Signal Processing最新文献_第3页

Meta-heuristic-based design of high-order stable digital filters using pole-zero placement 基于元启发式的零极放置高阶稳定数字滤波器设计

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-19 DOI: 10.1016/j.sigpro.2025.110291

Kiwook Baeck, Hyosang Yoon

{"title":"Meta-heuristic-based design of high-order stable digital filters using pole-zero placement","authors":"Kiwook Baeck, Hyosang Yoon","doi":"10.1016/j.sigpro.2025.110291","DOIUrl":"10.1016/j.sigpro.2025.110291","url":null,"abstract":"<div><div>This study presents a meta-heuristic optimization approach for digital IIR filter design that addresses fundamental limitations of conventional coefficient-based methods. Rather than optimizing filter coefficients directly, the proposed method identifies optimal locations of zeros, poles, and gain in the z-plane for a given frequency response. This pole-zero formulation provides an intuitive framework for managing filter characteristics, particularly stability constraints. The fitness function simultaneously optimizes magnitude and phase responses, enabling frequency response shaping for a wide range of applications. Extensive simulations across four complex design scenarios – including low-order filter, low-pass filters, curved frequency responses, and stabilized inverse systems – demonstrate the algorithm’s superior performance compared to related work for high-order implementations. Results show that the proposed approach maintains strong exploration capability even in high-dimensional optimization landscapes while guaranteeing stable filter realizations. This methodology provides engineers with a flexible and reliable tool for prototyping digital filters that accommodate specific operational requirements beyond conventional filter designs.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110291"},"PeriodicalIF":3.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118297","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

CM-CSAMFNet: A cross-modality channel and spatial attention module fusion network for multimodal medical image fusion CM-CSAMFNet：一种用于多模态医学图像融合的跨模态通道和空间关注模块融合网络

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-19 DOI: 10.1016/j.sigpro.2025.110288

Yixiang Lu, Changqing Xu, Jingyun Gong, Qingwei Gao, Dong Sun, De Zhu

{"title":"CM-CSAMFNet: A cross-modality channel and spatial attention module fusion network for multimodal medical image fusion","authors":"Yixiang Lu, Changqing Xu, Jingyun Gong, Qingwei Gao, Dong Sun, De Zhu","doi":"10.1016/j.sigpro.2025.110288","DOIUrl":"10.1016/j.sigpro.2025.110288","url":null,"abstract":"<div><div>The fusion technology of functional and structural images contributes to clinical diagnosis by integrating complementary information from different modalities. However, traditional state-of-the-art fusion methods and convolutional networks still require manually designed fusion strategies, which are inefficient and struggle to effectively merge complementary information across modalities. In addition, multiscale fusion methods suffer from excessive model parameters and inadequate consideration of long-range dependencies. To overcome these limitations, an attention-based end-to-end framework (CM-CSAMFNet) is proposed for medical image fusion using a multiscale autoencoder architecture. To design a learnable fusion strategy, we introduce a convolutional block attention module fusion network (CBAMFNet), which leverages cross-modal channel and spatial attention mechanisms to replace conventional fusion approaches. To reduce the number of parameters in multiscale network, the entire network employs ghost convolution techniques, which require only a small number of convolutional operations while extensively utilizing linear computations. Furthermore, to fully account for long-range dependencies, a cross-domain attention mechanism named the cross-modal residual convolutional block attention module (RCBAM) is proposed. This mechanism aims to comprehensively integrate locally complementary features and enhance global brightness information. More specifically, the cross-domain attention module incorporates spatial and channel attention mechanisms to integrate long-range dependencies within and across different modalities. Compared to existing approaches, the proposed fusion algorithm achieves superior performance in SPECT–MRI and PET–MRI image fusion tasks, as evaluated by both subjective and objective metrics. The code of the proposed method is available at <span><span>https://github.com/ahu-dsp/CM-CSAMFNet</span><svg><path></path></svg></span></div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110288"},"PeriodicalIF":3.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158183","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

Efficient rank-recovery-based coherent source localization framework for non-uniform FDA 基于秩恢复的非均匀FDA相干源定位框架

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-18 DOI: 10.1016/j.sigpro.2025.110286

Feng Shi , Shengheng Liu , Hao Chi Zhang , Kaiyan Xu , Le Peng Zhang , Qi Yang

引用次数: 0

Vertex–frequency hypergraph signal processing: Analytic tools and applications 顶点频率超图信号处理：分析工具和应用

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-17 DOI: 10.1016/j.sigpro.2025.110277

Alcebiades Dal Col , Fabiano Petronetto , José R. de Oliveira Neto , Juliano B. Lima

引用次数: 0

A novel digital predistortion technique based on partial least squares smooth twin support vector regression 一种新的基于偏最小二乘平滑双支持向量回归的数字预失真技术

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-15 DOI: 10.1016/j.sigpro.2025.110281

Shuman Kong , Mingchen Jiang , Mingyu Li , Yi Jin , Xin Luo , Tianfu Cai

{"title":"A novel digital predistortion technique based on partial least squares smooth twin support vector regression","authors":"Shuman Kong , Mingchen Jiang , Mingyu Li , Yi Jin , Xin Luo , Tianfu Cai","doi":"10.1016/j.sigpro.2025.110281","DOIUrl":"10.1016/j.sigpro.2025.110281","url":null,"abstract":"<div><div>In this article, a low-complexity digital predistortion (DPD) method based on a partial least squares smooth twin support vector regression (PLS-STSVR) model is proposed to jointly compensate for power amplifier (PA) nonlinearity, in-phase/quadrature (IQ) imbalance, and local oscillator (LO) leakage in modern communication transmitters. The proposed model enhances the conventional twin support vector regression (TSVR) framework by introducing a smooth loss function, which enables efficient optimization via Newton’s method, and by incorporating a model pruning strategy combining random deletion with partial least squares (PLS) to reduce kernel matrix complexity. To validate its effectiveness, two transmitter setups with IQ imbalance—one based on a Class-F PA and the other on a Doherty PA—are employed for experimental evaluation. Results show that the PLS-STSVR model not only improves modeling accuracy but also significantly reduces training time and coefficient complexity. Moreover, the DPD system based on this model achieves superior adjacent channel power ratio (ACPR) performance compared to existing methods, providing up to 2.34 dB ACPR improvement over TSVR and a 12.1% reduction in FLOPs relative to the PRVTDCNN model, while maintaining the lowest overall computational complexity. These results demonstrate the robustness and efficiency of the proposed PLS-STSVR model for practical RF front-end linearization.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110281"},"PeriodicalIF":3.6,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105449","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

MambaMTT: A deep learning method based on mamba structure for maneuvering target tracking MambaMTT：一种基于曼巴结构的机动目标跟踪深度学习方法

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-15 DOI: 10.1016/j.sigpro.2025.110285

Hongping Zhou, Chengwei Zhang, Peng Peng, Zhongyi Guo

{"title":"MambaMTT: A deep learning method based on mamba structure for maneuvering target tracking","authors":"Hongping Zhou, Chengwei Zhang, Peng Peng, Zhongyi Guo","doi":"10.1016/j.sigpro.2025.110285","DOIUrl":"10.1016/j.sigpro.2025.110285","url":null,"abstract":"<div><div>The research area of maneuvering target-tracking in radar system has emerged as a critical and valuable research frontier. The diverse and unpredictable movements of maneuvering targets make it hard to estimate their state accurately. This challenge often makes previous methods unreliable in dealing with maneuvering targets, especially the highly maneuvering ones. Although Transformer-based models possess global modeling capabilities, they encounter computational challenges when applied to long trajectory sequences due to their inherent computational complexity. To address the problem of highly maneuvering targets tracking, this paper proposes a Mamba-based maneuvering target tracking algorithm, termed as MambaMTT. MambaMTT is specifically designed to model trajectory change patterns by focusing on local and global feature correlation information of the trajectory sequence, facilitating the effective processing of highly maneuvering targets. Meanwhile, we introduce a multi-scale feature fusion module to capture spatial and temporal correlations at different scales within the trajectory sequence. With this module, the MambaMTT network is able to capture local and global trajectory features more efficiently, improving the model’s adaptability and accuracy for complex maneuvering targets. Experimental results show that the proposed MambaMTT algorithm exhibits higher tracking efficiency and accuracy in various maneuvering target tracking, and it also has better generalization ability on data beyond the training range.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110285"},"PeriodicalIF":3.6,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105447","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 hybrid Chebyshev-SVD based approach for robust audio watermarking application 一种基于chebyhev - svd的混合音频水印方法

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-14 DOI: 10.1016/j.sigpro.2025.110279

Priyadharsini S., Aniruddha Kanhe

{"title":"A hybrid Chebyshev-SVD based approach for robust audio watermarking application","authors":"Priyadharsini S., Aniruddha Kanhe","doi":"10.1016/j.sigpro.2025.110279","DOIUrl":"10.1016/j.sigpro.2025.110279","url":null,"abstract":"<div><div>This paper presents a robust and imperceptible audio watermarking algorithm using Fast Chebyshev Transform (FCT) and Singular Value Decomposition (SVD) with energy based and perceptually guided frame selection. The method embeds binary watermark data into selected high-energy frames of audio signal, leveraging the energy and Zero Crossing Count (ZCC) to identify voiced regions in speech or high energy frames in music. The watermark bits are embedded into the singular values of FCT transformed frame matrices, ensuring minimal distortion to the host signal while maintaining resilience against signal processing attacks. The use of FCT enables efficient frequency-domain representation with reduced computational complexity compared to traditional transforms. Experimental results on speech and music signals demonstrate high transparency, measured by Signal-to-Noise Ratio (SNR) consistently above 61db and 0 Bit Error Rate(BER) before any attacks. This method achieves a payload capacity of 1200 bits/sec and robustness against noise addition, filtering, compression, resampling and various stirmark Benchmark attacks. Compared to existing methods, the proposed approach achieves lower distortion and improved robustness, making it suitable for copyright protection and secure audio authentication.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110279"},"PeriodicalIF":3.6,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105528","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

Statistical modeling and likelihood ratio testing for resampling detection in TIFF images TIFF图像重采样检测的统计建模和似然比检验

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-13 DOI: 10.1016/j.sigpro.2025.110282

Nhan Le , Florent Retraint , Hichem Snoussi

{"title":"Statistical modeling and likelihood ratio testing for resampling detection in TIFF images","authors":"Nhan Le , Florent Retraint , Hichem Snoussi","doi":"10.1016/j.sigpro.2025.110282","DOIUrl":"10.1016/j.sigpro.2025.110282","url":null,"abstract":"<div><div>Resampling, including resizing, rotating, skewing, is a common technique in digital image tampering, typically used in conjunction with manipulations such as cloning or splicing to create visually seamless forgeries. Despite its sophistication, the resampling process inevitably leaves two main artifacts: (<em>i</em>) <em>periodicity</em> of resampled pixels, and (<em>ii</em>) <em>variance incoherence</em> between original and interpolated pixels. We exploit these artifacts to distinguish between authentic and resampled TIFF images though a two-step detection process: (<em>i</em>) analyzing and modeling characteristics of both authentic and resampled TIFF images, then (<em>ii</em>) developing statistical detectors by quantifying statistical deviations in these models. Compared to the current state-of-the-art methods, our contributions are threefold. First, we examine the complete processing pipeline, from a RAW image to a resampled TIFF image, to construct appropriate statistical noise models for both authentic and resampled images. Second, we leverage the periodic artifact to extract residual noise data and exploit their variance incoherence to develop (generalized) likelihood ratio test-based detectors for the resampling detection. Third, we derive closed-form expressions for the power function of the proposed detectors and provide an analytical performance evaluation. Numerical experiments on six well-known image databases using diverse interpolation kernels (i.e., nearest neighbor, linear, cubic convolution and cubic spline) validate the mathematical formulation of our detection approach and empirically demonstrate its superior performance.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110282"},"PeriodicalIF":3.6,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105445","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

Risk estimate under a time-varying autoregressive model for data-driven reproduction number estimation 数据驱动的时变自回归模型下的风险估计

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-13 DOI: 10.1016/j.sigpro.2025.110246

Barbara Pascal , Samuel Vaiter

{"title":"Risk estimate under a time-varying autoregressive model for data-driven reproduction number estimation","authors":"Barbara Pascal , Samuel Vaiter","doi":"10.1016/j.sigpro.2025.110246","DOIUrl":"10.1016/j.sigpro.2025.110246","url":null,"abstract":"<div><div>COVID-19 pandemic has brought to the fore epidemiological models which, though describing a wealth of behaviors, have previously received little attention in signal processing literature. In this work, a generalized time-varying autoregressive model is considered, encompassing, but not reducing to, a state-of-the-art model of viral epidemics propagation. The time-varying parameter of this model is estimated via the minimization of a penalized likelihood estimator. A major challenge is that the estimation accuracy strongly depends on hyperparameters fine-tuning. Without available ground truth, hyperparameters are selected by minimizing specifically designed data-driven oracles, used as proxy for the estimation error. Focusing on the time-varying autoregressive Poisson model, Stein’s Unbiased Risk Estimate formalism is generalized to construct asymptotically unbiased risk estimators based on the derivation of an original autoregressive counterpart of Stein’s lemma. The accuracy of these oracles and of the resulting estimates are assessed through intensive Monte Carlo simulations on synthetic data. Then, elaborating on recent epidemiological models, a novel weekly scaled Poisson model is proposed, better accounting for intrinsic variability of the contaminations while being robust to reporting errors. Finally, the data-driven procedure is particularized to the estimation of COVID-19 reproduction number from weekly infection counts demonstrating its ability to tackle real-world applications.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110246"},"PeriodicalIF":3.6,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105452","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

Joint estimation of multipath signal parameters using variational SBL-inspired SAGE algorithm 基于变分sbl - SAGE算法的多径信号参数联合估计

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-09-13 DOI: 10.1016/j.sigpro.2025.110264

Yankun Wang, Dongtang Ma, Dengke Guo, Linjin Kong, Yuan Mi, Xiaoying Zhang, Jun Xiong

{"title":"Joint estimation of multipath signal parameters using variational SBL-inspired SAGE algorithm","authors":"Yankun Wang, Dongtang Ma, Dengke Guo, Linjin Kong, Yuan Mi, Xiaoying Zhang, Jun Xiong","doi":"10.1016/j.sigpro.2025.110264","DOIUrl":"10.1016/j.sigpro.2025.110264","url":null,"abstract":"<div><div>In this paper, we jointly estimate the model order, amplitude gain and dispersion parameters of the received signal vector using a variational sparse Bayesian framework. Contrasting with the Gamma-Gaussian model typically employed in classical sparse Bayesian learning, we select the Bernoulli–Gaussian model as the hierarchical prior and infer a pruning condition for a single specular component within the SAGE framework. The adaptive thresholds derived from this approach are better suited to varying signal-to-noise ratios and provide improved model order estimation. Moreover, two novel joint estimation algorithms are proposed within this framework: (1) optimizing the alternating iterative process inherent in the variational solving approach, while jointly optimizing a portion of the dispersion parameters and amplitude gain to enhance model order estimation without adding to the computational complexity; (2) additionally, introducing a time-delay estimation computation method based on the autocorrelation characteristics of the sounding sequence, aimed at reducing algorithm complexity and speeding up convergence. Finally, the performance advantages of the proposed algorithm are validated through simulations and measured data. Comparisons with related algorithms demonstrate that the proposed algorithm effectively accomplishes joint estimation of model order and channel parameters. Particularly, it achieves more accurate estimation of model order and dispersion parameters in scenarios with high signal-to-noise ratios.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110264"},"PeriodicalIF":3.6,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046133","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