Signal Processing最新文献

A unified algorithmic framework for dynamic compressive sensing

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-02-01 DOI: 10.1016/j.sigpro.2025.109926

Xiaozhi Liu, Yong Xia

{"title":"A unified algorithmic framework for dynamic compressive sensing","authors":"Xiaozhi Liu, Yong Xia","doi":"10.1016/j.sigpro.2025.109926","DOIUrl":"10.1016/j.sigpro.2025.109926","url":null,"abstract":"<div><div>We present a unified algorithmic framework, termed PLAY-CS, for dynamic tracking and reconstruction of signal sequences exhibiting intrinsic structured dynamic sparsity. By leveraging specific statistical assumptions on the dynamic filtering of these sequences, our framework integrates a variety of existing dynamic compressive sensing (DCS) algorithms. This is facilitated by the introduction of a novel Partial-Laplacian filtering sparsity model, which is designed to capture more complex dynamic sparsity patterns. Within this unified DCS framework, we derive a new algorithm, <span><math><msup><mrow><mtext>PLAY</mtext></mrow><mrow><mo>+</mo></mrow></msup></math></span>-CS. Notably, the <span><math><msup><mrow><mtext>PLAY</mtext></mrow><mrow><mo>+</mo></mrow></msup></math></span>-CS algorithm eliminates the need for a priori knowledge of dynamic signal parameters, as these are adaptively learned through an expectation–maximization framework. Moreover, we extend the <span><math><msup><mrow><mtext>PLAY</mtext></mrow><mrow><mo>+</mo></mrow></msup></math></span>-CS algorithm to tackle the dynamic joint sparse signal reconstruction problem involving multiple measurement vectors. The proposed framework demonstrates superior performance in practical applications, such as real-time massive multiple-input multiple-output (MIMO) communication for dynamic channel tracking and background subtraction from online compressive measurements, outperforming existing DCS algorithms.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109926"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168356","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

DC-Reg: A triple-task collaborative framework for few-shot biomedical image registration

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-31 DOI: 10.1016/j.sigpro.2025.109924

Jun Wu , Yong Zhang , Zaiyang Tao , Meng Li , Tingting Han , Yuanyuan Li , Lingfei Zhu , Yiwei Niu , Lei Qu

{"title":"DC-Reg: A triple-task collaborative framework for few-shot biomedical image registration","authors":"Jun Wu , Yong Zhang , Zaiyang Tao , Meng Li , Tingting Han , Yuanyuan Li , Lingfei Zhu , Yiwei Niu , Lei Qu","doi":"10.1016/j.sigpro.2025.109924","DOIUrl":"10.1016/j.sigpro.2025.109924","url":null,"abstract":"<div><div>Deep learning (DL)-based deformable biomedical image registration (DIR) enables automatic information fusion and facilitates rapid diagnosis by aligning multi-source data into a unified coordinate system. However, achieving accurate similarity measurement and obtaining adequate training data pose significant challenges in biomedical image processing tasks. In this paper, we propose a few-shot DIR method that leverages spatial encoding within a triple-task collaborative framework to solve these issues. Firstly, we propose a registration network based on the spatial encoding, which represent images using voxel spatial positions, highlighting the importance of structural information in registration network while reducing modality difference between different images. Secondly, we propose a segmentation network based on data augmentation, which is achieved through the registration network. Specifically, we have designed a contrastive learning based discrimination network to suppress the unreliable augmented training data, which is also our third important component of the collaborative framework. Furthermore, the discrimination network also automatically learns similarity measure for the registration network. By iteratively refining the segmentation, registration, and discrimination networks, we are able to obtain a highly accurate registration model. Our experimental results on four mono-modal and multi-modal datasets demonstrate the effectiveness and superiority of the proposed method.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109924"},"PeriodicalIF":3.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168358","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

Finite blocklength approach for the two-user MISO multiple-access channel with noisy feedback and its performance analysis

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-31 DOI: 10.1016/j.sigpro.2025.109900

Guangfen Xie , Rong Luo , Bin Dai

引用次数: 0

Performance analysis of trace proximity based distributed Kalman filter

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-30 DOI: 10.1016/j.sigpro.2025.109906

Wei Liu , Peng Shi , Shuoyu Wang

{"title":"Performance analysis of trace proximity based distributed Kalman filter","authors":"Wei Liu , Peng Shi , Shuoyu Wang","doi":"10.1016/j.sigpro.2025.109906","DOIUrl":"10.1016/j.sigpro.2025.109906","url":null,"abstract":"<div><div>This paper analyzes the performance of the distributed Kalman filter based on trace proximity criterion and neighboring-node measurements (TPCNM) proposed in Liu et al. (2022) where the performance analysis includes the boundness, convergence, mean square error and estimation error covariance. First, we prove that the boundness of the distributed Kalman filter based on TPCNM is ensured under proper conditions. Second, the convergence conditions for the distributed Kalman filter based on TPCNM with some constraints for the value of node are established using a novel matrix difference equation (MDE), two equalities in the distributed Kalman filter based on TPCNM and some results presented in this paper where one equality contains a term with both the measurement matrix and the measurement noise covariance matrix. In addition, the mean square error performance for the distributed Kalman filter based on TPCNM is analyzed, and it is proved that the matrix <span><math><mi>P</mi></math></span> in the distributed Kalman filter based on TPCNM is the real estimation error covariance. A scalar dynamic system example and a radar tracking example are provided to illustrate the validity and correctness of the developed methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109906"},"PeriodicalIF":3.4,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168360","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

A MISO acoustic echo cancellation algorithm based on a two-layer filter decomposition

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-28 DOI: 10.1016/j.sigpro.2025.109892

Zi Cao , Tianci Yan , Jie Chen , Jingdong Chen , Jacob Benesty

引用次数: 0

Simultaneously transmitting and reflecting (STAR) RIS enhanced covert transmission with noise uncertainty

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-28 DOI: 10.1016/j.sigpro.2025.109907

Jinsong Hu , Beixi Cheng , Youjia Chen , Jun Wang , Feng Shu , Zhizhang Chen

{"title":"Simultaneously transmitting and reflecting (STAR) RIS enhanced covert transmission with noise uncertainty","authors":"Jinsong Hu , Beixi Cheng , Youjia Chen , Jun Wang , Feng Shu , Zhizhang Chen","doi":"10.1016/j.sigpro.2025.109907","DOIUrl":"10.1016/j.sigpro.2025.109907","url":null,"abstract":"<div><div>To break through the topological restriction imposed by conventional reflecting/transmitting-only reconfigurable intelligent surface (RIS) in covert communication systems, a simultaneously transmitting and reflecting RIS (STAR-RIS) is adopted in this paper. A transmitter Alice communicates with both users Willie and Bob, where Bob is the covert receiver. Moreover, Willie also plays a warden seeking to detect the covert transmission since it forbids Alice from illegally using the communication resources like energy and bandwidth allocated for them. To obtain the maximum covert rate, we first design the transmission schemes for Alice in the case of sending and not sending covert information and further derive the necessary conditions for Alice to perform covert communication. We also deduce Willie’s detection error probability, the minimum value of which is obtained as well in terms of an optimal detection threshold. Furthermore, through the design of Alice’s transmit power for covert transmission together with transmission and reflection beamforming at STAR-RIS, we achieve the maximum effective covert rate. Our numerical results show the correctness of the proposed theorems and indicate that utilizing STAR-RIS to enhance covert communication is feasible and effective.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109907"},"PeriodicalIF":3.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168361","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

Direct localization of wideband sources using distributed arrays: A subspace focusing and dimension reduction approach

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-27 DOI: 10.1016/j.sigpro.2025.109902

Dawei Li , Jianfeng Li , Mingyi You , Wanghao Tang , Xiaofei Zhang , Wutao Qin

引用次数: 0

Notch power detector for multiple vehicle trajectory estimation with distributed acoustic sensing

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-25 DOI: 10.1016/j.sigpro.2025.109905

Marco Fontana, Ángel F. García-Fernández, Simon Maskell

引用次数: 0

Softmax-kernel reproduced gradient descent for stochastic optimization on streaming data

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-24 DOI: 10.1016/j.sigpro.2025.109904

Yifu Lin , Wenling Li , Yang Liu , Jia Song

引用次数: 0

High-fidelity reversible data hiding based on enhanced IPPVO and adaptive 2D histogram modification

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-23 DOI: 10.1016/j.sigpro.2025.109901

Haorui Wu , Xiang Li , Qi Chang , Mengyao Xiao , Xiaolong Li , Yao Zhao

{"title":"High-fidelity reversible data hiding based on enhanced IPPVO and adaptive 2D histogram modification","authors":"Haorui Wu , Xiang Li , Qi Chang , Mengyao Xiao , Xiaolong Li , Yao Zhao","doi":"10.1016/j.sigpro.2025.109901","DOIUrl":"10.1016/j.sigpro.2025.109901","url":null,"abstract":"<div><div>Reversible data hiding (RDH) based on pixel-based pixel-value-ordering (PPVO) has demonstrated superior embedding performance compared to traditional blockwise PVO methods, owing to its pixel-wise approach. Recently, an enhanced version known as Improved PPVO (IPPVO) has been introduced, incorporating an upgraded predictor and embedding strategy based on multiple histogram generation and modification within multi-sized prediction contexts. Despite these advancements, IPPVO still faces challenges, such as inaccurate due to underutilization of nearby pixels and suboptimal embedding performance resulting from individual modification of prediction errors. To address these limitations and fully exploit local image correlation, this paper introduces an enhanced IPPVO-based RDH method. The proposed method introduces a novel predictor to redefine prediction contexts for improved accuracy. Simultaneously, a multi-layer embedding strategy ensures reversibility and comprehensive pixel utilization. Subsequently, two-dimensional (2D) multi-histograms are generated, accompanied by a novel 2D mapping design method. This method identifies optimal candidates through extensive statistical analysis of actual embedding results. Experimental results demonstrate the superiority of the proposed method over IPPVO and other state-of-the-art PVO-based RDH methods. Notably, the proposed method achieves average PSNR values of 60.67 dB with 10,000 bits and 57.80 dB with 20,000 bits, showing maximum average PSNR enhancements of 1.01 dB and 0.88 dB on USC-SIPI images.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109901"},"PeriodicalIF":3.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168367","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