EURASIP Journal on Advances in Signal Processing最新文献_第4页

New graphical models for sequential data and the improved state estimations by data-conditioned driving noises 用于序列数据的新图形模型，以及通过数据条件驱动噪声改进状态估计

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-04-10 DOI: 10.1186/s13634-024-01145-z

Wonjung Lee

{"title":"New graphical models for sequential data and the improved state estimations by data-conditioned driving noises","authors":"Wonjung Lee","doi":"10.1186/s13634-024-01145-z","DOIUrl":"https://doi.org/10.1186/s13634-024-01145-z","url":null,"abstract":"A prevalent problem in statistical signal processing, applied statistics, and time series analysis arises from the attempt to identify the hidden state of Markov process based on a set of available noisy observations. In the context of sequential data, filtering refers to the probability distribution of the underlying Markovian system given the measurements made at or before the time of the estimated state. In addition to the filtering, the smoothing distribution is obtained from incorporating measurements made after the time of the estimated state into the filtered solution. This work proposes a number of new filters and smoothers that, in contrast to the traditional schemes, systematically make use of the process noises to give rise to enhanced performances in addressing the state estimation problem. In doing so, our approaches for the resolution are characterized by the application of the graphical models; the graph-based framework not only provides a unified perspective on the existing filters and smoothers but leads us to design new algorithms in a consistent and comprehensible manner. Moreover, the graph models facilitate the implementation of the suggested algorithms through message passing on the graph.","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"300 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140601330","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

EEG emotion recognition based on differential entropy feature matrix through 2D-CNN-LSTM network 通过二维-CNN-LSTM 网络，基于差分熵特征矩阵进行脑电图情感识别

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-04-08 DOI: 10.1186/s13634-024-01146-y

Teng Wang, Xiaoqiao Huang, Zenan Xiao, Wude Cai, Yonghang Tai

{"title":"EEG emotion recognition based on differential entropy feature matrix through 2D-CNN-LSTM network","authors":"Teng Wang, Xiaoqiao Huang, Zenan Xiao, Wude Cai, Yonghang Tai","doi":"10.1186/s13634-024-01146-y","DOIUrl":"https://doi.org/10.1186/s13634-024-01146-y","url":null,"abstract":"Emotion recognition research has attracted great interest in various research fields, and electroencephalography (EEG) is considered a promising tool for extracting emotion-related information. However, traditional EEG-based emotion recognition methods ignore the spatial correlation between electrodes. To address this problem, this paper proposes an EEG-based emotion recognition method combining differential entropy feature matrix (DEFM) and 2D-CNN-LSTM. In this work, first, the one-dimensional EEG vector sequence is converted into a two-dimensional grid matrix sequence, which corresponds to the distribution of brain regions of the EEG electrode positions, and can better characterize the spatial correlation between the EEG signals of multiple adjacent electrodes. Then, the EEG signal is divided into equal time windows, and the differential entropy (DE) of each electrode in this time window is calculated, it is combined with a two-dimensional grid matrix and differential entropy to obtain a new data representation that can capture the spatiotemporal correlation of the EEG signal, which is called DEFM. Secondly, we use 2D-CNN-LSTM to accurately identify the emotional categories contained in the EEG signals and finally classify them through the fully connected layer. Experiments are conducted on the widely used DEAP dataset. Experimental results show that the method achieves an average classification accuracy of 91.92% and 92.31% for valence and arousal, respectively. The method performs outstandingly in emotion recognition. This method effectively combines the temporal and spatial correlation of EEG signals, improves the accuracy and robustness of EEG emotion recognition, and has broad application prospects in the field of emotion classification and recognition based on EEG signals.","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"48 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140601333","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 reinforcement approach for computation offloading in MEC dynamic networks 用于 MEC 动态网络计算卸载的深度强化方法

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-04-08 DOI: 10.1186/s13634-024-01142-2

Yibiao Fan, Xiaowei Cai

引用次数: 0

On the asymptotic performance of time-delay and Doppler estimation with a carrier modulated by a band-limited signal 关于使用带限信号调制载波的时延和多普勒估计的渐近性能

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-04-06 DOI: 10.1186/s13634-024-01134-2

Joan M. Bernabeu, Lorenzo Ortega, Antoine Blais, Yoan Grégoire, Eric Chaumette

{"title":"On the asymptotic performance of time-delay and Doppler estimation with a carrier modulated by a band-limited signal","authors":"Joan M. Bernabeu, Lorenzo Ortega, Antoine Blais, Yoan Grégoire, Eric Chaumette","doi":"10.1186/s13634-024-01134-2","DOIUrl":"https://doi.org/10.1186/s13634-024-01134-2","url":null,"abstract":"Time-delay and Doppler estimation is crucial in various engineering fields, as estimating these parameters constitutes one of the key initial steps in the receiver’s operational sequence. Due to its importance, several expressions of the Cramér–Rao Bound (CRB) and Maximum Likelihood Estimation (MLE) have been derived over the years. Previous contributions started from the assumption that the transmission process introduces an unknown phase, which hindered the explicit consideration of the time-delay parameter in the carrier-phase component in theoretical derivations. However, this contribution takes into account this additional term under the assumption that such an unknown phase is inferred and compensated for. This new condition leads to the derivation of a novel MLE. Subsequently, a closed-form expression of the achievable Mean Squared Error (MSE) for the time-delay and Doppler parameters is provided for the asymptotic region, assuming the signal is band-limited. Both expressions are validated via Monte Carlo simulations. This analysis reveals five distinct regions of operation of the MLE, refining existing knowledge and providing valuable insights into time-delay estimation","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"17 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140601569","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

Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization 最近主张：基于事件的新型时延估计算法，用于多传感器时间序列数据同步

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-04-05 DOI: 10.1186/s13634-024-01143-1

Christoph Schranz, Sebastian Mayr, Severin Bernhart, Christina Halmich

{"title":"Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization","authors":"Christoph Schranz, Sebastian Mayr, Severin Bernhart, Christina Halmich","doi":"10.1186/s13634-024-01143-1","DOIUrl":"https://doi.org/10.1186/s13634-024-01143-1","url":null,"abstract":"Estimating time delays in event-based time-series is a crucial task in signal processing as it affects the data quality and is a prerequisite for many subsequent analyses. In particular, data acquired from wearable devices often suffer from a low timestamp precision or clock drift. Current state-of-the-art methods such as Pearson Cross-Correlation are sensitive to typical data quality issues, e.g. misdetected events, and Dynamic Time Warping is computationally expensive. To overcome these limitations, we propose Nearest Advocate, a novel event-based time delay estimation method for multi-sensor time-series data synchronisation. We evaluate its performance using three independent datasets acquired from wearable sensor systems, demonstrating its superior precision, particularly for short, noisy time-series with missing events. Additionally, we introduce a sparse variant that balances precision and runtime. Finally, we demonstrate how Nearest Advocate can be used to solve the problem of linear as well as non-linear clock drifts. Thus, Nearest Advocate offers a promising opportunity for time delay estimation and post-hoc synchronization for challenging datasets across various applications.","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"54 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140601566","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

Object tracking method based on edge detection and morphology 基于边缘检测和形态学的物体跟踪方法

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-04-03 DOI: 10.1186/s13634-024-01144-0

Jie Xu, Sijie Niu, Zhifeng Wang

{"title":"Object tracking method based on edge detection and morphology","authors":"Jie Xu, Sijie Niu, Zhifeng Wang","doi":"10.1186/s13634-024-01144-0","DOIUrl":"https://doi.org/10.1186/s13634-024-01144-0","url":null,"abstract":"With the continuous development of science and technology, intelligent surveillance technology using image processing and computer vision is also progressing. To improve the performance of target detection and tracking, an improved target tracking method is proposed, which uses a combination of the Canny operator and morphology for the detection part, and a Kalman filter extended Kernel Correlation Filter (KCF) tracking algorithm approach for the tracking part. First, a convolution kernel of (3times 3) is improved to a convolution kernel of (2times 2) in the traditional Canny algorithm, and the pixel gradient in the diagonal direction is increased. Secondly, a mathematical morphology theory of nonlinear filtering is applied to the Canny edge detection algorithm, and this method effectively improves the clarity of image edges. Finally, the extended kernel correlation filtering algorithm is applied to video surveillance and Online Object Tracking Benckmark2013 (OTB2013) datasets for testing. The experimental results show that the method proposed in this paper can accurately detect moving targets and the algorithm has good accuracy and success rate.","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"34 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140601361","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 novel outlier detection method based on Bayesian change point analysis and Hampel identifier for GNSS coordinate time series 基于贝叶斯变化点分析和 Hampel 识别器的新型离群点检测方法，适用于 GNSS 坐标时间序列

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-04-02 DOI: 10.1186/s13634-023-01097-w

{"title":"A novel outlier detection method based on Bayesian change point analysis and Hampel identifier for GNSS coordinate time series","authors":"","doi":"10.1186/s13634-023-01097-w","DOIUrl":"https://doi.org/10.1186/s13634-023-01097-w","url":null,"abstract":"<h3>Abstract</h3> The identification and removal of outliers in time series are important problems in numerous fields. In this paper, a novel method (BCP-HI) is proposed to enhance the accuracy of outlier detection in GNSS coordinate time series by combining Bayesian change point (BCP) analysis and the Hampel identifier (HI). By using BCP, change points (cps) in the time series are lidentified, and so the time series is divided into subsegments that have properties of a normal distribution. In each of these separated segments, outliers are detected using HI. Each data element identified as an outlier is corrected by a median filter of window size (w) to obtain the corrected signal. The BCP-HI method was tested on both simulated and real GNSS coordinate time series. Outliers from three different synthetic test datasets with different sampling frequencies and outlier amplitudes were detected with approximately 98% accuracy after processing. After this process, Signal-to-Noise Ratio (SNR) increased from 0.0084 to 10.8714 dB and Root Mean Square (RMS) decreased from 24 to 23 mm. Similarly, for real GNSS data, approximately 98% accuracy was achieved, with an increase in SNR from 0.0003 to 4.4082 dB and a decrease in RMS from 7.6 to 6.6 mm observed. In addition, the output signals after BCP-HI were examined graphically using Lomb–Scargle periodograms and it was observed that clearer power spectrum distributions emerged. When the input and output signals were examined using the Kolmogorov–Smirnov (KS) test, they were found to be statistically similar. These results indicate that the BCP-HI algorithm effectively removes outliers, and enhances processing accuracy and reliability, and improves signal quality.","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"74 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140601356","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

Unmanned aerial vehicle-assisted wideband cognitive radio network based on DDQN-SAC 基于 DDQN-SAC 的无人机辅助宽带认知无线电网络

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-04-02 DOI: 10.1186/s13634-024-01141-3

Leibing Yan, Yiqing Cai, Hui Wei

{"title":"Unmanned aerial vehicle-assisted wideband cognitive radio network based on DDQN-SAC","authors":"Leibing Yan, Yiqing Cai, Hui Wei","doi":"10.1186/s13634-024-01141-3","DOIUrl":"https://doi.org/10.1186/s13634-024-01141-3","url":null,"abstract":"Cognitive radio (CR) systems have emerged as effective tools for improving spectrum efficiency and meeting the growing demands of communication. This study focuses on a flexible CR system based on opportunistic spectrum access technology, which enables secondary networks to efficiently utilize unoccupied spectrum resources for information transmission by actively sensing the spectrum utilization of primary networks. Specifically, we introduce unmanned aerial vehicles (UAV) technology into the CR system to further enhance its flexibility and adaptability, which enables the transmission efficiency of low-altitude UAV networks. In this CR system, UAVs are employed for more flexible spectrum management. The objective of this research is to maximize the average achievable rate of SUs by jointly optimizing the trajectories of secondary UAV, the trajectories of primary UAV, the beamforming of secondary UAV, subchannel allocation and sensing time. To achieve this goal, we employ deep reinforcement learning (DRL) algorithms to optimize these variables. Compared to traditional optimization algorithms, DRL algorithms not only have lower computational complexity but also achieve faster convergence. To address the mixed-action space problem, we propose a Dueling DQN-Soft Actor Critic algorithm. Simulation results demonstrate that the proposed approach in this paper significantly enhances the performance of the CR system compared to traditional baseline schemes. This is manifested in higher spectrum efficiency and data transmission rates, while minimizing interference with the primary network. This innovative research combines drone technology and DRL algorithms, bringing new opportunities and challenges to the future development of cognitive communication systems.","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"25 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140601702","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

Specific emitter identification based on ensemble domain adversarial neural network in multi-domain environments 基于多域环境中集合域对抗神经网络的特定发射器识别

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-03-28 DOI: 10.1186/s13634-024-01138-y

Dingshan Li, Bin Yao, Pu Sun, Peitong Li, Jianfeng Yan, Juzhen Wang

{"title":"Specific emitter identification based on ensemble domain adversarial neural network in multi-domain environments","authors":"Dingshan Li, Bin Yao, Pu Sun, Peitong Li, Jianfeng Yan, Juzhen Wang","doi":"10.1186/s13634-024-01138-y","DOIUrl":"https://doi.org/10.1186/s13634-024-01138-y","url":null,"abstract":"Specific emitter identification is pivotal in both military and civilian sectors for discerning the unique hardware distinctions inherent to various launchers, it can be used to implement security in wireless communications. Recently, a large number of deep learning-based methods for specific emitter identification have been proposed, achieving good performance. However, these methods are trained based on a large amount of data and the data are independently and identically distributed. In actual complex environments, it is very difficult to obtain reliable labeled data. Aiming at the problems of difficulty in data collection and annotation, and the large difference in distribution between training data and test data, a method for individual radiation source identification based on ensemble domain adversarial neural network was proposed. Specifically, a domain adversarial neural network is designed and a Transformer encoder module is added to make the features obey Gaussian distribution and achieve better feature alignment. Ensemble classifiers are then used to enhance the generalization and reliability of the model. In addition, three real and complex migration environments, Alpine–Montane Channel, Plain-Hillock Channel, and Urban-Dense Channel, were constructed, and experiments were conducted on WiFi dataset. The simulation results show that the proposed method exhibits superior performance compared to the other six methods, with an accuracy improvement of about 3%.","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140314256","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 highly efficient resource slicing and scheduling optimization algorithm for power heterogeneous communication networks based on hypergraph and congruence entropy 基于超图和全等熵的电力异构通信网络高效资源切分和调度优化算法

IF 1.9 4区工程技术

EURASIP Journal on Advances in Signal Processing Pub Date : 2024-03-22 DOI: 10.1186/s13634-024-01135-1

Wendi Wang, Chengling Jiang, Linqing Yang, Hong Zhu, Dongxu Zhou

{"title":"A highly efficient resource slicing and scheduling optimization algorithm for power heterogeneous communication networks based on hypergraph and congruence entropy","authors":"Wendi Wang, Chengling Jiang, Linqing Yang, Hong Zhu, Dongxu Zhou","doi":"10.1186/s13634-024-01135-1","DOIUrl":"https://doi.org/10.1186/s13634-024-01135-1","url":null,"abstract":"New services, such as distributed photovoltaic regulation and control, pose new service requirements for communication networks in the new power system. These requirements include low latency, high reliability, and large bandwidth. Consequently, power heterogeneous communication networks face the challenge of maintaining quality of service (QoS) while enhancing network resource utilization. Therefore, this paper puts forward a highly efficient optimization algorithm for resource slicing and scheduling in power heterogeneous communication networks. Our first step involves establishing an architectural description model of heterogeneous wireless networks for electric power based on hypergraph. This model characterizes complex dynamic relationships among service requirements, virtual networks, and physical networks. The system congruence entropy characterizes the degree of matching between the service demand and resource supply. Then an optimization problem is formed to maximize the system congruence entropy through dynamic resource allocation. To solve this problem, a joint resource allocation and routing method based on Lagrangian dual decomposition is proposed. These methods provide the optimal solutions of the nodes and link mappings of service function chains. The simulation results demonstrate that the proposed algorithm in this paper can greatly enhance resource utilization and also meet the QoS requirements of various services.","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"17 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140204403","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