2021 2nd Information Communication Technologies Conference (ICTC)最新文献_第5页

A Neighbor Immunization Strategy to Block Virus Spreading on Networks 阻止病毒在网络上传播的邻居免疫策略

2021 2nd Information Communication Technologies Conference (ICTC) Pub Date : 2021-05-07 DOI: 10.1109/ICTC51749.2021.9441616

Hao Yaohui, Liu Yingze, Zhao Baole, Wang Qingxian

引用次数: 0

Experimental Demonstration of OFDM-VLC System Employing Clustering Algorithm 基于聚类算法的OFDM-VLC系统实验验证

2021 2nd Information Communication Technologies Conference (ICTC) Pub Date : 2021-05-07 DOI: 10.1109/ICTC51749.2021.9441663

Yuandi Wang, Minglun Zhang, Hongyu Zhou, Xiaozheng Wang, Chongshuang Qin, Wen He, X. Ren

引用次数: 0

A Novel Event Detection Method for OTDR Trace with High Sensitivity Based on Machine Learning 一种基于机器学习的高灵敏度OTDR轨迹事件检测新方法

2021 2nd Information Communication Technologies Conference (ICTC) Pub Date : 2021-05-07 DOI: 10.1109/ICTC51749.2021.9441614

Zhimin Yang, Dan-Ke Hong, Xiaofang Feng, Junyi Xie

引用次数: 3

LSTM and ResNets Deep Learning Aided End-to-End Intelligent Communication Systems LSTM和ResNets深度学习辅助端到端智能通信系统

2021 2nd Information Communication Technologies Conference (ICTC) Pub Date : 2021-05-07 DOI: 10.1109/ICTC51749.2021.9441657

Haotian Zhang, Lin Zhang, Yuan Jiang, Zhiqiang Wu

{"title":"LSTM and ResNets Deep Learning Aided End-to-End Intelligent Communication Systems","authors":"Haotian Zhang, Lin Zhang, Yuan Jiang, Zhiqiang Wu","doi":"10.1109/ICTC51749.2021.9441657","DOIUrl":"https://doi.org/10.1109/ICTC51749.2021.9441657","url":null,"abstract":"Deep learning (DL) technology enables communication systems to provide intelligent transmissions to adapt to time changing wireless channel conditions. In this paper, with considerations that information bits are usually grouped into blocks or sequences for transmissions, we propose a DL end-to-end intelligent communication system. In our design, we construct a neural network (NN) constituted by long short-term memory (LSTM) units and residual networks (ResNets) architecture, to process the information-bearing sequences. More explicitly, we propose to add a forward feed path to compose the ResNets, thus the residual learning can be implemented to accelerate the convergence. Moreover, with the LSTM units, information-bearing sequences can be processed sequentially by the NN. Thus for larger number of sequences, better symbol error rate (SER) performances can be achieved since the features of the correlation between messages delivered at different time slots will be extracted to improve the detection performances. Subsequently, simulation results are provided to demonstrate that the proposed LSTM and ResNets based intelligent transmission systems can achieve better performances than benchmark systems over wireless channels undergoing the multiplicative fading and additive noises, while providing satisfactory robustness and convergence performances.","PeriodicalId":352596,"journal":{"name":"2021 2nd Information Communication Technologies Conference (ICTC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132876643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Coarse-to-Fine UAV Image Geo-Localization Using Multi-stage Lucas-Kanade Networks 基于多级Lucas-Kanade网络的无人机图像粗到精地理定位

2021 2nd Information Communication Technologies Conference (ICTC) Pub Date : 2021-05-07 DOI: 10.1109/ICTC51749.2021.9441503

Songbing Wu, C. Du, Hao Chen, N. Jing

{"title":"Coarse-to-Fine UAV Image Geo-Localization Using Multi-stage Lucas-Kanade Networks","authors":"Songbing Wu, C. Du, Hao Chen, N. Jing","doi":"10.1109/ICTC51749.2021.9441503","DOIUrl":"https://doi.org/10.1109/ICTC51749.2021.9441503","url":null,"abstract":"Unmanned aerial vehicles (UAVs) have become an indispensable part of our intelligent society. Accurate UAV image geo-localization is fundamental in many applications such as search and rescue, precision agriculture, change detection. Previously, the UAV image geo-localization was usually calculated by photogrammetry method or by matching with the well-located reference images. The photogrammetry method relies heavily on the accuracy of the UAV positioning parameters, however, the construction and use of high-precision positioning equipment are expensive and difficult to be widely used. The method based on image matching requires good similarity between matching images, which is hard to be satisfied as the reference image and UAV image are heterologous. The existing methods are difficult to achieve accurate image geo-location on UAVs equipped with economical positioning devices. To address this issue, we propose a coarse-to-fine UAV image geo-localization pipeline. Firstly, we implement the coarse UAV image geo-localization by utilizing the UAV’s position parameters. Then, a deep learning algorithm multi-stage Lucas-Kanade (MS-LK) is proposed to refine the UAV image geo-localization results iteratively. MS-LK can make full use of the UAV images’ global texture. We validate the effectiveness of the proposed method on two real UAV data sets. The results show that the proposed UAV image geo-localization method can promote the results of the traditional photogrammetry-based approaches.","PeriodicalId":352596,"journal":{"name":"2021 2nd Information Communication Technologies Conference (ICTC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133664443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A High-Speed Parallel Decoding Algorithm for Erasure Codes in the Distributed Storage System 分布式存储系统中擦除码的高速并行译码算法

2021 2nd Information Communication Technologies Conference (ICTC) Pub Date : 2021-05-07 DOI: 10.1109/ICTC51749.2021.9441660

Ruizhen Wu, Yan Wu, Mingming Wang, Lin Wang

引用次数: 0

Time Sensitive Routing Strategy Based on Flow Label 基于流标签的时间敏感路由策略

2021 2nd Information Communication Technologies Conference (ICTC) Pub Date : 2021-05-07 DOI: 10.1109/ICTC51749.2021.9441575

Ziwei Hu, Wei Bai, Yang Lu, Guangshuo Chen, Letian Li, Yunqi Wang, Xin Xv

引用次数: 1

Construction and Thinking of Track Engineering 轨道工程的建设与思考

2021 2nd Information Communication Technologies Conference (ICTC) Pub Date : 2021-05-07 DOI: 10.1109/ICTC51749.2021.9441565

Hong Xiao, Xiaoyu Wang, Feng Jin

引用次数: 0

Improved K-Means Text Clustering Algorithm Based on BERT and Density Peak 基于BERT和密度峰的改进K-Means文本聚类算法

2021 2nd Information Communication Technologies Conference (ICTC) Pub Date : 2021-05-07 DOI: 10.1109/ICTC51749.2021.9441505

Wenhao Hu, Dong Xu, Zhihua Niu

{"title":"Improved K-Means Text Clustering Algorithm Based on BERT and Density Peak","authors":"Wenhao Hu, Dong Xu, Zhihua Niu","doi":"10.1109/ICTC51749.2021.9441505","DOIUrl":"https://doi.org/10.1109/ICTC51749.2021.9441505","url":null,"abstract":"Since the K-Means algorithm was proposed, it has been widely concerned by researchers. Its advantage lies in its simplicity and efficiency, but it also has shortcomings. Aiming at the problems that random selection of initial clustering centers in K-Means algorithm may lead to the algorithm falling into a local optimal solution, making the clustering results unstable and the K-value need to be determined manually, an improved K-Means text clustering algorithm based on BERT and density peak value is proposed(BD-K-Means). First, the BERT model is used to generate the vector representation of the text, and then the density peak clustering algorithm is used to obtain the cluster center. However, aiming at some shortcomings of the density peak clustering algorithm, the Gini coefficient is introduced to find the best cutoff distance, and then the clustering center is obtained from adaptation through the drop change ratio. Finally, the obtained initial centroid and k-value are input into the K-Means algorithm. The experimental results show that the algorithm is better than K-Means algorithm, K-Means++ algorithm, DBSCAN algorithm and DCC-K-Means algorithm in F1 value and ARI value. It can effectively improve the text clustering effect and make the clustering result more accurate.","PeriodicalId":352596,"journal":{"name":"2021 2nd Information Communication Technologies Conference (ICTC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125201555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Direction-Finding Technology of Aliased Sources Based on Pattern Division of Array Data 基于阵列数据模式划分的混叠源测向技术

2021 2nd Information Communication Technologies Conference (ICTC) Pub Date : 2021-05-07 DOI: 10.1109/ICTC51749.2021.9441607

Zhang Jinfeng, Li Lichu, Pu Mingang, Jiang Heng

引用次数: 0