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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Marek Rydel Rafał Stanisławski Department of Electrical Control and Computer Engineering Opole University of Technology ul. Prószkowska 76 Opole Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

The paper presents a new algorithm for the selection of poles for modeling fractional-order difference based on Takenaka-Malmquist functions. The main advantage of the proposed approach to the selection of poles is better approximation accuracy in the chosen frequency adequacy range as well as in the time domain of finite-length implementation of the Takenaka-Malmquist-base fractional-order difference. The proposed Takenaka-Malmquist-based difference implementation can also be applied to model fractional-order integrators and non-commensurate state-space systems.

关键词： Time-frequency analysis Accuracy Automation Approximation algorithms Time-domain analysis Robots

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ENABLING HIGH SENSITIVITY AIRFLOW MEASUREMENT USING PHASE-CHANGE MATERIALS 26

ENABLING HIGH SENSITIVITY AIRFLOW MEASUREMENT USING PHASE-CH...

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26th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2022

作者： Zhou, Yushan Cao, Yunqi Sepúlveda, Nelson Zhang, Hongjian State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou310027 China Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States

ISBN: (纸本)9781733419048

A lab-on-a-chip (LOC) thermal mass flow sensor based on microelectromechanical systems (MEMS) technology is designed, fabricated, and characterized. Vanadium dioxide (VO2), a nonlinear phase-change material with 3-order of change in resistance is placed in the down streaming direction of the heat source as the sensing element. The resistance of VO2 is monitored under gas flow rates ranging from 0 to 37.8 μL·s-1 with a maximum sensitivity of 368.56 ppm/(μL·min-1). The studies presented in this research may enable the application of utilizing nonlinear metamaterial in microfluidic LOC sensors with orders of magnitude improvement in sensitivity. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

关键词： Phase change materials

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HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Wang, Xianbin Cheng, Zhipeng Hosseinalipour, Seyyedali Dai, Huaiyu Jiao, Zhenzhen School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Japan Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Suzhou China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering NC State University RaleighNC United States iF-Labs Beijing Teleinfo Technology Company Ltd. CAICT Beijing China

The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient machine learning (ML) solutions that can handle high vehicular mobility and decentralized data. This has motivated the emergence of Hierarchical Federated Learning over vehicle-edge-cloud architectures (VEC-HFL). Nevertheless, one aspect which is underexplored in the literature on VEC-HFL is that vehicles often need to execute multiple ML tasks simultaneously, where this multi-model training environment introduces crucial challenges. First, improper aggregation rules can lead to model obsolescence and prolonged training times. Second, vehicular mobility may result in inefficient data utilization by preventing the vehicles from returning their models to the network edge. Third, achieving a balanced resource allocation across diverse tasks becomes of paramount importance as it majorly affects the effectiveness of collaborative training. We take one of the first steps towards addressing these challenges via proposing a framework for multi-model training in dynamic VEC-HFL with the goal of minimizing global training latency while ensuring balanced training across various tasks—a problem that turns out to be NP-hard. To facilitate timely model training, we introduce a hybrid synchronous-asynchronous aggregation rule. Building on this, we present a novel method called Hybrid Evolutionary And gReedy allocaTion (HEART). The framework operates in two stages: first, it achieves balanced task scheduling through a hybrid heuristic approach that combines improved Particle Swarm Optimization (PSO) and Genetic Algorithms (GA);second, it employs a low-complexity greedy algorithm to determine the training priority of assigned tasks on vehicles. Experiments on real-world datasets demonstrate the superiority of HEART over existing methods. © 2025, CC BY-NC-ND.

关键词： Federated learning

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Bio-Inspired Home Localization Using Event-Based Vision and Spiking Neural Networks in Simulated Environment

Bio-Inspired Home Localization Using Event-Based Vision and ...

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2025 IEEE International Conference on Simulation, Modeling, and Programming for autonomous Robots, SIMPAR 2025

作者： Kausar, Rizwana Zayer, Fakhreddine Sudevan, Vidya Viegas, Jaime Dias, Jorge Khalifa University Department of Electrical Engineering United Arab Emirates Khalifa University Center for Autonomous Robotic Systems United Arab Emirates Khalifa University Department of Computer and Information Engineering United Arab Emirates

ISBN: (纸本)9798331516857

In this work, we present a bio-inspired approach for home localization using event-based visual data and spiking convolutional neural networks (S-CNNs) in a simulated environment within NVIDIA Omniverse. Drawing inspiration from the navigational strategies of insects, which utilize visual cues and homing vectors to return to their nests, our approach leverages event cameras that mimic the asynchronous change-driven visual processing of insect eyes. To process this sparse, event-driven input, we employ spiking neural networks (SNNs), which mirror the spike-based transmission of information in biological neural systems. The proposed system utilizes a quadrotor equipped with an event camera to capture dynamic, asynchronous visual data and processes it using an S-CNN trained to estimate relative home vectors. This vector encoding method is inspired by the unit-circle representation of gaze directions observed in insect homing behavior, as outlined in recent studies. By integrating event-based vision and SNNs, our approach ensures energy-efficient computation, robustness to lighting variations, and adaptability to dynamic scenes. We validate the framework in a 3D environment modeled within ISAAC Sim, where the quadrotor autonomously navigates back to a designated "nest"location. Comparative analysis with conventional frame-based neural networks demonstrates the superiority of the proposed system in terms of efficiency, robustness, and accuracy. This work establishes a novel bio-inspired framework for integrating event-based data and spiking neural networks, paving the way for energy-efficient localization in robotics and smart home environments. Future work will explore the deployment of the system in a multi-quadrotor environment to coordinate collaborative tasks including the integration of multi-modal sensory inputs such as Camera, IMU, Gas sensors and GPS data, and the extension of the framework to real-world settings for further validation and scalability. © 2025 IE

关键词： Convolutional neural networks

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Innovative Image Privacy Preservation Applied to Data Perturbation in Cloud

Innovative Image Privacy Preservation Applied to Data Pertur...

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Applied Intelligence and Sustainable Computing (ICAISC), International Conference on

作者： A. N. Karthikeyan N. Yuvaraj K Gowsic Mohammad Jahidul Islam Manickam Ramasamy Department of Information Technology Mahendra Institute of Technology(Autonomous) Namakkal India Department of Computer Science and Engineering KSR College of Engineering Tiruchengode India Department of Computer Science and Engineering Mahendra Institute of Technology(Autonomous) Namakkal India Department of Electrical and Electronic Engineering (EEE) Bangladesh University of Business and Technology (BUBT) Bangladesh Department of Electrical & Electronics UCSI university Malaysia

The proposed method is to protect the privacy of images in real-time by using encryption, salt key, and blurring techniques. The method is particularly useful in cloud environments where images may be processed or stored remotely. The method uses the Advanced Encryption Standard (AES) algorithm, which is a widely-used encryption method that is known for its security. The AES algorithm is applied to the image data, which is then protected with a unique salt key, making it even more difficult to decipher. In addition to encryption, the method also uses the Gaussian Blur algorithm to blur the image data, making it more difficult to recognize any details that might compromise privacy. This blurring technique can be adjusted to achieve varying degrees of blur, depending on the level of privacy required. Furthermore, the method applies data perturbation, which involves adding random noise to the image data. This helps to further obscure any sensitive information and makes it even more difficult for unauthorized users to extract useful data. Overall, the proposed system is a robust method for protecting image privacy in real-time, and it has many potential applications in various fields, such as healthcare, finance, and law enforcement.

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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On the Existence of Static Equilibria of a Cable-Suspended Load with Non-stopping Flying Carriers

On the Existence of Static Equilibria of a Cable-Suspended L...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Chiara Gabellieri Antonio Franchi Robotics and Mechatronics Department Electrical Engineering Mathematics and Computer Science (EEMCS) Faculty University of Twente Enschede AE The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This work answers positively the question whether non-stop flights are possible for maintaining constant the pose of cable-suspended objects. Such a counterintuitive answer paves the way for a paradigm shift where energetically efficient fixed-wing flying carriers can replace the inefficient multirotor carriers that have been used so far in precise cooperative cable-suspended aerial manipulation. First, we show that one or two flying carriers alone cannot perform non-stop flights while maintaining a constant pose of the suspended object. Instead, we prove that three flying carriers can achieve this task provided that the orientation of the load at the equilibrium is such that the components of the cable forces that balance the external force (typically gravity) do not belong to the plane of the cable anchoring points on the load. Numerical tests are presented in support of the analytical results.

关键词： Task analysis Aircraft Gravity

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Secure Communication Protocols for IoT-Enabled Smart Grids

Secure Communication Protocols for IoT-Enabled Smart Grids

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Innovative Computing, Intelligent Communication and Smart electrical Systems (ICSES), International Conference on

作者： Vandana Soni Naveen Kumar C Virendra Jain G. Praveen Santhoshkumar Yuvasri B Rajendiran M Department of Computer Engineering Shah & Anchor Kutchhi Engineering College Mumbai Maharashtra India Department of Computer Science and Engineering Global Academy of Technology Bengaluru Karnataka India Department Electrical & Electronics Engineering Mandsaur University Madhya Pradesh India Department Electronics and Communication Engineering Nandha Engineering College(Autonomous) Erode Tamil Nadu Department of Computer Science and Engineering R.M.K College of Engineering and Technology Thiruvallur Tamil Nadu Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The integration of the Internet of Things (IoT) into smart grids has changed energy management and distribution, and among its benefits are enhanced operational efficiency and real-time monitoring. However, there are significant security dangers associated with this growing interconnectedness, especially about the communication protocols. The goal of this project is to safeguard Internet of Things (IoT)-enabled smart grids from data breaches and cyberattacks by looking at secure communication methods. We provide a study of the available data integrity checks, authentication methods, and encryption technologies to suit the unique requirements of smart grid infrastructure. Furthermore, we assess how well different protocols mitigate common security threats including denial-of-service attacks, man-in-the-middle attacks, and unauthorized data access assaults. The study's conclusions highlight the importance of lightweight security solutions that can maintain high-performance levels and offer reliable protection at the same time. Advanced secure communication protocols can be implemented to increase the resilience of IoT-enabled devices.

关键词： Protocols Data integrity Authentication Smart grids Encryption Security Internet of Things Reliability computer crime Resilience

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Enhancing Alzheimer's Disease Detection Using Optimized Attribute Selection and Random Forest Classifier for Improved Accuracy

Enhancing Alzheimer's Disease Detection Using Optimized Attr...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： A. Sathiya CH Hussaian Basha Vasanth. S Jasphin Jeni Sharmila P Prabakaran S R. Indhumathi Department of Electronics and Communication Engineering M.Kumarasamy College of Engineering (Autonomous) Karur Department of Electrical and Electronics Engineering SR University Warangal Department of Electronics and communication Engineering Rajalakshmi Institute of Technology Chennai Department of computer science and Engineering Chennai Institute of Technology Chennai Department of Electronics and communication Engineering CMS College of Engineering and Technology Coimbatore Department of Biomedical Engineering Velalar College of Engineering and Technology Erode

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This research aims at improving the identification of Alzheimer's dementia by using the optimized attribute selection method and preliminary diagnosis data-driven learning algorithm, namely Random Forest Classifier. Alzheimer's is a progressive neurological disorder exhibiting barriers in preliminary diagnosis primarily due to the high density and intricate nature of medical information. In this study, attribute selection techniques are used to minimize the data dimensionality, by selecting only key features for classification with low computational complexity and improved model accuracy. The Random Forest Classifier is used because it is accurate, can process big data and because of its diverse uses. Through selecting the most relevant features of the model, it provides a prominent level of precise and acute identification of the early stage of Alzheimer's and could significantly enhance the medical treatment effects. The outcomes from this study indicate that feature optimization can be used interchangeably with preliminary diagnosis data-driven learning algorithms for accurate disease prediction.

关键词： Accuracy Computational modeling Volume measurement Visual analytics Classification algorithms Alzheimer's disease Slabs Random forests Medical diagnostic imaging Monitoring

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Symbiotic control of Uncertain Dynamical Systems: Harnessing Synergy Between Fixed-Gain control and Adaptive Learning Architectures

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IEEE Transactions on Automatic control 2025年

作者： Yucelen, Tansel Sarsilmaz, Selahattin Burak Yildirim, Emre University of South Florida Department of Mechanical Engineering Laboratory for Autonomy Control Information and Systems TampaFL33620 United States Utah State University Department of Electrical and Computer Engineering LoganUT84322 United States

Both fixed-gain control and adaptive learning architectures aim to mitigate the effects of uncertainties. In particular, fixed-gain control offers more predictable closed-loop system behavior but requires the knowledge of uncertainty bounds. In contrast, while adaptive learning does not necessarily require such knowledge, it often results in less predictable closed-loop system behavior compared to fixed-gain control. To this end, this paper presents a novel symbiotic control framework that offers the strengths of fixed-gain control and adaptive learning architectures. Specifically, this framework synergistically integrates these architectures to mitigate the effects of uncertainties in a more predictable manner as compared to adaptive learning alone and it does not require any knowledge on such uncertainties. Both parametric and nonparametric uncertainties are considered, where we utilize neural networks to approximate the unknown uncertainty basis for the latter case. Counterintuitively, the proposed framework has the ability to achieve a desired level of closed-loop system behavior even with an insufficient number of neurons (e.g., when the neural network approximation error is large) or in the face of injudiciously selected adaptive learning parameters (e.g., high leakage term parameters). © 1963-2012 IEEE.

关键词： Closed loop control systems

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