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arXiv 2025年

作者： Prayoonyong, Chawaphon Szydzik, Crispin Ren, Guanghui Corcoran, Bill Dubey, Aditya Murray, Caitlin E. Mitchell, Toby Mitchell, Arnan Photonic Communications Laboratory Dept. Electrical and Computer Systems Engineering Monash University ClaytonVIC Australia MelbourneVIC Australia

We propose an alternative way to determine GaAs carrier lifetime using pump-probe measurement based on fibre optics and integrated waveguides. We find that our GaAs samples have the lifetime ranging from 30-80 ps, supporting the bandwidth ≥ 12.5 GHz. The platform utilised in this work could offer a cost-effective way to investigate photocarrier lifetime. Moreover, it may have the potential for high-speed switches and detectors, or could be exploited for possible all-optical modulation. © 2025, CC BY-NC-ND.

关键词： Gallium arsenide

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Securing Digital Media in the Age of AI and Deepfakes: A Hardware-Based Solution

Securing Digital Media in the Age of AI and Deepfakes: A Har...

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IEEE Southeastcon

作者： Eric Scott Sisson Steven C. Puckett Dept. of Electrical and Computer Engineering Mississippi State University Starkville AL USA Computer Science and Information Systems University of North Alabama Florence AL USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The current capabilities of deepfake and Artificial Intelligence (AI) technologies make it difficult to distinguish what is original from what is fabricated. Electronic documents can be digitally signed to verify and authenticate their information, making them admissible as evidence in court. In contrast, images and videos from Internet of Things (IoT) devices-such as police body cameras, security cameras, and smartphones are susceptible to deepfake manipulation, which can undermine their authenticity in legal proceedings. This research hypothesizes that digitally signing image and video data utilizing hardware cryptographic coprocessors could render them admissible in court. Utilizing our processes will also identify if any manipulation of the image data has occurred. Our approach addresses a critical need in the justice system to combat the challenges deepfakes pose and could pave the way for enhanced trust in digital evidence, bolstering the integrity of the judicial process.

关键词： Deepfakes Law enforcement Media Cameras Hardware Cryptography Internet of Things Artificial intelligence Digital signatures Coprocessors

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On the Connection Between Magnetic-Field Odometry Aided Inertial Navigation and Magnetic-Field SLAM

arXiv

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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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Frequency domain identification for multivariable motion control systems: Applied to a prototype wafer stage

arXiv

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arXiv 2025年

作者： van der Hulst, M. González, R.A. Classens, K. Tacx, P. Dirkx, N. van de Wijdeven, J. Oomen, T. Dept. of Mechanical Engineering Eindhoven University of Technology Netherlands ASML Veldhoven Netherlands Delft Center for Systems and Control Delft University of Technology Netherlands

Multivariable parametric models are essential for optimizing the performance of high-tech systems. The main objective of this paper is to develop an identification strategy that provides accurate parametric models for complex multivariable systems. To achieve this, an additive model structure is adopted, offering advantages over traditional black-box model structures when considering physical systems. The introduced method minimizes a weighted least-squares criterion and uses an iterative linear regression algorithm to solve the estimation problem, achieving local optimality upon convergence. Experimental validation is conducted on a prototype wafer-stage system, featuring a large number of spatially distributed actuators and sensors and exhibiting complex flexible dynamic behavior, to evaluate performance and demonstrate the effectiveness of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Frequency domain analysis

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Predicting Optimal Placement of Electric Vehicle Charge Stations Using Machine Learning: A Case Study in Glasgow, UK 12

Predicting Optimal Placement of Electric Vehicle Charge Stat...

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12th Iranian Conference on Renewable Energies and Distributed Generation, ICREDG 2025

作者： Mousaei, Arash Naderi, Yahya University of Tabriz Dept. Computer and Electrical Engineering Tabriz Iran Ricardo Aea Sustainable Energy Systems Department Glasgow United Kingdom

ISBN: (纸本)9798331525132

Given the reduced costs of establishing Electric Vehicle (EV) charging stations and the need to improve their accessibility, it is essential for EV developers and governments to identify optimized locations for new stations. This study presents a predictive modeling approach using Machine Learning to determine the optimal sites for EV charging stations in Glasgow. While various machine learning methods are available for this purpose, we focus on Linear Regression due to its simplicity and high accuracy. By analyzing a comprehensive dataset of 207 AC charging stations-including factors latitude, longitude, population density, number of kinds of plugs, total number of plugs, price per kWh, traffic conditions, distance to the nearest station, Air Quality Index (AQI), and land value - we aim to provide urban planners and policymakers with actionable insights. The study evaluates the performance of the Linear Regression model in predicting the most suitable locations for charging stations. The results indicate that the proposed model achieved impressive accuracy, with R2, Mean Absolute Error (MAE), and Mean Squared Error (MSE) values of 0.89, 0.05, and 0.08, respectively. This research contributes to the ongoing efforts in urban planning for sustainable mobility by offering a data-driven methodology for EV infrastructure development. © 2025 IEEE.

关键词： Linear regression

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Analysis of Oscillations in Nonlinear Human-Operated Robotic System

Analysis of Oscillations in Nonlinear Human-Operated Robotic...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Boris Andrievsky Nikolay V. Kuznetsov Iuliia Zaitceva dep. Control of Complex Systems Institute of Problems in Mechanical Engineering of RAS dept. Applied Cybernetics St. Petersburg State University St. Petersburg Russia dep. Information and Control Systems Institute of Problems in Mechanical Engineering of RAS

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The development of automation is characterized by the increasing complexity of control tasks associated with various technical objects. However, this does not exclude the human element from the control process. Manual control has certain drawbacks that negatively impact the quality of management, primarily arising from the interaction between the operator and the nonlinear dynamics of the robot. This study examines a manual control system for a robotic manipulator, taking into account nonlinear constraints that may manifest as limitations on the power of the actuating mechanism or as part of a local regulator. The parameters of the operator's control actions exhibit a certain range due to individual characteristics of the human operator or the control technique employed. Additionally, signal transmission delays may be present in the control loop. Considering these system characteristics provides a more comprehensive understanding of control quality and allows for the establishment of acceptable control techniques for the human operator. Depending on the frequency of input disturbances, this work analyzes the closed-loop system comprising the operator and manipulator using methods based on harmonic linearization of nonlinearity and harmonic balance equations: locus of a perturbed relay system and harmonic stabilization. The results of calculations and simulations using these methods indicate that there exists a parameter space within which oscillations may arise. Thus, when designing manual control systems for robotic manipulators, it is essential to pay attention to the control techniques employed by the human operator and to overall delays in order to avoid undesirable operational modes of the system.

关键词： Training Service robots System performance Human factors Harmonic analysis Mathematical models Delays Relays Oscillators Remote control

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GraFIT: A toolbox for fast and accurate frequency response identification in Gravitational Wave Detectors

arXiv

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arXiv 2025年

作者： van Dael, Mathyn van Haren, Max Witvoet, Gert Swinkels, Bas Oomen, Tom Eindhoven University of Technology Dept. of Mechanical Engineering Control Systems Technology Eindhoven Netherlands TNO Optomechatronics Department Delft Netherlands Nikhef Amsterdam Netherlands Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Frequency response function (FRF) measurements are widely used in Gravitational Wave (GW) detectors, e.g., for the design of controllers, calibrating signals and diagnostic problems with system dynamics. The aim of this paper is to present GraFIT: a toolbox that enables fast, inexpensive, and accurate identification of FRF measurements for GW detectors compared to the commonly used approaches, including common spectral analysis techniques. The toolbox consists of a single function to estimate the frequency response function for both open-loop and closed-loop systems and for arbitrary input and output dimensions. The toolbox is validated on two experimental case studies of the Virgo detector, illustrating more than a factor 3 reduction in standard deviation of the estimate for the same measurement times, and comparable standard deviations with up to 10 times less data for the new method with respect to the currently implemented Spectral Analysis method. © 2025, CC BY-NC-ND.

关键词： Closed loop systems

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Formal Verification of Unknown Dynamical systems Via Gaussian Process Regression

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IEEE Transactions on Automatic control 2025年

作者： Skovbekk, John Laurenti, Luca Frew, Eric Lahijanian, Morteza The University of Colorado Smead Aerospace Engineering Sciences Dept. 3775 Discovery Dr BoulderCO80305 United States TU Delft Delft Center for Systems and Control Netherlands The University of Colorado Smead Aerospace Engineering Sciences Dept. BoulderCO80305 United States

Leveraging autonomous systems in safety-critical scenarios requires verifying their behaviors in the presence of uncertainties and black-box components that influence the system dynamics. In this work, we develop a framework for verifying discrete-time dynamical systems with unmodelled dynamics and noisy measurements against temporal logic specifications from an input-output dataset. The verification framework employs Gaussian process (GP) regression to learn the unknown dynamics from the dataset and abstracts the continuous-space system as a finite-state, uncertain Markov decision process (MDP). This abstraction relies on space discretization and transition probability intervals that capture the uncertainty due to the error in GP regression by using reproducible kernel Hilbert space analysis as well as the uncertainty induced by discretization. The framework utilizes existing model checking tools for verification of the uncertain MDP abstraction against a given temporal logic specification. We establish the correctness of extending the verification results on the abstraction created from noisy measurements to the underlying system. We show that the computational complexity of the framework is polynomial in the size of the dataset and discrete abstraction. The complexity analysis illustrates a trade-off between the quality of the verification results and the computational burden to handle larger datasets and finer abstractions. Finally, we demonstrate the efficacy of our learning and verification framework on several case studies with linear, nonlinear, and switched dynamical systems. © 1963-2012 IEEE.

关键词： Markov processes

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Predicting Optimal Placement of Electric Vehicle Charge Stations Using Machine Learning: A Case Study in Glasgow, UK

Predicting Optimal Placement of Electric Vehicle Charge Stat...

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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Arash Mousaei Yahya Naderi dept. Computer and Electrical Engineering University of Tabriz Tabriz Iran Sustainable Energy Systems Department Ricardo AEA Glasgow U.K

ISBN: (数字)9798331525132

ISBN: (纸本)9798331525149

Given the reduced costs of establishing Electric Vehicle (EV) charging stations and the need to improve their accessibility, it is essential for EV developers and governments to identify optimized locations for new stations. This study presents a predictive modeling approach using Machine Learning to determine the optimal sites for EV charging stations in Glasgow. While various machine learning methods are available for this purpose, we focus on Linear Regression due to its simplicity and high accuracy. By analyzing a comprehensive dataset of 207 AC charging stations-including factors latitude, longitude, population density, number of kinds of plugs, total number of plugs, price per kWh, traffic conditions, distance to the nearest station, Air Quality Index (AQI), and land value - we aim to provide urban planners and policymakers with actionable insights. The study evaluates the performance of the Linear Regression model in predicting the most suitable locations for charging stations. The results indicate that the proposed model achieved impressive accuracy, with R 2 , Mean Absolute Error (MAE), and Mean Squared Error (MSE) values of 0.89, 0.05, and 0.08, respectively. This research contributes to the ongoing efforts in urban planning for sustainable mobility by offering a data-driven methodology for EV infrastructure development.

关键词： Accuracy Atmospheric modeling Linear regression Machine learning Predictive models Charging stations Electric vehicle charging Indexes Population density Plugs

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Reinforcement Learning for Unrelated Parallel Machine Scheduling with Release Dates, Setup Times, and Machine Eligibility 24

Reinforcement Learning for Unrelated Parallel Machine Schedu...

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Proceedings of the Winter Simulation Conference

作者： Sang-Hyun Cho Hyun-Jung Kim Lars Mönch Dept. of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Dept. of Mathematics and Computer Science University of Hagen Universitätsstraße 1 Hagen GERMANY

ISBN: (纸本)9798331534202

This paper presents a novel approach for solving unrelated parallel machine scheduling problems through reinforcement learning. Notably, we consider three main constraints: release date, machine eligibility, and sequence- and machine-dependent setup time to minimize total weighted tardiness. Our work presents a new graph representation for solving the problem and utilizes graph neural networks combined with reinforcement learning. Experimental results show that our proposed method outperforms traditional dispatching rules and an apparent tardiness cost-based algorithm. Furthermore, since we represent and solve the problem using graphs, our method can be used regardless of the number of jobs or machines once trained.

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