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International Conference on Autonomous Robots and Agents, ICARA

作者： Marco Baracca Andre Meixner Giuseppe Averta Paolo Salaris Tamim Asfour Matteo Bianchi Dept. of Information Engineering Research Center “E. Piaggio” University of Pisa Pisa Italy Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Dep. of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Dual-arm manipulation is a key enabler for significantly enhancing the interaction between humans and robots, and their capabilities to purposefully shape the surrounding environment. However, the spatiotemporal coordination between the motion of the hands required for this type of actions makes their planning not trivial. A proper definition of these coordination patterns moving from the human example could simplify their translation on the robot side, fostering the generation of effective bimanual tasks. In this work, we propose Multivariate functional Principal Component Analysis (MfPCA) as a mathematical tool to encode inter-hands temporal kinematic covariations in terms of principal spatiotemporal coordination patterns in the Cartesian domain. We compared these patterns extracted from a dataset of human bimanual tasks with those resulting from the usage of classical fPCA, applied independently to each hand (univariate fPCA). We found that MfPCA allows for a better classification of the tasks, with respect to a state of the art taxonomy. For what concerns motion planning, MfPCA and fPCA yield similar accuracy in the reconstruction of the motion, but with a smaller number of principal components needed in the MfPCA case. These results, although preliminary, can open interesting perspectives for the usage of MfPCA for human-like bimanual motion planning and control of robotic manipulators, as well as for action recognition, to enable a more effective human-robot interaction.

关键词： Hands Translation Shape Robot kinematics Taxonomy Human-robot interaction Functional analysis Planning Spatiotemporal phenomena Principal component analysis

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Is Almost Sure Asymptotic Stabilizability Sufficient for Input Delay Tolerability of Nonlinear Stochastic systems?

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IEEE Transactions on Automatic control 2025年

作者： Yu, Xin Lin, Wei Jiangsu Normal University School of Electrical Engineering and Automation Xuzhou221116 China Case Western Reserve University Dept. of Electrical Computer and Systems Engineering ClevelandOH44106 United States

This article considers the problem of input delay tolerance (IDT) for stochastic control systems that are almost surely asymptotically stabilizable but inherently nonlinear (e.g., neither locally Lipschitz continuous nor linear growth and non-smoothly stabilizable). Using the notions of almost sure forward completeness [42] and weighted homogeneity, together with the existence of the solutions of continuous time-delay stochastic systems, we prove under Lyapunov-like conditions that : i) almost sure asymptotic stabilizability by nonsmooth homogeneous state feedback and dominated homogeneity of degree zero imply input delay tolerability of stochastic nonlinear systems, and ii) almost sure asymptotic stabilizability by homogeneous output feedback also implies input delay tolerability of the same class of multi-input/multi-output stochastic nonlinear systems. Our results contain not only the existing IDT results for the global Lipschitz or linear growth stochastic systems as special cases, but also important applications to significant classes of stochastic control systems with unstabilizable and undetectable linearization. © 1963-2012 IEEE.

关键词： Stochastic control systems

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Incorporating the Com-B Model for Behavior Change into an Agent-Based Model of Smoking Behaviors: An Object-Oriented Design 24

Incorporating the Com-B Model for Behavior Change into an Ag...

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Proceedings of the Winter Simulation Conference

作者： David Tian Hazel Y. Squires Charlotte Buckley Duncan Gillespie Harry Tattan-Birch Lion Shahab Robert West Alan Brennan Jamie Brown Robin C. Purshouse Dept. of Automatic Control and Systems Engineering University of Sheffield UK Sheffield Centre for Health and Related Research School of Medicine and Population Health University of Sheffield UK Institute of Epidemiology and Health University College London UK

ISBN: (纸本)9798331534202

Modeling trajectories in cigarette smoking prevalence, initiation and quitting for populations and subgroups of populations is important for policy planning and evaluation. This paper proposes an agent-based model (ABM) design for simulating the smoking behaviors of a population using the Capability, Opportunity, Motivation - Behavior (COM-B) model. Capability, Opportunity and Motivation are modeled as latent composite attributes which are composed of observable factors associated with smoking behaviors. Three forms of the COM-B model are proposed to explain the transitions between smoking behaviors: initiating regular smoking uptake, making a quit attempt and quitting successfully. The ABM design follows object-oriented principles and extends an existing generic software architecture for mechanism-based modeling. The potential of the model to assess the impact of smoking policies is illustrated and discussed.

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Evaluation of a Terrestrial Laser Scanner According to the ISO 17123-9 Standard

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Journal of Surveying engineering 2025年第3期151卷

作者： Polo, María-Eugenia Sánchez-Ríos, Alonso Pardo, Pedro J. Dept. of Graphic Expression Centro Universitario de Mérida Universidad de Extremadura Santa Teresa de Jornet 38 MeridaE06800 Spain Dept. of Computer and Networks Systems Engineering Centro Universitario de Merida Universidad de Extremadura Santa Teresa de Jornet 38 MeridaE06800 Spain

Currently, the use of terrestrial laser scanners (TLSs) for point cloud recording is widespread in the fields of architecture, engineering, and construction (AEC), with dimensional tolerance requirements ranging from a few millimeters to several centimeters. The technical specifications of TLSs define the nominal three-dimensional (3D) point accuracy based on distance. In practice, both the design of the TLS and the proprietary software calculation algorithms are key factors in the competition between manufacturers and are therefore not public. This, together with the multiple determinants of each type of work, makes it difficult to assume those values;so, it becomes necessary to use external models to study TLS error sources and standardized evaluation test procedures to determine and evaluate the precision of a TLS and its measurement uncertainties. In this work, we use the international standard ISO 17123-9 to evaluate a Leica TLS RTC360 scanner. We consider observation distances from 10 to 50 m, and use targets with different sizes and reflectance values measured with a Micro-Hyperspec hyperspectral camera. The results show that the use of optimal materials increases the range of the instrument used to automatically detect targets. Additionally, the uncertainties range between 2.7 and 3.9 mm (distances between 10 and 40 m), reaching values of 7.7 mm when the distance is extended to 50 m. Finally, some recommendations for improving the field procedure of this standard are described, with a focus on the interests of surveyors and highlighting the importance of the type of target used. © 2025 American Society of Civil Engineers.

关键词： Laser recording

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Key Exchange in the Quantum Era: Evaluating a Hybrid System of Public-Key Cryptography and Physical-Layer Security

Key Exchange in the Quantum Era: Evaluating a Hybrid System ...

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Annual Conference on Information Sciences and systems (CISS)

作者： Paul Staat Meik Dörpinghaus Azadeh Sheikholeslami Christof Paar Gerhard Fcllwcis Dennis Goeckel Max Planck Institute for Security and Privacy Bochum Germany Vodafone Chair Mobile Communications Systems Technische Universität Dresden Dresden Germany Math and Computer Science Department Suffolk University Boston Dept. Electrical and Computer Engineering University of Massachusetts Amherst USA

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

Today's information society relies on cryptography to achieve security goals such as confidentiality, integrity, authentication, and non-repudiation for digital communications. Here, public-key cryptosystems play a pivotal role to share encryption keys and create digital signatures. However, quantum computers threaten the security of traditional public-key cryptosystems as they can tame computational problems underlying the schemes, i.e., discrete logarithm and integer factorization. The prospective arrival of capable-enough quantum computers already threatens today's secret communication in terms of their long-term secrecy when stored to be later decrypted. Therefore, researchers strive to develop and deploy alternative *** this work, we evaluate a key exchange protocol based on combining public-key schemes with physical-layer security, anticipating the prospect of quantum attacks: If a powerful quantum attacker cannot immediately obtain a private key, legitimate parties have a window of short-term secrecy to perform a physical-layer jamming key exchange (JKE) to establish a long-term shared secret. Thereby, the protocol constraints the computation time available to the attacker to break the employed public-key cryptography. In this paper, we outline the protocol, discuss its security, and point out challenges to be resolved.

关键词： computers Protocols Quantum computing Quantization (signal) Non-repudiation Public key cryptography Security Time factors Jamming Signal resolution

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD)

Blockchain Integration for Robust Intellectual Property Prot...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： P. Chinnasamy Subashini. B Nijanthan Nijanthan Madasamy Raja. G Devulapally Swetha Sreenivasulu Reddy L Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Data Science and Business Systems School of Computing SRMIST India Computer Science and Engineering Paavai Engineering College Namakkal Tamilnadu India Information Technology Paavai Engineering College Namakkal Tamilnadu India Dept. of Computer Science and Engineering Vaagdevi Engineering College Telangana India Department of Mathematics School of Advanced Sciences Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms.

关键词： Patents Technological innovation Costs Smart contracts Intellectual property Product development Blockchains Security Protection Resilience

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2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring System using Mobile Application

2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring Sy...

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International Electrical engineering Congress (iEECON)

作者： Pruek Vanna-Iampikul Visutr Boonnateephisit Somphop Limsoontharakul Jednipat Moonrinta Matthew Dailey Mongkol Ekpanyapong Department of Electrical Engineering Burapha University Chonburi Thailand School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Dept. Computational Science Prince of Songkla University Songkhla Thailand Computer Science Field of Study School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Information & Communication Tech. School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Dept. of Industrial Systems Engineering School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

LiDAR technology has rapidly advanced, offering highly accurate solutions for tree geometry estimation, which is particularly valuable in agriculture and forestry. Its ability to capture precise 2D and 3D data has revolutionized tree detection and measurement. In Eucalyptus plantations, continuous monitoring is critical to maximizing growth, enhancing paper production, and mitigating environmental damage. However, existing solutions face challenges in real-time monitoring due to factors such as parasitic interference, low-light conditions, and uneven terrain. In this work, we present a novel, end-to-end framework that enhances Eucalyptus tree measurement and monitoring. Our framework significantly improves data collection efficiency by allowing measurements at walking speed, reducing measurement distance by 50% compared to traditional manual approaches. This system integrates 2D-LiDAR with additional environmental sensors to extract key tree parameters, including circumference, position, and tree count. The collected data is stored in real-time and uploaded to a centralized server for further analysis. Experimental evaluations across 160 trees, under five distinct farm conditions, demonstrate that our system achieves 89% accuracy in tree diameter estimation and 98.5% accuracy in tree counting, significantly outperforming traditional methods. These results establish our framework as a highly effective solution for improving the accuracy and efficiency of tree monitoring in challenging environments.

关键词： Geometry Accuracy Laser radar Plantations Estimation Vegetation Production Real-time systems Time measurement Monitoring

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Enhanced deep learning framework for real-time pain assessment using multi-modal fusion of facial features and video streams

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engineering Applications of Artificial Intelligence 2025年 154卷

作者： El-Ghaish, Hany Al-Basiouny, Mohamed Yousry Alshewimy, Mahmoud A.M. Tanta University Faculty of Engineering Computer and Control Engineering Dept. Tanta Egypt

This paper introduces an enhanced deep learning multimodal framework for real-time pain assessment that addresses the limitations of the single-mode approaches. Specifically, time-related disadvantages and its poor temporal analysis, noted in recent studies. The proposed framework fuses VGG16 (Visual Geometry Group 16-layer network), a pre-trained convolutional neural network (CNN), with a bidirectional long short-term memory network (Bi-LSTM) augmented with attention mechanisms. VGG16 has a proven ability to capture fine-grained spatial information in facial expressions to extract powerful spatial features, whereas the Bi-LSTM augmented with attention mechanisms because of its computational efficiency and concisely captures temporal features. Moreover, adaptive boosting has used seeking generalization across a broad base of settings and Gradient-weighted Class Activation Mapping (Grad-CAM) to provide comprehensible and interpretable visual explanations of the decision-making processes of the framework. Extensive evaluations were conducted on diverse popular benchmark datasets: BioVid, UNBC-McMaster, and MIntPAIN. Our framework achieves an AUC (the Area under the Receiver Operating Characteristic Curve) of 0.99 and 99% cross-validation accuracy, corresponding to a 12.5% improvement in accuracy and a 20% reduction in false positives relative to state-of-the-art convolutional neural network–long short-term memory (CNN–LSTM) models. Real-time deployment on an NVIDIA Jetson Nano yields 120 ms (ms) inference per frame with less than 1% accuracy loss, demonstrating its feasibility for edge-based telemedicine. Combined with explainability via Grad-CAM and privacy-preserving vision-only inputs, this framework offers an ethical, scalable solution for nonverbal pain detection. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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LiDAR-Based Climate Change Imaging in Geoscience Using Spatio Extreme Fuzzy Gradient Model

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Remote Sensing in Earth systems Sciences 2025年第2期8卷 465-472页

作者： Krushnasamy, V.S. Al-Omari, Omaia Sundaram, Ajith Varghese, Issac K. Muniyandy, Elangovan Rao, M. V. A. L. Narasimha Dept. of EIE Dayananda Sagar College of Engineering Bangalore India Information Systems Department College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Amrita School of Business Amrita Vishwa Vidyapeetham Kochi India T A Pai Management Institute Manipal Academy of Higher Education Manipal India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of MBA Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522302 India

In order to estimate high-resolution air temperatures, computationally demanding procedures are required because climate models do not provide the resolution required for urban climate studies. Data-driven methods, on the other hand, provide air temperature downscaling that is quicker and more precise. Lidar technology has become a vital tool for researching these atmospheric components because of its capacity to measure backscattered signals and investigate the atmosphere at extremely high spatial and temporal resolution. However, machine learning has become a potent technique for sophisticated data processing and analysis because of the complexity and diversity of lidar technology. This research proposes a novel technique in urban–rural interface analysis in remote sensing by machine learning (ML) model. Here the input is collected as urban–rural landscape data and processed for noise removal as well as normalisation. Then these data features have been extracted and classified using a spatio extreme fuzzy gradient model with reinforcement Markov vector perceptron neural networks. Experimental analysis has been carried out for various geographical regions in terms of training accuracy, average precision, recall, RMSE, and F-1 score. The proposed model attained an F-1 score of 92%, training accuracy of 98%, average precision of 95%, recall of 93%, and RMSE of 55%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Lidar technology Machine learning model Reinforcement Markov vector Remote sensing Spatio extreme fuzzy gradient

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Real-Time IoT Cybersecurity using Machine Learning-based AI Threat Detection System to Train Generative Robots

Real-Time IoT Cybersecurity using Machine Learning-based AI ...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： P Srilakshmi Koushik Reddy Chaganti Talachendri Suryam Sweety Julia I D. Chaithanya Jami Kavitha Department of CSE G Pulla Reddy Engineering College Dept. of Computer and Information Systems University of the Cumberlands Department of CSE (DATA SCIENCE) Vignan Institute of Technology and Science Department of AI&ML Malla Reddy University Department of CSE CVR College of Engineering Department of CSE Sanketika Vidya Parishad Engineering College

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Existing security protocols encounter major cybersecurity difficulties because the online devices' growing popularity continues to spread across networks. These security protocols are ineffective because they do not properly handle current cyber threats. The main goal of this study involves developing enhanced IoT cybersecurity through the development of a threat detection system which brings together adversarial training and deep learning models (CNN-LSTM) and Federated Learning (FL). The system enables distributed Internet of Things devices to work on security model development through Federated Learning while maintaining total privacy of their information. Security procedures controlled by generative artificial intelligence robots alongside real-time attack protection functions decrease security response durations. Through its Federated CNN-LSTM model the system upholds a 1.2% false positive rate alongside a 98.3% accuracy evaluation and 160 milliseconds of exact threat tracking time. The designed system sustains a minimal occurrence of incorrect alarm activations. The developed system provides real-time security for the Internet of Things framework because it enables adaptive protection systems while preserving user privacy in current IoT settings.

关键词： Training Privacy Protocols Federated learning Real-time systems Threat assessment Internet of Things computer security Protection Robots

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