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ACDV: Adaptive Content Delivery for Vehicular Digital Twin Networks

IEEE Transactions on Vehicular Technology 2025年第5期74卷 7084-7098页

作者： Zheng, Jinkai Luan, Tom H. Li, Guanjie Yin, Zhisheng Wu, Yuan Dong, Mianxiong Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan523808 China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China Xidian University School of Cyber Engineering Xi'an710071 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore Xidian University State Key Laboratory of Isn Xi'an710071 China University of Macau State Key Laboratory of Internet of Things for Smart City 999078 China University of Macau Department of Computer Information Science 999078 China Muroran Institute of Technology Department of Sciences and Informatics Muroran05-08585 Japan

Digital Twins (DTs), serving personalized cloud-based digital assistants, hold great promise for supporting infotainment applications of the Internet of Vehicles (IoVs), in which personalized content such as high-definition maps, system updates, and social video streaming can be delivered from individualized DTs to vehicular users (VUEs) to enhance the intelligence of IoV services and improve their driving experience. This paper proposes a novel content caching framework tailored to DT-enabled IoVs, where DTs selectively cache on-demand contents on edge devices (i.e., cellular base stations and roadside units) managed by the edge service manager (ESM) along the VUE's trip to avoid backbone congestion yet save download time. However, ESMs are inherently selfish and reluctant to contribute resources to cache contents without benefits. Furthermore, considering the diverse trajectories of vehicles, it is inefficient to cache data along a single path. To address these challenges, we propose ACDV, which first models the interactions between the ESMs and DTs as a Stackelberg game to incentivize ESMs to actively participate in the content caching process. We then deploy a Markov model to predict the VUE distribution, which enables DTs to cache contents with a focus on predicted trajectories. To save storage costs, we derive an upper bound of content sizes that the VUE can download within a limited network connection time. Considering the lack of network information and users' private utility model in practical scenarios, we further develop a learning-based algorithm to find the optimal pricing scheme and content size strategies of ESMs and DTs. Through extensive simulations, we show that our proposal can effectively find the optimal strategy and achieve a fast convergence speed and high-level performance compared to the baselines. © 1967-2012 IEEE.

关键词： Markov processes

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GestureTeach: A gesture guided online teaching interactive model

GestureTeach: A gesture guided online teaching interactive m...

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作者： Liu, Hongjun Yao, Chao Zhang, Yalan Ban, Xiaojuan Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial Intelligence School of Computer and Communication Engineering School of Intelligence Science and Technology Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China Institute for Materials Intelligent Technology Liaoning Academy of Materials Shenyang China

Online education has become more popular and effective due to the availability of high-speed internet and technological innovations, which allow people from different locations to access educational resources and opportunities. However, online classes often face challenges such as limited interactivity and display options, which can affect the quality and effectiveness of the online learning experience. In this article, we propose GestureTeach, a new pedagogical paradigm that enables free handwriting interaction and animation generation for online teaching. GestureTeach uses gestures as a natural and intuitive way of interaction, which enhances the teacher's intention expression and the student's engagement. GestureTeach also generates animations from handwritten sketches, which improves the display effects of the interaction and the student's knowledge comprehension. We conducted a two-stage study with 15 teachers and 90 students to evaluate the effectiveness of GestureTeach in facilitating classroom interaction. The results show that GestureTeach is preferred by both teachers and students over traditional online teaching methods and has the potential to transform the online teaching landscape by providing a seamless and interactive experience. © 2023 John Wiley & Sons Ltd.

关键词： Students

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OpenGL Based Simulation Test Bed for Aircraft Ground Telemetry System using Antenna Beam Forming 4

OpenGL Based Simulation Test Bed for Aircraft Ground Telemet...

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4th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2022

作者： Surya, Mangu Aman Lakshmi, P.H. Sri, Kuruganti Madhu Saketh, Amancherla Shanmukha Sai Kailash, Devendra J. Patra Tarun Sai, P. Mathur, Parul Jain, Vineetha Kurup, Dhanesh G. RF and Wireless Systems Laboratory Department of Electronics and Communication Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9781665402392

A simulation test-bed based on OpenGL graphics tool is developed for studying the telemetry communication link of aircraft with ground station. The smooth aircraft movement has been implemented using cubic spline algorithm and antenna beamforming algorithms has been used to track the aircraft movement by estimating the AoA (Angle of Arrival). The paper compares the performances of LMS (Least Mean Square) and RLS (Recursive Least Square) antenna beamforming algorithms to track the movement of aircraft in the presence of noise. The simulation test-bed was able to track the aircraft movement successfully for AOA of less than 75° with signal to noise ratio of more than 4dB. © 2022 IEEE.

关键词： Aircraft

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Automatic Scene Generation: State-of-the-Art Techniques, Models, Datasets, Challenges, and Future Prospects

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IEEE Access 2025年 13卷 95753-95796页

作者： Awal Ahmed Fime Saifuddin Mahmud Arpita Das Md. Sunzidul Islam Jong-Hoon Kim School of Computing Southern Illinois University Carbondale IL USA Department of Computer Science and Information Systems Bradley University Peoria IL USA Department of Computer Science and Engineering The University of Texas at Arlington Arlington TX USA Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna Bangladesh Department of Computer Science Advanced Telerobotics Research Laboratory Kent State University Kent OH USA

Automatic scene generation is an essential area of research with applications in robotics, recreation, visual representation, training and simulation, education, and more. This survey provides a comprehensive review of the current state-of-the-arts in automatic scene generation, focusing on techniques that leverage machine learning, deep learning, embedded systems, and natural language processing (NLP). We categorize the models into four main types: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, and Diffusion Models. Each category is explored in detail, discussing various sub-models and their contributions to the field. We also review the most commonly used datasets, such as COCO-Stuff, Visual Genome, and MS-COCO, which are critical for training and evaluating these models. Methodologies for scene generation are examined, including image-to-3D conversion, text-to-3D generation, graph-based methods, and interactive scene generation. Evaluation metrics such as Fréchet Inception Distance (FID), Kullback-Leibler (KL) Divergence, Inception Score (IS), Intersection over Union (IoU), and Mean Average Precision (mAP) are discussed in the context of their use in assessing model performance. The survey identifies key challenges and limitations in the field, such as maintaining realism, handling complex scenes with multiple objects, and ensuring consistency in object relationships and spatial arrangements. By summarizing recent advances and pinpointing areas for improvement, this survey aims to provide a valuable resource for researchers and practitioners working on automatic scene generation.

关键词： Autoencoders Image reconstruction Transformers Training Three-dimensional displays Diffusion models Visualization Surveys Image synthesis Decoding

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Prescriptive Analytics for Logistics: Bridging Predictive Models and Actionable Decisions Through Advanced Optimization Techniques

Prescriptive Analytics for Logistics: Bridging Predictive Mo...

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Innovative Research in Applied science, engineering and Technology (IRASET),Conference on

作者： Oussama El Azzouzy Tarik Chanyour Said Jai Andaloussi Department of Mathematics and Computer Science Laboratory of Data Engineering and Intelligent Systems(LIDSI) Hassan II University Casablanca Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Modern logistics is facing increasing challenges due to the growing complexity of supply chains, high consumer expectations, and economic and environmental pressures. While predictive analytics, powered by artificial intelligence (AI) and massive data, has transformed the ability to forecast trends and risks, its application remains limited by the lack of effective mechanisms to convert these predictions into actionable decisions in dynamic and constrained environments. This paper proposes an innovative hybrid approach that combines advanced predictive models, notably the Temporal Fusion Transformer (TFT), with prescriptive algorithms, such as linear programming and deep reinforcement learning (DRL). The TFT demonstrated superior performance in terms of predictive accuracy (significant reduction in MAE, RMSE, and MAPE), while the prescriptive algorithms optimized complex logistics decisions in real time, even in uncertain environments. This integrated methodology was validated through use cases simulating critical logistics scenarios, such as delivery route optimization. Experimental results show a substantial improvement in operational efficiency, with reductions in costs, lead times and resource utilization. The prescriptive approach made it possible to automate complex decisions, while taking into account multiple constraints, such as costs, lead times and sustainability objectives. By integrating real-time data collected via IoT devices and logistics management systems, this architecture offers a robust, adaptable and scalable solution. It thus meets the requirements of modern logistics by enabling proactive, optimized and resilient decision-making. This research makes a significant contribution by bridging the gap between predictive and prescriptive analysis, while laying the foundations for new applications in the field of intelligent logistics.

关键词： Costs Supply chains Predictive models Prediction algorithms Linear programming Transformers Real-time systems Thin film transistors Resource management Optimization

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Refining Boundary Sample Space for Differentiable Rendering 16

Refining Boundary Sample Space for Differentiable Rendering

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Gao, Ruicheng Qi, Yue State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Jingdezhen Research Institute Beihang University Jingdezhen333000 China Qingdao Research Institute Beihang University Qingdao266104 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The objective of physics-based differentiable rendering (PBDR) is to propagate gradients between scene parameters and the intensities of image pixels in a manner that is physically correct. The gradients obtained can be applied in optimization algorithms for the reconstruction of 3D geometry or materials, or they can be further propagated into neural network to learn neural representations of the scene. However, applying automatic differentiation techniques directly to the primary rendering process will result in biased gradients, as the rendering integral contains moving high-dimensional discontinuities. Based on how these discontinuities are managed-either implicitly or explicitly-existing PBDR methods can be categorized into two groups: reparameterization methods and boundary sampling methods. Boundary sampling methods need to construct paths that have one segment tangent to the geometry being differentiated in order to estimate a boundary integral to address the discontinuities explicitly. Such paths are usually constructed by sampling the tangent segment first and then extending it to complete the paths for subsequent processing. Fortunately, the number of dimensions in the space composed of such tangent segments is only three. In scenes comprised solely of triangle meshes, the first dimension is used to parameterize all the edges on the mesh, which determines a point on the tangent segment. The remaining two dimensions are used to parameterize the direction of the tangent segment. However, state-of-the-art boundary sampling methods parameterize the first dimension uniformly, which is inefficient because only a small portion of the edges contributes to the boundary integral, resulting in wasted parameter space. In this paper, we parameterize the first dimension by considering both edge length and contributions, thereby allocating more parameter space to important edges. Experiments demonstrate that our methods achieve lower variance gradients in the forward dif

关键词： Mesh generation

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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A Reputation-Aware Randomization Consensus Algorithm for Performance Optimization in Blockchain systems

A Reputation-Aware Randomization Consensus Algorithm for Per...

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International Conference on computer Supported Cooperative Work in Design

作者： Jingyu Zhang Yongtao Sun Zhennan Zhang Weifeng Ren Lailong Luo School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Blockchain technology, due to its decentralized, traceable, and tamper-resistant characteristics, has been applied in a wide range of collaborative computing scenarios, including smart grid, industrial production, and smart cities. Consensus algorithm, as the key blockchain technology, plays a decisive role in terms of performance, security, and scalability, ensuring that system nodes reach consensus on transaction data. However, with the increase of user nodes and the diversity of application scenarios, existing consensus algorithms face some performance and security challenges. To overcome these challenges, this article proposes a novel blockchain consensus mechanism, namely the Reputation-Aware Randomization Consensus Algorithm (RRCA). The proposed mechanism first constructs a dynamic reputation evaluation model to divide nodes into ordinary ones and candidate ones. Secondly, a selection model is constructed for candidate nodes to select consensus nodes, which run the consensus algorithm. Finally, a random selection mechanism for the leader node of the proposed consensus algorithm is constructed to ensure the unpredictability and reduce the probability of the leader node being subjected to malicious attacks. Theoretical analysis and experimental results indicate that the RRCA can significantly reduce consensus latency and increase the unpredictability of the leader node, which improves the performance and security of blockchain systems.

关键词： Smart cities Federated learning Scalability Evaluation models Consensus algorithm Production Blockchains

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InfinityRand: Blockchain Non-Interactive Randomness Beacon Protocol Based on Trapdoor Verifiable Delay Function

InfinityRand: Blockchain Non-Interactive Randomness Beacon P...

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International Conference on computer Supported Cooperative Work in Design

作者： Jingyu Zhang Jiejun Ou Di Lan Bojian Ma Lailong Luo School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Due to the prosperity of Decentralized Finance (DeFi) ecosystems and the rise of Decentralized Autonomous Organization (DAO) groups, blockchain as the underlying revolutionary theory has been attracted a lot of attention. How to achieve cryptographically unpredictable randomness in the publicly verifiable blockchain network, one of the typical collaborative systems, is a critical issue. Since Ethereum finished merging its mainnet with beacon chain, the research on randomness beacon in the blockchain field has become a hotspot. Most of the current distributed randomness beacon schemes are interactive protocols. They are constructed with Public Verifiable Secret Sharing (PVSS), leading high communication complexity O(n 2 ). In contrast, randomness beacons constructed based on Verifiable Delay Functions (VDFs) rely on the sequentiality and uniqueness of VDFs could solve this problem. This paper proposes a blockchain non-interactive randomness beacon protocol: InfinityRand (IR), which decoupled from the underlying message distribution mechanism. It could generate publicly verifiable, strongly bias-resistant, and fair random numbers. In designing InfinityRand, we also design a new trapdoor VDF scheme, which is constructed using negative wrapped convolution (NWC) based number theoretic transform (NTT) on polynomial ring. We conduct security analysis and evaluation experiments. Experiments show that InfinityRand could provide well unpredictability, leader election fairness and scalability guarantees.

关键词： Protocols Scalability Decentralized autonomous organization Voting Finance Collaboration Polynomials

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Distributed Optimal Contract for Data Rewarding with Network Effects

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IEEE Transactions on Control of Network systems 2024年

作者： Baneshi, Alireza Montazeri, Mina Kebriaei, Hamed University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran Swiss Federal Laboratories for Materials Science and Technology Urban Energy Systems Laboratory Dübendorf Switzerland School of Computer Science Tehran Iran

Data rewarding is a novel business model that leads to an economic trend in mobile networks. In this scheme, the advertiser incentivizes mobile users (MUs) to watch advertisement (ads) and, in return, receive a reward in the form of mobile data. In this work, we model the interaction between an advertiser who has asymmetric information about MUs and MUs who are connected to each other under a network, using the contract theory approach. We obtain the necessary and sufficient conditions for an optimal and practical contract to motivate MUs to participate in the data rewarding scheme and encourage them to declare their private information truthfully. The formulation of this contract is a non-convex-constrained optimization problem. Using lemmas and propositions, we reformulate the initial optimization problem that is challenging to solve as an optimization problem with convex constraints and prove that these two problems are equivalent. Then, with the help of a distributed and non-convex algorithm, we obtain the amount of ads demand and incentive reward. © 2014 IEEE.

关键词： asymmetric information contract theory Data rewarding distributed algorithm incentive compatibility

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