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A DQN-Based Approach for Online Service Placement in Mobile Edge Computing 16th

A DQN-Based Approach for Online Service Placement in Mobile ...

16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Jie, Xiaogan Liu, Tong Gao, Honghao Cao, Chenhong Wang, Peng Tong, Weiqin School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Data Science Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Shanghai Engineering Research Center of Intelligent Computing System Shanghai China

ISBN: (纸本)9783030675394

Due to the development of 5G networks, computation intensive applications on mobile devices have emerged, such as augmented reality and video stream analysis. Mobile edge computing is put forward as a new computing paradigm, to meet the low-latency requirements of applications, by moving services from the cloud to the network edge like base stations. Due to the limited storage space and computing capacity of an edge server, service placement is an important issue, determining which services are deployed at edge to serve corresponding tasks. The problem becomes particularly complicated, with considering the stochastic arrivals of tasks, the additional latency incurred by service migration, and the time spent for waiting in queues for processing at edge. Benefiting from reinforcement learning, we propose a deep Q network based approach, by formulating service placement as a Markov decision process. Real-time service placement strategies are output, to minimize the total latency of arrived tasks in a long term. Extensive simulation results demonstrate that our approach works effectively. © 2021, ICST institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Reinforcement learning

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GRADIENT WEIGHTING FOR SPEAKER VERIFICATION IN EXTREMELY LOW SIGNAL-TO-NOISE RATIO

arXiv

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arXiv 2024年

作者： Ma, Yi Lee, Kong Aik Hautamäki, Ville Ge, Meng Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong School of Computing University of Eastern Finland Finland Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China

Speaker verification is hampered by background noise, particularly at extremely low Signal-to-Noise Ratio (SNR) under 0 dB. It is difficult to suppress noise without introducing unwanted artifacts, which adversely affects speaker verification. We proposed the mechanism called Gradient Weighting (Grad-W), which dynamically identifies and reduces artifact noise during prediction. The mechanism is based on the property that the gradient indicates which parts of the input the model is paying attention to. Specifically, when the speaker network focuses on a region in the denoised utterance but not on the clean counterpart, we consider it artifact noise and assign higher weights for this region during optimization of enhancement. We validate it by training an enhancement model and testing the enhanced utterance on speaker verification. The experimental results show that our approach effectively reduces artifact noise, improving speaker verification across various SNR levels. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Iae-Sdnet: An End-to-End Image Adaptive Enhancement and Wheat Scab Detection Network Using Uav

SSRN

引用

SSRN 2024年

作者： Yang, Zihao Hu, Gensheng Bao, Wenxia Zhang, Pengfei Huang, Linsheng Yang, Xianjun National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei230601 China Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China Zhengzhou Tobacco Research Institute of CNTC Zhengzhou450001 China

Scab is one of the major diseases that significantly impacts both the productivity of wheat. The precise detection of scab during the wheat growth process, along with the implementation of effective preventive measures, is crucial for improving the wheat yield. The utilization of RGB images captured by unmanned aerial vehicle (UAV) combined with deep learning methods can effectively enhance the wheat scab detection efficiency. However, UAV images are influenced by environmental conditions such as illumination and weather conditions. Additionally, the scab target in the image is extremely small and the background is complex. Therefore, to boost the wheat scab detection accuracy in UAV images, this study proposes an end-to-end image adaptive enhancement and wheat scab detection network (IAE-SDNet). In the head of the network, an image adaptive enhancement (IAE) module aims to enhance the quality of UAV images and reduce the interference caused by complex backgrounds. In the IAE module, through end-to-end learning with the Transformer-based object detection module SDNet, there is no need for the manual adjustment of additional parameters. In the backbone of the network, a cascading attention inverse residual module (CIRMB) is designed to boost the feature extraction capacity for the disease area. In the encoder of the network, a deformable multi-head attention encoder (DMHA-Encoder) is deployed to augment the semantic information of the advanced features of wheat while maintaining the representation ability of global attention. In the fusion part of the network, a multi-scale feature fusion block (MFFB) is designed to effectively fuse wheat disease features of different scales, reducing the impact caused by the inconsistency in size between the complex field background and the disease area. Finally, to optimize the detection of small lesion areas, an MNIoU bounding-box regression loss function is proposed. The results of the experiment indicate that this end-to-end det

关键词： Object detection

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FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures 23

FlexiFed: Personalized Federated Learning for Edge Clients w...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

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High power non-electrolytic capacitor LED driver with three ports based on energy distribution 5

High power non-electrolytic capacitor LED driver with three ...

引用

2022 5th International Conference on Power Electronics and Control engineering, ICPECE 2022

作者： Li, Zejun Zhang, Junzhao Xu, Bingshe Han, Xiaoqing Li, Fan Lu, Xiaofeng College of Electrical and Power Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Key Laboratory of Interface Science and Engineering of New Materials Ministry of Education Taiyuan University of Technology Shanxi Taiyuan030024 China Shanxi Electric Drive and Internet of Things Engineering Research Center Shanxi Taiyuan030024 China Intelligent City Lighting Data Sharing and Pub. Serv. Platform Eng. Res. Ctr. of 1331 Proj. in Shanxi Prov. Based on Big Data Shanxi Taiyuan030024 China Shanxi Intelligent Lighting Joint Laboratory Shanxi Taiyuan030024 China Shanxi Intelligent Digital Culture and Tourism Industry Research Institute Shanxi Taiyuan030024 China

To eliminate the influence of electrolytic capacitors on the driving life of high-power LED, a three-port high-power LED without electrolytic capacitor driving power supply based on energy distribution is proposed. The energy difference between the input terminal and the output terminal is balanced by the auxiliary DC-DC circuit to reduce the ripple of the output current. The working principle of the topology and switching mode is analyzed in detail. According to the proposed circuit topology, the mechanism of the output current ripple of the low-frequency circuit is analyzed, and a low-frequency ripple control strategy suitable for the circuit topology is proposed. The specific implementation scheme and the key parameters of the circuit design are given, and the model is verified, which proves that the proposed model is feasible. © Published under licence by IOP Publishing Ltd.

关键词： Light emitting diodes

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NC-ALG: Graph-Based Active Learning Under Noisy Crowd

NC-ALG: Graph-Based Active Learning Under Noisy Crowd

引用

International Conference on data engineering

作者： Wentao Zhang Yexin Wang Zhenbang You Yang Li Gang Cao Zhi Yang Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications Key Lab of High Confidence Software Technologies Peking University Department of Data Platform TEG Tencent Inc. Beijing Academy of Artificial Intelligence Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active Learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed.

关键词： Crowdsourcing Costs Noise Predictive models Graph neural networks Labeling Noise measurement

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Movable Superdirective Pairs: A Phase Shifter-Free Approach to mmWave Communications

arXiv

引用

arXiv 2024年

作者： Han, Liangcheng Yin, Haifan Gao, Mengying Zhang, Rui The School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

In this letter, we propose a novel Movable Superdirective Pairs (MSP) approach that combines movable antennas with superdirective pair arrays to enhance the performance of millimeter-wave (mmWave) communications on the user side. By controlling the rotation angles and positions of superdirective antenna pairs, the proposed MSP approach maximizes the received signal-to-noise ratio (SNR) of multipath signals without relying on phase shifters or attenuators. This approach addresses the limitations of traditional superdirective antennas, which are typically restricted to the endfire direction and suffer from reduced scanning bandwidth and increased complexity. An efficient algorithm based on alternating optimization and the gradient projection method is developed to solve the non-convex optimization problem of antennas’ joint rotating positioning. Simulation results demonstrate that the MSP approach achieves significant performance gains over fixed-position array (FPA) employing Maximum Ratio Combining (MRC), while reducing system complexity and hardware costs. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Movable Antenna Aided Multiuser Communications: Antenna Position Optimization Based on Statistical Channel Information

arXiv

引用

arXiv 2025年

作者： Yan, Ge Zhu, Lipeng Zhang, Rui The NUS Graduate School National University of Singapore Singapore119077 Singapore The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

The movable antenna (MA) technology has attracted great attention recently due to its promising capability in improving wireless channel conditions by flexibly adjusting antenna positions. To reap maximal performance gains of MA systems, existing works mainly focus on MA position optimization to cater to the instantaneous channel state information (CSI). However, the resulting real-time antenna movement may face challenges in practical implementation due to the additional time overhead and energy consumption required, especially in fast time-varying channel scenarios. To address this issue, we propose in this paper a new approach to optimize the MA positions based on the users’ statistical CSI over a large timescale. In particular, we propose a general field response based statistical channel model to characterize the random channel variations caused by the local movement of users. Based on this model, a two-timescale optimization problem is formulated to maximize the ergodic sum rate of multiple users, where the precoding matrix and the positions of MAs at the base station (BS) are optimized based on the instantaneous and statistical CSI, respectively. To solve this non-convex optimization problem, a log-barrier penalized gradient ascent algorithm is developed to optimize the MA positions, where two methods are proposed to approximate the ergodic sum rate and its gradients with different complexities. Finally, we present simulation results to evaluate the performance of the proposed design and algorithms based on practical channels generated by ray-tracing. The results verify the performance advantages of MA systems compared to their fixed-position antenna (FPA) counterparts in terms of long-term rate improvement, especially for scenarios with more diverse channel power distributions in the angular domain. © 2025, CC BY.

关键词： Convex optimization

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Time-series surface water reconstruction method(TSWR)based on spatial distance relationship of multi-stage water boundaries

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International Journal of Digital Earth 2022年第1期15卷 2335-2354页

作者： Mingyang Li Shanlong Lu Cong Du Yong Wang Chun Fang Xinru Li Hailong Tang Muhammad Hasan Ali Baig Harrison Odion Ikhumhen International Research Center of Big Data for Sustainable Development Goals Key Laboratory of Digital Earth ScienceAerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China College of Resources and Environment University of Chinese Academy of SciencesBeijingPeople’s Republic of China School of Earth Sciences and Spatial Information Engineering Hunan University of Science and TechnologyXiangtanPeople’s Republic of China School of Geospatial Engineering and Science Sun Yat-Sen UniversityZhuhaiPeople’s Republic of China Institute of Geo-Information & Earth-Observation (IGEO) PMAS Arid Agriculture University RawalpindiRawalpindiPakistan Key Laboratory of Ministry of Education for Coastal Wetland Ecosystems College of the Environment and EcologyXiamen UniversityXiamenPeople’s Republic of China

Spatiotemporal continuity of surface water datasets widely known for its significance in the surface water dynamic monitoring and assessments,are faced with drawbacks like cloud influence,which hinders the direct extraction of data from time-series remote sensing *** study proposes a Time-series Surface Water Reconstruction method(TSWR).The initial stage of this method involves the effective use of remote sensing images to automatically construct multi-stage surface water boundaries based on Google Earth Engine(GEE).Then,we reconstructed regions the reconstruction of regions with missing water pixels using the distance relationship between the multi-stage water boundaries in previous and later *** applied to 10 large rivers around the world,this method yielded an overall accuracy of 98%for water extraction,an RMSE of 0.41 ***,time-series reconstruction tests conducted in 2020 on the Lancang and Danube rivers revealed a significant improvement in the image *** findings demonstrated that this method could not only be used to accurately reconstruct the surface water distribution missing water images,but also to depict a more pronounced time variation *** successful application of this method on GEE demonstrates its importance for use on large scales or in global studies.

关键词： Google earth engine sentinel-2 surface water reconstruction time-series surface water data

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Embedding VLAD in Transformer for Video Question Answering

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Jisuanji Xuebao/Chinese Journal of Computers 2023年第4期46卷 671-689页

作者： Guo, Dan Yao, Shen-Tao Wang, Hui Wang, Meng School of Computer and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230094 China Key Laboratory of Knowledge Engineering with Big Data（Hefei University of Technology） Ministry of Education Hefei230601 China Intelligent Interconnected Systems Laboratory of Anhui Province（Hefei University of Technology） Hefei230601 China

Video question answering（VideoQA）is a typical cross-modal understanding task. Its challenge lies in how to learn appropriate multimodal representation and cross-modal correlation for answer inference. Most existing video question answering methods focus on the latter，e. g.，relationship learning between each video frame or clip and word. In this work， we devote to advanced feature embedding of both video and query. We develop a clustering-based VLAD technique for VideoQA. The novelty of our work is the joint exploitation of temporal aggregation and correlation in multimodality. We propose an end-to-end trainable Transformed VLAD embedding network， named TVLAD-Net. TVLAD-Net constructs a differentiable aggregation network module（i. e.， convolutional Residual-less VLAD Block）to generate compact VLAD descriptors（transforming N frames，clips or words to compact K descriptors while K © 2023 science Press. All rights reserved.

关键词： Semantics

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