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arXiv 2025年

作者： Skog, Isaac Kok, Manon Hendeby, Gustaf Huang, Chuan Edridge, Thomas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Delft Center for Systems and Control Delft University of Technology Delft Netherlands Dept. of Electrical Engineering Linköping University Linköping Sweden

Magnetic-field simultaneous localization and mapping (SLAM) using consumer-grade inertial and magnetometer sensors offers a scalable, cost-effective solution for indoor localization. However, the rapid error accumulation in the inertial navigation process limits the feasible exploratory phases of these systems. Advances in magnetometer array processing have demonstrated that odometry information, i.e., displacement and rotation information, can be extracted from local magnetic field variations and used to create magnetic-field odometry-aided inertial navigation systems. The error growth rate of these systems is significantly lower than that of standalone inertial navigation systems. This study seeks an answer to whether a magnetic-field SLAM system fed with measurements from a magnetometer array can indirectly extract odometry information — without requiring algorithmic modifications — and thus sustain longer exploratory phases. The theoretical analysis and simulation results show that such a system can extract odometry information and indirectly create a magnetic field odometry-aided inertial navigation system during the exploration phases. However, practical challenges related to map resolution and computational complexity remain significant. © 2025, CC BY.

关键词： Inertial navigation systems

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Active RIS-Aided Massive MIMO With Imperfect CSI and Phase Noise

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IEEE Transactions on Wireless Communications 2025年

作者： Peng, Zhangjie Zhu, Jianchen Pan, Cunhua Zhang, Zaichen Da Costa, Daniel Benevides Elkashlan, Maged Karagiannidis, George K. Shanghai Normal University College of Information Mechanical and Electrical Engineering Shanghai200234 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece Artificial Intelligence and Cyber Systems Research Center Lebanon

As a recently proposed reconfigurable intelligent surface (RIS) architecture, active RIS has drawn considerable interest. The important feature of the active RIS is its ability to strengthen the impinging signals to mitigate the multiplicative fading effect inherent in passive RIS-aided systems. Herein, we explore the performance of an active RIS-aided uplink multi-user massive multiple-input multiple-output (MIMO) system, considering the phase noise at the RIS. Furthermore, a two-timescale scheme is utilized, where the base station (BS) beamforming is designed based on the instantaneous aggregated channel state information (CSI), while the statistical CSI is used for designing the phase shifts of the active RIS. In addition, the linear minimum mean square error (LMMSE) estimator is adopted to estimate the aggregated channel, which combines both the cascaded and direct channels. According to the estimated channel, a closedform expression for the lower bound of achievable rate is derived. Based on the theoretical expressions, the power scaling laws for the considered system are also investigated. Specifically, when each user’s transmit power is proportionally reduced by the quantity of BS antennas M or RIS elements N, we find that the amplified thermal noise causes the lower bound of the achievable rate to approach zero as M or N tends to infinity. Moreover, an optimization approach based on a genetic algorithm (GA) is introduced to obtain the optimal phase shifts for maximizing the achievable rate. Numerical results reveal that the active RIS can greatly enhance the performance of the massive MIMO system compared to its passive counterpart. © 2002-2012 IEEE.

关键词： Genetic algorithms

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Data-driven model discovery with Kolmogorov-Arnold networks

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Physical Review Research 2025年第2期7卷 023037-023037页

作者： Shirin Panahi Mohammadamin Moradi Erik M. Bollt Ying-Cheng Lai School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Electrical and Computer Engineering Clarkson Center for Complex Systems Science Clarkson University Potsdam New York 13699 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

Data-driven model discovery of complex dynamical systems can be done using sparse optimization, but it has a fundamental limitation: sparsity in that the underlying governing equations of the system contain only a small number of elementary mathematical functions. Examples where sparse optimization fails abound, including the classic Ikeda or optical-cavity map in nonlinear dynamics, as well as a wide variety of ecosystems. Another approach is based on machine learning, e.g., deep neural networks, which excels at capturing system behavior from data but functions as black boxes, offering little insight into how inputs influence the outputs. We propose a general model-discovery framework based on the Kolmogorov-Arnold networks (KANs) that are not constrained by the sparsity condition. The KAN framework with a simple structure is capable of accurately capturing the complex behavior of dynamical systems that do not meet the sparsity requirement while offering greater interpretability compared to conventional neural networks. This interpretability provides insights into the dynamics generating the data, which are typically inaccessible in traditional black-box function approximation methods. We demonstrate nonuniqueness in that a large number of approximate models of the system can be found that generate the same invariant set with the true statistics such as the Lyapunov exponents and Kullback–Leibler divergence. An analogy to shadowing of numerical trajectories in chaotic systems is pointed out.

关键词： Deep neural networks

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Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Fu, Liqun Su, Yuhan Hosseinalipour, Seyyedali Wang, Xianbin Hong, Yiguang School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China School of Electronic Science and Engineering Xiamen University Xiamen China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering Western University ON Canada

Hierarchical Federated Learning (HFL) introduces intermediate aggregation layers, addressing the limitations of conventional Federated Learning (FL) in geographically dispersed environments with limited communication infrastructure. An application of HFL is in smart IoT systems, such as remote monitoring, disaster response, and battlefield operations, where cellular connectivity is often unreliable or unavailable. In these scenarios, UAVs serve as mobile aggregators, providing connectivity to the terrestrial IoT devices. This paper studies an HFL architecture for energy-constrained UAVs in smart IoT systems, pioneering a solution to minimize global training cost increased caused by UAV disconnection. In light of this, we formulate a joint optimization problem involving learning configuration, bandwidth allocation, and device-to-UAV association, and perform global aggregation in time before UAV drops disconnect and redeployment of UAVs. The problem explicitly accounts for the dynamic nature of IoT devices and their interruptible communications and is unveiled to be NP-hard. To address this, we decompose it into three subproblems. First, we optimize the learning configuration and bandwidth allocation using an augmented Lagrangian function to reduce training costs. Second, we propose a device fitness score, integrating data heterogeneity (via Kullback-Leibler divergence), device-to-UAV distances, and IoT device resources, and develop a twin-delayed deep deterministic policy gradient (TD3)-based algorithm for dynamic device-to-UAV assignment. Third, We introduce a low-complexity two-stage greedy strategy for finding the location of UAVs redeployment and selecting the appropriate global aggregator UAV. Experiments on real-world datasets demonstrate significant cost reductions and robust performance under communication interruptions. © 2025, CC BY-NC-ND.

关键词： Federated learning

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Reinforcement Learning in Mechatronic systems: A Case Study on DC Motor Control

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Circuits and systems 2025年第1期16卷 1-24页

作者： Alexander Nüßgen Alexander Lerch René Degen Marcus Irmer Martin de Fries Fabian Richter Cecilia Boström Margot Ruschitzka CAD CAM Center Cologne Institute of Automotive Engineering Cologne (IFK) Faculty of Automotive Systems and Production Cologne University of Applied Science Cologne Germany Division of Electricity Department of Electrical Engineering Uppsala University Uppsala Sweden Heringer Consulting GmbH Cologne Germany

The integration of artificial intelligence into the development and production of mechatronic products offers a substantial opportunity to enhance efficiency, adaptability, and system performance. This paper examines the utilization of reinforcement learning as a control strategy, with a particular focus on its deployment in pivotal stages of the product development lifecycle, specifically between system architecture and system integration and verification. A controller based on reinforcement learning was developed and evaluated in comparison to traditional proportional-integral controllers in dynamic and fault-prone environments. The results illustrate the superior adaptability, stability, and optimization potential of the reinforcement learning approach, particularly in addressing dynamic disturbances and ensuring robust performance. The study illustrates how reinforcement learning can facilitate the transition from conceptual design to implementation by automating optimization processes, enabling interface automation, and enhancing system-level testing. Based on the aforementioned findings, this paper presents future directions for research, which include the integration of domain-specific knowledge into the reinforcement learning process and the validation of this process in real-world environments. The results underscore the potential of artificial intelligence-driven methodologies to revolutionize the design and deployment of intelligent mechatronic systems.

关键词： Artificial Intelligence in Product Development Mechatronic systems Reinforcement Learning for Control System Integration and Verification Adaptive Optimization Processes Knowledge-Based engineering

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A Two-stage Reconfiguration in Network Function Virtualization: Toward Service Function Chain Optimization

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IEEE Transactions on Network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： Network function virtualization

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Energy-Based Trajectory Tracking for Underactuated Mechanical systems: Velocity-Free and Disturbance Rejection Methods

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International Journal of Robust and Nonlinear Control 2025年

作者： Javanmardi, N. Borja, P. van der Schaft, A.J. Yazdanpanah, M.J. Scherpen, J.M.A. Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen Netherlands Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom Jan C. Willems Center for Systems and Control Bernoulli Institute Faculty of Science and Engineering University of Groningen Groningen Netherlands

This work investigates three energy-shaping control approaches to address the trajectory-tracking problem for specific classes of underactuated mechanical systems. In particular, the notions of contractive systems and dynamic extensions are utilized to solve the trajectory-tracking problem while addressing implementation issues such as the lack of velocity sensors and the presence of constant disturbances. To this end, a first tracking controller is developed without the need for velocity measurements. A second controller is introduced, solving the trajectory-tracking problem while rejecting matched constant disturbances. Then, a third approach establishes conditions to combine both mentioned controllers. It is shown that the proposed design methods guarantee exponential convergence of closed-loop systems for specific classes of underactuated mechanical systems. The third method is illustrated with an example. © 2025 The Author(s). International Journal of Robust and Nonlinear Control published by John Wiley & Sons Ltd.

关键词： Disturbance rejection

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A Preliminary Multi-Physics Study for Intra-Cardiac Communication Based on Biventricular Model

A Preliminary Multi-Physics Study for Intra-Cardiac Communic...

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European Conference on Antennas and Propagation, EuCAP

作者： Maoyuan Li Takahiro Aoyagi Ilangko Balasingham Ali Khaleghi Dept. of Electrical and Electronic Engineering Institute of Science Tokyo Japan Dept. of Electronic Systems Norw. Uni. of Science and Tech. and the Intervention Center Oslo University Hospital Norway

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

Wireless modules have become a crucial component of many modern implantable medical devices (IMDs), including those used for cardiac applications. In this study, we conducted a preliminary multi-physics simulation for intra-cardiac communication based on a simplified biventricular geometry. Initially, we simulated the cardiac cycle, demonstrating the deformation of the biventricular, and in one scenario, two cardiac implants were positioned within the cardiac chamber, with relative displacement observed. Based on electromagnetic simulations, we analyzed the fluctuations in coupling at different frequencies (6.78, 13.56, 433, 915, and 2400 MHz) during myocardial mechanical deformation over a simulated cardiac cycle. The method used in this study provides a paradigm for the intra-cardiac implant investigation, and the results are a major component in the link budget analysis.

关键词： Wireless communication Geometry Couplings Time-frequency analysis Solid modeling Medical devices Deformation Implants Timing Numerical models

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Exploiting the Potential of Self-Supervised Monocular Depth Estimation via Patch-Based Self-Distillation

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IEEE Internet of Things Journal 2025年第11期12卷 18042-18055页

作者： Yu, Ting Pan, Shan Chen, Wei Tian, Zijian Wang, Zehua Yu, F. Richard Leung, Victor C. M. Postdoctoral Research Station China RongTong Academy of Sciences Group Corporation Limited Beijing100022 China China RongTong Artificial Intelligence Research Center Beijing100022 China China University of Mining and Technology School of Artificial Intelligence Beijing100083 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China School of Artificial Intelligence Beijing100083 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Perceiving scene depth and 3D structure is one of the key tasks for Internet of Video Things (IoVT) devices to understand and interact with the environment. Self-supervised monocular depth estimation has demonstrated significant potential in leveraging large-scale unlabeled datasets to achieve competitive performance, thereby playing an increasingly important role in depth estimation. Despite recent methods providing additional supervisory signals through self-distillation strategies to improve depth estimation, an effective method for generating pseudo-depth labels suitable for addressing occlusion issues among elements far from the camera remains unexplored. To address this limitation, we propose a Patch-based Self-Distillation Learning Framework to exploit the potential of self-supervised monocular depth estimation in recovering fine-grained scene depth. In the proposed framework, elements far from the camera within the input image are enlarged by enlarging and cropping operations in the patch-based self-distillation branch. Guided by photometric consistency, the model learns the detailed occlusion relationships among elements from the enlarged patches, producing patch depth maps with fine structures. In the main branch, which takes full-scale images as input, patch depth maps serve as pseudo-depth labels through self-distillation loss to provide additional supervisory signals for regions where photometric consistency fails to offer effective supervision. This forces the depth estimation network to recover fine structures of elements far from the camera in full-scale input images. Regarding the architecture of the depth estimation network, we introduce a bin-center prediction. In this prediction, a global aggregator based on self-attention provides additional scene structure queries for adaptive scene depth discretization. Finally, to encourage the model to explore more general cues for depth inference beyond road plane cues, we propose a PatchMix data augmentati

关键词： Image enhancement

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A Variable Parameter Series Compensation Method Featuring Fast Dynamic Response for Dual Active Bridge Converter in Battery Charging/Discharging Applications

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IEEE Transactions on Power Electronics 2025年

作者： Wang, Feng Wang, Xuehua Yuan, Jialing Ruan, Xinbo Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Wuhan430074 China Nanjing University of Aeronautics and Astronautics Center for More-Electric-Aircraft Power Systems College of Automation Engineering Nanjing210016 China

The extended-phase-shift (EPS) modulated dual-active-bridge (DAB) converter has become prevalent in battery charging/discharging systems, where low overshoot and short settling time are critically required. However, wide variations in battery voltage and current and abrupt operating mode transitions tend to challenge the dynamic performance of the converter. In this paper, a small-signal model is developed, the potential challenge due to the wide-range variations or abrupt mode transitions is evaluated, and a variable parameter series compensation (VPSC) method is proposed. The proposed method can adjust its parameters in real time according to the variations in battery voltage and current, making the dynamics of the EPS-DAB converter immune to the variations. For practical implementation, a polynomial-fit approximation is conducted to minimize the computational complexity. An execution-optimized flowchart is also provided. Experimental results on a 45-kW prototype demonstrate the effectiveness of the VPSC method. Even under the worst case when the battery voltage is 107 V and the battery current steps from 100 A to -100 A, no overshoot arises and the settling time is less than 6 ms. © 1986-2012 IEEE.

关键词： Polynomial approximation

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