An adaptive method for individual tree segmentation synthesizing canopy cover and competitive mechanism using UAV data by Qiyu Guo, Kangning Li, Xiaojun Qiao, Jinbao Jiang, Yinpeng Zhao

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Network- and Demand-Driven Initialization Strategy for Enhanced Heuristic in Uncapacitated Facility Location Problem by Jayson Lin, Shuo Yang, Kai Huang, Kun Wang, Sunghoon Jang

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FTDZOA: An Efficient and Robust FS Method with Multi-Strategy Assistance by Fuqiang Chen, Shitong Ye, Lijuan Xu, Rongxiang Xie

“…Feature selection (FS) is a pivotal technique in big data analytics, aimed at mitigating redundant information within datasets and optimizing computational resource utilization. This study introduces an enhanced zebra optimization algorithm (ZOA), termed FTDZOA, for superior feature dimensionality reduction. …”
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Revolutionizing RPAS logistics and reducing CO2 emissions with advanced RPAS technology for delivery systems by Armin Mahmoodi, Leila Hashemi, Jeremy Laliberte, Richard C. Millar, Robert Walter Meyer

“…The use of the NSGA-II meta-heuristic method for validation enhances the credibility and practicality of the model. The optimization model’s performance over 250 generations shows rapid initial improvements in cost, time, risk, and battery usage, followed by stabilization, indicating efficient convergence and effective evolutionary computation. …”
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A novel approximation of underwater robotic vehicle controller exploiting multi-point matching by Umesh Kumar Yadav, V. P. Singh, Luigi Fortuna, Umesh Kumar Sahu

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Wireless Sensor Network Coverage Optimization: Comparison of Local Search-Based Heuristics by Krzysztof Trojanowski, Artur Mikitiuk

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Evaluation and optimization of carbon emission for federal edge intelligence network by Peng ZHANG, Yong XIAO, Jiwei HU, Liang LIAO, Jianxin LYU, Zegang BAI

“…In recent years, the continuous evolution of communication technology has led to a significant increase in energy consumption.With the widespread application and deep deployment of artificial intelligence (AI) technology and algorithms in telecommunication networks, the network architecture and technological evolution of network intelligent will pose even more severe challenges to the energy efficiency and emission reduction of future 6G.Federated edge intelligence (FEI), based on edge computing and distributed federated machine learning, has been widely acknowledged as one of the key pathway for implementing network native intelligence.However, evaluating and optimizing the comprehensive carbon emissions of federated edge intelligence networks remains a significant challenge.To address this issue, a framework and a method for assessing the carbon emissions of federated edge intelligence networks were proposed.Subsequently, three carbon emission optimization schemes for FEI networks were presented, including dynamic energy trading (DET), dynamic task allocation (DTA), and dynamic energy trading and task allocation (DETA).Finally, by utilizing a simulation network built on real hardware and employing real-world carbon intensity datasets, FEI networks lifecycle carbon emission experiments were conducted.The experimental results demonstrate that all three optimization schemes significantly reduce the carbon emissions of FEI networks under different scenarios and constraints.This provides a basis for the sustainable development of next-generation intelligent communication networks and the realization of low-carbon 6G networks.…”
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Parallel Primal-Dual Method with Linearization for Structured Convex Optimization by Xiayang Zhang, Weiye Tang, Jiayue Wang, Shiyu Zhang, Kangqun Zhang

“…The proposed algorithm operates in a parallel framework, simultaneously updating primal and dual variables, and offers potential computational advantages. …”
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Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea) by Degninou Yehadji, Degninou Yehadji, Geraldine Gray, Carlos Arias Vicente, Petros Isaakidis, Petros Isaakidis, Abdourahimi Diallo, Saa Andre Kamano, Thierno Saidou Diallo

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Improved Multiobjective Genetic Algorithm for Partitioning Distributed Photovoltaic Clusters: Balancing Spatial Distance and Power Similarity by Yansen Chen, Kai Cheng, Zhuohuan Li, Shixian Pan, Xudong Hu

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Frequency Optimization Objective during System Prototyping on Multi-FPGA Platform by Mariem Turki, Zied Marrakchi, Habib Mehrez, Mohamed Abid

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Bit Rate Optimization with MMSE Detector for Multicast LP-OFDM Systems by Ali Maiga, Jean-Yves Baudais, Jean-François Hélard

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Source-detector trajectory optimization for FOV extension in dental CBCT imaging by S M Ragib Shahriar Islam, Ander Biguri, Claudio Landi, Giovanni Di Domenico, Benedikt Schneider, Pascal Grün, Cristina Sarti, Ramona Woitek, Andrea Delmiglio, Carola-Bibiane Schönlieb, Dritan Turhani, Gernot Kronreif, Wolfgang Birkfellner, Sepideh Hatamikia

“…The main goal of this study was to extend the FOV algorithmically by acquiring projection data with optimal projection angulation and isocenter location rather than upgrading any physical parts of the device. …”
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Exploring the Impact of Resource Management Strategies on Simulated Edge Cloud Performance: An Experimental Study by Nikolaos Kaftantzis, Dimitrios G. Kogias, Charalampos Z. Patrikakis

“…Optimizing the utilization of limited edge cloud resources and improving the performance of edge computing systems requires efficient resource-management techniques. …”
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Multi group merging algorithm for solving data Shuffle and data skew of securities companies by CAO Yakun, TANG Xiaoyong

“…Experimental results showed that, compared to the no optimized（NO） control group, MGMA algorithm achieved a 20% data skew rate, 72% memory usage, and 61% computation time. …”
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Low complexity hybrid precoding algorithm for massive MIMO based on modified Newton method by Bo HU, Anding WANG, Guiyi WEI

“…A phase tracking algorithm was proposed for massivemultiple-input multiple-output(MIMO) systems based on the modified Newton (MN) method, which effectively reduced the high computational complexity in traditional high-performance hybrid precoding schemes.The algorithm optimized the analog precoding matrix from the perspective ofsub-dimensional vector recovery.In each sub-dimension optimization, the phase tracking method was used to transform the recovery of the analog precoding vectors into an unconstrained nonlinear optimization problem, which was then solved using the MN method.Concurrently, this strategy led to a marked reduction in the computational intricacy pertaining to both the computation of correction factors and the inversion of the Hessian matrix within the framework of the MN method.This was achieved through the insightful incorporation of Gerschgorin’s Disk theorem and the Hermitian matrix block-inverse lemma.Simulation results show that the proposed algorithm has higher spectral efficiency and lower computational complexity than several conventional high-performance hybrid precoding schemes.…”
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An Analysis Dictionary Learning Algorithm under a Noisy Data Model with Orthogonality Constraint by Ye Zhang, Tenglong Yu, Wenwu Wang

“…This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high), as represented by the Analysis K-SVD (AK-SVD) algorithm. …”
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Suboptimal allocation algorithm software components (PC) distributed control system (DSC) telecommunications by Dmitrij Valerevich Mochalov

“…Developed and implemented a recursive algorithm for optimizing the interaction of computer modules distributed control system…”
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A Fast Algorithm of Generalized Radon-Fourier Transform for Weak Maneuvering Target Detection by Weijie Xia, Ying Zhou, Xue Jin, Jianjiang Zhou

“…In this paper, a fast implementation algorithm of GRFT using the BSSL learning-based modified wind-driven optimization (BMWDO) is proposed. …”
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Predictive PID Control for Automated Guided Vehicles Using Genetic Algorithm and Machine Learning by Kinza Nazir, Yong-Woon Kim, Yung-Cheol Byun

“…This study introduces a hybrid framework combining traditional Proportional-Integral-Derivative (PID) control with advanced machine learning to optimize AGV performance. A genetic algorithm (GA) was employed to generate ground truth PID parameters for diverse track configurations, ensuring superior path-tracking accuracy. …”
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