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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Ethan Fettes Pablo G. Madoery Halim Yanikomeroglu Gunes Karabulut Kurt Colin Bellinger Stéphane Martel Khaled Ahmed Sameera Siddiqui Department of Systems and Computer Engineering Non-Terrestrial Networks (NTN) Lab Carleton University Canada Department of Electrical Engineering Poly-Grames Research Center Polytechnique Montréal Montréal Canada National Research Council Canada Canada Satellite Systems MDA Canada Defence Research and Development Canada Canada

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

For decades, satellites have facilitated remote internet of things (IoT) services. However, the recent proliferation of increasingly capable sensors and a surge in the number deployed, has led to a substantial growth in the volume of data that needs to be transmitted via satellites. In response to this growing demand, free space optical communication systems have been proposed, as they allow for the use of large bandwidths of unlicensed spectrum, enabling high data rates. However, optical communications are highly vulnerable to weather-induced disruptions, thereby limiting their high potential. This paper proposes the use of high altitude platform station (HAPS) systems in conjunction with delay-tolerant networking techniques to increase the amount of data that can be transmitted to the ground from satellites when compared to the use of traditional ground station network architectures. The architectural proposal is evaluated in terms of delivery ratio and buffer occupancy, and the subsequent discussion analyzes the advantages, challenges and potential areas for future research.

关键词： Limiting Satellite broadcasting Optical buffering Optical fiber networks Network architecture Internet of Things Optical sensors Proposals Surges Next generation networking

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Satellite Selection for In-Band Coexistence of Dense LEO Networks

arXiv

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arXiv 2025年

作者： Kim, Eunsun Roberts, Ian P. Lee, Taekyun Andrews, Jeffrey G. 6G@UT Research Center Wireless Networking and Communications Group University of Texas Austin United States Wireless Lab Department of Electrical and Computer Engineering UCLA United States

We study spectrum sharing between two dense low-earth orbit (LEO) satellite constellations, an incumbent primary system and a secondary system that must respect interference protection constraints on the primary system. In particular, we propose a secondary satellite selection framework and algorithm that maximizes capacity while guaranteeing that the time-average interference and absolute interference inflicted upon each primary ground user never exceeds specified thresholds. We solve this NP-hard constrained, combinatorial satellite selection problem through Lagrangian relaxation to decompose it into simpler problems which can then be solved through subgradient methods. A high-fidelity simulation is developed based on public FCC filings and technical specifications of the Starlink and Kuiper systems. We use this case study to illustrate the effectiveness of our approach and that explicit protection is indeed necessary for healthy coexistence. We further demonstrate that deep learning models can be used to predict the primary satellite system associations, which helps the secondary system avoid inflicting excessive interference and maximize its own capacity. Copyright © 2025, The Authors. All rights reserved.

关键词： Orbits

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Eco-Friendly Materials for Facile Sensors in Wearable Applications

Eco-Friendly Materials for Facile Sensors in Wearable Applic...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Mina Khalid Muhammad Hamza Zulfiqar Muhammad Qasim Mehmood Rifaqat Hussain Akram Alomainy Department of Biomedical Engineering University of Engineering and Technology Lahore Pakistan Electrical Engineering Department Micro Nano Lab Information Technology University (ITU) of the Punjab Lahore Pakistan Antenna and Electromagnetics Research Group School of Electronic Engineering and Computer Science Queen Mary University of London London UK

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

Electronic waste (e-waste) production is increasing day by day, polluting the environment and causing life-threatening scenarios. Non-rechargeable batteries are a major source of e-waste because of the increasing demand for battery-powered devices. Here, we report the utilization of abandoned batteries in recycling to develop graphite-based conductive paste for textile-based flexible strain sensors. A modified interdigitated electrode (MIDE) structure is developed on fabric using a facile and solvent-free fabrication process. The strain sensors are utilized in wearable applications to monitor the movement of body joints at different angles. The sensors efficiently detect the stain applied by human wrist, knee, and elbow joints with high sensitivity GF > 350. The proposed facile paste from e-waste is a promising candidate for developing green electronics for wearable applications.

关键词： Wrist Electronic waste Capacitive sensors Recycling Batteries Biomedical monitoring Wearable devices

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Simulation and Operation of 6DoF Robotic Arms: a Microservices Approach 16th

Simulation and Operation of 6DoF Robotic Arms: a Microser...

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16th EAI International Conference on Simulation Tools and Techniques, SIMUTools 2024

作者： Munoz-Gonzalez, Tomas Farias, Adalberto Piñero-Fuentes, Enrique Rios-Navarro, Antonio Segura-Ramos, Sergio Linares-Barranco, Alejandro Smart Computer Engineering Research Lab I3US Universidad de Sevilla Seville Spain Systems and Computing Department Federal University of Campina Grande Campina Grande Brazil

ISBN: (纸本)9783031873447

The use of robotic arms in industrial and academic settings has grown exponentially, however, the process of simulating and operating these systems continues to be a significant challenge, particularly for users without prior experience. The learning curve is steep due to the technical complexity associated with programming, calibrating and controlling precise movements, which can be daunting. Robotic devices are usually controlled using proprietary software or ad-hoc low-level programs, hindering their shared use and integration with other applications. This work presents a software application for robotic arm trajectory simulation, with a remote service for allowing access control and arbitration in the use of robots as a shared service. This software allows the design of trajectories in robot and Cartesian coordinate spaces in a simple and convenient way for the user. The service allows the execution of said trajectories whenever the robot is available. Robotic arms ED-RobotAnno and ROS-RobotAnno are presented as use cases for this service. Concepts such as closed-loop motor control or direct robot kinematics are abstracted from the user, allowing the creation of unsupervised learning iterations of closed-loop trajectories or the creation of datasets for supervised training. Moreover, the servitization is based on a heterogeneous ecosystem composed of microservices that use different platforms/languages to provide a novel Web experience to users, while still being flexible to allow easy addition of new robotic arms. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Robotic arms

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Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images

arXiv

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arXiv 2023年

作者： Li, Yanwen Luo, Luyang Lin, Huangjing Heng, Pheng-Ann Chen, Hao The Imsight Ai Research Lab The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Convolutional Neural Networks (CNNs) have shown remarkable progress in medical image segmentation. However, lesion segmentation remains a challenge to state-of-the-art CNN-based algorithms due to the variance in scales and shapes. On the one hand, tiny lesions are hard to be delineated precisely from the medical images which are often of low resolutions. On the other hand, segmenting large-size lesions requires large receptive fields, which exacerbates the first challenge. In this paper, we present a scale-aware super-resolution network to adaptively segment lesions of various sizes from the low-resolution medical images. Our proposed network contains dual branches to simultaneously conduct lesion mask super-resolution and lesion image super-resolution. The image super-resolution branch will provide more detailed features for the segmentation branch, i.e., the mask super-resolution branch, for fine-grained segmentation. Meanwhile, we introduce scale-aware dilated convolution blocks into the multi-task decoders to adaptively adjust the receptive fields of the convolutional kernels according to the lesion sizes. To guide the segmentation branch to learn from richer high-resolution features, we propose a feature affinity module and a scale affinity module to enhance the multi-task learning of the dual branches. On multiple challenging lesion segmentation datasets, our proposed network achieved consistent improvements compared to other state-of-the-art methods. © 2023, CC BY.

关键词： Convolution

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Health Care and Management based on Block chain and Machine Learning

Health Care and Management based on Block chain and Machine ...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Bakh, Ruhi Sarkar, VeenaNair Auguskani, Jerlin Priya Lovelin Poornima, Galiveeti Shetty, Monali N. Raj, Meerjumla Govind Dr. Ambedkar Institute of Management Studies and Research India Iihmr University India Department of Nursing College Aldrab Jazan University Saudi Arabia High Performance Computing Lab School of Computer Science and Information Science Presidency University Bengaluru India Computer Science Engineering Fr. Crce India St. Martin and #39 s Engineering College Survey No. 563 Dulapally Kompally Telangana Secunderabad500100 India

ISBN: (纸本)9798350334357

With the coming of technology, we currently have an immense measure of information accessible in each field, making it conceivable to offer responses to many issues. We will utilize machine learning and block chain technology to resolve issues with healthcare information the executives in this review. With the guide of machine learning, separating just the relevant information from the data is plausible. Using prepared calculations, this is finished. The reliability of information trade turns into a test after this information has been put away. This is where block chain technology is valuable. Block chain technology's agreement guarantees that information is credible and exchanges are secure. By putting the patient at the focal point of the healthcare framework and upgrading the security and interoperability of health information, block chain technology can possibly further develop health care organization. This article fundamentally centres around settling issues with healthcare information the board using Block chain technology and including a few urgent highlights utilizing Machine Learning. The freshest technology, block chain, offers a strong and secure groundwork. With these offices, block chain has gotten some momentum in an assortment of use areas, including the food business, store network the board, energy, government support offices, and healthcare. © 2023 IEEE.

关键词： Health care

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Container lifecycle-aware scheduling for serverless computing

Container lifecycle-aware scheduling for serverless computin...

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作者： Wu, Song Tao, Zhiheng Fan, Hao Huang, Zhuo Zhang, Xinmin Jin, Hai Yu, Chen Cao, Chun National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Elastic scaling in response to changes on demand is a main benefit of serverless computing. When bursty workloads arrive, a serverless platform launches many new containers and initializes function environments (known as cold starts), which incurs significant startup latency. To reduce cold starts, platforms usually pause a container after it serves a request, and reuse this container for subsequent requests. However, this reuse strategy cannot efficiently reduce cold starts because the schedulers are agnostic of container lifecycle. For example, it may ignore soon available containers or evict soon needed containers. We propose a container lifecycle-aware scheduling strategy for serverless computing, CAS. The key idea is to control distribution of requests and determine creation or eviction of containers according to different lifecycle phases of containers. We implement a prototype of CAS on OpenWhisk. Our evaluation shows that CAS reduces 81% cold starts and therefore brings a 63% reduction at 95th percentile latency compared with native scheduling strategy in OpenWhisk when there is worker contention between workloads, and does not add significant performance overhead. © 2021 John Wiley & Sons Ltd.

关键词： Containers

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Adaptive Incentivize for Federated Learning with Cloud-Edge Collaboration under Multi-level Information Sharing

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IEEE Transactions on computers 2025年第07期74卷 2445-2460页

作者： Yuan, Shijing Dong, Beiyu Li, Jie Guo, Song Chen, Hongyang Wu, Chentao Wu, Jie Zhao, Wei China Telecom Research Institute Department of Fundamental Network Operations Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Hong Kong University of Science and Technology Department of Computing Hong Kong Zhejiang Lab Hangzhou311121 China Temple University Department of Computer and Information Sciences United States CAS Shenzhen Institute of Advanced Technology Shenzhen518055 China

Federated Learning with Cloud-Edge Collaboration (FL-CEC) has emerged as a cutting-edge paradigm in distributed learning. Efficient resource investment incentive mechanisms are crucial to encouraging clients in FL-CEC to contribute the necessary data and computational resources for training. However, existing studies are inadequate in meeting the incentive design requirements under multi-level information-sharing scenarios. Moreover, current works often rely on specific functional relationships between resource investment and global model accuracy. To bridge these gaps, this paper investigates the incentive problem for data and computational resource investment under multi-level information-sharing levels. We design a resource investment incentive mechanism based on a weighted potential game without depending on any specific functional relationship between data investment and model accuracy. Furthermore, we propose four algorithms to solve resource investment strategies for different levels of information sharing. The complexity and convergence rates of the proposed algorithms are thoroughly analyzed. Finally, we construct a simulation incentive platform on the Aliyun. Extensive evaluations demonstrate that the proposed scheme effectively enhances social welfare, and improves collaborative training accuracy and efficiency. © 1968-2012 IEEE.

关键词： Open access

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Reinforcement Learning-Enhanced Active Disturbance Rejection Control for Mobile Robot Trajectory Tracking

Reinforcement Learning-Enhanced Active Disturbance Rejection...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： H. Usama M. Nasir R. Fareh J. Ghommam S. Khadraoui M. Bettayeb Research Institute of Sciences and Engineering University of Sharjah Sharjah UAE Department of Electrical Engineering University of Sharjah Sharjah UAE Department of Electrical and Computer Engineering Sultan Qaboos University Oman Control and Energy Management Laboratory (CEM Lab) Ecole National d'Ingenieur de Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

In recent years, the application of robotics has significantly advanced many fields, providing robust performance and efficiency in complex tasks without the need for human intervention. Robot control is a key area of robotics that has received a lot of interest and technological development. Active Disturbance Rejection Controllers (ADRCs) are widely adopted due to several features, including their ability to maintain robust performance and effectively reject disturbances. The manual tuning of controller parameters is time-consuming and highlights the need for automation. This paper presents an innovative approach to tune the parameters of ADRC using Reinforcement Learning (RL) based on Deep Deterministic Policy Gradient (DDPG) for a Differential Drive Mobile Robot (DDMR). The RL agent learns the ideal parameters of the ADRC through real-time, iterative interactions with the simulated environment, improving ADRC's performance without manual tuning. Simulation results demonstrate the effectiveness of the proposed idea in improving the trajectory tracking performance. Combining RL and ADRC provides a promising automated controller tuning solution, opening the door to more intelligent and adaptive robotic systems. 10.0pt.

关键词： Robust control Trajectory tracking Simulation Reinforcement learning Manuals Scattering parameters Mobile robots Artificial intelligence State estimation Tuning

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Hybrid-Excited, Variable-Flux, and Inter-Modular Biased-Flux Motors: A Comparative Analysis

Hybrid-Excited, Variable-Flux, and Inter-Modular Biased-Flux...

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Iranian Conference on Electrical engineering (ICEE)

作者： Mohammad Amirkhani Ehsan Farmahini Farahani Alireza Eikani Mojtaba Mirsalim Javad Shokrollahi Moghani Department of Electrical Engineering Electrical Machines and Transformers Research Laboratory Amirkabir University of Technology Tehran Iran Electrical Power Research Group School of Engineering Newcastle University Newcastle upon Tyne U.K Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

This paper analyses three prominent biased-flux permanent magnet (BFPM) motors and compares them with each other. The structures of these BFPMs are different from each other, but they share the same operation principle. These structures are, namely, hybrid-excited multi-tooth slot-PM (HETSPM), multi-tooth variable flux reluctance (TVFR), and inter-modular PM (IMPM). The reason for this selection of structure is because of complementary features that each one possesses. For example, the HETSPM motor places in between the TVFR and IMPM motors in terms of PM consumption and demagnetization. However, the IMPM structure produces high amount torque at high efficiency. The demagnetization of the HETSPM and IMPM structures is also investigated and is shown that, in the similar situation, the HETSPM provides a safer operation environment for the PMs. Two-dimensional finite element method is used to extract the important profiles of the mentioned structures.

关键词： Electrical engineering Magnetic flux Torque Motors Permanent magnets Finite element analysis Topology

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