Signal Processing最新文献_第2页

Hyperbolic frequency modulated signal analysis using Mellin transform

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-18 DOI: 10.1016/j.sigpro.2025.110007

Liang Zhang , Qinglei Du , Ruihui Peng , Xiangyu Zhang , Hui Chen

引用次数: 0

Periodicity-constrained reweighted generalized minimax-concave regularization and its application in gearbox fault diagnosis

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-17 DOI: 10.1016/j.sigpro.2025.110005

Lichao Yu , Chenglong Wang , Cunfu Wang , Fanghong Zhang , Huageng Luo

{"title":"Periodicity-constrained reweighted generalized minimax-concave regularization and its application in gearbox fault diagnosis","authors":"Lichao Yu , Chenglong Wang , Cunfu Wang , Fanghong Zhang , Huageng Luo","doi":"10.1016/j.sigpro.2025.110005","DOIUrl":"10.1016/j.sigpro.2025.110005","url":null,"abstract":"<div><div>In vibration-based gearbox fault diagnosis, accurately extracting periodic transient signals caused by defects is crucial for achieving fault diagnosis. However, the vibration signals measured in practice are often dominated by interference such as random noise and harmonic signals, making the extraction of the transient component highly challenging. To address the issue, this paper proposes a periodicity-constrained reweighted generalized minimax-concave (PC-ReGMC) regularization, which can effectively filter out interference and accurately reconstruct the periodic transient signals. This method is based on a weighted generalized minimax concave regularization model. First, the weight coefficients are initialized to perform the initial transient signal extraction. Then, the square envelope harmonic product spectrum (SEHPS) is introduced to identify the period of the transient signal, and a periodic weighting strategy based on the sinusoidal function is designed to update the weight coefficients. Finally, by reweighting the generalized minimax concave regularization model, the periodicity constraint is embedded into the optimization process of sparse coefficients, thus imposing penalties on non-periodic components to enhance denoising performance. Through the analysis results of simulations and practical cases, it is demonstrated that the proposed method outperforms other sparse regularization methods and the spectral kurtosis in terms of the reconstruction accuracy of the periodic transient signals, and thus provides more precise gearbox fault diagnosis results.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"234 ","pages":"Article 110005"},"PeriodicalIF":3.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716180","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

Signed graph learning with hidden nodes

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-17 DOI: 10.1016/j.sigpro.2025.109995

Rong Ye , Xue-Qin Jiang , Hui Feng , Jian Wang , Runhe Qiu

{"title":"Signed graph learning with hidden nodes","authors":"Rong Ye , Xue-Qin Jiang , Hui Feng , Jian Wang , Runhe Qiu","doi":"10.1016/j.sigpro.2025.109995","DOIUrl":"10.1016/j.sigpro.2025.109995","url":null,"abstract":"<div><div>Signed graphs, which are characterized by both positive and negative edge weights, have recently attracted significant attention in the field of graph signal processing (GSP). Existing works on signed graph learning typically assume that all graph nodes are available. However, in some specific applications, only a subset of nodes can be observed while the remaining nodes stay hidden. To address this challenge, we propose a novel method for identifying signed graph that accounts for hidden nodes, termed <em>signed graph learning with hidden nodes under column-sparsity regularization</em> (SGL-HNCS). Our method is based on the assumption that graph signals are smooth over signed graphs, i.e., signal values of two nodes connected by positive (negative) edges are similar (dissimilar). Rooted in this prior assumption, the topology inference of a signed graph is formulated as a constrained optimization problem with column-sparsity regularization, where the goal is to reconstruct the signed graph Laplacian matrix without disregarding the influence of hidden nodes. We solve the constrained optimization problem using a tailored block coordinate descent (BCD) approach. Experimental results using synthetic data and real-world data demonstrate the efficiency of the proposed SGL-HNCS method.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"234 ","pages":"Article 109995"},"PeriodicalIF":3.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679306","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

High-dimensional false discovery rate control for dependent variables

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-15 DOI: 10.1016/j.sigpro.2025.109990

Jasin Machkour , Michael Muma , Daniel P. Palomar

{"title":"High-dimensional false discovery rate control for dependent variables","authors":"Jasin Machkour , Michael Muma , Daniel P. Palomar","doi":"10.1016/j.sigpro.2025.109990","DOIUrl":"10.1016/j.sigpro.2025.109990","url":null,"abstract":"<div><div>Algorithms that ensure reproducible findings from large-scale, high-dimensional data are pivotal in numerous signal processing applications. In recent years, multivariate false discovery rate (FDR) controlling methods have emerged, providing guarantees even in high-dimensional settings where the number of variables surpasses the number of samples. However, these methods often fail to reliably control the FDR in the presence of highly dependent variable groups, a common characteristic in fields such as genomics and finance. To tackle this critical issue, we introduce a novel framework that accounts for general dependency structures. Our proposed dependency-aware T-Rex selector integrates hierarchical graphical models within the T-Rex framework to effectively harness the dependency structure among variables. Leveraging martingale theory, we prove that our variable penalization mechanism ensures FDR control. We further generalize the FDR-controlling framework by stating and proving a clear condition necessary for designing both graphical and non-graphical models that capture dependencies. Numerical experiments and a breast cancer survival analysis use-case demonstrate that the proposed method is the only one among the state-of-the-art benchmark methods that controls the FDR and reliably detects genes that have been previously identified to be related to breast cancer. An open-source implementation is available within the R package TRexSelector on CRAN.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"234 ","pages":"Article 109990"},"PeriodicalIF":3.4,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A state-of-the-art review on acoustic preservation of historical worship spaces through auralization

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-15 DOI: 10.1016/j.sigpro.2025.109992

Hannes Rosseel, Toon van Waterschoot

引用次数: 0

Pulse processing — Overview and challenges

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-13 DOI: 10.1016/j.sigpro.2025.109978

Jonathan H. Manton

引用次数: 0

Frequency offset deception velocity-based method for discriminating between physical targets and active false targets 基于频率偏移欺骗速度的方法，用于区分物理目标和主动假目标

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-12 DOI: 10.1016/j.sigpro.2025.109977

Deyu Li, Siyi Yao, Yi Liao, Jian Yan, Yusheng Fu

{"title":"Frequency offset deception velocity-based method for discriminating between physical targets and active false targets","authors":"Deyu Li, Siyi Yao, Yi Liao, Jian Yan, Yusheng Fu","doi":"10.1016/j.sigpro.2025.109977","DOIUrl":"10.1016/j.sigpro.2025.109977","url":null,"abstract":"<div><div>The discrimination of false targets by multistatic radar utilizing data-level fusion is contingent upon the differences in spatial information observed by receivers of different orientations. Nevertheless, if the target is situated in the far field, the jamming discrimination performance of multistatic radar is limited by the baseline distance of the receivers. To address this challenge, this paper designs a multistatic frequency diversity radar to mislead the carrier frequency estimation of the target jammer. This scheme introduces a difference in spectral information to the spatial information of the multistatic radar, thereby enhancing the jamming discrimination performance. In light of the above, we propose a frequency offset deception velocity-based (FODVB) method to discriminate active false targets. The method effectively fuses the spatial and frequency information to discriminate the false targets accurately and applies to multi-target scenarios. Furthermore, an optimal frequency selection strategy based on target orientation information is proposed to achieve the optimal discrimination performance of the FODVB method. The effectiveness of the proposed method is validated through Monte Carlo simulations.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"234 ","pages":"Article 109977"},"PeriodicalIF":3.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628569","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

Low interactive direct position determination of radio emitters with hybrid measurements

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-11 DOI: 10.1016/j.sigpro.2025.109987

Ming-Yi You , Lin Gao , Yun-Xia Ye , Wei Wang

{"title":"Low interactive direct position determination of radio emitters with hybrid measurements","authors":"Ming-Yi You , Lin Gao , Yun-Xia Ye , Wei Wang","doi":"10.1016/j.sigpro.2025.109987","DOIUrl":"10.1016/j.sigpro.2025.109987","url":null,"abstract":"<div><div>This paper proposes a direct position determination (DPD) method for stationary and moving non-cooperative sources. Employing an unbalanced group of measurements consisting of uncompressed measurements at the central receiver and compressed measurements from all other auxiliary receivers, the method estimates the source position directly in the hybrid measurement domain without original signal recovering, where the compressing matrix is not restricted to any specific form. A block coordinate descent (BCD)-like iterative algorithm is proposed to handle the high-dimensional optimization problem of joint position and velocity estimation for moving emitters, where a generalized cross ambiguity function (GCAF) is proposed to extract the time-differences-of-arrival (TDOA) parameters from the hybrid measurements to initialize the iteration process. In addition, the hybrid measurements-based Cramér–Rao lower bound (CRLB) for emitter position is derived for performance evaluation. Several numerical case studies are carried out to evaluate the effectiveness of the proposed DPD method as well as the proposed GCAF. The proposed method is expected to extend the applicability of compressive sensing (CS)-based DPD to cases where there is relative radial motion between the source.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"234 ","pages":"Article 109987"},"PeriodicalIF":3.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621478","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

TOA estimation via cross-correlation-based atomic norm minimization

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-10 DOI: 10.1016/j.sigpro.2025.109989

Shuang Wei , Zhichao Niu , Di He , Jiawei Lei

{"title":"TOA estimation via cross-correlation-based atomic norm minimization","authors":"Shuang Wei , Zhichao Niu , Di He , Jiawei Lei","doi":"10.1016/j.sigpro.2025.109989","DOIUrl":"10.1016/j.sigpro.2025.109989","url":null,"abstract":"<div><div>This paper proposes a novel Cross-Correlation-based Atomic Norm Minimization method (CC-ANM) to estimate Time-of-Arrival (TOA) parameters with enhanced accuracy. It leverages a gridless approach based on atomic norm to address the cross-correlation model, which is effective in mitigating the impact of non-independently and identically distributed (non-i.i.d.) Gaussian noise. A new optimization framework is formulated to tackle this challenge, and its dual problem expressed through a Semi-Definite Programming (SDP) is derived. By utilizing the characteristics of dual problem, the proposed method can estimate TOA parameters without the need for prior information of the path count under high Signal-to-Noise Ratio (SNR) conditions. To overcome the constraints imposed by traditional root polynomials in low SNR scenarios, the proposed method develops a derivative-based root-finding algorithm to extract TOA parameters from the dual polynomial. It can not only significantly reduce the estimation errors introduced by the discretization process, but also address the performance limitations under low SNR conditions. Simulation results demonstrate that the proposed CC-ANM method closely approximates the Cramer–Rao Lower Bound (CRLB) and outperforms existing methods across a range of SNR levels and path configurations.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"234 ","pages":"Article 109989"},"PeriodicalIF":3.4,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601445","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

High-accuracy image steganography with invertible neural network and generative adversarial network

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-10 DOI: 10.1016/j.sigpro.2025.109988

Ke Wang, Yani Zhu, Qi Chang, Junyu Wang, Ye Yao

{"title":"High-accuracy image steganography with invertible neural network and generative adversarial network","authors":"Ke Wang, Yani Zhu, Qi Chang, Junyu Wang, Ye Yao","doi":"10.1016/j.sigpro.2025.109988","DOIUrl":"10.1016/j.sigpro.2025.109988","url":null,"abstract":"<div><div>Image steganography conceals secret messages imperceptibly within cover images. However, many existing deep learning-based image steganography methods have limitations in visual quality, payload size, and security. Specifically, they often require error correction codes for complete message extraction. In this paper, we propose a novel image steganography network architecture based on Invertible Neural Network (INN) and Generative Adversarial Network (GAN). Leveraging the reversibility of INN and the connection with the hidden network, we design three extraction networks based on DenseNet, shared weights, and unshared weights. These are respectively combined with the hidden network and discriminator network to create new network structures, effectively improving invisibility and message extraction accuracy. Furthermore, the discriminator participates in adversarial training by comparing cover and stego images in a patch-to-patch manner, thereby enhancing visual quality and security. Extensive experiments demonstrate the effectiveness of our proposed method across various aspects, including image quality, payload, extraction accuracy, and security, particularly achieving close to 100% message extraction accuracy without requiring error correction codes.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"234 ","pages":"Article 109988"},"PeriodicalIF":3.4,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601402","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