International Conference on Signal Processing Systems最新文献_第3页

Intention recognition based on Hidden Markov Model for aerial target 基于隐马尔可夫模型的航空目标意图识别

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631555

Huimin Chai, Yong Zhang, Yanan Song

引用次数: 0

GLNet: low-light image enhancement via grayscale priors GLNet:通过灰度先验增强弱光图像

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631466

Li Guo, Junwei Xie, Yuyang Xue, Ru Li, Weixin Zheng, T. Tong, Qinquan Gao

{"title":"GLNet: low-light image enhancement via grayscale priors","authors":"Li Guo, Junwei Xie, Yuyang Xue, Ru Li, Weixin Zheng, T. Tong, Qinquan Gao","doi":"10.1117/12.2631466","DOIUrl":"https://doi.org/10.1117/12.2631466","url":null,"abstract":"Low-light images are generally produced by shooting in a low light environment or a tricky shooting angle, which not only affect people's perception, but also leads to the bad performance of some artificial intelligence algorithms, such as object detection, super-resolution, and so on. There are two difficulties in the low-light enhancement algorithm: in the first place, applying image processing algorithms independently to each low-light image often leads to the color distortion; the second is the need to restore the texture of the extremely low-light area. To address these issues, we present two novel and general approaches: firstly, we propose a new loss function to constrain the ratio between the corresponding RGB pixel values on the low-light image and the high-light image; secondly, we propose a new framework named GLNet, which uses the dense residual connection block to obtain the deep features of the low-light images, and design a gray scale channel network branch to guide the texture restoration on the RGB channels by enhancing the grayscale image. The ablation experiments have demonstrated the effectiveness of the proposed module in this paper. Extensive quantitative and perceptual experiments show that our approach obtains state-of-the-art performance on the public dataset.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133438628","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

The elliptic function in graph filters 图滤波器中的椭圆函数

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631597

Zirui Ge, Zhenghong Yang

引用次数: 0

Rural road environment segmentation of LiDAR dataset with deep learning 基于深度学习的LiDAR数据集农村道路环境分割

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631445

Zakria, Jianhua Deng, Jiani He, Jingye Cai, Muhammad Saddam Khokhar

引用次数: 0

A multi-channel phase calibration method based on deep neural network 一种基于深度神经网络的多通道相位标定方法

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631453

Zhang Yuxin, Yao Xin, Zhang Yilong

{"title":"A multi-channel phase calibration method based on deep neural network","authors":"Zhang Yuxin, Yao Xin, Zhang Yilong","doi":"10.1117/12.2631453","DOIUrl":"https://doi.org/10.1117/12.2631453","url":null,"abstract":"In order to address problem about the channel phase error, a channel phase calibration method based on deep learning is proposed. Using data mining to replace the traditional method can not only improve the flexibility and stability of the method, but also achieve better results. Firstly, we use the frequency response function to model the channel characteristics, and the channel mismatch model is established to simulate the errors of the channel. Secondly, the error generated by the channel is introduced into the signal to generate the analog data set. Through the training and fitting, we achieved the all-phase calibration. At the same time, a variety of different channel parameters are simulated, and the generalization ability of different channel parameters get verified. Finally, the model network is evaluated in the form of test standard deviation. According to the results, the standard deviation can be controlled within 3°, which proves the effectiveness of this method. In this paper, Octave was used to generate the simulated data set for preprocessing, PyCharm platform was used to build the neural network, and the model was trained based on TensorFlow.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"14 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131956907","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

Conduction study of electrophysiological signals based on in-vitro myocardial tissue 体外心肌组织电生理信号传导研究

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631447

Qingmei Chen, Helin Zhang

引用次数: 0

Design and test of signal processing hardware for two-dimensional phased array digital multi-beam system 二维相控阵数字多波束系统信号处理硬件设计与测试

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631423

Teng Wang, Renhong Xie, Dengbo Sun, Jingwei Hu, Pengcheng Li, Yibin Rui

引用次数: 0

FPGA realization of signal processing for two-dimensional phased array digital multi-beam radar 二维相控阵数字多波束雷达信号处理的FPGA实现

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631442

Jing Hu, Pengcheng Li, D. Sun, Jiangong Shao, Renhong Xie, Yibin Rui

引用次数: 0

A CPM signal denoising method based on attention network 一种基于注意网络的CPM信号去噪方法

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631551

Xiaopeng Zhang, Xiaolin Zhang, Hao Chen

{"title":"A CPM signal denoising method based on attention network","authors":"Xiaopeng Zhang, Xiaolin Zhang, Hao Chen","doi":"10.1117/12.2631551","DOIUrl":"https://doi.org/10.1117/12.2631551","url":null,"abstract":"Cognitive communication countermeasure system utilizes artificial intelligence technology to quickly realize electromagnetic dynamic perception and electronic jamming strategy generation. In the complex electromagnetic environment of the modern battlefield, continuous phase modulation (CPM) signals are getting more and more attention due to high spectral efficiency and power efficiency. CPM signal denoising processing helps to improve electromagnetic dynamic perception performance. In this paper, a novel model, namely attentional denoising autoencoder (ADE), is proposed with enhanced signal denoising by introducing self-attentional mechanism into the autoencoder. The proposed method divides the one-dimensional communication signal sequence into fixed-size signal patches satisfying the same modulation law, and then utilizes the parallel computing of the self-attention mechanism to model the dependencies between the signal patches, and finally average pooling is used to synthesize the information of each signal patch to reconstruct the signal. The simulation results demonstrate that the proposed model is superior to other methods in terms of the denoising effect, and has a high degree of waveform recovery, which is helpful for the subsequent perception and processing of CPM signals.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125000699","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

Low-light image enhancement based on variational Retinex model 基于变分Retinex模型的微光图像增强

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631428

Xuesong Li, Hongyi Zhang, Jinfeng Pan, Qilei Li, Guofeng Zou, Mingliang Gao

引用次数: 2