that one of the two ESS stays connected all the time to the DC bus to ensure uninterruptable power supply to loads. Due to the use of DC bus, the system complexity decreases as there is no need to control frequency. Two cases were considered to verify the performance of the proposed energy management strategy in Matlab/Simulink. In the first case, during the unavailability of supercapacitor, the battery supplies 50 W to the load in addition to PV whereas supercapacitor supplies 136 W in the absence of battery along with PV. Furthermore, it was shown that the absence of PV has no negative effect in terms of power flow by ESS to meet load demand. © 2025 IEEE.

关键词： Energy security Energy Storage Systems Microgrid Power sharing Supercapacitor

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Ontology-Driven Semantic Web Framework with Sentiment Analysis for Risk Assessment and Diagnosis of Diabetes Mellitus 4

Ontology-Driven Semantic Web Framework with Sentiment Analys...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Muruganantham, A. Velmurugan, R. Kumar, R. Rajan, M. Sundar Sivaraj, V. Karnataka Bengaluru India Arba Minch Institute of Technology Arba Minch University Faculty of Electrical and Computer Engineering Ethiopia Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798331523923

This paper uses semantic web and ontology techniques to predict the risk analysis of patients with diabetes mellitus. The data is collected from patients through personal interaction and by accessing their previous medical history. However, during personal interactions, medical staff often fail to gather all the necessary details for an accurate diagnosis, and patients may lose their previous medical records, which hinders doctors' ability to gain a full understanding of the problem. To address this issue, ontology techniques are employed, and additional details are collected from patients, such as their food habits, smoking history, alcohol consumption, sexual activity, and cardiovascular health. Using the collected data, ontology and semantic web technologies are applied to predict the risk analysis of diabetic patients. The results are visualized in a graph, providing a more comprehensive and accurate assessment of the patients' health risks. © 2025 IEEE.

关键词： Electronic health record

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Polarization Insensitive Broadband Zero Indexed Nano-Meta Absorber for Optical Region Applications

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computers, Materials & Continua 2022年第4期71卷 993-1009页

作者： Ismail Hossain Md Samsuzzaman Ahasanul Hoque Mohd Hafiz Baharuddin Norsuzlin Binti Mohd Sahar Mohammad Tariqul Islam Space Science Center(ANGKASA) Universiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia Department of Computer and Communication Engineering Faculty of Computer Science and EngineeringPatuakhali Science and Technology UniversityDumki8602PatuakhaliBangladesh Department of Electrical Electronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia

Broadband response metamaterial absorber(MMA)remains a challenge among researchers.A nanostructured new zero-indexed metamaterial(ZIM)absorber is presented in this study,constructed with a hexagonal shape resonator for optical region *** design consists of a resonator and dielectric layers made with tungsten and quartz(Fused).The proposed absorbent exhibits average absorption of more than 0.8972(89.72%)within the visible wavelength of 450–600 nm and nearly perfect absorption of 0.99(99%)at 461.61 *** on computational analysis,the proposed absorber can be characterized as *** developments of ZIM absorbers have demonstrated plasmonic resonance characteristics and a perfect impedance *** incidence obliquity in typically the range of 0◦–90◦both in TE and TM mode with maximum absorbance is more than 0.8972(∼89.72%),and up to 45◦angular stability is suitable for solar cell applications,like exploiting solar *** proposed structure prototype is designed and simulated by studying microwave technology numerical computer simulation(CST)*** finite integration technique(FIT)based simulator CST and finite element method(FEM)based simulator HFSS also helps validate the numerical data of the proposed ZIM *** proposed MMA design is appropriate for substantial absorption,wide-angle stability,absolute invisible layers,magnetic resonance imaging(MRI),color images,and thermal imaging applications.

关键词： Hexagonal resonator loaded star shaped polarization insensitive compact broadband metamaterial absorber optical region applications

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Dynamic-Matrix-Encryption Based Secure Strong PUF for Device Authentication Protocols 17

Dynamic-Matrix-Encryption Based Secure Strong PUF for Device...

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17th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

作者： Zhao, Liangxiao Li, Gang Wang, Pengjun Ma, Xuejiao Zhou, Ziyu The College of Electrical and Electronic Engineering Wenzhou University Wenzhou325000 China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325035 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China

ISBN: (纸本)9798350361834

Strong physical unsolvable function (PUF) has a wide range of applications in internet of things (IoT) security. However, it is vulnerable to machine learning (ML) modeling attacks. This paper proposes a strong PUF anti-ML attack method based on dynamic-matrix encryption. It obfuscates the challenge by multiplying the dynamic encryption matrix (DEM) with the challenge matrix synchronizing initial values during the registration phase of the device security authentication protocol prevents our obfuscation from being invalidated when the algorithms and structures motivating the obfuscation are made public. Each round of DEM is updated with respect to each previous round of challenge which is similar to the properties of sequential logic circuits in that the current response does not depend only on the current challenge but is related to previous challenge. Experimental results show that even if an attacker collects 1 million challenge response pairs, the prediction accuracies of the several ML attacks we use are below 53%, with negligible effects on randomness, reliability, and uniqueness. © 2024 IEEE.

关键词： Authentication Protocol

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A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

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computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security Information and Event Management Systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： Cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

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Remodelled and Reduced complexity-OMP-based Channel Estimation Schemes for Intelligent Reflecting Surface-Aided Millimeter Wave Systems 16

Remodelled and Reduced complexity-OMP-based Channel Estimati...

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16th International Conference on Signal Processing and Communication System, ICSPCS 2023

作者： Oyerinde, Olutayo Oyeyemi Flizikowski, Adam Marciniak, Tomasz School of Electrical and Information Engineering University of the Witwatersrand Johannesburg2050 South Africa Bydgoszcz University of Science and Technology Faculty of Telecommunications Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350333510

Intelligent reflecting surface (IRS) has been put forward as one of the key candidate technologies to achieve high spectrum efficiency. However, to reach its full potential there is a need for the availability of accurate channel state information, which is quite challenging to estimate. This paper, therefore, focuses on proposing two compressive sensing algorithm-based channel estimation schemes to be used in the IRS-aided millimeter wave (mmWave) wireless communication systems. The first channel estimation named reduced complexity-orthogonal matching pursuit (OMP) (RedCOMP)-based channel estimation scheme seeks to achieve optimum performance while reducing the computational complexity associated with the traditional OMP algorithm. The second scheme named remodelled OMP (RemOMP)-based channel estimation method aims at obtaining enhanced channel estimate with the aid of side information known a priori at the receiver. Simulation results indicate that the proposed RemOMP-based channel estimator exhibits the best performance among the three channel estimation schemes with similar computational complexity cost to the traditional OMP-base channel estimator. However, the RedCOMP-based channel estimation scheme, while not showing good performance, incurred the least computational complexity cost among all the schemes. © 2023 IEEE.

关键词： Millimeter waves

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AI-Infused Predictive Digital Twin Systems for Resilient IoT Software Architectures 4

AI-Infused Predictive Digital Twin Systems for Resilient IoT...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Gupta, Raj Kumar Dhasmana, Gulshan Benvin, Shalet Bhargav, H.K. Jadhav, Girish Rao, Tarini Department of Physics Bhabua India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Bgs Institute of Technology Adichunchanagiri University Department of Computer Science and Engineering Mandya India Shridevi Institute of Engineering and Technology Department of Computer Science and Engineering Tumakuru India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electrical Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The proliferation of Internet of Things (IoT) networks has significantly increased the complexity of software architectures, leading to heightened vulnerabilities and system inefficiencies. AI-infused Predictive Digital Twin Systems provide a promising approach by integrating real-time data analytics with predictive AI models to monitor, forecast, and enhance the health and security of IoT software architectures. This paper introduces an AI-Infused Predictive Digital Twin System designed to create resilient IoT software architectures. Experimental validation was conducted using a large-scale IoT network with 1,000 interconnected devices. The AI-Infused Predictive Digital Twin System achieved a 97.8% accuracy in anomaly detection, with a 28% reduction in system disruptions compared to conventional monitoring solutions. The RL-based optimization module resulted in a 20% improvement in resource utilization efficiency and a 30% increase in overall system resilience under varying load conditions. The integration of the blockchain audit layer significantly improved traceability and reduced security breach recovery time by 40%. The AI-Infused Predictive Digital Twin System provides an innovative solution for developing resilient IoT software architectures. By integrating predictive AI models and real-time digital twin technology, the framework enables continuous monitoring, proactive anomaly detection, and dynamic optimization. The experimental results demonstrate the system's capability to enhance resilience, efficiency, and security in complex IoT environments, making it a valuable tool for managing and securing next-generation IoT architectures. Future work will explore expanding the system's capabilities to support multi-cloud IoT networks and developing more sophisticated AI models for enhanced predictive accuracy and operational efficiency. © 2024 IEEE.

关键词： Blockchain

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Integration Of Convolutional Neural Network and Support Vector Regression For Electricity Price Forecasting

Integration Of Convolutional Neural Network and Support Vect...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Iqbal, Rashed Mokhlis, Hazlie Khairuddin, Anis Salwa Mohd Mansor, Nurulafiqah Nadzirah Zahari, Nur Elida Mohamad Sankaranarayanan, Suresh School of Electrical Computer and Telecommunications Engineering University of Wollongong NSW Australia Department of Electrical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia Department of Computer Science King Faisal University Saudi Arabia

ISBN: (纸本)9798350325188

Accurate electricity price forecasting (EPF) plays a crucial role in bid strategies and risk reduction for market participants in the competitive electricity market. However, forecasting of electricity prices accurately is a highly demanding task due to the intricate nonlinearity present in the price dynamics. This paper introduces a combination of deep learning (DL) technique with machine learning (ML) model to forecast electricity prices in the Australian energy market operator (AEMO) for hourly price prediction utilizing load and renewable energy supply data from five major economical states New South Wales (NSW), Queensland (QLD), South Australia (SA), Tasmania (TAS) and Victoria (VIC). The proposed work integrates the convolutional neural network (CNN) and support vector regression (SVR) to identify short-term local dependency patterns among variables and uncover long-term correlations for time series patterns and forecast the hourly ahead price. The suggested model (CNN-SVR) is contrasted to various models such as CNN-LSTM, BiLSTM, SVR, and their performance is evaluated using various metrics, including mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). © 2023 IEEE.

关键词： convolutional neural network deep learning electricity price forecasting machine learning

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Confidence Bands Based on Rating Demonstrated on the CEC 2021 Competition Results 13

Confidence Bands Based on Rating Demonstrated on the CEC 202...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ravber, Miha Mernik, Marjan Liu, Shih-Hsi Šmid, Marko Črepinšek, Matej University of Maribor Faculty of Electrical Engineering and Computer Science Koroška cesta 46 Maribor2000 Slovenia California State University Fresno Department of Computer Science FresnoCA93740 United States

ISBN: (纸本)9798350308365

Benchmarking plays a vital role in evaluating and comparing evolutionary algorithms. Our research addresses this critical need by introducing an innovative approach: rating-based confidence bands. This method serves to evaluate algorithm performance across various execution stages, enabling statistical comparison at different cutpoints of maximum Function Evaluations (maxFEs). Using publicly available results from the IEEE Congress on Evolutionary Computation (CEC) benchmark, we demonstrate the effectiveness of our methodology. Our approach uses the function error values recorded at distinct cutpoints to plot confidence bands. The confidence bands offer a comprehensive and intuitive comparison, revealing statistical significance in algorithm performances at different stages of execution. The method is integrated seamlessly into the EARS framework, facilitating the incorporation of additional algorithm results for a robust comparative analysis. Our results present a detailed analysis of CEC benchmark outcomes through rating-based confidence bands. This approach enables a nuanced evaluation of algorithmic performance, revealing statistical differences between algorithms at various cutpoints that were previously undisclosed. The framework encourages researchers to compare their results directly with state-of-the-art algorithms from benchmark competitions, enhancing result credibility and promoting a standardized evaluation approach within the research community. Our findings highlight the significance of our approach in advancing algorithm benchmarking practices. Furthermore, they emphasize the need for transparent evaluation of algorithms at different stages of execution. © 2024 IEEE.

关键词： benchmarking confidence bands evolutionary algorithms performance comparison rating intervals

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