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An analysis of second-order sav-filtered time-stepping finite element method for unsteady natural convection problems

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2025年 140卷

作者： Jiang, Mengru Wu, Jilian Li, Ning Feng, Xinlong Xinjiang Univ Coll Math & Syst Sci Urumqi 830046 Peoples R China Xinjiang Univ Inst Math & Phys Urumqi 830046 Peoples R China Henan Univ Technol Sch Math & Stat Zhengzhou 450001 Henan Peoples R China Henan Agr Univ Coll Informat & Management Sci Zhengzhou 450002 Peoples R China

This paper presents an unconditionally stable time-filtering algorithm for natural convection equations. The algorithm is based on the scalar auxiliary variables in the exponential function and adopts a completely discrete Back-Euler combining time filter scheme. The proposed scheme requires minimal invasive modification of the existing program to improve the time accuracy from first-order to second-order without increasing the computational complexity, and we demonstrate the unconditional stability of the proposed algorithm and analyze its second-order convergence. In addition, due to the increasing demand for low-memory solvers, the application of a time-adaptive algorithm can improve the accuracy and efficiency of the proposed algorithm, so we extend the method to variable step sizes and construct an adaptive algorithm. Finally, the effectiveness of the proposed method and the accuracy of the theoretical results are verified by numerical experiments.

关键词： Time filter adaptive algorithms Convergence Scalar auxiliary variable Natural convection

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Usability and Perceived Efficiency of an adaptive Route Optimization Solution for Commercial Vehicles

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IEEE ACCESS 2025年 13卷 61091-61108页

作者： Anghelache, Florian Goga, Nicolae Marian, Constantin Viorel Alexandru Mitrea, Dan Pavaloiu, Ionel-Bujorel iSYS Profess Res & Dev Dept Bucharest 020312 Romania Natl Univ Sci & Technol POLITEHN Bucharest Fac Engn Foreign Languages FILS Dept Engn Foreign Languages Bucharest 060042 Romania Univ Groningen Mol Dynam Grp NL-9747 AG Groningen Netherlands

This paper details the outcome of a European scientific research and development project, funded to prototype a commercial solution for vehicle fleets. While current route optimization solutions have data sources such as real-time road traffic and weather conditions, our solution was developed to offer companies a proposed plan with adapted optimized routes, considering critical operational requirements, local conditions, and drivers' driving experience. The iRoute solution consists of a module for receiving vehicle positions from GPS devices, an integration layer for end-customer applications, a route optimization engine, and applications for web and mobile users. Unlike other approaches, this innovative solution leverages historical, well-known, and familiar route data to enhance, refine, and adapt the optimized operational routes. This way, the drivers benefit from a more effective daily plan to follow based on business requirements such as locations, quantities, time, and distance. At the end of the day, adding these adjustments results in achieving up to a 20% improvement in the accuracy of executed routes, in terms of distance and time, compared to the planned route, outperforming standard optimization algorithms. This allows business owners to rely more confidently on the optimization results, providing a safer itinerary for their drivers. Also, due to lower incident rates on these known and preferred itineraries, the overall business will benefit from increased vehicle fleet availability and fewer days in service, with lower maintenance costs. The paper evaluates the usability and perceived efficiency value of the proposed solution by statistically analyzing feedback from dispatchers and business owners.

关键词： Vehicles Costs Clustering algorithms Business Routing Optimization Companies Usability Software algorithms Software adaptive algorithms adaptive systems path planning roadmaps (technology planning) road traffic control vehicle routing

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Dynamic Optimization of User and Cell Association in UAV-Assisted THz 6G Networks

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IEEE ACCESS 2025年 13卷 52406-52431页

作者： Negassa, Workeneh Geleta Gelmecha, Demissie Jobir Singh, Ram Sewak Adama Sci & Technol Univ Dept Elect & Commun Engn Adama 1888 Ethiopia

The dynamic optimization of user and cell association in UAV-assisted THz 6G networks ensures high reliability, spectral efficiency, and real-time adaptability in high-mobility environments. However, beam misalignment, Doppler effects, and path delay distortions significantly degrade network performance. This paper introduces a novel machine learning-driven framework that integrates advanced optimization techniques for beam alignment, Doppler compensation, and user association. The proposed framework consist of three key algorithms: the Deep Reinforced Hybrid Angle Estimation Algorithm (DRHAEA), which employed deep reinforcement learning and transformer-based beamforming to dynamically adjust azimuth and elevation angles, mitigating beam squint effects and ensuring precise beam alignment;the adaptive Neural Doppler Refinement Algorithm (ANDRA), which integrates Recurrent Neural Networks (RNN) and Multi-Stage Neural Networks (MSNN) to refine Doppler shift estimation and compensate for path delay distortions, thereby enhancing synchronization even in complex urban environments and the Neurodynamic Channel Tracker Algorithm (NCTA), which utilizes Deep Neural Networks (DNN), RNN, and Graph Neural Networks (GNN) for real-time channel tracking, efficient user clustering, and scalable connectivity. By mitigating beam squint, optimizing Doppler shift compensation, and improving user association, the proposed framework significantly enhanced spectral efficiency, reduced signal distortions, and improved UAV-ground communication reliability. Simulation results demonstrated that adaptive angle estimation and Doppler compensation effectively minimize beam misalignment and synchronization errors, achieving low-latency, high-throughput, and stable UAV communications. The framework achieved near-optimal spectral efficiency at SNR levels above 5 dB, outperforming conventional approaches. Comprehensive benchmarking against IEEE 802.15.3d and 6G performance metrics confirms its superi

关键词： Autonomous aerial vehicles Terahertz communications 6G mobile communication Spectral efficiency Optimization Throughput Three-dimensional displays Machine learning Heuristic algorithms Estimation adaptive algorithms channel estimation terahertz (THz) communication UAV-assisted communication user association

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adaptive Joint Estimation of Temporal Vertex and Edge Signals

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2025年 11卷 215-229页

作者： Yan, Yi Xie, Tian Kuruoglu, Ercan E. Tsinghua Univ Inst Data & Informat Tsinghua Shenzhen Int Grad Sch Shenzhen 518071 Peoples R China Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA 90007 USA Meta Inc Menlo Pk CA 94025 USA

The adaptive estimation of coexisting temporal vertex (node) and edge signals on graphs is a critical task when a change in edge signals influences the temporal dynamics of the vertex signals. Existing Graph Signal Processing algorithms have extensively studied signals on graph vertices, and while recent advancements have started exploring signals on edges, a framework for systematically representing interactive time-varying signals across vertices, edges, and higher-order structures has yet to be fully realized. We propose an adaptive Joint Vertex-Edge Estimation (AJVEE) algorithm for jointly estimating time-varying vertex and edge signals through a time-varying regression, incorporating both vertex signal filtering and edge signal filtering. Accompanying AJVEE is a newly proposed adaptive Least Mean Square procedure based on the Hodge Laplacian (ALMS-Hodge), which is inspired by classical adaptive filters combining simplicial filtering and simplicial regression. AJVEE is able to operate jointly on the vertices and edges by merging two ALMS-Hodge specified on the vertices and edges into a unified formulation. A more generalized case extending AJVEE beyond the vertices and edges is being discussed. Experimenting on real-world traffic networks and population mobility networks, we have confirmed that our proposed AJVEE algorithm could accurately and jointly track time-varying vertex and edge signals on graphs.

关键词： Laplace equations Information processing Filters Signal processing algorithms Image edge detection Adaptation models Imputation Heuristic algorithms Time series analysis Symbols adaptive algorithms graph learning graph signal processing (GSP) simplicial complexes time series

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Performance weighted blended spectrogram

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JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2025年第3期157卷 2106-2116页

作者： Tucker, Jeff Wage, Kathleen E. Buck, John R. Van Uffelen, Lora J. George Mason Univ Elect & Comp Engn Dept Fairfax VA 22030 USA Univ Massachusetts Dartmouth Elect & Comp Engn Dept Dartmouth MA 02747 USA Univ Rhode Isl Dept Ocean Engn Narragansett RI 02882 USA

Spectrograms are used for time-frequency analysis and as preprocessing for signal classifiers and other algorithms. The conventional spectrogram is a tapered short-time Fourier transform, equivalent to a bank of bandpass filters. The taper defines filter-bank characteristics such as bandwidth and sidelobe levels. Although the conventional spectrogram uses minimal computational resources, its design requires a compromise between resolution and interference suppression. adaptive spectrogram algorithms adjust the filter-bank based on incoming data, thereby allowing different bandwidth/sidelobe trade-offs at each frequency and time. Adaptation can simultaneously improve tonal resolution and reveal quiet sources but typically costs substantially more to implement. This paper presents an adaptive spectrogram designed for applications with limited computational resources, e.g., autonomous vehicles. The performance weighted blended (PWB) spectrogram combines the output of a set of conventional filter-banks designed with different tapers. By adapting its blend weights at each frequency and time, the new algorithm separates loud closely spaced tones and identifies quiet signals. Because it relies on conventional filter-banks, the PWB spectrogram requires significantly less computation than other adaptive algorithms that require expensive matrix computations. Analysis of underwater glider data demonstrates the algorithm's ability to reveal a quiet chirp signal in the presence of vehicle self-noise.

关键词： adaptive algorithms

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Reconfigurable Intelligent Surface Direct Data Modulation With adaptive Beam Steering Backscatter Communication

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IEEE Internet of Things Journal 2025年第11期12卷 15600-15619页

作者： Amri, Muhammad Miftahul Aziz, Arif Abdul Tran, Nguyen Minh Park, Je Hyeon Kim, Dong In Choi, Kae Won Sungkyunkwan University College of Information and Communication Engineering Department of Electrical and Computer Engineering Suwon16419 Korea Republic of Telkom University University Center of Excellence for Intelligent Sensing-IoT Bandung Indonesia Department of Wireless Communications Hanoi Viet Nam

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for next-generation wireless communications due to its ability to manipulate electromagnetic (EM) waves. This work introduces a novel RIS direct modulation scheme based on the backscatter communication concept. In this work, the RIS acts as a sole information modulator. Unlike conventional RIS systems that relay incoming data-carrying waves, this work enables RIS to encode its own information into unmodulated waves without requiring any hardware modifications. We propose an adaptive algorithm that maximizes modulation order while meeting the quality of service (QoS) requirements by selecting appropriate constellation points to minimize error probability. The proposed scheme was validated through simulations and experiments using a passive 5.8 GHz RIS prototype. The validations demonstrate consistent performance in both distributed and co-located transmitter-receiver scenarios. These results highlight its feasibility as an alternative approach for RIS-assisted communications and contribute to further exploration of RIS-enabled symbiotic radio (SR) and over-the-air (OTA) modulation systems. © 2014 IEEE.

关键词： adaptive algorithms

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Amari Alpha Divergence-Based Channel Estimator for Pilot Contaminated Multicell MIMO System

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IEEE COMMUNICATIONS LETTERS 2025年第1期29卷 35-39页

作者： Singh, Shekhar Pratap Sharma, Parth Pradhan, Pyari Mohan Indian Inst Technol Roorkee Dept Elect & Commun Engn Roorkee 247667 India

The multiple input multiple output (MIMO) system deployed in multicell multiuser scenario suffers from pilot contamination due to interference. The contaminated pilot used in the channel estimation leads to inaccurate estimates of the channel. In this letter, a multicell multiuser channel estimation technique based on the Amari alpha divergence based LMS algorithm (AALMS) is proposed for MIMO system. The mean stability analysis has been carried out, and the upper bound on the step size parameter of the AALMS algorithm is derived. The steady state mean square deviation (MSD) analysis is also presented. Monte-carlo simulations have been carried out considering a Rayleigh fading channel. The simulation results show that the proposed scheme provides better estimates of the channel as indicated by the lower mean square error (MSE) and MSD.

关键词： Channel estimation MIMO communication Noise Stability analysis Contamination Cost function adaptive algorithms Steady-state Convergence Computational complexity MIMO system channel estimation adaptive filter Amari alpha divergence LMS algorithm

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An adaptive Variant of The Frank-Wolfe Method for Relative Smooth Convex Optimization Problems

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COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS 2025年第3期65卷 591-602页

作者： Vyguzov, A. A. Stonyakin, F. S. Moscow Inst Phys & Technol Dolgoprudnyi 141701 Russia Innopolis Univ Innopolis 420500 Russia VI Vernadsky Crimean Fed Univ Simferopol 295007 Russia

This paper proposes a new variant of the adaptive Frank-Wolfe algorithm for relatively smooth convex minimization problems. It suggests using a divergence different from half of the squared Euclidean norm in the step size adjustment formula. Convergence rate estimates for this algorithm are proven for minimization problems involving relatively smooth convex functions with the triangle scaling property. We also conducted computational experiments for the Poisson linear inverse problem and SVM models. The paper also identifies the conditions under which the proposed algorithm shows a clear advantage over the adaptive proximal gradient Bregman method and its accelerated variants.

关键词： relative smoothness Frank-Wolfe algorithm adaptive algorithms convex minimization triangle scaling property Bregman divergence

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JLOS: a Cooperative UAV-Based Optical Wireless Communication With Multi-Agent Reinforcement Learning

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IEEE Transactions on Network and Service Management 2025年第2期22卷 1345-1356页

作者： Liu, Jiangang Luo, Hanjiang Tao, Hang Liu, Jiahong Zhou, Jiehan Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China University of Oulu Faculty of Information Technology and Electrical Engineering Oulu Finland

In maritime Internet of Things (IoT) systems, leveraging a swarm of Unmanned Aerial Vehicles (UAVs) and optical communication can achieve a variety of potential maritime missions. However, due to the high directionality of the optical beam and interference from the marine environment, the optical link via UAVs as relays is prone to interruption. To address this challenge, we propose a Joint Link Optimization Scheme (JLOS) that includes Wind Disturbance Resistance (WDR) and adaptive Beamwidth Adjustment (ABA). In WDR, we first model the problem as a Partially Observed Markov Decision Process (POMDP), and then design a collaborative Multi-Agent Reinforcement Learning (MARL) approach to control a swarm of UAVs in windy conditions, to maintain mechanical stability and prevent link interruption. Furthermore, in ABA, to reduce uncertainties from control activities and environmental factors like sunlight and fog, we design an adaptive algorithm using distributed MARL. It adjusts beamwidth based on historical UAV locations and link Bit Error Ratio (BER) to improve communication reliability. Numerical simulations confirm its effectiveness in enhancing robust data transmission. © 2004-2012 IEEE.

关键词： adaptive algorithms

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ML computational inference techniques and indicator metrics for analyzing uncertainties in stock market data

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Multimedia Tools and Applications 2025年 1-21页

作者： Henge, Santosh Kumar Mandal, Sanjeev Kumar Sharma, Amit Upadhyay, Aditya Kundu, Shakti Iyer, Srinivasan Saini, Ashok Kumar Yadav, Manoj Kumar Singh, Bhupinder Department of Computer Science and Engineering School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Bangalore India Department of Computer Applications Lovely Professional University Punjab Jalandhar India Center for Distance and Online Education Manipal University Jaipur Rajasthan Jaipur India Haryana Gurugram122413 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science & Engineering Lovely Professional University Punjab Jalandhar India

In a growing demand of accurately predicting the stock market and inefficient complex markets the rising accurate relationship prediction is not adequately addressed by the conventional methods. The dynamic and complex natures of data sources become the issue and we need to propose an effective method for adaptive algorithms to accurately forecasting and make decision efficiently. The system framed with two stages of execution: The first stage has covered time series-based predictions for different stock values involving the LSTM Model;the second stage integrated the experimental scenarios based on trend, value, indicators, and it's supporting impacted data metrics, which experimented on various large-cap stock companies. The dataset is sourced online by yahoo finance API with consecutive days data and dependable, non-dependable influenced indicators, which helped to intend trend-based evaluation performances. The proposed model experimented on distinct stock values and test cases executed based on indicator-based prediction with a 76.92% accurate performance rate using a Random Forest Classifier and 76.92% of Logistic Regression. The value-based prediction test case has achieved a performance rate of 97.5% using SVR Regressor and 97.2% using Linear Regressor. It computed value of root mean square error for the Ten large cap companies for performance evaluation. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： adaptive algorithms

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