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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua science and technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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PBCounter:weighted model counting on pseudo-boolean formulas

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Frontiers of computer science 2025年第3期19卷 55-63页

作者： Yong LAI Zhenghang XU Minghao YIN College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China School of Computer Science and Information Technology Northeast Normal UniversityChangchun 130017China

In Weighted Model Counting(WMC),we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its *** current WMC solvers work on Conjunctive Normal Form(CNF)***,CNF is not a natural representation for human-being in many *** by the stronger expressive power of Pseudo-Boolean(PB)formulas than CNF,we propose to perform WMC on PB *** on a recent dynamic programming algorithm framework called ADDMC for WMC,we implement a weighted PB counting tool *** compare PBCounter with the state-of-the-art weighted model counters SharpSAT-TD,ExactMC,D4,and ADDMC,where the latter tools work on CNF with encoding methods that convert PB constraints into a CNF *** experiments on three domains of benchmarks show that PBCounter is superior to the model counters on CNF formulas.

关键词： weighted model counting pseudo-boolean constraint algebraic decision diagrams preprocessing techniques

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Purely Azimuthal UAV Passive Localization Based on a Triangular Model 6

Purely Azimuthal UAV Passive Localization Based on a Triangu...

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6th International Conference on Communications, Information System and computer engineering, CISCE 2024

作者： Huang, Xihao Ma, Jianqiao Wang, Qiang Liang, Ruoyu School of Information and Communication Guilin University of Electronic Technology Guilin China International College Zhengzhou University Zhengzhou China College of Optoelectronic Engineering Guilin University of Electronic Science and Technology Guilin China

ISBN: (纸本)9798350353174

When a cluster of UAVs executes formation flight, in order to maintain a good formation shape, a purely azimuthal passive localization method is usually used to adjust the UAVs that show positional deviations. This method reduces the emission of electromagnetic wave signals to the outside and enhances the stealthiness of the formation. We address the problem of localization using more frequent electromagnetic signal communications and propose a purely mathematical geometric method, triangulation, to determine UAV orientation by passively receiving signals. Finally, we test the model using small error data and observe that the model results have high accuracy. © 2024 IEEE.

关键词： Triangulation

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Dual-Enhanced High-Order Self-Learning Tensor Singular Value Decomposition for Robust Principal Component Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3564-3578页

作者： Xu, Honghui Fang, Chuangjie Wang, Renfang Chen, Shengyong Zheng, Jianwei Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Zhejiang Wanli University College of Big Data and Software Engineering Ningbo315200 China Tianjin University of Technology College of Computer Sciences and Engineering Tianjin300384 China

Recently, tensor singular value decomposition (TSVD) within high-order (Ho) algebra framework has shed new light on tensor robust principal component analysis (TRPCA) problem. However, HoTSVD lacks flexibility in handling the hidden correlations along different modes of large-scale multidimensional data. Moreover, the utilization of fixed or data-independent transformations in HoTSVD may result in suboptimality. For a relief, we propose a dual-enhanced self-learning TSVD along all modes to address computational flaws and learn a lossless transformation that induces a lower average-rank tensor. Specifically, we multiply the learnable semiorthogonal matrices obtained through Tucker compression with the original tensor along all modes, thus obtaining a core tensor with more inherent low rankness. Building upon this foundation, a new TNN is introduced by generalizing HoTSVD to Mode-k TSVD, followed by the facilitation to the core tensor, achieving dual-enhancement. Moreover, a reweighting scheme is imposed on the Mode-k HoTSVD to learn the global low-rank correlation and provide an efficient numerical solution. Finally, an alternating direction method of multipliers (ADMM)-based algorithm is developed as a solver. Experimental results on several types of multidimensional visual data, including light field images (LFI) and color videos, demonstrate the superiority of the proposal over previous state-of-the-art methods. © 2020 IEEE.

关键词： Discrete Fourier transforms

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Modified Bat Algorithm: a newly proposed approach for solving complex and real-world problems

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Soft Computing 2024年第13-14期28卷 7983-7998页

作者： Umar, Shahla U. Rashid, Tarik A. Ahmed, Aram M. Hassan, Bryar A. Baker, Mohammed Rashad Department of Software College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler KRI Erbil Iraq Department of Computer Science College of Science Charmo University Sulaimani Chamchamal46023 Iraq

Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of bats, which acts as a signal system to estimate the distance and hunt prey. Although the BA has proven effective for various optimization problems, it exhibits limited exploration ability and susceptibility to local optima. The algorithm updates velocities and positions based on the current global best solution, causing all agents to converge toward a specific location, potentially leading to local optima issues in optimization problems. On this premise, this paper proposes the Modified Bat Algorithm (MBA) as an enhancement to address the local optima limitation observed in the original BA. MBA incorporates the frequency and velocity of the current best solution, enhancing convergence speed to the optimal solution and preventing local optima entrapment. While the original BA faces diversity issues, both the original BA and MBA are introduced. To assess MBA’s performance, three sets of test functions (classical benchmark functions, CEC2005, and CEC2019) are employed, with results compared to those of the original BA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). The outcomes demonstrate the MBA’s significant superiority over other algorithms. In addition, MBA successfully addresses a real-world assignment problem (call center problem), traditionally solved using linear programming methods, with satisfactory results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Swarm intelligence

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COVID-19 Pandemic: A Worldwide Critical Review with the Machine Learning Model-Based Prediction

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 339-349页

作者： Misra, Neeraj Kumar Das, Sanchali Satpathy, Sambit Addula, Santosh Reddy Trivedi, Munesh Chandra School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravathi522237 India School of Computer Science Engineering and Technology Bennett University Greater Noida India Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India Information Technology University of the Cumberlands Williamsburg United States Computer Science and Engineering PSSCIVE Bhopal Bhopal India

Machine learning (ML) with data analysis has many successful applications and is widely employed daily. Additionally, they have played a significant role in combating the global coronavirus (COVID-19) outbreak. International occurrences of the COVID-19 pandemic have resulted from the rapid global spread of the SARS-CoV-2 virus. In this study, we examine the ML and data analysis techniques for diagnosis, treatment, and impact of COVID-19, which are based on artificial intelligence (AI). Moreover, we provide an overview of the current state of the art in AI-based machine learning and deep learning techniques as well as the datasets, tools, and performance of the tools that can be used to combat COVID-19. In this survey, we have attempted to provide a comprehensive overview of the most recent state-of-the-art approaches available to researchers in Mathematical and Statistical analysis and the larger medical community as a whole. It also includes information on how data and ML can be used to improve COVID-19 status and encourage further research to prevent COVID-19 outbreaks. Future directions and problems are also described in detail. © The Institution of Engineers (India) 2024.

关键词： COVID-19

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Data-driven output regulation control for constrained linear systems

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science China(Information sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Tailoring magnetic softness of Fe-based amorphous alloys with superior magnetization by magnetic field annealing

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Journal of Materials science & technology 2024年第33期200卷 27-37页

作者： Long Hou Benjun Wang Li Liu Xinhao Mao Mingya Zhang Chenchen Yuan Zhong Li Wenwei Ju Hanchen Feng Chengying Tang Ailin Xia Weihuo Li School of Materials Science and Engineering School of Metallurgical EngineeringAnhui University of TechnologyMa’anshan 243002China Wuhu Technology and Innovation Research Institute Wuhu 242000China School of Materials Science and Engineering Instrumental Analysis CenterSoutheast UniversityNanjing 211189China Institute of Advanced Magnetic Materials College of Materials and Environmental EngineeringHangzhou Dianzi UniversityHangzhou 310012China Guangxi Key Laboratory for Informational Materials&School of Materials Science and Engineering Guilin University of Electronic TechnologyGuilin 541004China

The inverse relationship between the saturation magnetic flux density(Bs)and coercivity(Hc)of Febased amorphous alloys is a very active research topic that has been extensively *** this work,we conducted a detailed investigation on the magnetic softness of Fe_(83.2-x)Co_(x)B_(10)C_(6)Cu_(0.8)(x=0 and 6 at.%)amorphous alloys based on analysis of the surface morphology,microstructure,magnetic anisotropy,and magnetic domain *** magnetic softness-magnetization synergy was realized in the present alloys by magnetic field annealing(MFA)during the de-stressing process.A dramatic 84%reduction of Hc to 2.2 A/m was achieved for the Co-doped alloy under MFA,exhibiting excellent magnetic performance with a superb Bs of 1.86 *** consistency between the experimental results and theoretical analysis revealed that the MFA process can mitigate the trade-off between stress-induced anisotropy and induced uniaxial anisotropy owing to the homogenized structure formed by field ***,the process favored a low Hc due to the significant continuous decline in the total magnetic anisotropy,which coincided well with the results of Magneto-optical Kerr *** study elucidates a mechanism for tuning Hc in Co-doped alloy systems and affords a possible pathway for softening amorphous alloys with high Bs.

关键词： Amorphous alloy De-stressing magnetic field annealing Magnetic softness Microstructure Magnetic anisotropy

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Unleashing the potential of ChatGPT in education: Revolutionizing learning and collaboration

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IEEE Potentials 2024年第6期43卷 13-22页

作者： Chandrasekaran, Balaji Nair, Smrithy Girijakumari Sreekantan College of Engineering Guindy Anna University Department of Computer Science and Engineering Chennai600025 India Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India

ChatGPT, an advanced language model powered by artificial intelligence, has emerged as a transformative tool in the field of education. This article explores the potential of ChatGPT in revolutionizing learning and collaboration within educational settings. By leveraging natural language processing and machine learning, ChatGPT offers an interactive and personalized learning experience, enabling students to engage in dynamic conversations and receive instant feedback. Moreover, ChatGPT may facilitate collaborative learning by promoting effective communication and knowledge sharing among students and educators. This article examines the various applications of ChatGPT in education, including virtual tutoring, adaptive learning, and intelligent chatbots. It also discusses the benefits and considerations associated with implementing ChatGPT, such as ethical considerations, data privacy, and potential biases. While acknowledging the limitations and challenges, this article emphasizes the promising potential of ChatGPT in transforming education by enhancing student engagement, fostering critical thinking skills, and expanding access to quality education. © 1988-2012 IEEE.

关键词： Adversarial machine learning

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Differential Evolution Strategy with Chebyshev Chaos Based Mutation

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 613-628页

作者： Sitenda, Amos Song, Pei-Cheng Chu, Shu-Chuan Chen, Shi-Huang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Computer Science and Information Engineering Shu-Te UniversityKaohsiung Kaohsiung824 Taiwan

Differential Evolution (DE) is a potent stochastic evolutionary optimization algorithm garnering increasing research attention. Over the years, it has been found applicable in solving diverse real-world problems. DE employs mutation, crossover, and selection operators to guide populations toward optimal or nearly optimal solutions. However, the standard DE mutation strategies have found limitations in balancing exploration and exploitation effectively, thus prompting research into possible improvements. This study introduces a novel mutation strategy named Chebyshev Infused Chaos Mutation Strategy (CICMS). where a chaotic sequence partially guides the process of donor vector generation. Rigorous evaluations were conducted, comparing our modified DE against the standard DE and seven other metaheuristic algorithms, including Genetic Algorithm, Particle Swarm Optimization and Gravitational Search Algorithm. Experiments were performed using the challenging CEC 2014 benchmark functions, consisting of 30 objective functions. Results indicate substantial improvements in convergence speed and solution quality, highlighting the potential of our novel mutation strategy to enhance DE’s practicality in addressing complex optimization problems. This research contributes valuable insights to the dynamic field of optimization algorithms with implications for a wide range of applications. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Chaos theory

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