Eurasip Journal on Advances in Signal Processing最新文献_第2页

Ris-aided integrated satellite duplex UAV relay terrestrial networks with imperfect hardware and co-channel interference ris辅助集成卫星双工无人机中继地面网络，硬件不完善和同信道干扰

4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2023-10-28 DOI: 10.1186/s13634-023-01067-2

Jiu Sun, Kefeng Guo, Feng Zhou, Xueling Wang, Mingfu Zhu

{"title":"Ris-aided integrated satellite duplex UAV relay terrestrial networks with imperfect hardware and co-channel interference","authors":"Jiu Sun, Kefeng Guo, Feng Zhou, Xueling Wang, Mingfu Zhu","doi":"10.1186/s13634-023-01067-2","DOIUrl":"https://doi.org/10.1186/s13634-023-01067-2","url":null,"abstract":"Abstract Increasing the spectrum and time utilization rate is the goal of the next wireless communication networks. This work studies the outage performance of the reconfigurable intelligent surface (RIS)-aided integrated satellite duplex unmanned-aerial-vehicle relay terrestrial networks. Especially, the RIS is installed in the tall building to enhance the communication. To further increase the time utilization rate, the duplex unmanned aerial vehicle is utilized to enhance the time utilization efficiency. However, owing to the practical reasons, the imperfect hardware and co-channel interference are further researched in this paper. Particularly, the accurate expression for the outage probability (OP) is gotten to confirm the effects of RIS parameters, channel parameters and imperfect hardware on the considered network. To gain more insights of the OP at high signal-to-noise ratios, the asymptotic analysis for the OP is derived. Finally, some Monte Carlo simulations are provided to verify the rightness of the theoretical analysis. The simulations indicate that the OP is mainly judged by the satellite transmission link. The results also indicate that although RIS can enhance the system performance, the system performance is not decided by RIS.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136158568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An improved wavelet threshold denoising approach for surface electromyography signal 一种改进的小波阈值去噪方法用于表面肌电信号

4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2023-10-25 DOI: 10.1186/s13634-023-01066-3

Chuanyun Ouyang, Liming Cai, Bin Liu, Tianxiang Zhang

{"title":"An improved wavelet threshold denoising approach for surface electromyography signal","authors":"Chuanyun Ouyang, Liming Cai, Bin Liu, Tianxiang Zhang","doi":"10.1186/s13634-023-01066-3","DOIUrl":"https://doi.org/10.1186/s13634-023-01066-3","url":null,"abstract":"Abstract Background The surface electromyography (sEMG) signal presents significant challenges for the dynamic analysis and subsequent examination of muscle movements due to its low signal energy, broad frequency distribution, and inherent noise interference. However, the conventional wavelet threshold filtering techniques for sEMG signals are plagued by the Gibbs-like phenomenon and an overall decrease in signal amplitude, leading to signal distortion. Purpose This article aims to establish an improved wavelet thresholding method that can filter various types of signals, with a particular emphasis on sEMG signals, by adjusting two independent factors. Hence, it generates the filtered signal with a higher signal-to-noise ratio (SNR), a lower mean square error (MSE), and better signal quality. Results After denoising Doppler and Heavysine signals, the filtered signal exhibits a higher SNR and lower MSE than the signal generated from traditional filtering algorithms. The filtered sEMG signal has a lower noise baseline while retaining the peak sEMG signal strength. Conclusion The empirical evaluation results show that the quality of the signal processed by the new noise reduction algorithm is better than the traditional hard thresholding, soft thresholding, and Garrote thresholding methods. Moreover, the filtering performance on the sEMG signal is improved significantly, which enhances the accuracy and reliability of subsequent experimental analyses.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"97 3-4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135170214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A wavelet selection scheme in underwater discharge signal analysis 水下放电信号分析中的小波选择方法

4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2023-10-24 DOI: 10.1186/s13634-023-01065-4

Xiaobing Zhang, Binjie Lu, Liang Qiao

引用次数: 0

Fast algorithms for band-limited extrapolation by over sampling and Fourier series 基于过采样和傅立叶级数的带限外推快速算法

4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2023-10-24 DOI: 10.1186/s13634-023-01060-9

Weidong Chen

引用次数: 0

Joint channel and impulse noise estimation based on compressed sensing and Kalman filter for OFDM system 基于压缩感知和卡尔曼滤波的OFDM系统联合信道和脉冲噪声估计

4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2023-10-21 DOI: 10.1186/s13634-023-01064-5

Yiting Zhao, Youming Li, Shoudong Shi, Jianding Yu

{"title":"Joint channel and impulse noise estimation based on compressed sensing and Kalman filter for OFDM system","authors":"Yiting Zhao, Youming Li, Shoudong Shi, Jianding Yu","doi":"10.1186/s13634-023-01064-5","DOIUrl":"https://doi.org/10.1186/s13634-023-01064-5","url":null,"abstract":"Abstract Impulse noise (IN) widely exists in many communication systems, which seriously affects the performance of OFDM communication systems. A joint channel and IN estimation method based on all subcarriers is designed. This method uses a sparse Bayesian learning (SBL) algorithm incorporating forward–backward Kalman filter (FB-Kalman) to tackle the problem of joint channel and IN estimation and data detection for OFDM systems. Firstly, the channel impulse response and IN are regarded as unknown sparse vectors, and a SBL framework using all subcarriers is proposed to estimate the unknown vector. The SBL theory is used based on the prior distribution of variables, and then the forward–backward joint system is established, which applies the data detection simultaneously. We also propose the FB-Kalman implementation algorithm by using the expectation maximization updates. Explicit expressions of mean and covariance matrix of the posterior distribution are derived in the E-step. Simulation results show that the proposed algorithm improves the normalized mean square error and bit error rate performance of OFDM system in the presence of IN communication environment.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"70 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135512057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An intelligent signal processing method against impulsive noise interference in AIoT 一种抗AIoT中脉冲噪声干扰的智能信号处理方法

4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2023-10-19 DOI: 10.1186/s13634-023-01061-8

Bin Wang, Ziyan Jiang, Yanjing Sun, Yan Chen

{"title":"An intelligent signal processing method against impulsive noise interference in AIoT","authors":"Bin Wang, Ziyan Jiang, Yanjing Sun, Yan Chen","doi":"10.1186/s13634-023-01061-8","DOIUrl":"https://doi.org/10.1186/s13634-023-01061-8","url":null,"abstract":"Abstract In complex industrial environments such as the Internet of Things in coal mines, large mechanical and electrical equipment can generate powerful impulsive noise, which can cause sudden errors. Because it is difficult to establish an accurate channel model, the performance of current error control techniques is limited. To enhance the reliability of information recovery in the Internet of Things in coal mines, the traditional method of shortening the communication distance between sensors is often utilized, but this can be costly. Therefore, this article proposes an intelligent signal processing method against impulsive noise interference that draws on the concept of the Artificial Intelligence of Things (AIoT) and incorporates deep learning technology. This method replaces the traditional sensor signal processing module with a Convolutional Neural Network (CNN), which learns the intricate mapping relationship between transmitted information and sensor signals in impulsive noise environments. Simulation results demonstrate that the proposed method outperforms the traditional sensor signal processing method in three impulsive noise environments by achieving a lower Bit Error Rate (BER). Moreover, this method adopts an improved lightweight neural network, which is more conducive to the deployment of mobile terminals in the Internet of Things.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135729972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep learning-based load forecasting algorithm for energy consumption monitoring system using dimension expansion 基于深度学习的能耗监测系统负荷预测维数展开算法

4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2023-10-18 DOI: 10.1186/s13634-023-01068-1

Wei-guo Zhang, Qing Zhu, Lin-Lin Gu, Hui-Jie Lin

引用次数: 0

Image embedding and user multi-preference modeling for data collection sampling 图像嵌入和用户多偏好建模的数据采集采样

4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2023-10-18 DOI: 10.1186/s13634-023-01069-0

Anju Jose Tom, Laura Toni, Thomas Maugey

引用次数: 0

A fault diagnosis method for rolling bearings based on graph neural network with one-shot learning 基于单次学习的图神经网络滚动轴承故障诊断方法

4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2023-10-11 DOI: 10.1186/s13634-023-01063-6

Yan Gao, Haowei Wu, Haiqian Liao, Xu Chen, Shuai Yang, Heng Song

引用次数: 0

Urban localization using robust filtering at multiple linearization points 在多个线性化点上使用鲁棒滤波的城市定位

4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2023-10-06 DOI: 10.1186/s13634-023-01062-7

Shubh Gupta, Adyasha Mohanty, Grace Gao

{"title":"Urban localization using robust filtering at multiple linearization points","authors":"Shubh Gupta, Adyasha Mohanty, Grace Gao","doi":"10.1186/s13634-023-01062-7","DOIUrl":"https://doi.org/10.1186/s13634-023-01062-7","url":null,"abstract":"Abstract We propose a robust Bayesian filtering framework for state and multi-modal uncertainty estimation in urban settings by fusing diverse sensor measurements. Our framework addresses multi-modal uncertainty from various error sources by tracking a separate probability distribution for linearization points corresponding to dynamics, measurements, and cost functions. Multiple parallel robust Extended Kalman filters (R-EKF) leverage these linearization points to characterize the state probability distribution. Employing Rao–Blackwellization, we combine the linearization point distribution with the state distribution, resulting in a unified, efficient, and outlier-resistant Bayesian filter that captures multi-modal uncertainty. Furthermore, we introduce a gradient descent-based optimization method to refine the filter parameters using available data. Evaluating our filter on real-world data from a multi-sensor setup comprising camera, Global Navigation Satellite System (GNSS), and Attitude and Heading Reference System (AHRS) demonstrates improved performance in bounding position errors based on uncertainty, while maintaining competitive accuracy and comparable computation to existing methods. Our results suggest that our framework is a promising direction for safe and reliable localization in urban environments.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135350259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0