EURASIP Journal on Advances in Signal Processing最新文献_第7页

Purposeful co-design of OFDM signals for ranging and communications 有目的地共同设计用于测距和通信的 OFDM 信号

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-01-30 DOI: 10.1186/s13634-024-01110-w

Andrew Graff, Todd E. Humphreys

引用次数: 0

De-noising classification method for financial time series based on ICEEMDAN and wavelet threshold, and its application 基于 ICEEMDAN 和小波阈值的金融时间序列去噪分类方法及其应用

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-01-26 DOI: 10.1186/s13634-024-01115-5

Bing Liu, Huanhuan Cheng

{"title":"De-noising classification method for financial time series based on ICEEMDAN and wavelet threshold, and its application","authors":"Bing Liu, Huanhuan Cheng","doi":"10.1186/s13634-024-01115-5","DOIUrl":"https://doi.org/10.1186/s13634-024-01115-5","url":null,"abstract":"This paper proposes a classification method for financial time series that addresses the significant issue of noise. The proposed method combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and wavelet threshold de-noising. The method begins by employing ICEEMDAN to decompose the time series into modal components and residuals. Using the noise component verification approach introduced in this paper, these components are categorized into noisy and de-noised elements. The noisy components are then de-noised using the Wavelet Threshold technique, which separates the non-noise and noise elements. The final de-noised output is produced by merging the non-noise elements with the de-noised components, and the 1-NN (nearest neighbor) algorithm is applied for time series classification. Highlighting its practical value in finance, this paper introduces a two-step stock classification prediction method that combines time series classification with a BP (Backpropagation) neural network. The method first classifies stocks into portfolios with high internal similarity using time series classification. It then employs a BP neural network to predict the classification of stock price movements within these portfolios. Backtesting confirms that this approach can enhance the accuracy of predicting stock price fluctuations.","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"21 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139590185","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

Energy-efficient access point clustering and power allocation in cell-free massive MIMO networks: a hierarchical deep reinforcement learning approach 无小区大规模多输入多输出网络中的高能效接入点聚类和功率分配：一种分层深度强化学习方法

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-01-26 DOI: 10.1186/s13634-024-01111-9

Fangqing Tan, Quanxuan Deng, Qiang Liu

{"title":"Energy-efficient access point clustering and power allocation in cell-free massive MIMO networks: a hierarchical deep reinforcement learning approach","authors":"Fangqing Tan, Quanxuan Deng, Qiang Liu","doi":"10.1186/s13634-024-01111-9","DOIUrl":"https://doi.org/10.1186/s13634-024-01111-9","url":null,"abstract":"Cell-free massive multiple-input multiple-output (CF-mMIMO) has attracted considerable attention due to its potential for delivering high data rates and energy efficiency (EE). In this paper, we investigate the resource allocation of downlink in CF-mMIMO systems. A hierarchical depth deterministic strategy gradient (H-DDPG) framework is proposed to jointly optimize the access point (AP) clustering and power allocation. The framework uses two-layer control networks operating on different timescales to enhance EE of downlinks in CF-mMIMO systems by cooperatively optimizing AP clustering and power allocation. In this framework, the high-level processing of system-level problems, namely AP clustering, enhances the wireless network configuration by utilizing DDPG on the large timescale while meeting the minimum spectral efficiency (SE) constraints for each user. The low layer solves the link-level sub-problem, that is, power allocation, and reduces interference between APs and improves transmission performance by utilizing DDPG on a small timescale while meeting the maximum transmit power constraint of each AP. Two corresponding DDPG agents are trained separately, allowing them to learn from the environment and gradually improve their policies to maximize the system EE. Numerical results validate the effectiveness of the proposed algorithm in term of its convergence speed, SE, and EE.","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"22 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139585556","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

Electrocardiogram prediction based on variational mode decomposition and a convolutional gated recurrent unit 基于变异模式分解和卷积门控递归单元的心电图预测

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-01-25 DOI: 10.1186/s13634-024-01113-7

HongBo Wang, YiZhe Wang, Yu Liu, YueJuan Yao

{"title":"Electrocardiogram prediction based on variational mode decomposition and a convolutional gated recurrent unit","authors":"HongBo Wang, YiZhe Wang, Yu Liu, YueJuan Yao","doi":"10.1186/s13634-024-01113-7","DOIUrl":"https://doi.org/10.1186/s13634-024-01113-7","url":null,"abstract":"Electrocardiogram (ECG) prediction is highly important for detecting and storing heart signals and identifying potential health hazards. To improve the duration and accuracy of ECG prediction on the basis of noise filtering, a new algorithm based on variational mode decomposition (VMD) and a convolutional gated recurrent unit (ConvGRU) was proposed, named VMD-ConvGRU. VMD can directly remove noise, such as baseline drift noise, without manual intervention, greatly improving the model usability, and its combination with ConvGRU improves the prediction time and accuracy. The proposed algorithm was compared with three related algorithms (PSR-NN, VMD-NN and TS fuzzy) on MIT-BIH, an internationally recognized arrhythmia database. The experiments showed that the VMD-ConvGRU algorithm not only achieves better prediction accuracy than that of the other three algorithms but also has a considerable advantage in terms of prediction time. In addition, prediction experiments on both the MIT-BIH and European ST-T databases have shown that the VMD-ConvGRU algorithm has better generalizability than the other methods.","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"65 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559664","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

Bias-free estimation of the covariance function and the power spectral density from data with missing samples including extended data gaps 从包含扩展数据缺口的缺失样本数据中无偏估计协方差函数和功率谱密度

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-01-25 DOI: 10.1186/s13634-024-01108-4

Nils Damaschke, Volker Kühn, Holger Nobach

{"title":"Bias-free estimation of the covariance function and the power spectral density from data with missing samples including extended data gaps","authors":"Nils Damaschke, Volker Kühn, Holger Nobach","doi":"10.1186/s13634-024-01108-4","DOIUrl":"https://doi.org/10.1186/s13634-024-01108-4","url":null,"abstract":"Nonparametric estimation of the covariance function and the power spectral density of uniformly spaced data from stationary stochastic processes with missing samples is investigated. Several common methods are tested for their systematic and random errors under the condition of variations in the distribution of the missing samples. In addition to random and independent outliers, the influence of longer and hence correlated data gaps on the performance of the various estimators is also investigated. The aim is to construct a bias-free estimation routine for the covariance function and the power spectral density from stationary stochastic processes under the condition of missing samples with an optimum use of the available information in terms of low estimation variance and mean square error, and that independent of the spectral composition of the data gaps. The proposed procedure is a combination of three methods that allow bias-free estimation of the desired statistical functions with efficient use of the available information: weighted averaging over valid samples, derivation of the covariance estimate for the entire data set and restriction of the domain of the covariance function in a post-processing step, and appropriate correction of the covariance estimate after removal of the estimated mean value. The procedures abstain from interpolation of missing samples as well as block subdivision. Spectral estimates are obtained from covariance functions and vice versa using Wiener–Khinchin’s theorem.","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"7 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559588","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

Detecting GNSS spoofing using deep learning 利用深度学习检测全球导航卫星系统欺骗行为

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-01-18 DOI: 10.1186/s13634-023-01103-1

引用次数: 0

SHC: soft-hard correspondences framework for simplifying point cloud registration SHC：简化点云注册的软硬对应框架

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-01-17 DOI: 10.1186/s13634-023-01104-0

Zhaoxiang Chen, Feng Yu, Shuqing Liu, Jiacheng Cao, Zhuohan Xiao, Minghua Jiang

{"title":"SHC: soft-hard correspondences framework for simplifying point cloud registration","authors":"Zhaoxiang Chen, Feng Yu, Shuqing Liu, Jiacheng Cao, Zhuohan Xiao, Minghua Jiang","doi":"10.1186/s13634-023-01104-0","DOIUrl":"https://doi.org/10.1186/s13634-023-01104-0","url":null,"abstract":"Point cloud registration is a multifaceted problem that involves a series of procedures. Many deep learning methods employ complex structured networks to achieve robust registration performance. However, these intricate structures can amplify the challenges of network learning and impede gradient propagation. To address this concern, the soft-hard correspondence (SHC) framework is introduced in the present paper to streamline the registration problem. The framework encompasses two modes: the hard correspondence mode, which transforms the registration problem into a correspondence pair search problem, and the soft correspondence mode, which addresses this new problem. The simplification of the problem provides two advantages. First, it eliminates the need for intermediate operations that lead to error fusion and counteraction, thereby improving gradient propagation. Second, a perfect solution is not necessary to solve the new problem, since accurate registration results can be achieved even in the presence of errors in the found pairs. The experimental results demonstrate that SHC successfully simplifies the registration problem. It achieves performance comparable to complex networks using a simple network and can achieve zero error on datasets with perfect correspondence pairs.","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"53 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139481414","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

Unsupervised domain adaptive bearing fault diagnosis based on maximum domain discrepancy 基于最大域差异的无监督域自适应轴承故障诊断

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-01-11 DOI: 10.1186/s13634-023-01107-x

Cuixiang Wang, Shengkai Wu, Xing Shao

{"title":"Unsupervised domain adaptive bearing fault diagnosis based on maximum domain discrepancy","authors":"Cuixiang Wang, Shengkai Wu, Xing Shao","doi":"10.1186/s13634-023-01107-x","DOIUrl":"https://doi.org/10.1186/s13634-023-01107-x","url":null,"abstract":"In the existing domain adaptation-based bearing fault diagnosis methods, the data difference between the source domain and the target domain is not obvious. Besides, parameters of target domain feature extractor gradually approach that of source domain feature extractor to cheat discriminator which results in similar feature distribution of source domain and target domain. These issues make it difficult for the domain adaptation-based bearing fault diagnosis methods to achieve satisfactory performance. An unsupervised domain adaptive bearing fault diagnosis method based on maximum domain discrepancy (UDA-BFD-MDD) is proposed in this paper. In UDA-BFD-MDD, maximum domain discrepancy is exploited to maximize the feature difference between the source domain and target domain, while the output feature of target domain feature extractor can cheat the discriminator. The performance of UDA-BFD-MDD is verified through comprehensive experiments using the bearing dataset of Case Western Reserve University. The experimental results demonstrate that UDA-BFD-MDD is more stable during training process and can achieve higher accuracy rate.","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"8 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139423902","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

Average effective subcarrier-domain sparse representation approach for target information estimation in CP-OFDM-based passive bistatic radar 基于 CP-OFDM 的无源双稳态雷达中目标信息估计的平均有效子载波域稀疏表示方法

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-01-09 DOI: 10.1186/s13634-023-01106-y

Zhixin Zhao, Yanghang Gong, Huilin Zhou, Yulong Cao

{"title":"Average effective subcarrier-domain sparse representation approach for target information estimation in CP-OFDM-based passive bistatic radar","authors":"Zhixin Zhao, Yanghang Gong, Huilin Zhou, Yulong Cao","doi":"10.1186/s13634-023-01106-y","DOIUrl":"https://doi.org/10.1186/s13634-023-01106-y","url":null,"abstract":"Although some existing sparse representation (SR) methods are robust for target detection in passive bistatic radar (PBR), they still face the challenges of high computational complexity and poor detection performance for extremely low-signal-to-clutter ratio (SCR) target. So, an average effective subcarrier (AES)-domain sparse representation approach is investigated in this paper. Firstly, the AES-based SR model is proposed to solve the problem of high computational complexity, which is established by utilizing the sparseness of the orthogonal frequency-division multiplexing (OFDM) with cyclic prefix (CP) signals in each effective subcarrier domain. Then, considering the difficulty of detecting extremely low-SCR targets, clutter cancellation is implemented by the SR-based optimization model. Two AES-S algorithms, namely AES-S-based clutter cancellation in the time domain (AES-S-T) and AES-S-based clutter cancellation in the subcarrier domain (AES-S-C), are proposed, and the computational complexity is further reduced. Finally, extensive simulation and experimental results illustrate that the proposed algorithms have good detection performance and low computational complexity in PBR detection scene.","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"84 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139415062","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

Multi-UAV-assisted Internet of Remote Things communication within satellite–aerial–terrestrial integrated network 卫星-航空-地面综合网络中的多无人机辅助远程物联网通信

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-01-09 DOI: 10.1186/s13634-023-01101-3

Yuanyuan Yao, Dengyang Dong, Changjun Cai, Sai Huang, Xin Yuan, Xiaocong Gong

{"title":"Multi-UAV-assisted Internet of Remote Things communication within satellite–aerial–terrestrial integrated network","authors":"Yuanyuan Yao, Dengyang Dong, Changjun Cai, Sai Huang, Xin Yuan, Xiaocong Gong","doi":"10.1186/s13634-023-01101-3","DOIUrl":"https://doi.org/10.1186/s13634-023-01101-3","url":null,"abstract":"Due to the limited transmission capabilities of terrestrial intelligent devices within the Internet of Remote Things (IoRT), this paper proposes an optimization scheme aimed at enhancing data transmission rate while ensuring communication reliability. This scheme focuses on multi-unmanned aerial vehicle (UAV)-assisted IoRT data communication within the satellite–aerial–terrestrial integrated network (SATIN), which is one of the key technologies for the sixth generation (6G) networks. To optimize the system’s data transmission rate, we introduce a multi-dimensional coverage and power optimization (CPO) algorithm, rooted in the block coordinate descent (BCD) method. This algorithm concurrently optimizes various parameters, including the number and deployment of UAVs, the correlation between IoRT devices and UAVs, and the transmission power of both devices and UAVs. To ensure comprehensive coverage of a large-scale randomly distributed array of terrestrial devices, combined with machine learning algorithm, we present the Dynamic Deployment based on K-means (DDK) algorithm. Additionally, we address the non-convexity challenge in resource allocation for transmission power through variable substitution and the successive convex approximation technique (SCA). Simulation results substantiate the remarkable efficacy of our CPO algorithm, showcasing a maximum 240% improvement in the uplink transmission rate of IoRT data compared to conventional methods.","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"18 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139415061","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