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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yonghao Yu Yongde Guo Dawei Zhao Kexue Fu Yongwei Tang Qinyuan Wang Faculty of Data Science City University of Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center(National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China School of Mechanical Engineering Key Laboratory of High Efficiency and Clean Mechanical Manufacture Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Existing indoor visible light positioning methods, such as those based on fingerprint database algorithms, are complex to construct and consume significant resources. In response to this issue, the text proposes an end-to-end indoor visible light positioning method that samples continuous signals at a small number of random coordinate positions in indoor environments. Through a deep learning method based on attention mechanism, a nonlinear mapping between the received signal and spatial position coordinates is constructed, and the time series properties in the received signal are encoded as a feature in the data. The method is further improved by combining residual networks to improve accuracy. Through simulation experiments, it has been verified that the deep learning based localization method described in this article can achieve sub meter level accuracy on small sample datasets, and it has been proven that time series features have a significant impact on localization accuracy in indoor visible light positioning algorithms based on deep learning.

关键词： Location awareness Deep learning Meters Training Accuracy Attention mechanisms Time series analysis Training data Encoding Data models

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International Conference on computer Supported Cooperative Work in Design

作者： Wei Ding Luyao Wang Wei Zhang Myung Jin Lee Ling Wang Kwang Woo Nam Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Industry-University Cooperstion Foundation Chungbuk National University Cheongju Korea School of Computer Science Northeast Electric Power University Jilin China School of Computer Science and Engineering Kunsan National University Gunsan Korea

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In this paper, we propose a new UAV video similarity measurement algorithm to cluster and analyze UAV videos by measuring the similarity between UAV videos and train them in sub-groups, which in turn improves the efficiency of UAV video data mining. Our preliminary work introduces the maximum common view subsequence (LCVS) to compute the similarity between UAV videos, but LCVS needs to frequently compute the common visible region of video frames generating extremely high time cost. In order to solve the problem of high computational cost of LCVS, we propose a similarity algorithm based on minimum tilted outer rectangle clustering (MTORC) for UAV videos. The MTORC algorithm adopts minimum tilted outer rectangle to cluster the field of view of the marked points in each frame, which reduces the time complexity of calculating the common visible region. By comparing experiments with LCSS and LCVS, the experimental results prove that MTORC is better than the present previous research in terms of algorithm accuracy and time cost.

关键词： Training Visualization Costs Accuracy Clustering algorithms Autonomous aerial vehicles Time measurement

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Blind Joint Multi-DFO Estimation for High-Mobility OFDM Communication Systems

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IEEE Transactions on Vehicular Technology 2025年

作者： Feng, Yunqi Zhu, Zhehua Lu, Weidang Zhao, Nan Nallanathan, Arumugam Zhejiang University of Technology College of Information Engineering Key Laboratory of Communication Networks and Applications of Zhejiang Province Hangzhou310023 China Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

In high-mobility communication systems, the relative motion between the transceivers will cause severe Doppler shifts, resulting in the doubly-selective fading channel. In this correspondence, a blind joint multiple Doppler frequency offsets (DFOs) estimator is proposed for high-mobility communication with orthogonal frequency division multiplexing (OFDM). Both the receive beamforming and multi-beamformer redundancy are employed to estimate and compensate multiple DFOs, thereby effectively mitigating the channel time-variation. Moreover, the inter-channel interference (ICI) has been derived to further analyze the impact of residual DFOs due to the estimation error on the system performance. Numerical results verify that the proposed blind estimator substantially outperforms existing ones. © 1967-2012 IEEE.

关键词： Frequency allocation

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DA-BEV: Unsupervised Domain Adaptation for Bird's Eye View Perception

arXiv

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arXiv 2024年

作者： Jiang, Kai Huang, Jiaxing Xie, Weiying Lei, Jie Li, Yunsong Shao, Ling Lu, Shijian State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China S-lab School of Computer Science and Engineering Nanyang Technological University Singapore UCAS-Terminus AI Lab University of Chinese Academy of Sciences China School of Electrical and Data Engineering The University of Technology Sydney Australia

Camera-only Bird's Eye View (BEV) has demonstrated great potential in environment perception in a 3D space. However, most existing studies were conducted under a supervised setup which cannot scale well while handling various new data. Unsupervised domain adaptive BEV, which effective learning from various unlabelled target data, is far under-explored. In this work, we design DA-BEV, the first domain adaptive camera-only BEV framework that addresses domain adaptive BEV challenges by exploiting the complementary nature of image-view features and BEV features. DA-BEV introduces the idea of query into the domain adaptation framework to derive useful information from image-view and BEV features. It consists of two query-based designs, namely, query-based adversarial learning (QAL) and query-based self-training (QST), which exploits image-view features or BEV features to regularize the adaptation of the other. Extensive experiments show that DA-BEV achieves superior domain adaptive BEV perception performance consistently across multiple datasets and tasks such as 3D object detection and 3D scene segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Acoustic Sensing for Fitness Activities Recognition: A Deep Learning Approach

Acoustic Sensing for Fitness Activities Recognition: A Deep ...

引用

IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Chen Dai Weihao Zhou Linqing Gui Haotong Cao Sahil Garg Fu Xiao School of Computer Science Nanjing University of Posts and Telecommunications China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks China College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications China Electrical Engineering Department École de technologie supérieure Montréal Canada

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Currently, the demand for home fitness has increased as more people choose to work remotely. Acoustic sensing technology has gained popularity due to its practicality on fitness activity monitoring. Due to the fact that sound waves do not propagate uniformly in all directions, identifying fitness activities can be challenging when sound waves do not directly face the device. To address it, we propose a method that models the reflected signal based on the sound field characteristics of a piston sound source and introduces a novel signal processing technique to reduce angle correlation. Then we combine deep learning methods for fitness activity recognition. Experimental results demonstrate improved recognition accuracy for fitness activities performed at different angles around the device.

关键词： Pistons Deep learning Performance evaluation Correlation Accuracy Activity recognition Signal processing

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DH-Based Updatable Private Set Intersection

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International Journal of Network Security 2024年第6期26卷 1092-1101页

作者： Zhang, Yingbo Liu, Momeng Li, Yunyun Networking and Intelligent Information Services School of Computer Science Xi’an Polytechnic University State Key Laboratory of Integrated Services Networks Xidian University Shaanxi Key Laboratory of Clothing Intelligence State and Local Joint Engineering Research Center for Advanced No. 58 Shan-gu Road Lintong Xi’an710600 China

The rapid development of information technology provides convenience for online shopping, and many merchants try to increase their income through advertising conversion rates. However, in a virtual network environment, the transaction carries risks that may lead to private data leakage. Fortunately, private set intersection (PSI) provides an effective private data protection solution, allowing two parties to compute the intersection of their private sets without revealing any additional information beyond the intersection itself. This paper proposes an updatable PSI with a Diffie-Hellman-based key agreement paradigm. Our scheme allows dynamic updating of private sets of advertisers or merchants for computation, breaking the limitations of static scenarios. At the same time, we choose Rank and Select based Quotient Filter (RSQF) to process the data sets of participants in the scenario, it only needs small storage space to filter advertising data than Cuckoo filter. In addition, it allows merge operations between filters, and the filter is rebuilt when it reaches its maximum capacity without generating a new filter from scratch. Finally, we conducted comparison experiments and found that our solution has lower communication complexity and running time than related works. © (2024), (International Journal of Network Security). All rights reserved.

关键词： Information leakage

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Joint Scheduling, Computing, and Load Balancing for Time Sensitive Traffic in SDN-Enabled Space-Air-Ground Integrated 6 G networks: A Federated Reinforcement Learning Approach

引用

IEEE Transactions on Mobile Computing 2025年

作者： Sun, Haitong Zhang, Haijun Ma, Hui Leung, Victor C.M Hebei Key Laboratory of Space-Air-Ground Intelligent Communication Beijing100083 China University of Science and Technology Beijing Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Beijing100083 China Shenzhen University Department of Computer Science and Software Engineering Shenzhen518061 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Low Earth Orbit (LEO) constellations and Unmanned Aerial Vehicle (UAV) networks enable wide coverage for the sixth generation (6G) mobile communication. However, it is a challenge to achieve high scheduling success rate, ultra-low latency, and efficient load balance in the Space-Air-Ground Integrated 6G Network (SAGGIN). This paper addresses the following issue: How to effectively and orderly transmit time-sensitive traffic in SAGGIN under strict deadlines, limited computational ability, and restrained link capacity? Specifically, this paper uses Software-Defined Networking (SDN) and designs a joint optimization method to enhance the traffic transmission ability of SAGGIN. Considering response time, computing cost, and link capacity in SAGGIN, the scheduling, computing, and load balance issues are modeled as a multi-objective optimization problem that minimizes the worst-case response time and computing cost of data frames while maximizing the network flow. Then, this paper leverages a Federated Reinforcement Learning (FRL) scheme to solve the problem. Results show that the FRL could achieve great scheduling, computing, and load balance performance. Specifically, our method can successfully schedule 80% of the traffic at most when the current network load is around 90%. Furthermore, the computational delay could reduce around 50%. © 2002-2012 IEEE.

关键词： Reinforcement learning

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Outage Probability Analysis of Uplink Heterogeneous Non-terrestrial networks: A Novel Stochastic Geometry Model

arXiv

引用

arXiv 2024年

作者： Dong, Wen-Yu Yang, Shaoshi Lin, Wei Zhao, Wei Gui, Jia-Xing Chen, Sheng The School of Information and Communication Engineering Beijing University of Posts and Telecommunications The Key Laboratory of Universal Wireless Communications Ministry of Education China The Key Laboratory of Mathematics and Information Networks Beijing100876 China The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

In harsh environments such as mountainous terrain, dense vegetation areas, or urban landscapes, a single type of unmanned aerial vehicles (UAVs) may encounter challenges like flight restrictions, difficulty in task execution, or increased risk. Therefore, employing multiple types of UAVs, along with satellite assistance, to collaborate becomes essential in such scenarios. In this context, we present a stochastic geometry based approach for modeling the heterogeneous non-terrestrial networks (NTNs) by using the classical binomial point process and introducing a novel point process, called Matérn hard-core cluster process (MHCCP). Our MHCCP possesses both the exclusivity and the clustering properties, thus it can better model the aircraft group composed of multiple clusters. Then, we derive closed-form expressions of the outage probability (OP) for the uplink (aerial-to-satellite) of heterogeneous NTNs. Unlike existing studies, our analysis relies on a more advanced system configuration, where the integration of beamforming and frequency division multiple access, and the shadowed-Rician (SR) fading model for interference power, are considered. The accuracy of our theoretical derivation is confirmed by Monte Carlo simulations. Our research offers fundamental insights into the system-level performance optimization of NTNs. © 2024, CC BY-NC-SA.

关键词： Stochastic systems

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Resource-aware stochastic self-triggered model predictive control

arXiv

引用

arXiv 2021年

作者： Lian, Yingzhao Jiang, Yuning Stricker, Naomi Thiele, Lothar Jones, Colin N. Automatic Control Laboratory EPFL Switzerland Computer Engineering and Networks Laboratory ETH Zürich Switzerland

This paper considers the control of uncertain systems that are operated under limited resource factors, such as battery life or hardware longevity. We consider here resource-aware self-triggered control techniques that schedule system operation non-uniformly in time in order to balance performance against resource consumption. When running in an uncertain environment, unknown disturbances may deteriorate system performance by acting adversarially against the planned event triggering schedule. In this work, we propose a resource-aware stochastic predictive control scheme to tackle this challenge, where a novel zero-order hold feedback control scheme is proposed to accommodate a time-inhomogeneous predictive control update. © 2021, CC BY-NC-ND.

关键词： Model predictive control

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Rafmnet:Reinforced Attention Fusion and Multiscale Network For Noisy Infrared and Visible Image Fusion

Rafmnet:Reinforced Attention Fusion and Multiscale Network F...

引用

IEEE International Conference on Image Processing

作者： Guohua Lv Xiyan Wang Yongbiao Gao Yi Zhai Guixin Zhao Guangxiao Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Comuter Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The purpose of infrared and visible image fusion is to combine the advantages of different types of images to produce more robust and informative images. However, if the source images are noisy, existing fusion methods may not produce clear results. To address this issue, we propose a novel method for infrared and visible image fusion with noise reduction. This method enhances the visual perception of fused images by integrating features of different scales extracted by the denoising network into the fusion network. By using deformable convolutional denoising networks, noise in images can be removed and features can be enhanced. Then, a set of reinforced attention fusion modules (RAFM) are designed to fuse the features extracted by the denoising network. Experimental results demonstrate the effectiveness of our proposed method, which outperforms existing state-of-the-art methods in terms of fusion accuracy and visual perception.

关键词： Couplings Fuses Noise reduction Noise Feature extraction Noise measurement Task analysis

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