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Computing Power in the Sky: Digital Twin-assisted Collaborative Computing with Multi-UAV Networks

IEEE Transactions on Vehicular technology 2025年

作者： Wang, Chao Han, Yu Zhang, Long Jia, Ziye Zhang, Hongliang Hong, Choong Seon Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing211111 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Kyung Hee University Department of Computer Science and Engineering Gyeonggi-do Yongin-si17104 Korea Republic of University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The collaboration of computing powers (CPs) among unmanned aerial vehicles (UAVs)-mounted edge servers is essential to handle data-intensive tasks of user equipments (UEs). This paper presents a multi-UAV computing power network (CPN) that orchestrates the CP resources of edge servers to cooperatively process tasks through collaborative computing. Due to the frequent interactions among edge servers and the increased complexities for large-scale task executions, we propose a digital twin (DT)-driven multi-UAV CPN framework, where the aerial DT network is created as a digital replica of the multi-UAV network to assist collaborative computing. A joint optimization problem of the service association, offloading power control, task partitions, CP allocation, and UAV trajectory design is formulated to minimize the long-term average delay of UEs. We use the Lyapunov technique to tackle the long-term energy constraint, forming a tractable optimization problem, which is modeled as a Markov decision process to balance the UAV's deficit queue stability and the UE's task completion delay. A hybrid soft actor-critic based deep reinforcement learning algorithm is developed to optimize the joint design by sampling the hybrid action from the Gumbel and Gaussian distributions. Simulation results verify that the multi-UAV CPN contributes to significant performance gains when supported by DTs. © 2025 IEEE.

关键词： Markov processes

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Graph Convolutional Network with Long Time Memory for Skeleton-based Action Recognition

Graph Convolutional Network with Long Time Memory for Skelet...

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International Conference on computer Supported Cooperative Work in Design

作者： Yanpeng Qi Chen Pang Yiliang Liu Hong Liu Lei Lyu School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Academy of Education Recruitment and Examination Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

Skeleton-based action recognition task has been widely studied in recent years. Currently, the most popular researches use graph convolutional network (GCN) to solve this task by modeling human joints data as spatio-temporal graph. However, a large number of long-term temporal motion relationships cannot be effectively captured by GCN. Thus, recurrent neural network (RNN) is introduced to solve this defect. In this work, we propose a model namely graph convolutional network with long time memory (GCN-LTM). Specifically, there are two task streams in our proposed model: GCN stream and RNN stream, respectively. The GCN stream aims to capture the spatial motion relationships as well as the RNN stream focuses on extracting the long-term temporal patterns. In addition, we introduce the contrastive learning strategy to better facilitate feature learning between these two streams. The multiple ablation experiments have verified the feasibility of our proposed model. Numerous experiments show that the proposed model is superior to the current state-of-the-art method under two large-scale datasets including NTU-RGBD and NTU-RGBD-120.

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Swarm Intelligence for Multi-Robot Coordination in Industrial Environments

Swarm Intelligence for Multi-Robot Coordination in Industria...

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International Conference on science technology engineering and Management (ICONSTEM)

作者： Kode Jaya Prakash Muthukumaran M R. Sandhiya T Rajpradeesh Mrunalini A. Deshmukh Sathish Thanikodi Department of Mechanical Engineering VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad Telangana Department of Electrical and Electronics Engineering Karpagam College of Engineering Coimbatore Tamil Nadu India Department of Computer Science Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Mechanical Engineering Kalasalingam Academy of Research and Education Srivilliputhur Tamil Nadu Department of Civil Engineering Shree L. R. Tiwari College of Engineering Thane Maharashtra India Department of Mechanical Engineering Saveetha school of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The operation of mass intelligence in order to improve multi-robot collaboration within artificial environments. With the ever-increasing demand for robotization in the field of diligence, the efficient collaboration of several robots is becoming increasingly necessary for optimising operations such as manufacturing, examination, and shipping. The collaborative gesture of social nonentity colonies served as the inspiration for swarm intelligence, which provides a decentralised and tone-organizing solution to the coordination of a platoon of robots. The technique that has been offered investigates mass algorithms, such as Ant Colony Optimisation and flyspeck mass Optimisation, with the purpose of facilitating efficient work allocation, path planning, and resource optimisation among the robots. The purpose of this is to evaluate the performance of mass intelligence in terms of refining collaboration, reducing the amount of time required to complete tasks, and improving the overall effectiveness of multi-robot systems in complex artificial settings. This evaluation is carried out through extensive simulations and experiments conducted in the actual world. The findings bring significant perceptivity to the field of robotics and robotization, emphasising the possibility of mass intelligence for addressing the issues of scalability and rigidity in artificial settings.

关键词： Service robots Robot kinematics Scalability Collaboration Rigidity Resource management Particle swarm optimization

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Machine Learning Based Skin Disease Analyzer with Image Processing

Machine Learning Based Skin Disease Analyzer with Image Proc...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： V. Nivedita Kamalraj Subramaniam M. Ramya B D Parameshachari Department of Computer Science and Engineering SRM Institute Of Science And Technology Trichy India Department of Biomedical Engineering karpagam academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Excel Engineering College Namakkal India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

People may acquire skin illnesses as a consequence of hereditary factors, the natural ageing process, hormones, allergic responses, exposure to the sun or hazardous chemicals, and other environmental variables. Skin disorders are among the most prevalent and pervasive diseases in the world. The diagnosis is exceedingly challenging despite the fact that this condition is rather common since there are slight changes in skin tone, colour, and the presence of hair. When it is in its earlier phases, individuals sometimes fail to recognise the impacts of skin diseases. At the moment, dermatologists diagnose skin conditions by performing a sample, which is subsequently analysed, and then manually give drugs to patients. In this research, we offer a technique for the diagnosis of skin diseases that is based on image processing, Python, and the Yolov3 tool. Our goal is to eliminate the need for physical examination and to deliver reliable findings in a fast way. An picture of the portion of the patient's skin that is infected may be sent as an input to the system by the patient. On this picture, several image processing methods are used, and feature values are retrieved and located, in addition to the illness that is under investigation. The suggested approach is particularly helpful in geographical locations with a limited number of dermatologists. Acne, melanoma, blisters, and cold sores are the four forms of skin diseases that are discussed in this article, along with a technique for diagnosing each of them. The fundamental objective of this approach is to attain the greatest degree of accuracy in the prediction of skin diseases as is possibly conceivable.

关键词： Software algorithms Melanoma User interfaces Skin System software Reliability Sun

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Topological Valley Hall Edge State of Lamb Wave in Aluminum Scandium Nitride Thin Film

Topological Valley Hall Edge State of Lamb Wave in Aluminum ...

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IEEE Conference on Nanotechnology

作者： Zhifang Luo Shuai Shao Chengkuo Lee Tao Wu School of Information Science and Technology ShanghaiTech University Shanghai CHINA Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai CHINA University of Chinese Academy of Sciences Shijingshan Beijing CHINA Department of Electrical & Computer Engineering National University of Singapore SINGAPORE Center for Intelligent Sensors and MEMS National University of Singapore SINGAPORE

The lamb wave topological valley hall edge (VHE) state based on aluminum scandium nitride (Al0.7Sc0.3N) film is demonstrated. By designing miniaturized phononic crystals (PnCs) in AlScN film, we realize a valley hall edge transport for Lamb wave. The variation of the adjacent AlScN columns causes the topological transition of the band structure. The band edge state is topologically valley-locked, and obtained by gap Dirac point at K point to break inversion symmetry. Moreover, AlScN film is also utilized to efficiently generate acoustic wave by improving the piezoelectric constant. The topological PnCs with a broad bandwidth of 22.3 % is obtained utilizing fundamental symmetry (S0) Lamb wave. The proposed broad bandwidth topological PnCs and efficient guided Lamb wave in waveguide may promote future on-chip acoustic information transmission and processing.

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Graph-Tensor FISTA-Net: Edge Computing-Aided Deep Learning for Distributed Traffic Data Recovery

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IEEE Transactions on Network science and engineering 2025年

作者： Deng, Lei Liu, Xiao-Yang Zheng, Haifeng Feng, Xinxin Zhu, Ming Tsang, Danny H. K. Fuzhou University Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou China Internet of Things Thrust Guangdong Guangzhou511400 China Columbia University Department of Electrical Engineering NY United States Chinese Academy of Sciences Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Hong Kong

In intelligent transportation systems, deep learning is a widely adopted technique for traffic data recovery. In city-wide traffic data recovery tasks, traditional centralized deep-learning-model training strategies become inapplicable because of the expensive storage costs for large-scale traffic datasets. In this scenario, edge computing emerges as a natural choice, allowing decentralized data storage and distributed training on edge nodes. However, there is still a challenge: distributed training on edge nodes suffers from high communication costs for parameter transmission. In this paper, we propose a communication-efficient Graph-Tensor Fast Iterative Shrinkage-Thresholding Algorithm-based neural Network (GT-FISTA-Net) for distributed traffic data recovery. Firstly, we model the recovery task as a graph-tensor completion problem to better capture the low-rankness of traffic data. A recovery guarantee is also provided to characterize the performance bounds of the proposed scheme in terms of recovery error. Secondly, we propose a distributed graph-tensor completion algorithm and unfold it into a deep neural network called GT-FISTA-Net. GT-FISTA-Net requires small communication costs for distributed model training on edge nodes and thus it is applicable for city-wide traffic data recovery. Extensive experiments on real-world datasets show that the proposed GT-FISTA-Net can also provide excellent recovery accuracy compared with state-of-the-art distributed recovery methods. © 2013 IEEE.

关键词： distributed learning Edge computing Graph-tensor tensor completion traffic data recovery

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Neuromodulated Meta-Learning

arXiv

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arXiv 2024年

作者： Wang, Jingyao Guo, Huijie Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Xiong, Hui Hua, Gang University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Wormpex AI Research LLC BellevueWA98004 United States

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network structure, not as flexible as BNS. To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks. This reveals the crucial role of FNS in meta-learning, ensuring meta-learning to generate the optimal structure for each task, thereby maximizing the performance and learning efficiency of meta-learning. Motivated by this insight, we propose to define, measure, and model FNS in meta-learning. First, we define that an effective FNS should possess frugality, plasticity, and sensitivity. Then, to quantify FNS in practice, we present three measurements for these properties, collectively forming the structure constraint with theoretical supports. Building on this, we finally propose Neuromodulated Meta-Learning (NeuronML) to model FNS in meta-learning. It utilizes bi-level optimization to update both weights and structure with the structure constraint. Extensive theoretical and empirical evaluations demonstrate the effectiveness of NeuronML on various tasks. Code is publicly available at https://***/WangJingyao07/NeuronML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Surface profile recovery from electromagnetic field with physics–informed neural networks

arXiv

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arXiv 2024年

作者： Chen, Yuxuan Wang, Ce Hui, Yuan Spivack, Mark School of Mathematical Sciences Soochow University Suzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Institute of Intelligent Computing Technology Chinese Academy of Sciences in Suzhou Branch Suzhou China Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom

Physics–informed neural networks (PINN) have shown their potential in solving both direct and inverse problems of partial differential equations. In this paper, we introduce a PINN-based deep learning approach to reconstruct one-dimensional rough surfaces from field data illuminated by an electromagnetic incident wave. In the proposed algorithm, the rough surface is approximated by a neural network, with which the spatial derivatives of surface function can be obtained via automatic differentiation and then the scattered field can be calculated via the method of moments. The neural network is trained by minimizing the loss between the calculated and the observed field data. Furthermore, the proposed method is an unsupervised approach, independent of any surface data, rather only the field data is used. Both TE field (Dirichlet boundary condition) and TM field (Neumann boundary condition) are considered. Two types of field data are used here: full scattered field data and phaseless total field data. The performance of the method is verified by testing with Gaussian-correlated random rough surfaces. Numerical results demonstrate that the PINN-based method can recover rough surfaces with great accuracy and is robust with respect to a wide range of problem regimes. © 2024, CC BY.

关键词： Method of moments

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An Evaluation and Implementation of Pretty Good Privacy (PGP) in Wireless Network Security

An Evaluation and Implementation of Pretty Good Privacy (PGP...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Arvind Hans M. Vigenesh Yuvaraj S Suvarna R. Bhagwat Hamza Zaki Chiging Yamang Faculty Of Business Mangagement & Commerce Usha Martin University Ranchi India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department Of Computer Science Mangalayatan University Aligarh India Department of Bussiness & Management Himalayan University Itanagar India

Top privateers (PGP) is an encryption protocol that shields statistics saved, exchanged, or transmitted over wireless networks. PGP provides a green manner to comfy WiFi networks and communications using symmetric and publickey cryptography. This paper evaluates and examines the utility of PGP in wireless network protection. The paper critiques the layout and implementation of PGP on specific varieties of Wi-Fi networks, which include the partnerships to be had for implementing PGP in pre-present and destiny wireless infrastructures. Similarly to the numerous partnerships, the paper explores PGP's unique protocols and technologies and how they can be integrated inside Wi-Fi networks based on their extraordinary capabilities and functions. The paper additionally discusses the commercial advertising of PGP, its effect on wireless community protection, and the packages of PGP, in addition to a number of the demanding situations and threats related to its use. Moreover, the paper highlights the advantages of implementing PGP in wireless community safety. It gives a diffusion of trying out and implementation strategies that can be used to ensure a successful implementation. The paper concludes with a dialogue about the possible destiny directions of PGP and how it could be used to ease and protect wireless networks.

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Machine Learning in K–12 Educational Settings

Machine Learning in K–12 Educational Settings

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Ramy Riad Al-Fatlawy Dadamuxamedov Alimjon R. Udayakumar Ashish Sharma Jaspreet Kaur S. Padmanaban Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Senior Lecturer at the Department of "Modern Information and Communication Technologies" of International Islamic Academy of Uzbekistan Tashkent Uzbekistan Dean CS & IT Kalinga University India Department of Computer Engineering and Applications GLA University Mathura India Assistant Professor Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Science and Humanities Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The many real-world applications of machine learning-based approaches over the past several decades have shown the potential of data-driven methodology in a variety of computing fields. Higher education computer curricula are beginning to include machine learning, and more and more institutions are incorporating it into K–12 computer instruction. Given the growing prevalence of computational learning in K–12 computer teaching, it is imperative to investigate how agency and intuition develop in these kinds of settings. However, considering the challenges educators and schools already have in integrating conventional learning, understanding the challenges connected with teaching algorithms for learning across grades K–12 provides an even more challenging barrier for computer education research. the curriculum's incorporation of artificial intelligence and *** article outlines the potential for data mining education in grades K–12. These advancements include modifications to technology, philosophy, and practice. The study puts the present findings in the larger framework of computing education and discusses some differences that K–12 computer educators should keep in mind while tackling this issue. The study focuses on key components of the fundamental shift that is required in order to successfully integrate machine learning into more thorough K–12 computing curricula. A critical first step is to abandon the notion that next-generation computational thinking requires rule-based, "traditional" programming.

关键词： Training Technological innovation Philosophical considerations Education Machine learning Learning (artificial intelligence) Market research Telecommunication computing Programming profession Next generation networking

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