Signal Processing最新文献_第8页

Sound source localization via distance metric learning with regularization 通过正则化距离度量学习进行声源定位

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-30 DOI: 10.1016/j.sigpro.2024.109721

Mingmin Liu , Zhihua Lu , Xiaodong Wang , João Paulo J. da Costa , Tai Fei

{"title":"Sound source localization via distance metric learning with regularization","authors":"Mingmin Liu , Zhihua Lu , Xiaodong Wang , João Paulo J. da Costa , Tai Fei","doi":"10.1016/j.sigpro.2024.109721","DOIUrl":"10.1016/j.sigpro.2024.109721","url":null,"abstract":"<div><div>Sound source localization (SSL) or simply direction of arrival (DOA) classification is an important ingredient in acoustic applications. Traditional model-based algorithms are susceptible to the effects of noise and reverberation, while data-driven deep learning algorithms maintain strong performance across a variety of acoustic circumstances, but typically require a large amount of labeled data. Nevertheless, the existing datasets for SSL are not sufficiently big and diverse to achieve the full potential of deep learning algorithms. Then, it is an imperative work to develop a non-data-hungry algorithm of SSL using small or medium data volume. To this end, we propose a regularized distance metric learning algorithm, that is, by means of the kernel method, we design a nonlinear feature transformation from two aspects: feature points and feature distributions. It transforms the data into a new feature space that brings features of the same class as close as possible and removes features of different classes as far away as possible, which can significantly improve the output of a DOA classifier that follows. Experimental results show that the proposed algorithm outperforms deep learning algorithms in diverse acoustic conditions.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109721"},"PeriodicalIF":3.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418352","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

Robust fusion filter for networked uncertain descriptor systems with colored noise and cyber-attacks 具有彩色噪声和网络攻击的网络不确定描述符系统的鲁棒融合滤波器

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-30 DOI: 10.1016/j.sigpro.2024.109724

Yexuan Zhang, Chenjian Ran, Shuli Sun

{"title":"Robust fusion filter for networked uncertain descriptor systems with colored noise and cyber-attacks","authors":"Yexuan Zhang, Chenjian Ran, Shuli Sun","doi":"10.1016/j.sigpro.2024.109724","DOIUrl":"10.1016/j.sigpro.2024.109724","url":null,"abstract":"<div><div>The robust fusion filtering problem of multi-sensor networked uncertain descriptor systems (NUDSs) with colored noise, uncertain noise variances and cyber-attacks is investigated. During data transmission in unreliable communication networks, the data can be maliciously attacked by attackers. In other words, the local filters (LFs) may receive false data or may not receive data because of the cyber-attacks. By adopting the singular value decomposition (SVD) method, the original NUDSs can be converted into two reduced-order subsystems with uncertain correlated fictitious white noises, and the cyber-attacks are transformed into the fictitious noises. Cross-covariance matrices between local filtering errors are derived. The robust LFs are obtained according to the minimax robust estimation principle. Under the linear unbiased minimum variance criterion, three weighted fusion algorithms are applied to fuse the LFs. For all allowable uncertainties of noise variances and cyber-attacks, the minimal upper bounds of covariance matrices of the local and distributed fusion filters are guaranteed. The proof of their robustness is established through the minimax estimation principle and Lyapunov equation method. Finally, the correctness and effectiveness of the proposed algorithms are verified by a circuit system example.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109724"},"PeriodicalIF":3.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418354","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

Image deconvolution using hybrid threshold based on modified L1-clipped penalty in EM framework 在 EM 框架中使用基于修改后 L1 截断惩罚的混合阈值进行图像解卷积

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-29 DOI: 10.1016/j.sigpro.2024.109725

Ravi Pratap Singh , Manoj Kumar Singh

{"title":"Image deconvolution using hybrid threshold based on modified L1-clipped penalty in EM framework","authors":"Ravi Pratap Singh , Manoj Kumar Singh","doi":"10.1016/j.sigpro.2024.109725","DOIUrl":"10.1016/j.sigpro.2024.109725","url":null,"abstract":"<div><div>Image deconvolution remains a challenging task due to its inherent ill-posedness. While existing algorithms show strong numerical performance, their complexity often complicates analysis and implementation. This paper introduces a computationally efficient image deconvolution method within the expectation maximization (EM) framework. The proposed algorithm alternates between an E-step leveraging the fast Fourier transform (FFT) and an M-step utilizing the discrete wavelet transform (DWT). In the M-step, we introduce a novel L<sub>1</sub>-clipped penalty to compute the maximum a posteriori (MAP) estimate, resulting in a hybrid threshold that combines the strengths of soft and hard thresholding. This hybrid threshold is mathematically derived, overcoming the high variance of hard-thresholding and the high bias of soft-thresholding, thus optimizing the trade-off between variance and bias. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art techniques in terms of improved signal-to-noise ratio (ISNR) and peak signal-to-noise ratio (PSNR), as well as visual quality. Notably, the proposed method shows average PSNR improvements of 3.49 dB, 4.23 dB, and 1.44 dB for uniform blur and 0.76 dB, 3.57 dB, and 0.66 dB for Gaussian blur on the Set12, BSD68, and Set14 datasets, respectively.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109725"},"PeriodicalIF":3.4,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418351","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

Isolated point clutter suppression method for airborne STAP radar in wind farm environment 风电场环境中机载 STAP 雷达的孤立点杂波抑制方法

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-29 DOI: 10.1016/j.sigpro.2024.109723

Yuanyi Xiong , Wenchong Xie , Wei Chen , Ming Hou , Chengyin Liu , Yongliang Wang

{"title":"Isolated point clutter suppression method for airborne STAP radar in wind farm environment","authors":"Yuanyi Xiong , Wenchong Xie , Wei Chen , Ming Hou , Chengyin Liu , Yongliang Wang","doi":"10.1016/j.sigpro.2024.109723","DOIUrl":"10.1016/j.sigpro.2024.109723","url":null,"abstract":"<div><div>With the large-scale construction of wind farms, wind turbine isolated point clutter has an increasingly serious impact on airborne radar target detection performance. Traditional space-time adaptive processing methods cannot suppress wind turbine clutter (WTC) with spectrum broadening characteristics, which may lead to a decrease in target detection probability and an increase in false alarm rate. In this paper, a WTC suppression method for airborne radar based on micro-Doppler features is proposed, and we construct the feature subspace of wind turbine echo to distinguish wind turbine, target, clutter, and noise. First, the Sobel operator is used to process the radar range-Doppler spectrum, and the range cells of the wind turbines are preliminarily judged. Then the smallest of constant false alarm rate (SO<img>CFAR) method is used to further confirm the range cells where the wind turbines are located. Next, Mahalanobis distance is used to estimate the optimal dictionary atomic parameters of WTC, and the updated dictionary atoms are used to construct an orthogonal projection matrix to suppress WTC. Finally, short-range nonstationary clutter and sidelobe clutter are suppressed by space-time adaptive segment processing. On the one hand, the proposed method realizes the accurate positioning of wind turbines through image edge detection and constant false alarm detection. On the other hand, Mahalanobis distance is used to estimate the atomic parameters of the wind turbine dictionary, which ensures the homogeneity of wind turbine samples after clutter suppression. The simulation and measured data results show that the proposed method can significantly reduce the false alarm rate caused by WTC while ensuring the effective detection of the target.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109723"},"PeriodicalIF":3.4,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418349","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

EdgeStereoSR: A multi-task network with transformers for stereo image super-resolution considering edge prior EdgeStereoSR：带变换器的多任务网络，用于考虑边缘先验的立体图像超分辨率

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-28 DOI: 10.1016/j.sigpro.2024.109719

Anqi Liu, Sumei Li, Yongli Chang, Yonghong Hou

{"title":"EdgeStereoSR: A multi-task network with transformers for stereo image super-resolution considering edge prior","authors":"Anqi Liu, Sumei Li, Yongli Chang, Yonghong Hou","doi":"10.1016/j.sigpro.2024.109719","DOIUrl":"10.1016/j.sigpro.2024.109719","url":null,"abstract":"<div><div>Recently, stereo image super-resolution methods focusing on exploring cross-view information have been widely studied and achieved good performance. However, it is still challenging for them to reconstruct high-quality high-frequency details. In addition, they mainly focus on improving quantitative metrics, neglecting the perceptual quality of reconstructed images. In this paper, to improve the accuracy of high-frequency reconstruction, we propose a multi-task network with Transformers considering edge prior, named EdgeStereoSR, which achieves better stereo image SR under the guidance of edge detection. Basically, edge priors have two contributions. First, we propose a cross-view Transformer (CVT), which utilizes edge priors to guide the correspondence search, thus more accurate cross-view information can be captured. Second, we propose a cross-task Transformer (CTT), which exploits edge priors to guide the high-frequency reconstruction, thus images with more details and sharper edges can be reconstructed. To further improve the visual quality, we propose EdgeStereoSR-G, integrating the generative adversarial network into EdgeStereoSR. Specially, a spatial-view discriminator is designed to learn the stereo image distribution so as to make the reconstructed stereo image more photo-realistic and avoid parallax inconsistency. Extensive experiments show that the proposed methods are superior to other state-of-the-art methods in terms of both quantitative metrics and visual quality.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109719"},"PeriodicalIF":3.4,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418350","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

Adaptive radar target detection in nonzero-mean compound Gaussian sea clutter with random texture 具有随机纹理的非零均值复合高斯海杂波中的自适应雷达目标探测

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-28 DOI: 10.1016/j.sigpro.2024.109720

Haoqi Wu, Zhihang Wang, Hongzhi Guo, Zishu He

引用次数: 0

Protecting copyright of stable diffusion models from ambiguity attacks 保护稳定扩散模型版权免受模糊攻击

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-28 DOI: 10.1016/j.sigpro.2024.109722

Zihan Yuan, Li Li, Zichi Wang, Xinpeng Zhang

{"title":"Protecting copyright of stable diffusion models from ambiguity attacks","authors":"Zihan Yuan, Li Li, Zichi Wang, Xinpeng Zhang","doi":"10.1016/j.sigpro.2024.109722","DOIUrl":"10.1016/j.sigpro.2024.109722","url":null,"abstract":"<div><div>In recent years, the stable diffusion models (SDMs) have been widely used in text-to-image generative tasks, and their copyright protection problem has been concerned by scholars. The model owners can embed watermarks into SDMs by fine-tuning them, and use the prompt-watermark pair to complete model ownership authentication. However, the attackers can obfuscate model ownership by forging the relationship between the fake prompt and the watermark image. Therefore, this paper proposes a black-box copyright protection method for SDMs, which can effectively resist watermark ambiguity attacks. Specifically, we adopt an irreversible watermarking technology to complete watermark embedding. The hash function is used to ensure the unidirectional irreversible generation of the trigger prompts using the secret key. Then, the trigger set consisting of trigger prompts and watermarks is used to fine-tune the SDMs to embed the watermarks. Without the secret key, it is not possible for the attackers to reverse build the specific prompts with internal associations. Experiments show that our method can protect the copyright of SDMs effectively and resist ambiguity attacks without the model performance degradation.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109722"},"PeriodicalIF":3.4,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418356","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

Multi-objective network resource allocation method based on fractional PID control 基于分数 PID 控制的多目标网络资源分配方法

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-25 DOI: 10.1016/j.sigpro.2024.109717

Xintong Ni, Yiheng Wei, Shuaiyu Zhou, Meng Tao

引用次数: 0

Exploring high-order correlation for hyperspectral image denoising with hypergraph convolutional network 利用超图卷积网络探索高光谱图像去噪的高阶相关性

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-09-25 DOI: 10.1016/j.sigpro.2024.109718

Jun Zhang , Yaoxin Tan , Xiaohui Wei

{"title":"Exploring high-order correlation for hyperspectral image denoising with hypergraph convolutional network","authors":"Jun Zhang , Yaoxin Tan , Xiaohui Wei","doi":"10.1016/j.sigpro.2024.109718","DOIUrl":"10.1016/j.sigpro.2024.109718","url":null,"abstract":"<div><div>High-order correlation is an important property of hyperspectral images (HSIs) and has been widely investigated in model-based HSI denoising. However, the existing deep learning-based HSI denoising approaches have not fully utilized the high-order correlation. Hypergraph convolutional networks have shown great potential in capturing the high-order correlation. Therefore, in this paper, we propose a novel HSI denoising method by employing hypergraph convolution to characterize the high-order correlation at the patch level. Specifically, our framework is a symmetrically skip-connected 3D encoder–decoder architecture, which enhances the extraction and utilization of local features. Furthermore, to integrate competently the hypergraph convolutional modules into the 3D framework, we devise a dimensional transformation module that facilitates the fusion of 3D convolution and hypergraph convolution. Notably, in the hypergraph convolution operation, we use a data-driven technique to acquire the incidence matrix of a hypergraph, efficiently constructing the HSI into a high-order structure. Our proposed method excels in HSI denoising performance compared to state-of-the-art approaches, evidenced by extensive experiments on synthetic and real-world noisy HSIs.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109718"},"PeriodicalIF":3.4,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356905","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

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