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An efficient single unit for virtual-machine placement in cloud data centres

International Journal of Information and Communication Technology 2024年第1期25卷 1-24页

作者： Ismaeel, Salam Miri, Ali Al-Khazraji, Ayman Department of Computer Science Ryerson University Toronto Canada Department of Electrical and Electronics Engineering University of Bahrain Manama Bahrain

There are numerous energy minimisation plans that are adopted in today’s data centres (DCs). The highest important ones are those that depend on switching off unused physical machines (PMs). This is usually done by optimal distribution and/or reallocating of virtual machines (VMs) on the selected servers, while maintaining the quality of service (QoS) to ensure the performance of a DC. In this work, a novel server machine condition index (MCI) has been proposed, which includes all resources related to servers available in the DC using a single unit. The MCI represents a dynamic tool to compare services, increase effectiveness, reflect PM adequation, and ensure the optimal management of heterogeneous DC resources. The MCI will be used to convert the multi-objective VM allocation optimisation problem into a single-objective problem. This work will identify the MCI components and the way that can be used as a cloud resource unit, and modified VMP algorithms. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Electric power utilization

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Advancement in piezoelectric nanogenerators for acoustic energy harvesting

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Microsystems & Nanoengineering 2024年第6期10卷 21-54页

作者： Fandi Jean Muhammad Umair Khan Anas Alazzam Baker Mohammad Department of Computer and Information Engineering Khalifa UniversityAbu Dhabi 12778UAE System on Chip Lab Khalifa UniversityAbu Dhabi 12778UAE Department of Mechanical Engineering Khalifa UniversityAbu Dhabi 12778UAE

The demand for sustainable energy sources to power small electronics like IoT devices has led to exploring innovative solutions like acoustic energy harvesting using piezoelectric nanogenerators(PENGs).Acoustic energy harvesting leverages ambient noise,converting it into electrical energy through the piezoelectric effect,where certain materials generate an electric charge in response to mechanical stress or *** review paper provides a comprehensive analysis of the advancements in PENG technology,emphasizing their role in acoustic energy *** begin by discussing the essential principles of piezoelectricity and the design considerations for nanogenerators to optimize energy capture from sound *** discussion includes a detailed examination of various piezoelectric materials,such as polyvinylidene fluoride(PVDF),lead zirconate titanate(PZT),and zinc oxide(ZnO)nanowires,which are known for their superior piezoelectric properties.A critical aspect of this review is the exploration of innovative structural designs and resonance devices that enhance the efficiency of *** delve into the mechanisms and benefits of using Helmholtz resonators,quarter-wavelength tubes,and cantilever beams,which are instrumental in amplifying acoustic signals and improving energy conversion *** device’s design parameters and operational principles are scrutinized to highlight their contributions to the *** review addresses practical applications of PENGs in various *** monitoring systems,wearable electronics,and medical devices stand to benefit significantly from the continuous and sustainable power supplied by *** applications can reduce reliance on batteries and minimize maintenance by harnessing ambient acoustic energy,leading to more efficient and longer-lasting *** the promising potential of PENGs,several challenges remain,including material degradation,efficiency limitations,and integrating these devices i

关键词： piezoelectric ambient integrating

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike Information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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An Integrated Demand Side Management Framework Utilizing Smart Grid Technologies and IoT for Enhanced Residential Energy Efficiency

An Integrated Demand Side Management Framework Utilizing Sma...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Venkatesan, P. Gopinath, C. Prabhavathy, P. Department of Electrical and Electronics Engineering India Center for Integrated Smart Waste Management System Department of Electrical and Electronics Engineering Sriperumbudur India Anna University Department of Computer Science and Engineering College of Engineering Guindy Chennai India

ISBN: (纸本)9798331535476

Increasing energy demands and the need for efficient management necessitate innovative solutions. This study introduces an integrated Demand Side Management (DSM) framework tailored for residential buildings within smart grid systems. The proposed methodology employs Earth Worm Optimization Algorithm (EWOA) and Virulence Optimization Algorithm (VOA) to optimize scheduling for household appliance using integration of Renewable Energy Storage (RES) and Renewable Energy Resources (RERs). Real-Time Electricity Scheduling (RTES) is implemented using IoT technologies to enable adaptive energy management. This system demonstrates significant performance improvements, achieving a 76.19% reduction in the Peak-to-Average Ratio (PAR) using VOA and a 73.8% reduction with EWOA when RES is included. Comparative analyses highlight substantial cost reductions, enhanced grid stability, and improved energy efficiency. This work not only showcases the novelty of integrating advanced optimization algorithms with IoT but also provides a robust approach to achieve sustainable and cost-effective energy management. © 2024 IEEE.

关键词： Smart power grids

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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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Neutrosophic Fuzzy Power Management (NFPM): Tackling Uncertainty in Energy Harvesting for Sensor Networks

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Neutrosophic Sets and systems 2025年 76卷 99-114页

作者： Alzubi, Musallam M. Mohamed, Mohamed A. Amer, Hanan M. Salama, A.A. Electronics and Communications Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt

This paper presents a novel approach known as Neutrosophic Fuzzy Power Management (NFPM) aimed at addressing the critical challenge of uncertain energy availability in Energy Harvesting Sensor Networks (EHWSNs). The main objective of this research is to enhance the management of energy resources within these networks, which traditional fuzzy logic methods often fail to do, leading to power failures and reduced reliability. NFPM utilizes neutrosophic logic to effectively model uncertainty by representing the degrees of truth, indeterminacy, and falsity of both harvested and residual energy levels. Through a fuzzy inference system, NFPM dynamically allocates energy budgets for each time slot based on these neutrosophic sets, resulting in more adaptive and conservative energy distribution. The results are validated through numerical examples and extensive simulations, demonstrating NFPM's superiority over traditional fuzzy logic, with significant improvements such as a 25% reduction in power failures, 95% enhanced network connectivity, a 15% increase in data transmission success rates, and overall improvements in energy efficiency and robustness to fluctuations and noise. This research establishes NFPM as a promising solution to the uncertainties inherent in EHWSNs. Future research directions include exploring the integration of NFPM with machine learning algorithms for predictive energy management, assessing its scalability in larger networks, and examining its applicability in other domains requiring energy management under uncertainty. © 2025, University of New Mexico. All rights reserved.

关键词： Budget control

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Two-dimensional confined topological modes in all dielectric guided mode resonance structure through surface features

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

作者： Ramachandra, Swathy Menezes, Maclean Paul Tumuluru, Vamsi Krishna Kulkarni, Raghavendra G. Sinha, Rajat K. Bhowmick, Kaustav Department of Electronics and Communication Engineering PES University Karnataka Bangalore India Department of Electrical and Computer Engineering University of Toronto Toronto Canada

Guided mode resonance (GMR) structures offer simplicity and have spectral resonance capabilities such as narrowband resonance, high Q-factor, whereas topological surface features offer spatial light control. In this paper, we explore the integration of the GMR structures with topological photonics to achieve extreme two-dimensional (2D) spatial confinement of light. With this integration, we propose a novel design that leverages discontinuous surface features (crosscut) to excite Jackiw—Rebbi (JR) solution, which describes relationship between Dirac equation and topological insulators. Finite element modelling, considering various structural and material parameters, demonstrate a 40% improvement in light confinement compared to benchmark GMR structures, with a calculated full width half maximum (FWHM) of the confined optical mode measured to be approximately ~ 18 ± 3 nm. This FWHM value represent the spatial extent of the confined optical field, indicating the achieved field localization in the proposed structures. This significant enhancement showcases the potential of our approach for applications demanding ultra- precise spatial resolution. Furthermore, tolerance studies reveal good robustness to variations in the dimensions of the crosscut and angle of polarization, ensuring practical feasibility. The proposed structure exhibited good tolerance of up to ~ ± 10%. This work paves the way for highly confined 2D light manipulation within all- dielectric platforms, opening exciting avenues for nanophotonic devices and applications requiring light localization. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Topology

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Hybrid Recommender system Using Systolic Tree for Pattern Mining

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computer systems Science & engineering 2023年第2期44卷 1251-1262页

作者： S.Rajalakshmi K.R.Santha Department of Computer Science Engineering Sri Venkateswara College of EngineeringSriperumbudurIndia Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudurIndia

A recommender system is an approach performed by e-commerce for increasing smooth users’*** pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of *** work will present the implementation of sequence pattern mining for recommender systems within the domain of *** work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender *** feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target *** will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy *** work pre-sents a new hybrid recommender system based on optimized feature selection and systolic *** features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)*** systolic tree is used for pattern mining,and based on this,the recommendations are *** proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes conﬁrmed the efﬁciency of the *** was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativeﬁltering,the precision of 0.89 was achieved.

关键词： Recommender systems hybrid recommender systems frequent pattern mining collaborativeﬁltering systolic tree river formation dynamics particle swarm optimization

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From Past to Present: A Comprehensive Review of Antenna Technology in Modern Wireless Communication

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Journal of engineering Science and Technology Review 2024年第3期17卷 179-200页

作者： Mishra, Brijesh Singh, Aditya Kumar Satheesha, T.Y. Verma, Ramesh Kumar Singh, Vivek Department of Electronics and Communication Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology U.P. Greater Noida India School of Computer Science Engineering REVA University Karnataka Bengaluru India Department of Computer Science and Engineering IMS Engineering College Uttar Pradesh Ghaziabad India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Karnataka Bengaluru India

This research looks at microwave devices, specifically, patch antenna along with electromagnetic spectrum, shape, mechanism, analytical methods, simulation tools, and feeding procedures. Patch antennas are distinguished by their rectangular form, with a patch on one side and a ground plane on the other. Patch antennas work by exciting electromagnetic waves inside the patch, which are subsequently transmitted into the surrounding environment. The report also outlines numerous ways for evaluating the performance of microstrip patch antennas. The electromagnetic characteristics of the antenna are analyzed using the transmission line model, cavity model, and multiport network model (MNM). The integral equations that regulate the behavior of the antenna are solved using the method of moments (MoM) and the finite element method (FEM). The spectral domain technique (SDT) is used to analyze the antenna’s frequency response, while the finite difference time domain (FDTD) approach is used to analyze the antenna’s time-domain behavior. Overall, these methodologies give a thorough understanding of microstrip patch antenna performance and may be utilized to optimize their design. Furthermore, several patch antenna feeding methods, such as probe feed, microstrip line feeding, aperture coupling, proximity coupling, and CPW feed, are investigated. Attaching a microstrip line to the patch, which is subsequently linked to the RF source, is what microstrip line feeding entails. Aperture coupling entails making a hole in the ground plane that allows the RF source to feed the patch directly. Proximity coupling is accomplished by placing a probe near the patch, which creates an electromagnetic field on the patch. Patch antenna simulation software includes programmers such as HFSS, CST, and FEKO. These tools simulate the patch antenna’s performance, including its radiation pattern, gain, and input impedance. These simulations may be used to optimize the patch antenna design for specific a

关键词： Microstrip antennas

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An FPGA Prototype for Parkinson’s Disease Detection using Machine Learning on Voice Signal

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IEEE Access 2025年 13卷 91113-91128页

作者： Khan, Mujeev Moiz, Abdul Khan, Gani Nawaz Wajid, Mohd Usman, Mohammed Ali, Jabir Z.H.C.E.T. Aligarh Muslim University Department of Electronics Engineering India Bennett University Department of Electronics and Communication Engineering India Bennett University School of Computer Science Engineering and Technology India

Parkinson’s disease (PD) is a chronic neurological disorder caused by a reduction in dopamine levels in the brain. Early diagnosis is crucial for effective treatment. This paper proposes an efficient machine learning model for PD detection using voice-based features, which offer a non-invasive, cost-effective, and accessible alternative to complex imaging methods. To enhance classification performance and reduce computational complexity, we evaluate three feature selection algorithms—Chi-squared (χ2), Minimum Redundancy Maximum Relevance (mRMR), and Analysis of Variance (ANOVA) — and adopt an incremental feature selection approach, where each feature set increment is assessed across five classifiers: K-Nearest Neighbors (KNN), Decision Tree (DT), Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector Machine (SVM). To address dataset imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied. Among the evaluated approaches, the ANOVA algorithm yields the most optimal feature set, with the top 10 features enabling the Quadratic SVM to achieve a classification accuracy of 98.86%. The Quadratic SVM, optimized for minimal power consumption, is implemented on a Nexys A7 FPGA. This setup achieves a dynamic power consumption of just 23 mW and delivers a performance acceleration of 155× compared to equivalent computations on a 6th-generation Intel i5 processor. By combining a high-accuracy machine learning model with an energy-efficient FPGA implementation, our approach offers a powerful and portable solution for real-time PD detection. © 2013 IEEE.

关键词： Neurodegenerative diseases

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