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Spatio-temporal GNN-Based Cell-Free Massive MIMO Network with Maximal Benefit-Cost Ratio

IEEE Transactions on Vehicular technology 2025年

作者： Jiang, Jing Li, Yanni Ye, Yinghui Feng, Dan Zhang, Jiayi Sutthiphan, Worakrin Niyato, Dusit Xi'an Uni versity of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an710121 China Beijing Jiaotong University Frontiers Science Center for Smart High-Speed Railway System Beijing100044 China National Telecommunication Public Company Mobile System Development Department Bangkok10210 Thailand Nanyang Technological University College of Computing and Data Science Singapore

Due to the network's rapid expansion, mobile networks bear extra high deployment costs. Being a key enabler of 6th generation (6G) mobile networks, it is crucial that cell-free massive MIMO (CF mMIMO) networks achieve green and cost-effective development. In this correspondence, we propose spatio-temporal graph neural networks (STGNN)-based CF mMIMO networks with a maximal benefit-cost ratio. First, we formulate a new coverage cost metric named benefit-cost ratio (BCR), which can evaluate the deployment cost while fulfilling various traffic requirements. Next, CF mMIMO networks are modeled as a spatio-temporal graph to utilize STGNN to extract the spatial-temporal properties of traffic demands. Finally, STGNN optimizes the number and location of access points (APs) based on the maximal BCR criterion so that the CF mMIMO networks can achieve on-demand service capability with minimum deployment cost. Numerical results demonstrate that the proposed algorithm can significantly reduce the deployment cost while providing on-demand and high-quality mobile service. © 1967-2012 IEEE.

关键词： Mobile telecommunication systems

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An Improved Q-RRT^(*) Algorithm Based on Virtual Light

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computer systems science & Engineering 2021年第10期39卷 107-119页

作者： Chengchen Zhuge Qun Wang Jiayin Liu Lingxiang Yao Department of Computer Information and Cyber Security Jiangsu Police InsituteNanjing210031China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Jiangsu Police InsituteNanjing210031China Jiangsu Provincial Public Security Department Key Laboratory of Digital Forensics Jiangsu Police InsituteNanjing210031China University of Technology Sydney Sydney2007Australia

The Rapidly-exploring Random Tree(RRT)algorithm is an efficient path-planning algorithm based on random *** RRT^(*)algorithm is a variant of the RRT algorithm that can achieve convergence to the optimal ***,it has been proven to take an infinite time to do *** improved Quick-RRT^(*)(Q-RRT^(*))algorithm based on a virtual light source is proposed in this paper to overcome this *** virtual light-based Q-RRT^(*)(LQRRT^(*))takes advantage of the heuristic information generated by the virtual light on the *** this way,the tree can find the initial solution ***,the LQRRT^(*)algorithm combines the heuristic information with the optimization capability of the Q-RRT^(*)algorithm to find the approximate optimal ***^(*)further optimizes the sampling space compared with the Q-RRT^(*)algorithm and improves the sampling *** efficiency of the algorithm is verified by comparison experiments in different simulation *** results show that the proposed algorithm can converge to the approximate optimal solution in less time and with lower memory consumption.

关键词： Path planning RRT^(*) Q-RRT^(*) LQ-RRT^(*) virtual light

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WiWm-EP: Wi-Fi CSI-based Wheat Moisture Detection Using Equivalent Permittivity

WiWm-EP: Wi-Fi CSI-based Wheat Moisture Detection Using Equi...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Hu, Pengming Yang, Weidong Wang, Xuyu Mao, Shiwen Shen, Erbo Henan University of Technology Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Knight Foundation School of Computing and Information Sciences Florida International University FL33199 United States Auburn University Department of Electrical and Computer Engineering AuburnAL36849-5201 United States

ISBN: (纸本)9781665494274

Moisture content is one of the important indexes of food storage security. The existing detection methods are time-consuming and high cost such that it is difficult to realize online moisture detection. In this paper, according to the dielectric properties of wheat, we propose a wheat moisture content detection system with commercial Wi-Fi devices, termed WiWm-EP. First, we introduce the relationship between the moisture content of wheat and its dielectric constant. Then, we establish an equivalent permittivity (EP) model to characterize wheat moisture content, where the EP can be calculated from channel state information (CSI) of the dual antenna model. Besides, we build the fitting function between the EP and the moisture content as the wheat moisture detection model. Finally, we evaluate the performance of the system through different experiments. The average relative error of the detection results of five wheat samples with different moisture contents is less than 3%. © 2023 IEEE.

关键词： Permittivity

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Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval

arXiv

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arXiv 2022年

作者： Fang, Xiang Liu, Daizong Zhou, Pan Hu, Yuchong Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Wangxuan Institute of Computer Technology Peking University No. 128 Zhongguancun North Street Beijing China School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China

As an increasingly popular task in multimedia information retrieval, video moment retrieval (VMR) aims to localize the target moment from an untrimmed video according to a given language query. Most previous methods depend heavily on numerous manual annotations (i.e., moment boundaries), which are extremely expensive to acquire in practice. In addition, due to the domain gap between different datasets, directly applying these pre-trained models to an unseen domain leads to a significant performance drop. In this paper, we focus on a novel task: cross-domain VMR, where fully-annotated datasets are available in one domain ("source domain"), but the domain of interest ("target domain") only contains unannotated datasets. As far as we know, we present the first study on cross-domain VMR. To address this new task, we propose a novel Multi-Modal Cross-Domain Alignment (MMCDA) network to transfer the annotation knowledge from the source domain to the target domain. However, due to the domain discrepancy between the source and target domains and the semantic gap between videos and queries, directly applying trained models to the target domain generally leads to a performance drop. To solve this problem, we develop three novel modules: (i) a domain alignment module is designed to align the feature distributions between different domains of each modality;(ii) a cross-modal alignment module aims to map both video and query features into a joint embedding space and to align the feature distributions between different modalities in the target domain;(iii) a specific alignment module tries to obtain the fine-grained similarity between a specific frame and the given query for optimal localization. By jointly training these three modules, our MMCDA can learn domain-invariant and semantic-aligned cross-modal representations. Extensive experiments on three challenging benchmarks (ActivityNet Captions, Charades-STA and TACoS) illustrate that our cross-domain method MMCDA outperforms a

关键词： Alignment

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PCF-Grasp: Converting Point Completion to Geometry Feature to Enhance 6-DoF Grasp

arXiv

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arXiv 2025年

作者： Cheng, Yaofeng Zha, Fusheng Guo, Wei Wang, Pengfei Zeng, Chao Sun, Lining Yang, Chenguang State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Lanzhou University of Technology China Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

The 6-Degree of Freedom (DoF) grasp method based on point clouds has shown significant potential in enabling robots to grasp target objects. However, most existing methods are based on the point clouds (2.5D points) generated from single-view depth images. These point clouds only have one surface side of the object providing incomplete geometry information, which mislead the grasping algorithm to judge the shape of the target object, resulting in low grasping accuracy. Humans can accurately grasp objects from a single view by leveraging their geometry experience to estimate object shapes. Inspired by humans, we propose a novel 6-DoF grasping framework that converts the point completion results as object shape features to train the 6-DoF grasp network. Here, point completion can generate approximate complete points from the 2.5D points similar to the human geometry experience, and converting it as shape features is the way to utilize it to improve grasp efficiency. Furthermore, due to the gap between the network generation and actual execution, we integrate a score filter into our framework to select more executable grasp proposals for the real robot. This enables our method to maintain a high grasp quality in any camera viewpoint. Extensive experiments demonstrate that utilizing complete point features enables the generation of significantly more accurate grasp proposals and the inclusion of a score filter greatly enhances the credibility of real-world robot grasping. Our method achieves a 17.8% success rate higher than the state-of-the-art method in real-world experiments. Code and videos are available at https://***/ChengYaofeng/PCF-Grasp. Copyright © 2025, The Authors. All rights reserved.

关键词： Geometry

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Data Processing technology for Network Abnormal Traffic Detection

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Procedia computer science 2024年 243卷 610-618页

作者： Kun Wang Yu Fu Xueyuan Duan Jianqiao Xu Taotao Liu Department of Information Security Naval University of Engineering Wuhan 430033 China School of Mathematics and Information Engineering Xinyang Vocational and Technical College Xinyang 464000 China College of Computer and Information Technology Xinyang Normal University Xinyang 464000 China Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang Normal University Xinyang 464000 China

At present, the threats to network security are also increasing, among which abnormal traffic detection is the key link to ensure network security. Traditional detection methods based on signature or threshold are often difficult to adapt to the increasingly complex network environment and new attack methods. Therefore, this paper optimizes and improves the data processing technology, proposes a network ATD method based on particle swarm optimization (PSO) algorithm, and explores in detail the traffic data collection and pre-processing, the feature recognition of abnormal traffic, the application of PSO algorithm, real-time monitoring and response mechanism. The results of two sets of simulation experiments are as follows: compared with the traditional model, the accuracy rate of ATD of the improved algorithm is increased by 7.2% on average, and the detection time is reduced by 7.35s on average. This method not only enhances the adaptability of the model to new attacks, but also improves the degree of automation of detection.

关键词： Network Anomaly Traffic Detection Data Processing technology particle swarm optimization

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Identify Influential Nodes in Complex Networks: A K-Orders Entropy-Based Method

SSRN

引用

SSRN 2023年

作者： Wu, Yali Dong, Ang Ren, Yuanguang Jiang, Qiaoyong Department of Information and Control Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China The School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China

Identifying influential nodes is a recognized challenge for the tremendous number of nodes in complex networks. Most of proposed methods detect the influential nodes based on their degree or topological location, which only consider the local or global information of the network causing inaccuracy. In this paper, we propose a k-orders entropy-based method to identify influential nodes. The influence of node is determined by its entropy with local and global information. The entropy reflecting local information is measured by the different order neighbors' information of nodes while the entropy reflecting global information by the betweenness centrality. The experiments conducted on real-world networks demonstrate the proposed method is more accurate than other methods. © 2023, The Authors. All rights reserved.

关键词： Complex networks

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Research on Image Captioning Based on Vision-language Pre-trained Models

Research on Image Captioning Based on Vision-language Pre-tr...

引用

International Conference on Big Data and information Analytics (BigDIA)

作者： Shiwei Zou Yuxiang Xie Jie Yan Yingmei Wei Xidao Luan Department of System Engineering National University of Defense Technology Hunan China Key Laboratory of Big Data Department of System Engineering National University of Defense Technology Hunan Changsha China Department of Computer Science Changsha University Hunan China

Image captioning is a vision-language task that targets at describing an image by generating a coherent sentence automatically. This technology allows computers to understand and describe images like humans, enabling further processing of images. It combines computer vision and natural language processing, which has broad applications. This paper investigates the use of vision-language pre-trained models for image captioning. In this article, an encoder-decoder hybrid model based on PVT and BERT is pre-trained on a large number of image-text pairs, possessing both understanding and generation capabilities. It consists of three components: 1) an image encoder and a text encoder to extract image and text features respectively; 2) a multi-modal encoder to align the two modalities; and 3) a text decoder to generate text descriptions. This paper focuses on enhancing the image encoder of the model, which is crucial for image captioning models as it extracts high-resolution image features. Reducing training time and computational costs can also improve the applicability of pre-trained models. Improvements are made to the original model by modifying the ViT model architecture of the image encoding module. A feature pyramid structure and a spatial reduction attention mechanism is introduced to extract high-resolution image features while reducing computational complexity and memory usage, thereby enhancing the universality of the model. The model adopts a pure Transformer architecture, discarding the convolutional structure, and achieves excellent performance.

关键词：

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A provable-secure and practical two-party distributed signing protocol for SM2 signature algorithm

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Frontiers of computer science 2020年第3期14卷 203-215页

作者： Yudi ZHANG Debiao HE Mingwu ZHANG Kim-Kwang Raymond CHOO Key Laboratory of Aerospace Information Security and Trusted Computing of Ministry of Education School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China School of Computer Sciences Hubei University of TechnologyWuhan 430068China Department of Information Systems and Cyber Security and Department of Electrical and Computer Engineering University of Texas at San AntonioSan AntonioTX 78249USA

Mobile devices are widely used for data access,communications and ***,storing a private key for signature and other cryptographic usage on a single mobile device can be challenging,due to its computational ***,a number of(t,n)threshold secret sharing schemes designed to minimize private key from leakage have been proposed in the ***,existing schemes generally suffer from key reconstruction *** this paper,we propose an efficient and secure two-party distributed signing protocol for the SM2 signature *** latter has been mandated by the Chinese government for all electronic commerce *** proposed protocol separates the private key to storage on two devices and can generate a valid signature without the need to reconstruct the entire private *** prove that our protocol is secure under nonstandard ***,we implement our protocol using MIRACL Cryptographic SDK to demonstrate that the protocol can be deployed in practice to prevent key disclosure.

关键词： SM2 signature two-party signing wireless environment provable security threshold secret sharing

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Deep Adaptively Feature Extracting Network for Cervical Squamous Lesion Cell Detection 4th

Deep Adaptively Feature Extracting Network for Cervical Squ...

引用

4th International Conference on Machine Learning for Cyber security, ML4CS 2022

作者： Peng, Zhonghua Hu, Rong Wang, Fuen Fan, Haoyi Eng, Yee Wei Li, Zuoyong Zhou, Liwei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fujian Fuzhou350118 China School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fujian Fuzhou350121 China Department of Nutrition The First Affiliated Hospital of Zhengzhou University Henan Zhengzhou450052 China

ISBN: (纸本)9783031200984

Cervical cancer is one of the most widespread malignancies affecting women’s health worldwide today. However, the task of detection is particularly difficult due to the complex background of the cervical smear, where cells are previously stacked in clusters. To address this problem, we utilize the YOLOv5 as the baseline and build on YOLOv5 by using the simple Transformer Block which only combines with multi-head self attention layers and MLP to better extract cell features as well as to obtain global information. In addition, we allow the model to refine features adaptively to assist with detection by using Convolutional Block Attention Module (CBAM), an attention module being simple and effective for feed-forward convolutional neural networks, in the complex background information. Finally, we compare the model with YOLOv5 as baseline. In CDetector dataset, our model obtains 52.5% mAP@.5, which is 6% better than baseline. In transfer learning, it is 62.2%, which outperforms baseline by 3.2%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cells

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