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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge computing

IEEE Transactions on Vehicular technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Min Northeastern University School of Computer Science and Engineering The Engineering Research Center of Security Technology of Complex Network System Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system *** first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multiper-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Active Magnetic Compensation Based on Parametric Resonance Magnetometer

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Journal of Shanghai Jiaotong university(Science) 2024年第2期29卷 280-289页

作者：郭阳李绍良黄艺明骆曼箬刘华 School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Institute of Spacefight Control Technology Shanghai Key Laboratory of Space Intelligent Control TechnologyShanghai Engineer Research Center of InertiaShanghai 201109China

Based on the parametric resonance magnetometer(PRM)theory,this paper establishes an experimen-tal system of *** experimental results are consistent with the theoretical predictions.A PRM has been developed with sensitivity of 0.5 pT/Hz^(1/2),which can detect the magnitude of residual magnetic field;furthermore,a proportion-integration-differentiation(PID)closed-loop magnetic compensation system of the residual magnetic field also has been *** with open-loop compensation,the PID closed-loop compensation reduces the average value of the residual magnetic field in the z-axis direction from 0.0244nT to-0.0023nT,and the mean-square error from 0.2083 nT to 0.0691 *** the same way,the average value of the residual magnetic field in the y-axis direction is reduced from 0.0816nT to-0.0042nT,and the mean-square error from 0.1316nT to 0.0461 *** magnitude of residual magnetic fields in both directions is decreased to the order of picotesla(pT).In addition,based on the signal waveforms of the magnetometer,a method of verifying the effect of magnetic compensation is proposed.

关键词： magnetometer parametric resonance nuclear magnetic resonance gyroscope magnetic compensa-tion proportion-integration-differentiation(PID)

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Multi-attribute wearable pressure sensor based on multilayered modulation with high constant sensitivity over a wide range

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Nano Research 2023年第5期16卷 7583-7592页

作者： Ning Li Song Gao Yang Li Jianwen Liu Wenhao Song Guozhen Shen School of Information Science and Engineering University of JinanJinan 250022China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China School of Aeronautical and Astronautical Engineering Purdue University West LafayetteIN 47907USA

Flexible pressure sensors capable of monitoring diverse physiological signals and body movements have garnered tremendous attention in wearable electronic ***,high constant sensitivity over a wide pressure range combined with breathability,biocompatibility,biodegradability is pivotal for manufacturing of reliable pressure sensors in practical sensing *** this work,inspired by the multilayered structure of skin epidermis,we propose and demonstrate a multi-attribute wearable piezoresistive pressure sensor consisting of multilayered gradient conductive poly(ε-caprolactone)nanofiber membranes *** response to externally applied pressure,a layer-by-layer current path is activated inside the multilayered membranes composites,leading to the most salient sensing performance of high constant sensitivity of 33.955 kPa^(−1) within the pressure range of 0–80 *** proposed pressure sensor also exhibits a fast response–relaxation time,a low detection limit,excellent stability,which can be successfully used to measure human physiological ***,an integrated sensor array system that can locate objects’positions is constructed and applied to simulate sitting posture *** results indicate that the proposed pressure sensor holds great potential in health monitoring and wearable electronic devices.

关键词： wearable pressure sensors multilayer structure high constant sensitivity multi-attribute physiological signals monitoring

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Scattering Characteristics-guided Self-supervised Learning for Target Classification in SAR Images 24

Scattering Characteristics-guided Self-supervised Learning f...

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7th International Conference on Image and Graphics Processing, ICIGP 2024

作者： Zhong, Honghu Li, Jianhao Shi, Hao Fang, Zhonghao Chen, Liang Institute of Radar Technology Beijing Institute of Technology Beijing China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing China Innovation Center of Beijing Institute of Technology Chongqing Chongqing401120 China

ISBN: (纸本)9798400716720

With the advancement of science and technology, the spatial resolution of spaceborne Synthetic Aperture Radar (SAR) images has achieved sub-meter precision, enabling the classification and identification of targets. Notably, the rapid and accurate classification of aircraft targets using SAR images has become a significant application requirement. Nevertheless, challenges persist in the classification of targets within aircraft remote sensing images. This paper introduces a novel method aimed at enhancing the scattering characteristics in SAR images to address issues associated with discrete imaging pixels, strong scattering characteristics, and the consequent difficulty in distinguishing aircraft features. The proposed method systematically correlates the discrete SAR image scattering characteristics, thereby facilitating the extraction of characteristic information pertaining to the edge contours of aircraft targets. Recognizing the limitations of traditional supervised classification methods, which often demand substantial manually labeled information, this study integrates self-supervised contrastive learning to mitigate labeling costs. Additionally, acknowledging the imbalanced distribution of samples across different categories and the prevalent "long tail"effect, a weighted loss function is introduced to rectify the imbalance and enhance the network's focus on the learning of underrepresented samples. The efficacy of the proposed method is evaluated using a self-established dataset. The results demonstrate a 1.48% increase in accuracy compared to the original self-supervised method, indicating an improvement in the classification performance for categories characterized by an imbalanced sample distribution. © 2024 ACM.

关键词： Image classification

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Task Separation and Knowledge Sharing for Class Incremental Learning 5

Task Separation and Knowledge Sharing for Class Incremental ...

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5th International Conference on intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Zhang, Jiali Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China School of Mathematics and Information Science Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China

ISBN: (纸本)9798350368284

Methods based on dynamically expanding architectures can effectively mitigate catastrophic forgetting in class incremental learning (CIL), but they often overlook information sharing and integration between subnetworks, leading to performance degradation and increased parameter count and memory consumption as the network expands. To address these complex issues, we introduce a new method called Task Separation and Knowledge Sharing (TSKS), which transfers knowledge at both the logical and feature levels through Hybrid Knowledge Distillation (HKD). In logical distillation, we combine Global Knowledge Distillation (GKD) and Task Knowledge Distillation (TKD) to establish connections between tasks. To prevent the model from favoring new classes, we design an Attention- Based Feature Fusion (ABFF) mechanism to capture important features. Additionally, we introduce a Soft-Margin Auxiliary Loss (SMAL) to help the model learn new concepts. Extensive experiments show that TSKS outperforms existing models on CIFAR100 and ImageNet100, regardless of whether memory samples are used. Notably, TSKS demonstrates superior performance in memory-free settings, indicating a reduced reliance on rehearsal memory. © 2024 IEEE.

关键词： Adversarial machine learning

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A Weighted Ensemble Causal Discovery Method for Effective Connectivity Estimation 9

A Weighted Ensemble Causal Discovery Method for Effective Co...

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9th IEEE Smart World Congress, SWC 2023

作者： Zhang, Qiqi Zhang, Yingwei Ding, Yanhui Chen, Yiqiang Song, Shuchao Wu, Shuang Shandong Normal University School of Information Science and Engineering Jinan China Shandong Academy of Intelligent Computing Technology Jinan China Chinese Academy of Sciences Institute of Computing Technology Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China

ISBN: (纸本)9798350319804

Exploring and explaining the effective connectivity (EC) between brain regions can help us understand the mechanisms behind neurodegenerative diseases such as Alzheimer's disease, thus helping us to diagnose patients better. Causal discovery techniques have been widely used in this domain recently, but diverse methods may produce conflicting outcomes because of the differences in underlying principles and search methods. To solve this problem, we integrate the causal relationships generated by different algorithms to obtain high-confidence inferences, resulting in a more reliable weighted ensemble causal discovery (WECD) method with higher accuracy. First of all, WECD defines a distribution metric function to measure the weights of different individual methods. Then, WECD adaptively chooses the critical threshold to obtain the confident edges set based on the generated directed edge confident metric curve. Finally, the directed acyclic graph condition is used to refine the confident edges set and acquire the final causal graph. We validate the effectiveness of WECD with both the simulated dataset and real functional near-infrared spectroscopy (fNIRS) dataset collected on AD patients, MCI patients and healthy controls. By comparing with other existing methods, experimental results demonstrate the potential of WECD as an effective tool for estimating brain EC. © 2023 IEEE.

关键词： Alzheimer's disease causal discovery effective connectivity ensemble learning functional near-infrared spectroscopy

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Vision based intelligent traffic light management system using Faster R‐CNN

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CAAI Transactions on Intelligence technology 2024年第4期9卷 932-947页

作者： Syed Konain Abbas Muhammad Usman Ghani Khan Jia Zhu Raheem Sarwar Naif R.Aljohani Ibrahim A.Hameed Muhammad Umair Hassan Department of Computer Science and Engineering University of Engineering and TechnologyLahorePakistan Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal UniversityJinhuaChina OTHEM Manchester Metropolitan UniversityManchesterUK Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of ICT and Natural Sciences Norwegian University of Science and Technology(NTNU)ÅlesundNorway

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

关键词： access control artificial intelligence computer vision intelligent control

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Evaluating Performance Portability of SYCL and Kokkos: A Case Study on LBM Simulations 21

Evaluating Performance Portability of SYCL and Kokkos: A Cas...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud computing, 16th IEEE International Conference on Social computing and Networking and 13th International Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Ding, Yue Xu, Chuanfu Qiu, Haozhong Wang, Qingsong Dai, Weixi Lin, Yongzhen Che, Yonggang National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing Changsha China

ISBN: (纸本)9798350329223

Since modern high performance computing systems are evolving towards diverse and heterogeneous architectures, the emergence of high-level portable programming models leads to a particular focus on performance portability. In this paper, we evaluate the performance portability and explore performance optimization methods for two portable programming models SYCL and Kokkos. We take an open-source multi-phase Lattice Boltzmann Method (LBM) flow simulation code as a case study and implement portable versions with different optimizations. Then we compare our portable implementations with engineer-tuned OpenMP and CUDA versions on Intel CPUs and NVIDIA GPUs. Experimental results show that both SYCL and Kokkos can deliver superior performance than traditional programming models, but the best performance of the portable versions depends heavily on platform-specific optimizations. There is no single implementation that can achieve the best performance on both CPUs and GPUs. Consequently, we conclude that the performance portability still needs to be further improved for both SYCL and Kokkos. In addition, we present a comparative analysis of different optimization methods that qualify the performance enhancement when using SYCL and Kokkos on CPUs and GPUs. Our work offers valuable references for the development of both portable programming models and applications. © 2023 IEEE.

关键词： Program processors

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A High-Quality and Efficient Bus-Aware Global Router

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Chinese Journal of Electronics 2025年第2期34卷 444-456页

作者： Liu Genggeng Wei Ling Yu Yantao Xu Ning College of Computer and Data Science Fuzhou University Fuzhou 350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou 350108 China School of Information Engineering Wuhan University of Technology Wuhan 430070 China

As advanced technology nodes enter the nanometer era, the complexity of integrated circuit design is increasing, and the proportion of bus in the net is also increasing. The bus routing has become a key factor affecting the performance of the chip. In addition, the existing research does not distinguish between bus and non-bus in the complete global routing process, which directly leads to the expansion of bus deviation and the degradation of chip performance. In order to solve these problems, we propose a high-quality and efficient bus-aware global router, which includes the following key strategies: By introducing the routing density graph, we propose a routing model that can simultaneously consider the routability of non-bus and the deviation value of bus;A dynamic routing resource adjustment algorithm is proposed to optimize the bus deviation and wirelength simultaneously, which can effectively reduce the bus deviation;We propose a layer assignment algorithm consider deviation to significantly reduce the bus deviation of the 3D routing solution;And a depth-first search (DFS)-based algorithm is proposed to obtain multiple routing solutions, from which the routing result with the lowest deviation is selected. Experimental results show that the proposed algorithms can effectively reduce bus deviation compared with the existing algorithms, so as to obtain high-quality 2D and 3D routing solutions considering bus deviation.

关键词： Global routing Layer assignment Bus deviation Physical design Bus routing

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Image Augment Self-Supervised Network for Hand-Drawn Sketch 3D Reconstruction 3

Image Augment Self-Supervised Network for Hand-Drawn Sketch ...

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3rd International Conference on Advanced Algorithms and Neural Networks, AANN 2023

作者： Ma, Yang Kong, Dehui Li, Jinghua Yin, Baocai Beijing Key Laboratory of Multimedia & Intelligent Software Technology Beijing Institute of Artificial Intelligence Faculty of Information Technology Beijing University of Technology China

ISBN: (纸本)9781510668355

3D reconstruction from single hand-drawn sketches can be considered as a task of single-view reconstruction, which faces great challenge of lifting the dimension of the geometric representation of objects. Most of the reconstruction networks are based on deep learning technology with supervised training which are used to suffer from dataset labeling, while self-supervised sketch-based 3D reconstruction remains challenging. In this paper, we propose a self-supervised 3D reconstruction network for hand-drawn sketch (IASSReNet), which introduces image information as an auxiliary to address the ambiguity and sparsity of sketch. In order to obtain image information, an image generator is firstly designed to provide augmented information for the reconstruction through a sketch feature enhancement module. To integrate the information from sketch and image, we use a spatially corresponding feature transfer module to fuse their feature. Finally, silhouettes are obtained from the predicted 3D mesh, and similarity constraints are applied to the sketch contour to perform self-supervised training on the network. Experimental results on multiple datasets show that our method outperforms other unsupervised methods and is competitive with some supervised methods. © 2023 SPIE.

关键词： Image reconstruction

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