Signal Processing最新文献_第7页

Paramps: Convolutional neural networks based on tensor decomposition for heart sound signal analysis and cardiovascular disease diagnosis 帕兰普斯基于张量分解的卷积神经网络用于心音信号分析和心血管疾病诊断

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-24 DOI: 10.1016/j.sigpro.2024.109716

Lin Duan , Lidong Yang , Yong Guo

{"title":"Paramps: Convolutional neural networks based on tensor decomposition for heart sound signal analysis and cardiovascular disease diagnosis","authors":"Lin Duan , Lidong Yang , Yong Guo","doi":"10.1016/j.sigpro.2024.109716","DOIUrl":"10.1016/j.sigpro.2024.109716","url":null,"abstract":"<div><div>Currently, convolutional neural networks have demonstrated outstanding efficiency in heart sound detection and automatic diagnosis of cardiovascular diseases. However, due to the non-stationary nature and complex data patterns caused by environmental noise and stethoscope differences, traditional neural networks are limited in extracting discriminative features. This article proposes a convolutional neural network based on tensor decomposition to address this issue. This model uses a convolutional neural network with four parallel structures to extract audio features of heart sound signals and introduces a tensor network to use tensor decomposition to perform low-rank approximation on the convolutional kernel, compress model parameters, reduce redundancy, and improve performance. When processing feature data, the model divides large areas of features into locally unordered small areas to achieve feature compression and reorganization, ensuring that crucial information is preserved while compressing parameters. The model can accurately capture spatial structural information and critical features by refining the matrix product state layer. Experiments were conducted on the 2016 PhysioNet/CinC Challenge and the Yaseen heart sound public dataset, the experimental results show that the proposed method has an accuracy of 96.4% and 99.2% on two datasets, specificity of 99.1% and 99.8%, demonstrating its excellent generalization ability and diagnostic accuracy.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109716"},"PeriodicalIF":3.4,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Sparse recovery using expanders via hard thresholding algorithm 通过硬阈值算法使用扩展器进行稀疏恢复

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-24 DOI: 10.1016/j.sigpro.2024.109715

Kun-Kai Wen, Jia-Xin He, Peng Li

引用次数: 0

High-resolution multicomponent LFM parameter estimation based on deep learning 基于深度学习的高分辨率多分量 LFM 参数估计

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-18 DOI: 10.1016/j.sigpro.2024.109714

BeiMing Yan, Yong Li, Wei Cheng, Limeng Dong, Qianlan Kou

{"title":"High-resolution multicomponent LFM parameter estimation based on deep learning","authors":"BeiMing Yan, Yong Li, Wei Cheng, Limeng Dong, Qianlan Kou","doi":"10.1016/j.sigpro.2024.109714","DOIUrl":"10.1016/j.sigpro.2024.109714","url":null,"abstract":"<div><p>This paper addresses the complex challenge of parameter estimation in multi-component Linear Frequency Modulation (LFM) signals by introducing an innovative approach to high-resolution Fractional Fourier Transform (FrFT) parameter estimation, facilitated by convolutional neural networks. Initially, it analyzes the issues of peak shifts and the masking of weaker components due to spectral overlap in the FrFT domain of multi-component LFM signals. Convolutional neural networks are then employed to train and achieve high-resolution representations of FrFT parameters. Specifically, convolutional modules with residual structures are utilized to learn coarse features, while a weighted attention mechanism refines independent features across both channel and spatial dimensions. This approach effectively addresses the challenges posed by spectral peak overlap and frequency shifts in multi-component LFM signals, thereby enhancing the quality of high-resolution parameter estimation. Experimental results demonstrate that the proposed method significantly outperforms traditional methods in processing multi-component LFM signals. Moreover, it exhibits robust detection capabilities for both weak and compact components, thereby underscoring its potential applicability in the field of complex signal processing.</p></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109714"},"PeriodicalIF":3.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Channel estimation for Massive MIMO systems aided by intelligent reflecting surface using semi-super resolution GAN 利用半超分辨率广义泛函模型，在智能反射面辅助下进行大规模多输入多输出（MIMO）系统信道估计

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-16 DOI: 10.1016/j.sigpro.2024.109710

Mehrdad Momen-Tayefeh , Mehrshad Momen-Tayefeh , S. AmirAli GH. Ghahramani , Ali Mohammad Afshin Hemmatyar

{"title":"Channel estimation for Massive MIMO systems aided by intelligent reflecting surface using semi-super resolution GAN","authors":"Mehrdad Momen-Tayefeh , Mehrshad Momen-Tayefeh , S. AmirAli GH. Ghahramani , Ali Mohammad Afshin Hemmatyar","doi":"10.1016/j.sigpro.2024.109710","DOIUrl":"10.1016/j.sigpro.2024.109710","url":null,"abstract":"<div><p>Intelligent Reflecting Surfaces (IRSs) coupled with Massive Multiple-Input-Multiple-Output (MIMO) millimeter wave (mmWave) systems hold immense promise for the next generation of wireless communications. However, harnessing their full potential requires accurate channel state information (CSI). Despite the benefits of IRSs, such as passive element integration and energy efficiency, precise channel estimation becomes a formidable challenge due to the absence of active elements. In this paper, we tackle these challenges by employing generative adversarial networks (GANs) to estimate the channel’s cascade matrix between the base station (BS) and mobile users. To leverage the high correlation among adjacent elements in the IRS, we propose turning off a majority of these elements during the estimation phase, effectively creating a low-resolution channel. We then introduce the semi-super resolution GAN (SSRGAN) model, capable of inferring channel values for the deactivated elements based on existing correlations. Our new SSRGAN-based channel estimation method transforms low-resolution channel data into high-resolution channel data. Through a comprehensive comparative analysis, our study showcases the superior performance of our SSRGAN channel estimation method compared to established benchmark schemes.</p></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109710"},"PeriodicalIF":3.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Design of optimum two-dimensional non-redundant arrays 设计最佳二维非冗余阵列

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-15 DOI: 10.1016/j.sigpro.2024.109713

Seyed Mohammad Hosseini, Mahmood Karimi

{"title":"Design of optimum two-dimensional non-redundant arrays","authors":"Seyed Mohammad Hosseini, Mahmood Karimi","doi":"10.1016/j.sigpro.2024.109713","DOIUrl":"10.1016/j.sigpro.2024.109713","url":null,"abstract":"<div><div>Recent advancements in array signal processing focus on enhancing source detection and reducing the effects of mutual coupling among array elements. This has been achieved using Direction of Arrival (DOA) estimation via virtual arrays formed by sparse arrays. Non-Redundant Arrays (NRAs) are a very common structure among sparse arrays. Traditionally, one-dimensional NRAs capture either azimuth or elevation angles of sources, but practical scenarios often require both simultaneously. This paper introduces optimized methods for designing two-dimensional (2-D) NRAs to address this need. In addition to the optimized design approach for creating 2-D NRAs with minimum aperture, the optimized design approaches for creating 2-D NRAs with desired aperture, with minimized mutual coupling effect and with hybrid of both are proposed. The designed arrays can be in the form of a rectangle or a regular polygon with the number of sides being a multiple of 4. The proposed array design methods significantly enhance the flexibility in designing NRAs, allowing the creation of various array configurations for any desired number of sensors. Simulation results show that the proposed arrays outperform the existing 2-D arrays in estimating the DOAs of signal sources and show more robustness against the effects of mutual coupling.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109713"},"PeriodicalIF":3.4,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Smooth robust principal component analysis based on multidimensional transform tensor for dynamic MRI 基于多维变换张量的动态磁共振成像平滑稳健主成分分析

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-15 DOI: 10.1016/j.sigpro.2024.109712

Xiaotong Liu, Jingfei He, Zehan Wang, Chenghu Mi

{"title":"Smooth robust principal component analysis based on multidimensional transform tensor for dynamic MRI","authors":"Xiaotong Liu, Jingfei He, Zehan Wang, Chenghu Mi","doi":"10.1016/j.sigpro.2024.109712","DOIUrl":"10.1016/j.sigpro.2024.109712","url":null,"abstract":"<div><div>Dynamic magnetic resonance imaging (DMRI) stands as a sophisticated medical imaging technique pivotal to clinical practice, but the protracted duration of its imaging poses a substantial constraint on its practical application. This paper introduces a smooth robust principal component analysis model based on multidimensional transform tensors for accelerating DMR imaging. Specifically, the proposed method breaks down data into low-rank and sparse parts for reconstruction, respectively. The low-rank part employs a multidimensional adaptive transformation framework to generate transform tensors with favorable low-rank properties along three dimensions of DMR data. As for the sparse part, precise reconstruction can be achieved with the sparsity of the data after sparse transformation. In addition, to enhance the preservation of image details, this paper introduces a novel weighted tensor total variation regularization, imposing varying degrees of constraints based on smoothness in different dimensions. Experimental results demonstrate that the proposed method realizes superior reconstruction effects in comparison to existing advanced methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109712"},"PeriodicalIF":3.4,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Relative entropy based uncertainty principles for graph signals 基于相对熵的图信号不确定性原理

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-14 DOI: 10.1016/j.sigpro.2024.109708

Xu Guanlei, Xu Xiaogang , Wang Xiaotong

{"title":"Relative entropy based uncertainty principles for graph signals","authors":"Xu Guanlei, Xu Xiaogang , Wang Xiaotong","doi":"10.1016/j.sigpro.2024.109708","DOIUrl":"10.1016/j.sigpro.2024.109708","url":null,"abstract":"<div><p>In physical quantum mechanics, the uncertainty principle in presence of quantum memory [Berta M, Christandl M, Colbeck R,et al., Nature Physics] can reach much lower bound, which has resulted in a huge breakthrough in quantum mechanics. Inspired by this idea, this paper would propose some novel uncertainty relations in terms of relative entropy for signal representation and time-frequency resolution analysis. On one hand, the relative entropy measures the distinguishability between the known (priori) basis and the client basis, which implies that we have partial “memory” of the client basis so that the uncertainty bounds become sharper in some cases. On the other hand, in some cases, if the reference basis along with nearly the same energy distribution could be given, then the uncertainty bound would tend to zero, as shows that there is no uncertainty any longer. These novel uncertainty relationships with sharper bounds would give us the potential advantages over the classical counterpart. In addition, the detailed comparison with classical Shannon entropy based uncertainty principle has been addressed as well via combined uncertainty relations. Finally, the theoretical analysis and numerical experiments on certain application over graph signals have been demonstrated to show the efficiency of these proposed relations.</p></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109708"},"PeriodicalIF":3.4,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Optimal prototype filter design in GFDM systems for self-interference elimination: A novel signal processing approach GFDM 系统中消除自干扰的最佳原型滤波器设计：一种新颖的信号处理方法

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-12 DOI: 10.1016/j.sigpro.2024.109711

Behzad Mozaffari Tazehkand , Mohammad Reza Ghavidel Aghdam , Reza Abdolee , Aysan Kamyab

{"title":"Optimal prototype filter design in GFDM systems for self-interference elimination: A novel signal processing approach","authors":"Behzad Mozaffari Tazehkand , Mohammad Reza Ghavidel Aghdam , Reza Abdolee , Aysan Kamyab","doi":"10.1016/j.sigpro.2024.109711","DOIUrl":"10.1016/j.sigpro.2024.109711","url":null,"abstract":"<div><p>We present an innovative conceptual framework and a comprehensive mathematical model to advance the understanding and mitigation of self-interference phenomena within generalized frequency division multiplexing (GFDM). By introducing a novel analytical perspective, we decompose the self-interference effects inherent to GFDM into two orthogonal constituents through a vectorized representation. Our elucidation of the self-interference components in terms of prototype filter parameters in the frequency domain is of particular significance. This theoretical characterization allows us to derive explicit analytical expressions, thereby paving the way for the proposition of an optimal filter design strategy that effectively mitigates self-interference distortions within GFDM systems. Our investigation reveals a noteworthy linkage between the required bandwidth allocation for individual subcarriers and the sub-symbol configuration within the proposed optimal prototype filter. This relationship underscores the filter’s adeptness in optimizing spectrum utilization across the system. Through an analytical examination of the bit error rate (BER) performance within the GFDM framework, we establish the superior efficacy of our proposed optimal filter design relative to contemporary approaches documented in extant literature. Validation of our analytical findings is conducted via meticulous computer simulations, where a strong concurrence between the analytical predictions and the observed simulation outcomes is manifest.</p></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109711"},"PeriodicalIF":3.4,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142171660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Improved event-based fault detection filter for networked fuzzy systems under DoS attacks 基于事件的网络模糊系统在 DoS 攻击下的改进型故障检测滤波器

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-12 DOI: 10.1016/j.sigpro.2024.109699

Di Lun , Huiyan Zhang , Yongchao Liu , Ning Zhao , Wudhichai Assawinchaichote

引用次数: 0

DOA estimation of noncircular signals with direction-dependent mutual coupling 具有方向性相互耦合的非圆形信号的 DOA 估计

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-12 DOI: 10.1016/j.sigpro.2024.109688

Dandan Meng , Wei Wang , Xin Li

引用次数: 0