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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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Efficient Low-Resolution Face Recognition via Bridge Distillation

arXiv

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arXiv 2024年

作者： Ge, Shiming Zhao, Shengwei Li, Chenyu Zhang, Yu Li, Jia The Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China SenseTime Group Limited 100084 China The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The Peng Cheng Laboratory Shenzhen518055 China

Face recognition in the wild is now advancing towards light-weight models, fast inference speed and resolution-adapted capability. In this paper, we propose a bridge distillation approach to turn a complex face model pretrained on private high-resolution faces into a light-weight one for low-resolution face recognition. In our approach, such a cross-dataset resolution-adapted knowledge transfer problem is solved via two-step distillation. In the first step, we conduct cross-dataset distillation to transfer the prior knowledge from private high-resolution faces to public high-resolution faces and generate compact and discriminative features. In the second step, the resolution-adapted distillation is conducted to further transfer the prior knowledge to synthetic low-resolution faces via multi-task learning. By learning low-resolution face representations and mimicking the adapted high-resolution knowledge, a light-weight student model can be constructed with high efficiency and promising accuracy in recognizing low-resolution faces. Experimental results show that the student model performs impressively in recognizing low-resolution faces with only 0.21M parameters and 0.057MB memory. Meanwhile, its speed reaches up to 14,705, 934 and 763 faces per second on GPU, CPU and mobile phone, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi-task learning

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AugmentedBook: A collaborative e-learning augmented reality platform 1st

AugmentedBook: A collaborative e-learning augmented reality ...

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1st International Conference on Human Interaction and Emerging Technologies, IHIET 2019

作者： Alzahrani, Nouf M. Lajmi, Sonia Information Technology Department Faculty of Computer Science and Information Technology Albaha University Albaha Saudi Arabia MIRACL: Multimedia InfoRmation Systems and Advanced Computing Laboratory University of Sfax Technological Pole of Sfax Sakiet Ezzit Sfax3021 Tunisia

ISBN: (纸本)9783030256289

This paper introduces an augmented reality-based framework (called AugmentedBook) for e-learning that allows the creation of collaborative notes, illustrative media (i.e. video, 2D or 3D image, audio) for mobile devices or Google glass. The augmented content can be added to real-world educational support to make it more comprehensive, interactive and collaborative. In this platform, students and teachers can add collaborative notes to any part of the educational support system. They can also find illustrative media and indicate the pertinence of the result. Using our AugmentedBook platform, students can also download the enriched support using a mobile device. Our framework solves the problem of standard integration of augmented reality applications in education, offering a distributed framework which is e-learning compliant. © 2020, Springer Nature Switzerland AG.

关键词： Augmented reality

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Jointly Optimizing Terahertz based Sensing and Communications in Vehicular Networks: A Dynamic Graph Neural Network Approach

arXiv

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arXiv 2024年

作者： Li, Xuefei Chen, Mingzhe Hu, Ye Zhang, Zhilong Liu, Danpu Mao, Shiwen The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States

In this paper, the problem of vehicle service mode selection (sensing, communication, or both) and vehicle connections within terahertz (THz) enabled joint sensing and communications over vehicular networks is studied. The considered network consists of several service provider vehicles (SPVs) that can provide: 1) only sensing service, 2) only communication service, and 3) both services, sensing service request vehicles, and communication service request vehicles. Based on the vehicle network topology and their service accessibility, SPVs strategically select service request vehicles to provide sensing, communication, or both services. This problem is formulated as an optimization problem, aiming to maximize the number of successfully served vehicles by jointly determining the service mode of each SPV and its associated vehicles. To solve this problem, we propose a dynamic graph neural network (GNN) model that selects appropriate graph information aggregation functions according to the vehicle network topology, thus extracting more vehicle network information compared to traditional static GNNs that use fixed aggregation functions for different vehicle network topologies. Using the extracted vehicle network information, the service mode of each SPV and its served service request vehicles will be determined. Simulation results show that the proposed dynamic GNN based method can improve the number of successfully served vehicles by up to 17% and 28% compared to a GNN based algorithm with a fixed neural network model and a conventional optimization algorithm without using GNNs. Copyright © 2024, The Authors. All rights reserved.

关键词： Vehicles

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Efficient meta neural heuristic for multi-objective combinatorial optimization 23

Efficient meta neural heuristic for multi-objective combinat...

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Proceedings of the 37th International Conference on Neural information Processing systems

作者： Jinbiao Chen Jiahai Wang Zizhen Zhang Zhiguang Cao Te Ye Siyuan Chen School of Computer Science and Engineering Sun Yat-sen University P.R. China School of Computer Science and Engineering Sun Yat-sen University P.R. China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University P.R. China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China School of Computing and Information Systems Singapore Management University Singapore

Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta-model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. Specifically, for the training process, a (partial) architecture-shared multi-task model is leveraged to achieve parallel learning for the meta-model, so as to speed up the training; meanwhile, a scaled symmetric sampling method with respect to the weight vectors is designed to stabilize the training. For the fine-tuning process, an efficient hierarchical method is proposed to systematically tackle all the subproblems. Experimental results on the multi-objective traveling salesman problem (MOTSP), multi-objective capacitated vehicle routing problem (MOCVRP), and multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform the state-of-the-art neural heuristics in terms of solution quality and learning efficiency, and yield competitive solutions to the strong traditional heuristics while consuming much shorter time.

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Adaptive Stepwise Feature Selection Approach for EEG-Based Epileptic Seizure Classification 46

Adaptive Stepwise Feature Selection Approach for EEG-Based E...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Aboyeji, Sunday Timothy Zhou, Wenfang Tao, Yuan Zhu, Mingxing Samuel, Oluwarotimi Williams Ahmad, Ijaz Zhao, Guoru Yi, Ji Tong, Michael Chi Fai Wang, Xin Guo, Yi Chen, Shixiong Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen China Luohu District People's Hospital Department of Otolaryngology Shenzhen China Peking University Shenzhen Hospital Department of Otorhinolaryngology Shenzhen China Harbin Institute of Technology Shenzhen School of Electronics and Information Engineering Shenzhen China University of Derby School of Computing and Engineering Derby United Kingdom Johns Hopkins University School of Medicine Department of Ophthalmology Baltimore United States The Chinese University of Hong Kong Department of Otorhinolaryngology Head and Neck Surgery Hong Kong Shenzhen People's Hospital Department of Neurology Shenzhen China The Chinese University of Hong Kong Shenzhen School of Medicine Shenzhen China

ISBN: (纸本)9798350371499

Recent advancements in feature selection (FS) optimization algorithms have influenced the field of epileptic seizure classification. However, integrating these optimization algorithms into machine learning (ML) models often creates time complexity, limiting their clinical deployment. To address this issue, we propose an innovative adaptive stepwise FS method tailored for epileptic seizure detection (ESD). First, a discrete wavelet transform (DWT) was applied to the preprocessed signal to get three levels of the db4 wavelet family within the frequency range pertinent to epileptic seizure classification. Linear and nonlinear features are then extracted from each level of the DWT. The selected features are initially ranked using the minimum relevance, maximum redundancy (mRMR) FS technique. After that, a stepwise FS approach was applied to the ranked features to optimize the performance of Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) classifiers. The experiment was performed on a publicly accessible CHB-MIT dataset in a patient-independent approach. The model's performance was assessed using accuracy, sensitivity, and specificity. The results show an improved performance of the ML models with the integration of stepwise algorithm into the mRMR technique. Among the classifiers, RF exhibited superior performance with accuracy, sensitivity, and specificity of 87.69%, 91.53%, and 83.86%, respectively, when 12 features were selected. Our proposed stepwise feature selection method (PSFS) performs similarly to generalize forward feature selection (GFFS), with an average accuracy of 88.37% and 88.57%, respectively across selected features with less computation. This makes PSFS a very efficient and effective FS in epileptic seizure classification. © 2024 IEEE.

关键词： Discrete wavelet transforms

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DISCO Might Not Be Funky: Random Intelligent Reflective Surface Configurations That Attack

arXiv

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arXiv 2023年

作者： Huang, Huan Dai, Lipeng Zhang, Hongliang Zhang, Chongfu Tian, Zhongxing Cai, Yi Swindlehurst, A. Lee Han, Zhu The School of Electronic and Information Engineering Soochow University Suzhou215006 China The School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China The State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China The Center for Pervasive Communications and Computing University of California IrvineCA92697 United States The University of Houston HoustonTX77004 United States

Emerging intelligent reflective surfaces (IRSs) significantly improve system performance, but also pose a significant risk for physical layer security (PLS). Unlike the extensive research on legitimate IRS-enhanced communications, in this article we present an adversarial IRS-based fully-passive jammer (FPJ). We describe typical application scenarios for Disco IRS (DIRS)-based FPJ, where an illegitimate IRS with random, time-varying reflection properties acts like a "disco ball" to randomly change the propagation environment. We introduce the principles of DIRS-based FPJ and overview existing investigations of the technology, including a design example employing one-bit phase shifters. The DIRS-based FPJ can be implemented without either jamming power or channel state information (CSI) for the legitimate users (LUs). It does not suffer from the energy constraints of traditional active jammers, nor does it require any knowledge of the LU channels. In addition to the proposed jamming attack, we also propose an anti-jamming strategy that requires only statistical rather than instantaneous CSI. Furthermore, we present a data frame structure that enables the legitimate access point (AP) to estimate the DIRS-jammed channels’ statistical characteristics in the presence of the DIRS jamming. Typical cases are discussed to show the impact of the DIRS-based FPJ and the feasibility of the anti-jamming precoder (AJP). Moreover, we outline future research directions and challenges for the DIRS-based FPJ and its anti-jamming precoding to stimulate this line of research and pave the way for practical applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Channel state information

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Neural multi-objective combinatorial optimization with diversity enhancement 23

Neural multi-objective combinatorial optimization with diver...

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Proceedings of the 37th International Conference on Neural information Processing systems

作者： Jinbiao Chen Zizhen Zhang Zhiguang Cao Yaoxin Wu Yining Ma Te Ye Jiahai Wang School of Computer Science and Engineering Sun Yat-sen University P.R. China School of Computing and Information Systems Singapore Management University Singapore Department of Industrial Engineering & Innovation Sciences Eindhoven University of Technology Netherlands Department of Industrial Systems Engineering & Management National University of Singapore Singapore School of Computer Science and Engineering Sun Yat-sen University P.R. China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University P.R. China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO.

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Federated Graph Learning via Constructing and Sharing Feature Spaces for Cross-Domain IoT

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IEEE Internet of Things Journal 2025年

作者： Chen, Jiale Zhuo, Shengda He, Jinchun Qiu, Wangjie Zhang, Qinnan Xiong, Zehui Zheng, Zhiming Tang, Yin Chen, Min Wang, Changdong Huang, Shuqiang Jinan University College of Cyber Security Guangzhou China Beihang University Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China Zhongguancun Laboratory Beijing China Singapore University of Technology and Design Information Systems Technology and Design 487372 Singapore Jinan University School of Management Guangzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China Sun Yat-sen University School of Computer Guangzhou China Jinan University College of Cyber Security of Jinan University China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou China

The Internet of Things (IoT) collects large volumes of diverse data, with graph data as a critical component, and extensively utilizes Federated Graph Learning (FGL) to process this data while preserving data security. However, the graph data collected by different IoT institutions are relatively independent due to various factors (e.g., data collection methods, geographical locations), and data access is restricted to local environments due to privacy constraints, IoT institutions typically possess heterogeneous feature spaces. Aggregating under these conditions could potentially contaminate local graph representations. Unfortunately, most existing FGL methods tend to overlook this issue. To address this challenge, we propose Federated Graph Learning via Constructing and Sharing Features (FedCSF), a novel FGL framework designed to build a globally consistent feature space and share it among IoT institutions. To construct and extract the feature space, we employ an uniform feature initialization across IoT institutions and design an encoder to extract both global and local feature relationships, thereby facilitating effective collaboration across data from different IoT institutions. Furthermore, we introduce an independent adaptive aggregation strategy to eliminate the integration of harmful knowledge, ensuring that the contributions from each IoT institution are effectively integrated into the global model. We theoretically analyze the convergence of FedCSF. To validate the effectiveness of FedCSF, we conducted extensive experiments under various settings (i.e., cross-datasets, and cross-domains), demonstrating its significant advantages of FedCSF in terms of performance, convergence speed, and practical adaptability © 2014 IEEE.

关键词： Federated learning

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Analysis of Artifactual Components Rejection Threshold towards Enhanced Characterization of Neural Activity in Post-Stroke Survivor

Analysis of Artifactual Components Rejection Threshold towar...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Mojisola Grace Asogbon Yaping Huai Oluwarotimi Williams Samuel Zhengxiang Jing Yixin Ma Jingyu Liu Yibo Jiang Yunfa Fu Guanglin Li Yongcheng Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen Guangdong China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen Guangdong China School of Computing and Engineering University of Derby Derby United Kingdom Department of Information Engineering and Automation Kunming University of Science and Technology Yunnan China

Research advancement has spurred the usage of electroencephalography (EEG)-based neural oscillatory rhythms as a biomarker to complement clinical rehabilitation strategies for the recovery of motor functions in stroke survivors. However, the inevitable contamination of EEG signals with artifacts from various sources limits its utilization and effectiveness. Thus, the integration of Independent Component Analysis (ICA) and Independent Component Label (ICLabel) has been widely employed to separate neural activity from artifacts. A crucial step in the ICLabel preprocessing pipeline is the artifactual ICs rejection threshold (TH) parameter, which determines the overall signal's quality. For instance, selecting a high TH will cause many ICs to be rejected, thereby leading to signal over-cleaning, and choosing a low TH may result in under-cleaning of the signal. Toward determining the optimal TH parameter, this study investigates the effect of six different TH groups (NO-TH and TH1-TH6) on EEG signals recorded from post-stroke patients who performed four distinct motor imagery (MI) tasks including wrist and grasping movements. Utilizing the EEG-beta band signal at the brain's sensorimotor cortex, the performance of the TH groups was evaluated using three notable EEG quantifiers. Overall, the obtained result shows that the considered THs will significantly alter neural oscillatory patterns. Comparing the performance of the TH-groups, TH-3 with a confidence level of 60% showed consistently stronger signal desynchronization and lateralization. The correlation result shows that most of the electrode pairs with high correlation values are replicable across all the MI tasks. It also revealed that brain activity correlates linearly with distance, and a strong correlation between electrode pairs is independent of the different brain cortices. The study outcome may facilitate adequate therapeutic intervention for stroke *** Relevance: This study indicated that optimal s

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