2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)最新文献_第7页

Small-scale Data Underwater Acoustic Target Recognition with Deep Forest Model 基于深度森林模型的小尺度水声目标识别

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984335

Yafen Dong, Xiaohong Shen, Yongsheng Yan, Haiyan Wang

{"title":"Small-scale Data Underwater Acoustic Target Recognition with Deep Forest Model","authors":"Yafen Dong, Xiaohong Shen, Yongsheng Yan, Haiyan Wang","doi":"10.1109/ICSPCC55723.2022.9984335","DOIUrl":"https://doi.org/10.1109/ICSPCC55723.2022.9984335","url":null,"abstract":"Underwater acoustic target recognition is an issue of great interest, and its key lies in effective feature extraction. Nowadays, due to the rapid development of underwater acoustic signal processing technology and machine learning, some progress has been made in the field of underwater acoustic target recognition. However, traditional machine learning methods utilize shallow features, and the recognition ability needs to be further improved. Although neural network-based deep learning methods can extract deep features, they are prone to over-fitting and other undesirable phenomena in underwater small-scale data scenarios. This means that we need to find a method of underwater acoustic target recognition that can extract deep features, and it should be suitable for small-scale data scenarios. In this research, a method of underwater acoustic target recognition based on the deep forest model is come up with to meet the above requirements. This method adopts MFCC features and the deep forest model as the input feature vectors and classifier, respectively. Experimental results on the ShipsEar database show that the proposed method achieves satisfactory performance and has a promising application in the field of small-scale data underwater acoustic target recognition.","PeriodicalId":346917,"journal":{"name":"2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116434581","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

DAC-Sync: Research on underwater time synchronization algorithm based on Doppler effect and clustering model DAC-Sync:基于多普勒效应和聚类模型的水下时间同步算法研究

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984584

Yunfeng Han, Ziyi Guo, Yujie Ouyang, Jucheng Zhang

{"title":"DAC-Sync: Research on underwater time synchronization algorithm based on Doppler effect and clustering model","authors":"Yunfeng Han, Ziyi Guo, Yujie Ouyang, Jucheng Zhang","doi":"10.1109/ICSPCC55723.2022.9984584","DOIUrl":"https://doi.org/10.1109/ICSPCC55723.2022.9984584","url":null,"abstract":"Time synchronization technology plays an important role in underwater acoustic sensor networks, which is the foundation of the cooperation among network nodes to complete distributed tasks. To address the problem of long underwater propagation delay, the mobility of nodes, and the limited energy of nodes, this paper proposes an underwater time synchronization algorithm (DAC-Sync) based on the Doppler effect and the cluster model. The clustering model is used to realize the time synchronization between nodes in stages. Besides, the effect of clock frequency skew is considered when estimating the Doppler scale factor, the clock frequency skew is solved by multiple one-way interactions and obtain clock phase offset through a two-way interaction, to complete the whole time synchronization process. The performance of DAC-Sync was compared with CD-Sync under the same simulation conditions. The results show that, for punctuality accuracy, the algorithm drifts by an average of around 36 microseconds per second over a period of 106 seconds, while CD-Sync drifts by an average of around 146 microseconds per second, while, drifting 1.5 milliseconds and 6.1 milliseconds respectively for timing accuracy. For energy consumption, compared with CD-Sync, DAC-Sync has lower computing cost and simpler synchronization process. Therefore, this algorithm has higher energy efficiency and high synchronization accuracy.","PeriodicalId":346917,"journal":{"name":"2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126260595","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

Dual attention unit-based generative adversarial networks for low-light image enhancement 基于双注意单元的弱光图像增强生成对抗网络

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984400

Tian Ma, Chenhui Fu, Ming Guo, Jiayi Yang, Jia Liu

{"title":"Dual attention unit-based generative adversarial networks for low-light image enhancement","authors":"Tian Ma, Chenhui Fu, Ming Guo, Jiayi Yang, Jia Liu","doi":"10.1109/ICSPCC55723.2022.9984400","DOIUrl":"https://doi.org/10.1109/ICSPCC55723.2022.9984400","url":null,"abstract":"Images taken in low-light conditions would have insufficient light intensity and high noise. Many existing methods could not work very well in low-light environments, such as the noise and artifacts in dark conditions will be more obvious when enhanced. Therefore, low-light image enhancement is a challenging task in computer vision. To solve this problem, this paper proposes a lightweight generative adversarial network with dual-attention units to enhance underexposed photos. There is only a simple two-layer convolution in the generator section, and a dual-attention unit is added between the two convolutions to suppress the noise generated during the enhancement process and the deviation of color reduction. Then, non-local correlations of the image are used in the spatial attention module for denoising. Ours low-light image enhancement network is guided by the channel attention module to optimize redundant color features. In addition, the ideas of PatchGAN and Relativistic GAN are combined in the discriminator section to make the discriminator a better measure of the probability of changing from absolute true or false to relative true or false. The experiment results show that, our method could get better enhancement effects on low-illumination image datasets, which has more natural color, better exposure, and less noise and artifacts.","PeriodicalId":346917,"journal":{"name":"2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126700644","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

Specific Emitter Identification Based on Homomorphic Filtering and Support Vector Machine-2K 基于同态滤波和支持向量机的特定发射器识别[j]

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984423

Qi Wu, Zepeng Hu, Q. Wan

引用次数: 0

Mine pressure prediction model of fully mechanized mining face based on Improved Transformer 基于改进变压器的综采工作面矿井压力预测模型

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984378

Yaping Liu, Lihong Dong, Ou Ye

{"title":"Mine pressure prediction model of fully mechanized mining face based on Improved Transformer","authors":"Yaping Liu, Lihong Dong, Ou Ye","doi":"10.1109/ICSPCC55723.2022.9984378","DOIUrl":"https://doi.org/10.1109/ICSPCC55723.2022.9984378","url":null,"abstract":"With the increase of mining depth, the frequency of mine pressure disasters on the comprehensive mining face also increases, which has a significant impact on the safety production of coal mines, so the accurate prediction of mine pressure on the comprehensive mining face is of great significance to the prevention of coal mine disasters. In order to improve the prediction accuracy of mine pressure, an improved Transformer mine pressure prediction model is proposed in this paper. Firstly, the gray correlation is used to analyze and rank the mine pressure monitoring data of multiple supports at the working face; secondly, the trend-seasonality decomposition method is combined with Transformer to build the improved Transformer prediction model and optimize it with the optimization algorithm to realize the prediction of mine pressure at the comprehensive mining working face. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are used to evaluate the prediction effect of the model. The experimental results show that the prediction result of the improved Transformer model is better than the traditional BP neural network, GRU, LSTM and the basic Transformer model, and has higher accuracy.","PeriodicalId":346917,"journal":{"name":"2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130579661","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

Impulsive Noise Suppression and Concatenated Code for OFDM Underwater Acoustic Communications OFDM水声通信脉冲噪声抑制与串接码

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984322

Gang Tan, Shefeng Yan, Binbin Yang

{"title":"Impulsive Noise Suppression and Concatenated Code for OFDM Underwater Acoustic Communications","authors":"Gang Tan, Shefeng Yan, Binbin Yang","doi":"10.1109/ICSPCC55723.2022.9984322","DOIUrl":"https://doi.org/10.1109/ICSPCC55723.2022.9984322","url":null,"abstract":"Underwater acoustic (UWA) information transmission is vulnerable to the interference of impulsive noise, which has short duration, high energy and random occurrence, decreasing the reliability of the orthogonal frequency-division multiplexing (OFDM) systems seriously. In this paper, we introduce the symmetric alpha stable (SαS) distribution to model the underwater impulsive noise firstly, and verifies the accuracy by measured noise data. For strong impulsive interference, an adaptive window median filter algorithm based on Chebyshev inequality (AWMF-C) is proposed, which applies the Chebyshev inequality twice to filter out the impulses and designs an adaptive window median filter to suppress them. Secondly, the serial concatenated code with RS code as outer code and interleaver are applied to suppress the residual impulsive interference further. Simulation results show that the proposed algorithm can suppress impulsive noise effectively, and the concatenated coding system given in this paper can further reduce the bit error rate (BER) of the communications system under impulsive noise background.","PeriodicalId":346917,"journal":{"name":"2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132127523","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

Research on the Existence of Non-cooperative Underwater Acoustic Monitoring Network 非合作水声监测网络存在性研究

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984334

Lin Sun, Xiaohong Shen, Zhengguo Liu, Hong Wang, Yifan Yuan, Haodi Mei

引用次数: 0

Ship Recognition Algorithm Based on ResNet in SAR Images 基于ResNet的SAR图像船舶识别算法

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984594

Jinfeng He, Hongtu Xie, Xinqiao Jiang, Zhitao Wu, Guoqian Wang

引用次数: 0

Active Jamming Signal Recognition based on Residual Neural Network* 基于残差神经网络的有源干扰信号识别*

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984424

Mingqiu Ren, B. Cheng, Po Gao

{"title":"Active Jamming Signal Recognition based on Residual Neural Network*","authors":"Mingqiu Ren, B. Cheng, Po Gao","doi":"10.1109/ICSPCC55723.2022.9984424","DOIUrl":"https://doi.org/10.1109/ICSPCC55723.2022.9984424","url":null,"abstract":"The problem how to effectively identify the type of active jamming signal has important practical significance for the accurate perception of radar anti-jamming system. Therefore, a radar active jamming identification method based on fractional Fourier transform and residual neural network is proposed. Before the jamming signal pattern recognition, the time-frequency structure model of the signal is established, and the influence factors such as radar technical system and jamming signal processing cycle are comprehensively considered. The constraints of the time-frequency analysis kernel function and processing on the jamming signal type, the cross term of the composite modulation signal and the effectiveness of the distorted signal characteristics are analyzed. Then, according to the requirements of availability and recognition rate of subsequent signal classifiers, the recognition model based on residual neural network (RESNET) is used to solve the problem. The simulation results show that the recognition effect of multiple active jamming patterns under different interference to signal ratio is higher than 90%, which verifies the effectiveness and rationality of the method.","PeriodicalId":346917,"journal":{"name":"2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115077988","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

Multi-Temporal PolSAR Image Classification Based on Polarimetric Scattering Tensor Eigenvalue Decomposition and Deep CNN Model 基于偏振散射张量特征值分解和深度CNN模型的多时相偏振sar图像分类

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984546

Jun-Wu Deng, Haoliang Li, X. Cui, Siwei Chen

引用次数: 0