2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)最新文献_第6页

Research on Campus Network Based on QoS Technology 基于QoS技术的校园网研究

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232073

J. Xue, Yue Wu, Jun Tao, YunLing Zhang

引用次数: 2

DNN-Based Decoder for Four-Dimensional Modulation Superposition NOMA 基于dnn的四维调制叠加NOMA解码器

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232075

Meng Li, Jun Zou, Jiyuan Sun

引用次数: 0

A DOA Estimation algorithm for the vertical line array of vector hydrophone based on data fusion method 一种基于数据融合的矢量水听器垂直线阵列DOA估计算法

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232064

Yan Liang, Z. Meng, Yu Chen, Jianfei Wang, Xiaoxia Zhou, Mingyang Wang

{"title":"A DOA Estimation algorithm for the vertical line array of vector hydrophone based on data fusion method","authors":"Yan Liang, Z. Meng, Yu Chen, Jianfei Wang, Xiaoxia Zhou, Mingyang Wang","doi":"10.1109/ICICSP50920.2020.9232064","DOIUrl":"https://doi.org/10.1109/ICICSP50920.2020.9232064","url":null,"abstract":"According to the array signal processing theory, the horizontal directivity index of the vector hydrophone vertical array (VHVA) is not higher than that of a single vector hydrophone. In order to improve the direction-of-arrival (DOA) estimation performance, a data fusion method for the VHVA is proposed in this paper. The azimuth estimation results corresponding to each vector hydrophone at a series of single-frequency points are obtained through the cross-spectral processing algorithm. Then the narrow-band estimation results of multiple hydrophones are processed by data fusion method to achieve more accurate estimation results. By adopting the histogram statistics method, the synthetic high-resolution estimation result is obtained finally. To verify the significantly improved performance of the proposed algorithm, we conducted the simulation and sea trial. It is revealed that the DOA estimation performance of the VHVA is much better than that of a single vector hydrophone. In the fusion estimation result, the peak of the statistics values is much narrower, and the side lobe is obviously suppressed.","PeriodicalId":117760,"journal":{"name":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115571785","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

An Optimization Method for Sound Speed Profile Inversion Using Empirical Orthogonal Function Analysis 基于经验正交函数分析的声速剖面反演优化方法

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232017

Chen Liu, Kaifeng Han, Wen Zhang, Wen Chen

引用次数: 1

Fast TLAM: High-precision Fine Grain Smoking Behavior Detection Network Fast TLAM:高精度细粒冒烟行为检测网络

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232100

Zhang Yang, Dengfeng Yao

{"title":"Fast TLAM: High-precision Fine Grain Smoking Behavior Detection Network","authors":"Zhang Yang, Dengfeng Yao","doi":"10.1109/ICICSP50920.2020.9232100","DOIUrl":"https://doi.org/10.1109/ICICSP50920.2020.9232100","url":null,"abstract":"This paper proposes a fast two-level attention model (Fast TLAM) smoking behavior detection network to detect smoking behavior. The Fast TLAM network is mainly divided into the following three phases: 1. Pre-processing stage: EdgeBox candidate region selection algorithm is used to generate a large number of candidate regions and then filter them;, candidate regions containing foreground objects will be reserved for transmission to object-level and local-level models;2. Object-level model: a CNN network is trained to filter and classify candidate regions in the preprocessing stage; the network is also trained to filter out background information, leave only patches containing the target to be detected, and abtain classification results; 3. Local level model: (1) a network is trained to classify candidate regions in the preprocessing stage; (2) candidate regions screened at the object level are clustered with K-means algorithm and then classified. Finally, the classification results are obtained. The classification results of the first and second stages are categorized to complete the entire detection process. Tests are carried out on a self-made experimental data set. Experimental results show that the Fast TLAM network has a very high accuracy rate of 92.68% can be identified only by object-level graphics, and does not need labeling information at all. Moreover, the network solves several defects, namely, low accuracy, high cost, and poor convenience of the traditional smoking behavior detection method.","PeriodicalId":117760,"journal":{"name":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128954477","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

Utilizing Drone for Food Quality and Safety Detection using Wireless Sensors 利用无人机进行无线传感器食品质量安全检测

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232046

Faris. A. Almalki

引用次数: 15

Application of VMD in Fault Diagnosis for Rotor-Bearing System with Rub-Impact VMD在含碰摩转子轴承系统故障诊断中的应用

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232055

Rui Zhu, Xin Xia

引用次数: 2

Learning-Based Signal Detection for OFDM Systems with I/Q Imbalance 基于学习的OFDM系统I/Q不平衡信号检测

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232099

Jinglan Ou, Jiaying Wang, Qihao Peng, Xingxin Zhu, Haowei Wu

引用次数: 0

Passive Source Ranging Using Residual Neural Network With One Hydrophone in Shallow Water 基于残差神经网络的单水听器浅水被动源测距

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232070

Yonggang Lin, Min Zhu, Yanqun Wu, Wen Zhang

引用次数: 4

The Night Flame Detection Algorithm Based on Sequential Frame Difference 基于序列帧差的夜间火焰检测算法

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232086

Le Ma, Feng Yu, Changlong Zhou, Minghua Jiang, Xiong Wei

{"title":"The Night Flame Detection Algorithm Based on Sequential Frame Difference","authors":"Le Ma, Feng Yu, Changlong Zhou, Minghua Jiang, Xiong Wei","doi":"10.1109/ICICSP50920.2020.9232086","DOIUrl":"https://doi.org/10.1109/ICICSP50920.2020.9232086","url":null,"abstract":"Flame detection has significance to reduce the loss of life and property caused by fire. At present, there is a lack of outdoor night flame detection methods. Most of the existing flame detection methods are based on flame characteristics or models, and most of them require additional storage and computation, which will reduce the system performance. The difficult problem of outdoor night flame recognition is how to eliminate the interference of fixed and mobile light sources. The paper proposes a method to detect outdoor night flame by using flame brightness and location characteristics. This method can detect the night flame accurately without additional cost. The frame difference method and OTSU algorithm are used to extract and segment the suspected flame targets in three consecutive frames. It can effectively reduce the interference of fixed and mobile light sources. We calculate the position coincidence rate of the suspected flame, and then compare the coincidence rate with the preset threshold to determine whether the suspicious target is a flame. The effectiveness of our proposed method is validated by experiments carried out on our self-created dataset, which achieves 95.5% detection accuracy.","PeriodicalId":117760,"journal":{"name":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131284455","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