2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)最新文献_第8页

Research on Head Detection and State Estimation Algorithm in Classroom Scene 教室场景中头部检测与状态估计算法研究

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449186

Yuting Huang, Fan Bai, Chongwen Wang

{"title":"Research on Head Detection and State Estimation Algorithm in Classroom Scene","authors":"Yuting Huang, Fan Bai, Chongwen Wang","doi":"10.1109/ICCCS52626.2021.9449186","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449186","url":null,"abstract":"The penetration rate of mobile phones and tablet computers among college students is increasing, and the loose teaching environment has led to a large number of phubbers in college classrooms. The state of students' attendance in class is an intuitive indicator of classroom quality. Obtaining this data in real-time will bring great help to school evaluation and improvement of teaching standards. The data in this article comes from teaching videos collected by high-definition cameras in colleges. Through offline training, the face detector HDN can accurately extract the position coordinates of the student in the picture in the real teaching scene and pass the detected head information to the convolutional network responsible for judging the state of the student's head to obtain the student's current Class status. The HDN designed in this paper achieves a recall rate of more than 95% on the authoritative public dataset FDDB, and the accuracy of Wider Face's face dataset under three difficulty conditions is 93.9%, 93.2%, and 88.0%. The self-designed Raised Head Network achieves 88% accuracy on the RaisedHead dataset.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127339523","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: 0

A New Siamese Co-attention Network for Unsupervised Video Object Segmentation 一种新的Siamese共关注网络用于无监督视频对象分割

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449293

Zhenghao Zhang, Liguo Sun, Lingyu Si, C. Zheng

引用次数: 0

Phase Demodulation Method for Low SNR Laser Doppler Signals 低信噪比激光多普勒信号的相位解调方法

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449264

Siyou Su, D. Hao, Jian-Bin Huang, T. Zhang, Z. Cao, Ning Zhang

{"title":"Phase Demodulation Method for Low SNR Laser Doppler Signals","authors":"Siyou Su, D. Hao, Jian-Bin Huang, T. Zhang, Z. Cao, Ning Zhang","doi":"10.1109/ICCCS52626.2021.9449264","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449264","url":null,"abstract":"All Fiber Displacement Interferometer System for Any Reflector (AFDISAR) provides outstanding measurement accuracy and resolution for recording the dynamic damage evolution and fracture of materials in the shock wave and detonation physics research. Based on sine curve fitting, a new numerical phase demodulation method is presented for the low SNR AFDISAR Doppler signal while measuring low speed moving target. The phase difference between reference and measurement beam is determined by the displacement of the measurement target and changes the output current of the photodetector. Sine curve fitting method is introduced to modulate the phase from the differential frequency of Doppler signal. Traditional Hilbert transform and new sine curve fitting method are simulated separately for modulating the displacement from the low SNR laser Doppler signals with DC-bias offset. The simulation results show that the decoding accuracy of sine curve fitting method is better than the accuracy of Hilbert transform. When SNR downing to −5dB, the error of sine curve fitting is only 1°, while the value for Hilbert transform is as large as58.19°. The measurement results shows that sine curve fitting method has better robustness and tolerance to noise.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121342436","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: 0

Sparse Matrix Reconstruction Based on Sequential Sparse Recovery for Multiple Measurement Vectors 基于序列稀疏恢复的多测量向量稀疏矩阵重构

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449312

Xingyu He, Tao Liu

引用次数: 0

Temperature Prediction Based on Integrated Deep Learning and Attention Mechanism 基于集成深度学习和注意机制的温度预测

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449176

Xu Zhao, Lvwen Huang, Yanming Nie

{"title":"Temperature Prediction Based on Integrated Deep Learning and Attention Mechanism","authors":"Xu Zhao, Lvwen Huang, Yanming Nie","doi":"10.1109/ICCCS52626.2021.9449176","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449176","url":null,"abstract":"It is greatly significant to predict air temperature accurately for effective warning of extreme weather events, whereas the complex and nonlinear characteristics of meteorological data make this kind of forecast difficult to achieve high accuracy. To deal with this issue, a novel model named CNN-GRU-RPASM (Convolution Neural Networks - Gated Recurrent Unit - Relative Position-based Self-Attention Mechanism) was proposed in this paper. Apart from the traditional counterparts, the CNN-GRU-RPASM model innovatively combines the advantages of CNN and GRU, and introduces a gaussian amplifier model to improve the self-attention mechanism with relative position information. Firstly, CNN was used to extract the characteristics of the meteorological input data. Then, the improved self-attention mechanism was employed to extract key information from the data sequence. And finally, GRU was utilized to encode the relationship information among time-series data. The performance evaluation with the real meteorological data shows that the CNN-GRU-RP ASM model performs better than its traditional counterparts. This new model will be deployed in the agricultural production service system to provide technical supports for extreme weather disaster warning forecasting.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122826761","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: 3

Multi-objective Architecture Optimization Based on Evolutionary Algorithm with Grid Decomposition 基于网格分解进化算法的多目标结构优化

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449098

Rui Zhang, Lisong Wang, Xinye Cai, Guonan Cui, Yang Hong, Qin Zhang

引用次数: 0

Research on Resource Allocation Algorithm of 5G Network in Multi-Business Smart Grid 多业务智能电网下5G网络资源分配算法研究

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449187

Jing Jiang, Peizhe Xin, Jun Li, Liu Han, Bin Hou, Lixin He

引用次数: 3

Real-time Small-size Pixel Target Perception Algorithm Based on Embedded System for Smart City 基于智慧城市嵌入式系统的小尺寸像素目标实时感知算法

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449130

Ruirui Mao

{"title":"Real-time Small-size Pixel Target Perception Algorithm Based on Embedded System for Smart City","authors":"Ruirui Mao","doi":"10.1109/ICCCS52626.2021.9449130","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449130","url":null,"abstract":"With the continuous development of technology in the artificial intelligence era, smart city applications based on high-performance servers in large data centers have penetrated all walks of life. However, the current mainstream smart city application model is only data collection on the device side, and then calculations and inferences in the data center. Data transmission is difficult to achieve the real-time performance of the system, resulting in poor effects in many smart city applications. The intelligent perception for smart city is required to perceive the whole urban environment comprehensively. Among them, small-size pixel target detection and recognition is particularly critical. To this end, a real-time small-size pixel target perception algorithm based on embedded system for smart city is proposed, which uses lightweight neural networks and model pruning optimization to realize terminal intelligence for smart city applications, and integrates traditional machine learning filtering algorithms for improving the detection speed and the accuracy. The experimental results of the method show that the real-time performance and the accuracy of detection are greatly improved for different sizes of small-size pixel targets.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115333992","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

CTP: Correlated Trajectory Publication with Differential Privacy 基于差分隐私的相关轨迹发布

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449263

Yunkai Yu, Hong Zhu, Meiyi Xie

{"title":"CTP: Correlated Trajectory Publication with Differential Privacy","authors":"Yunkai Yu, Hong Zhu, Meiyi Xie","doi":"10.1109/ICCCS52626.2021.9449263","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449263","url":null,"abstract":"With the popularity of smart devices and social applications, vast amounts of trajectory data are generated that can be used for traffic planning, etc. However, when trajectory data are applied in these applications, the private information contained in the trajectories can be revealed. In this paper, we focus on trajectory correlation, which can reveal the social relations of users and further cause severe breaches of privacy. We present a method for correlated trajectory publication with differential privacy, called CTP. First, we discretize the continuous geographical space of raw trajectories to obtain a grid space via an adaptive grid partition method with the Laplace mechanism and convert the trajectories from locations into cells. Then, we quantify the trajectory correlation using the cell visit probability vectors of raw trajectories of the cell mode and turn to reducing the similarity of two cell visit probability vectors for the protection of trajectory correlation. Second, based on the correlations extracted from raw trajectories of the cell mode, we design a constrained optimization problem. By solving it via particle swarm optimization, which is modified to satisfy differential privacy, we can obtain an updated cell visit probability vector of a given trajectory, thus weakening the correlations between the given trajectory and other trajectories. Finally, based on the updated probability vector, we synthesize a trajectory corresponding to the given trajectory. We perform experiments on real trajectory datasets. The experimental results show that CTP is stable and achieves a better trade-off between the data utility and the privacy than the existing methods.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115935399","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

A New Deep Architecture for Digital Signal Modulation Classification over Rician Fading 一种新的数字信号调制衰落深度分类体系

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449146

Peicong Hu, Wendong Yang, Na Pu, Yunfei Peng, Xiang Ding

引用次数: 1