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Guest Editorial Special Issue on Near-Field Communications (NFCs) in Internet of Everything

IEEE Internet of Things Journal 2025年第12期12卷 18459-18460页

作者： Pin-Han Ho Miaowen Wen Zhiguo Ding Marco Di Renzo Wei Duan Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada School of Electronic and Information Engineering South China University of Technology Guangzhou China Department of Computer Communication Engineering Khalifa University Abu Dhabi UAE Intelligent Physical Communications group in the Laboratory of Signals and Systems (L2S) Paris-Saclay University Gif-sur-Yvette France School of Information Science and Technology Nantong University Nantong China

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Enhanced Gesture Recognition Through Graph-Based Multimodal Fusion

Enhanced Gesture Recognition Through Graph-Based Multimodal ...

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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Mobeen Ur Rehman Talha Ilyas Lakmal Seneviratne Irfan Hussain KU Center for Autonomous Robotic Systems Khalifa University Abu Dhabi United Arab Emirates Monash Medical AI Group Faculty of Engineering and Computer Science Clayton 3168 Monash University Victoria Australia

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

This study introduces an advanced framework for recognizing hand gestures from a first-person view, leveraging the integration of multimodal data including optical flow, pose, depth, and RGB video recordings. Adeptly navigating the challenges and opportunities presented by the integration of multimodal data. At its core, the framework employs two pivotal components: a cross-attention based adaptive graph convolutional network and relational graph interactions for modality fusion. The former is designed to extract features from skeleton-based gesture data, ensuring a nuanced capture of hand movements by emphasizing the interconnections within the hand's skeletal structure. The latter component innovatively models each output modality feature as a node in a fully connected relational graph, facilitating the fusion of heterogeneous data types through dynamic interactions between modalities. This approach allows for the leveraging of each data type's strengths and the mitigation of their weaknesses, significantly enhancing the system's classification accuracy and robustness. Tested on a public benchmark dataset, the framework achieved a remarkable accuracy of 98.48%, demonstrating its efficacy. Moreover, it proves resilient, maintaining strong performance (93.48% accuracy) even in scenarios where only one modality is available, highlighting its potential for real-world applications. This advancement sets a new benchmark in hand gesture recognition, promising future developments in multimodal data fusion.

关键词： Accuracy Computational modeling Prevention and mitigation Gesture recognition Benchmark testing Signal processing Feature extraction Robustness Video recording Optimization

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Opportunistic Constant-Time Underwater Acoustic Network Discovery

Opportunistic Constant-Time Underwater Acoustic Network Disc...

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Underwater Communications and Networking (UComms)

作者： Nils Morozs Florian Mahieu Benjamin T. Henson Lu Shen Paul D. Mitchell Yuriy Zakharov Tim C. Tozer Benoît Parrein Underwater Information Systems Group School of Physics Engineering and Technology University of York York UK Nantes Université Polytech Nantes Computer Science LS2N CNRS 6004 Nantes France

ISBN: (数字)9798331542511

ISBN: (纸本)9798331542528

Network discovery (ND) is a crucial task of underwater acoustic networks (UANs). When the UAN nodes are initially deployed, how can they discover the network topology and establish multi-hop routes to the surface gateway without any a priori information? To this end, we propose an opportunistic protocol, called OCTAVE, which has a fixed runtime specified at the start of ND that cannot be exceeded, regardless of the network size, density or channel conditions. It does not require clock synchronisation among the network nodes; instead, the gateway specifies the duration of the three stages of OCTAVE ND as part of its probe transmissions. Simulations show that OCTAVE provides a significantly higher probability of discovering all nodes of a network in the presence of random link fading, compared with a sequential collision-free ND protocol. OCTAVE was implemented in hardware and a 6-node UAN was deployed in a lake on 9 May 2024. This lake trial demonstrated successful node discovery and fixed time operation in challenging channel conditions and highly time-variable node connectivity.

关键词： Underwater communication Runtime Network topology Spread spectrum communication Logic gates Lakes Routing protocols Synchronization Underwater acoustics Probes

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Computationally Efficient Predictive Control Based on ANN State-Space Models

Computationally Efficient Predictive Control Based on ANN St...

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IEEE Conference on Decision and Control

作者： Jan H. Hoekstra Bence Cseppento Gerben I. Beintema Maarten Schoukens Zsolt Kollár Roland Tóth Dept. of Electrical Engineering at the Eindhoven Control Systems group University of Technology The Netherlands Dept. of Measurement and Information Systems Budapest University of Technology and Economics Hungary Institute for Computer Science and Control Budapest Hungary

Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for the identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinear model predictive control (NMPC), the resulting nonlinear optimization problem is often overly complex due to the size of the network, requires the use of high-order observers to track the states of the ANN model, and the overall control scheme does not exploit the available autograd tools for these models. In this paper, we propose an efficient approach to auto-convert ANN statespace models to linear parameter-varying (LPV) form and solve predictive control problems by successive solutions of linear model predictive problems, corresponding to quadratic programs (QPs). Furthermore, we show how existing deep-learning methods, such as SUBNET that uses a state encoder, enable efficient implementation of MPCs on identified ANN models. Performance of the proposed approach is demonstrated by a simulation study on an unbalanced disc system.

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Robot-mediated intervention can assist children with autism to develop visual perspective taking skills

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Paladyn 2021年第1期12卷 87-101页

作者： Lakatos, Gabriella Wood, Luke Jai Syrdal, Dag Sverre Robins, Ben Zaraki, Abolfazl Dautenhahn, Kerstin Adaptive Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom Cardiff University CardiffCF24 3AA United Kingdom Departments of Electrical and Computer Engineering Systems Design Engineering University of Waterloo WaterlooON N2L 3G1 Canada

In this work, we tested a recently developed novel methodology to assist children with Autism Spectrum Disorder (ASD) improve their Visual Perspective Taking (VPT) and Theory of Mind (ToM) skills using the humanoid robot Kaspar. VPT is the ability to see the world from another person's perspective, drawing upon both social and spatial information. Children with ASD often find it difficultto understand that others might have perspectives, viewpoints and beliefs that are different from their own, which is a fundamental aspect of both VPT and ToM. The games we designed were implementedas the first attempt to study if these skills can be improved in children with ASD through interacting with a humanoid robot in a series of trials. The games involved a number of different actions with the common goal of helping the children to see the world from the robot's perspective. Childrenwith ASD were recruited to the study according to specific inclusion criteria that were determined in a previous pilot study. In order to measure the potential impact of the games on the children, three pre- and post-tests (Smarties, Sally-Anne and Charlie tests) were conducted with the children Our findings suggest that children with ASD can indeed benefit from this approach of robot-assistedtherapy. © 2021 Gabriella Lakatos et al., published by De Gruyter.

关键词： Anthropomorphic robots

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Electric Power Industry Challenges due to Increasing Shares of Inverter-Based Resources in Power systems

Electric Power Industry Challenges due to Increasing Shares ...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Sajadi, A. Ranola, J.P. Kenyon, R.W. Hodge, B.M. Mather, B. BoulderCO80303 United States Group Palo AltoCA94304 United States Electrical Computer and Energy Engineering Department BoulderCO80309 United States Grid Planning and Analysis Center GoldenCO80401 United States Integrated Devices and Systems Group GoldenCO80401 United States

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

The large-scale integration of variable renewable energy technologies around the world is forcing electric power systems into an unprecedented transition. This paper reports on power grid modernization and reliability issues related to the planning and operation of power systems with high levels of variable renewable energy. The contents of this paper stem from recent qualitative research in the form of a summary of an industry survey. The technical feedback from the interviewed 37 industry experts helped us identify 12 key areas of potential concern, which are discussed throughout this paper. © 2022 IEEE.

关键词： Electric inverters

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Stores are Liable for Their Robots!? An Empirical Study on Liability in HRI with an Anthropomorphic Frontline Service Robot

Stores are Liable for Their Robots!? An Empirical Study on L...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Philip Busch Jérôme Kirchhoff Judith S. Heinisch Klaus David Oskar von Stryk Janine Wendt Department of Law and Economics Civil and Company Law Group Technical University of Darmstadt Germany Department of Computer Science Simulation Systems Optimization and Robotics Group Technical University of Darmstadt Germany Department of Electrical Engineering and Computer Science Chair for Communication Technology University of Kassel Germany

Everyday life scenarios where non-expert users (e.g., customers) are confronted with frontline service robots will become more and more likely. In particular, misunderstandings and incidents may occur during these interactions because of wrong expectations of the robot’s capabilities. Current applicable laws are based on technological assumptions from prior decades unsuitable to modern robotics and AI. The new AI Act as a part of the solution to this is still in development. In addition to the pure legal view, a technological viewpoint may be beneficial for establishing a fitting, trustful, and, thus, acceptable technology liability law. This work contributes to this by empirically evaluating the service robot non-expert user’s liability expectations, the use of robots, and well-being. The results in a DIY store environment significantly show that the store deploying the robot should be liable if an incident happens. Further, we examined that even a minor simulated incident affected the participants’ emotions and moods. Consequently, this influences their perception of liability while not mitigating their acceptance of frontline service robots.

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Simulating Cyber Attacks and Designing Malware using Python 10

Simulating Cyber Attacks and Designing Malware using Python

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10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Ankit, Akash Inder, Shubham Sharma, Anuj Johari, Rahul Vidyarthi, Deo Prakash University School of Information Communication and Technology Guru Gobind Singh Indraprastha University SWINGER: Security Wireless IoT Network Group of Engineering and Research Dwarka New Delhi110078 India MelbourneVIC3000 Australia Jawaharlal Nehru University Parallel and Distributed System Lab School of Computer and Systems Sciences New Delhi110067 India

ISBN: (纸本)9781665490993

In this Internet era, we have become heavily dependent on the World Wide Web and it has become a basic amenity for humans. A normal user can be trapped easily in this vicious web if not careful enough. It is a field that requires users to be on their toes every time they try to connect to this enormous interconnection of networks. Reports and Surveys have said that in the coming years, Cybercrime could become the biggest threat. In this proposed work, the authors have deployed applications and designed malware for the purpose of simulating various kinds of cyber-attacks so that they could study their effects & affects and ultimately devise solutions to resolve the vulnerabilities in a system. These applications included networking concepts & protocols and a software based keylogger which was developed with no malicious intent, but to analyse the architectural view or design of a malware and to study the stages of a malware attack like entry, distribution, exploitation, infection and execution. Efforts have been made to demonstrate spooting attacks with an idea of identifying vulnerabilities and security loopholes in a particular system that can be exploited by an attacker if not resolved within time. © 2023 IEEE.

关键词： Network security

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Optimizing UAV-UGV Coalition Operations: A Hybrid Clustering and Multi-Agent Reinforcement Learning Approach for Path Planning in Obstructed Environment

arXiv

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arXiv 2024年

作者： Brotee, Shamyo Kabir, Farhan Razzaque, Md. Abdur Roy, Palash Mamun-Or-Rashid, Md. Hassan, Md. Rafiul Hassan, Mohammad Mehedi Green Networking Research Group Department of Computer Science and Engineering University of Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh College of Arts and Sciences University of Maine at Presque IsleME04769 United States Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

One of the most critical applications undertaken by coalitions of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) is reaching predefined targets by following the most time-efficient routes while avoiding collisions. Unfortunately, UAVs are hampered by limited battery life, and UGVs face challenges in reachability due to obstacles and elevation variations. Existing literature primarily focuses on one-to-one coalitions, which constrains the efficiency of reaching targets. In this work, we introduce a novel approach for a UAV-UGV coalition with a variable number of vehicles, employing a modified mean-shift clustering algorithm to segment targets into multiple zones. Each vehicle utilizes Multi-agent Deep Deterministic Policy Gradient (MADDPG) and Multi-agent Proximal Policy Optimization (MAPPO) — two advanced reinforcement learning algorithms — to form an effective coalition for navigating obstructed environments without collisions. This approach of assigning targets to various circular zones, based on density and range, significantly reduces the time required to reach these targets. Moreover, introducing variability in the number of UAVs and UGVs in a coalition enhances task efficiency by enabling simultaneous multi-target engagement. The results of our experimental evaluation demonstrate that our proposed method substantially surpasses current state-of-the-art techniques, nearly doubling efficiency in terms of target navigation time and task completion rate. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Multiband Patch Antenna Design Using Nature-Inspired Optimization Method

IEEE Open Journal of Antennas and Propagation

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IEEE Open Journal of Antennas and Propagation 2021年 2卷 151-162页

作者： Boursianis, Achilles D. Papadopoulou, Maria S. Pierezan, Juliano Mariani, Viviana C. Coelho, Leandro S. Sarigiannidis, Panagiotis Koulouridis, Stavros Goudos, Sotirios K. ELEDIA@AUTH Research Group School of Physics Aristotle University of Thessaloniki Thessaloniki54124 Greece Department of Electrical Engineering Federal University of Parana Curitiba80060-000 Brazil Industrial and Systems Engineering Graduate Program and Mechanical Engineering Graduate Program Pontifical Catholic University of Parana Curitiba80215-901 Brazil Department of Electrical and Computer Engineering University of Western Macedonia Kozani501 00 Greece Electrical and Computer Engineering Department University of Patras Patras26504 Greece

Radio frequency energy harvesting has attracted considerable interest as a technique of enabling self-powered wireless networks. This technique faces several challenges, such as the receiving and the rectifying modules of a rectenna system. Multiband antennas provide several comparative advantages against the goal of maximizing the amount of energy harvesting. In this work, we present a multiband microstrip patch antenna with three slits operating in the LoRaWAN (Long Range Wide Area Network) and the cellular (GSM-1800 and UMTS) communication frequency bands. A feasible solution of the antenna is obtained by the application of a recently introduced nature-inspired optimization technique, namely the Coyote Optimization Algorithm. The proposed antenna operates satisfactorily in the LoRaWAN (Long Range Wide Area Network) and the cellular (GSM-1800 and UMTS) communication frequency bands. Measured results of the proposed antenna exhibit an acceptable performance (multiband frequency operation, maximum gain of 3.94 dBi, broadside operation) and demonstrate features of operation, which make it a strong candidate for various RF energy harvesting applications. © 2020 IEEE.

关键词： Microstrip antennas

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