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International Conference on Computers, Communications, and Systems (ICCCS)

作者： Shaofei Feng Xinxin Feng Lixia Xu Haifeng Zheng Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Predicting traffic flow effectively alleviates congestion. However, traditional methods tend to rely solely on historical traffic flow data, overlooking the correlation between multimodal traffic data, such as speed and occupancy collected by sensors placed on the road. This limitation results in low tolerance for abnormal situations. Moreover, the decentralization of multimodal data on edge devices may pose data anomalies or partial modal missing due to equipment damage or absence. To address these challenges, we propose a Block-Term tensor decomposition-based multimodal data feature fusion algorithm for traffic prediction. This approach enhances the accuracy and robustness of traffic flow prediction by considering correlations between various modal data, such as speed and occupancy rate. In response to the issues of scattered multimodal data anomalies and missing data on edge devices, and to ensure address privacy and security issues, we employ federated learning methods to achieve adaptive extraction and fusion of multi-modal data at the edges. Our method is tested on a real highway dataset, demonstrating superior prediction performance and robustness compared to traditional methods, particularly in the context of data anomalies or missing modalities.

关键词： Performance evaluation Data privacy Accuracy Tensors Correlation Federated learning Distributed databases

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Off-Grid Parameter Estimation for OFDM-Based ISAC Systems with Incomplete Data

Off-Grid Parameter Estimation for OFDM-Based ISAC Systems wi...

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IEEE Conference on Wireless Communications and Networking

作者： Muyao Ling Xinxin Feng Haifeng Zheng Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In the 6G environment, addressing the challenges of data loss and off-grid issues during target parameter estimation poses a significant challenge for the Integrated Sensing and Communication (ISAC) system. In the ISAC framework, a commonly used method for parameter estimation is compressive sensing. However, compressive sensing may encounter off-grid issues in continuous parameter estimation. In contrast, the atomic norm proves effective in addressing off-grid problems, making it more suitable for continuous parameter estimation. We explore the application of the atomic norm in ISAC and further derive an ISAC model based on OFDM (Orthogonal Frequency Division Multiplexing) utilizing the atomic norm under conditions of incomplete data. To ensure improved convergence speed and accuracy of our algorithm, we employ the Alternating Direction Method of Multipliers (ADMM) for iterative implementation. Experimental results demonstrate that our proposed AN algorithm accurately estimates target parameters in the presence of data loss, exhibiting higher precision and robustness compared to traditional methods.

关键词： Parameter estimation Accuracy OFDM Noise reduction Estimation Robustness Iterative algorithms

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Deep Unfolding Network for Target Parameter Estimation in OTFS-based ISAC Systems

Deep Unfolding Network for Target Parameter Estimation in OT...

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IEEE Conference on Wireless Communications and Networking

作者： Weizhi Lin Haifeng Zheng Xinxin Feng Youjia Chen Fujian Key Lab for Intelligent Processing Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Target parameter estimation in high-speed scenarios is one of the main challenges in the integrated sensing and communication (ISAC) systems. In an ISAC system, the orthogonal time frequency space (OTFS) signal is able to successfully combat time-frequency-selective channels since the channel exhibits significant delay-Doppler (DD) sparsity characteristic. In this paper, we investigate the problem of parameter estimation of moving targets using OTFS modulation. We firstly derive signal model in the DD domain equivalent channel and recast the problem of parameter estimation into a compressed sensing (CS) problem. In order to improve the estimation performance, we then propose ADMM-Net by deep unfolding the iterations of the Alternating Direction Method of Multipliers (ADMM) algorithm into a deep learning network. Experimental results demonstrate that the proposed ADMM-Net algorithm outperforms the other methods in terms of estimation accuracy and running time for OTFS-based parameter estimation.

关键词： Time-frequency analysis Parameter estimation Accuracy Simulation Estimation Modulation Channel estimation

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Communication-Efficient Personalized Federated Learning for Traffic Flow Prediction

Communication-Efficient Personalized Federated Learning for ...

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International Conference on Computer and Communications (ICCC)

作者： Haojie Lai Junxin Guo Xinxin Feng Haifeng Zheng Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

As an important component of intelligent transportation systems, accurate traffic flow prediction can improve road traffic efficiency. Traffic data is typically represented as graphs, but data from different regions is independently collected by various organizations (clients), resulting in the division of the complete graph structure into multiple subgraphs. Feder-ated learning enables the collaborative use of these independent datasets. However, limited communication bandwidth and computational resources result in high overhead in practical scenarios, hindering the application of federated learning. To address this, we introduce an efficient pruning-based federated learning algorithm (DPruneFL), which analyzes client differences and retains personalized model parts. Additionally, we developed the FedNe-DP system by integrating this with the personalized federated learning algorithm based on neighbor aggregation (FedNe), considering both the physical relationships among subgraph data and client personalization needs during aggregation. Experimental results on real datasets show that our approach not only improves prediction performance but also reduces communication costs, enhancing the efficiency of federated learning in actual traffic flow prediction.

关键词： Analytical models Accuracy Federated learning Computational modeling Heuristic algorithms Organizations Predictive models Prediction algorithms Road traffic Intelligent transportation systems

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Joint AP Clustering and Beamforming Design for RIS-Aided Cell-Free Networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8315-8320页

作者： Xu, Chunmei Jia, Yuanqi Chen, Youjia Huang, Wei Southeast University National Mobile Communications Research Laboratory School of Information Science and Engineering Nanjing210096 China Southeast University School of Information Science and Engineering Nanjing210096 China Fuzhou University Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Cell-free networks and reconfigurable intelligent surfaces (RIS) are two promising techniques for future wireless communications. The integration of RIS into cell-free networks, termed RIS-aided cell-free networks, offers the potential to significantly enhance network performance. However, the realization of this potential is constrained by the limited capacities of the fronthaul links. To address this challenge, we investigate the joint design of access point (AP) clustering, transmit and passive beamforming in RIS-aided cell-free networks. The objective is to maximize the weighted sum-rate performance while minimizing the number of clustered APs to alleviate the fronthaul overhead. The problem is formulated in a group sparse manner, employing a mixed zero-norm/two-norm term to represent the number of the clustered APs. To solve this problem, we first approximate the mixed zero-norm/two-norm term by the mixed one-norm/two-norm term and provide its equivalent formulation by introducing receive beamforming vectors and weight parameters. Then, an iterative method is proposed based on the block coordinate descent (BCD) technique, which is guaranteed to converge to a stationary point. Simulation results demonstrate the effectiveness of the proposed method. © 1967-2012 IEEE.

关键词： Iterative methods

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A Correction-Based Dynamic Enhancement Framework towards Underwater Detection

arXiv

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arXiv 2023年

作者： Qiu, Yanling Feng, Qianxue Cai, Boqin Wei, Hongan Chen, Weiling Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China

To assist underwater object detection for better performance, image enhancement technology is often used as a pre-processing step. However, most of the existing enhancement methods tend to pursue the visual quality of an image, instead of providing effective help for detection tasks. In fact, image enhancement algorithms should be optimized with the goal of utility improvement. In this paper, to adapt to the underwater detection tasks, we proposed a lightweight dynamic enhancement algorithm using a contribution dictionary to guide low-level corrections. Dynamic solutions are designed to capture differences in detection preferences. In addition, it can also balance the inconsistency between the contribution of correction operations and their time complexity. Experimental results in real underwater object detection tasks show the superiority of our proposed method in both generalization and real-time performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Traffic Forecasting with Adversarial Domain Adaptation in Edge-Computing Systems 7

Traffic Forecasting with Adversarial Domain Adaptation in Ed...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Chen, Geng Xu, Xianlu Zheng, Haifeng Feng, Xinxin Fuzhou University Fujian Key Lab For Intelligent Processing and Wireless Transmission of Media Information Fuzhou China

ISBN: (纸本)9781665409506

Traffic forecasting is one of the important functions of intelligent transportation systems (ITS), which is of great significance to user experience and urban traffic control. Edge computing, as an emerging technology, has been a promising candidate for real-time and accurate traffic flow prediction. In this paper, we propose an adversarial domain adaptation model for traffic forecasting in an edge computing system. We design the feature mapping and the discriminator optimization objective functions. The traffic features of target domain can be aligned with the traffic features of source domain by adversarial domain adaptation training. We utilize the proposed model for different traffic prediction tasks such as traffic flow and occupancy. We also present extensive simulations by using real-world traffic dataset. We show that the proposed model can achieve better prediction accuracy than the other algorithms when the number of training dataset is insufficient. © 2021 IEEE.

关键词： Edge computing

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Non-invasive Image Quality Assessment Based on Eye-tracking 7

Non-invasive Image Quality Assessment Based on Eye-tracking

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7th International Conference on Computer and Communications, ICCC 2021

作者： Wei, Hongan Lin, Sang Chen, Weiling Chen, Jing Zheng, Yannan Fuzhou University Fujian Key Lab For Intelligent Processing and Wireless Transmission of Media Information Fuzhou China

ISBN: (纸本)9781665409506

In order to measure the perceptual quality of images, it is important to find suitable Image Quality Assessment (IQA) methods. Compared with the traditional objective IQA methods, the subjective IQA methods can more truly reflect users' subjective feelings on image quality. However, the evaluation steps in subjective experiments will interfere with the process of subjects viewing images. We propose a non-intrusive IQA method based on eye-tracking technology to overcome the shortcoming. Through subjective experiments, we collect the eye movement data of subjects and calculate four main features from them. The analysis results of the eye movement parameters indicate that users pay attention to different areas of images with different qualities. Then, we use the eye movement parameters to design the Non-Intrusive Subjective (NIS) IQA algorithm which is a new subjective quality metric of images. Experimental results reveal that the validity and feasibility of the proposed metric. © 2021 IEEE.

关键词： Eye movements

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Graph Transformer Attention Networks for Traffic Flow Prediction 7

Graph Transformer Attention Networks for Traffic Flow Predic...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Ruan, Haochun Feng, Xinxin Zheng, Haifeng Fuzhou University Fujian Key Lab For Intelligent Processing and Wireless Transmission of Media Information Fuzhou China

ISBN: (纸本)9781665409506

As a significant function of an intelligent transportation system (ITS), accurate traffic flow prediction plays an important role in traffic management. However, the nonlinear and complex characteristics of traffic flow makes the traditional approaches unable to achieve satisfactory prediction performance. Although the existing methods based on deep learning models have improved the accuracy of traffic flow prediction, they still ignore some potential features of traffic data. In this paper, we propose a novel network model called Graph Transformer Attention Network (GTAN) for traffic flow prediction. Firstly, we combine Transformer with Graph Convolutional Network (GCN) to discover the relationships between nodes by considering the information of all the positions of nodes in the traffic network and efficiently extract the spatial and temporal features of traffic flow. Secondly, to better exploit the flow characteristic of each node, we assign learnable parameters for each node separately to learn its unique variance. Finally, the experimental results on two real datasets show that the proposed model can achieve better prediction performance and less computation complexity compared with the other existing methods. © 2021 IEEE.

关键词： Forecasting

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Human Activity Recognition Based on Data Fusion of FMCW Radar and Image 7

Human Activity Recognition Based on Data Fusion of FMCW Rada...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Li, Wenlong Feng, Xinxin He, Zhao Zheng, Haifeng Fuzhou University Fujian Key Lab For Intelligent Processing and Wireless Transmission of Media Information Fuzhou China

ISBN: (纸本)9781665409506

As we are getting deeper into the world of human-computer interaction, the human activity recognition technology becomes more and more important. Due to the complexity of the actual environment, we need a more reliable and powerful system that can recognize human activities in a variety of environments with high accuracy. Taking these factors into consideration, we propose a human activity recognition method based on data fusion of the FMCW radar and image, which uses the complementarity of the different data to improve the performance of the system. Apart from this, the domain adaptation is applied to reduce the data differences caused by the changes of environments and user habits in the practical application. Finally, we have implemented real-time applications based on the proposed algorithm on the edge computing platform. The experimental results show that the recognition accuracy of the fusion system can reach 98.7%, and the average running time of the real-time system is about 0.17s. © 2021 IEEE.

关键词： Image enhancement

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