IEEE Signal Processing Letters最新文献_第3页

Moving Average-Based Variable Projection for Separable Nonlinear Problems 基于移动平均的可分离非线性问题变量投影

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3563133

Peng Xue;Min Gan;Fang Yuan;Guang-Yong Chen;C. L. Philip Chen

{"title":"Moving Average-Based Variable Projection for Separable Nonlinear Problems","authors":"Peng Xue;Min Gan;Fang Yuan;Guang-Yong Chen;C. L. Philip Chen","doi":"10.1109/LSP.2025.3563133","DOIUrl":"https://doi.org/10.1109/LSP.2025.3563133","url":null,"abstract":"The identification of separable nonlinear models, prevalent in tasks such as signal analysis, image processing, time series analysis, and machine learning, presents a non-convex optimization challenge that necessitates the development of efficient identification algorithms. The Variable Projection (VP) algorithm has been proven to be quite effective for addressing these problems; however, traditional VP relying on the Hessian matrix and its inverse are highly time-consuming and unsuitable for complex, large-scale applications. This letter introduces a novel approach that employs the exponential moving average of gradient and gradient estimation bias to indirectly estimate the curvature of the objective landscape, proposing a Moving Average-based Variable Projection method (MAVP). The proposed algorithm utilizes only gradient information and can properly tackle the coupling relationships between different parameters during the optimization process, thereby achieving faster convergence. Numerical results on nonlinear time series analysis and image reconstruction demonstrate that the MAVP algorithm exhibits significant efficiency and effectiveness.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1900-1904"},"PeriodicalIF":3.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929826","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

Multilevel Representation Disentanglement Framework for Multimodal Sentiment Analysis 多模态情感分析的多层表示解纠缠框架

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562827

Nan Jia;Zicong Bai;Tiancheng Xiong;Mingyang Guo

引用次数: 0

Beyond $R$-Barycenters: An Effective Averaging Method on Stiefel and Grassmann Manifolds 超越R -重心：Stiefel和Grassmann流形的一种有效的平均方法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562735

Florent Bouchard;Nils Laurent;Salem Said;Nicolas Le Bihan

引用次数: 0

Enhancing Tumor Edge Consistency in Multimodal MRI Synthesis for Improved Glioma Segmentation 增强多模态MRI合成中肿瘤边缘一致性以改善胶质瘤分割

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562824

Can Chang;Li Yao;Xiaojie Zhao

{"title":"Enhancing Tumor Edge Consistency in Multimodal MRI Synthesis for Improved Glioma Segmentation","authors":"Can Chang;Li Yao;Xiaojie Zhao","doi":"10.1109/LSP.2025.3562824","DOIUrl":"https://doi.org/10.1109/LSP.2025.3562824","url":null,"abstract":"Precise segmentation of glioma subregions using multimodal MRI is crucial for accurate diagnosis and effective treatment. However, the absence of certain MRI modalities in clinical settings often leads to incomplete information, necessitating cross-modality synthesis to fill the gaps. A significant challenge in this synthesis is the blurring of tumor subregion boundaries, which affects subsequent segmentation accuracy. Existing methods, while improving boundary clarity, fail to ensure consistent depiction across different modalities due to varying contrasts and sensitive areas. To address these issues, we propose CSEC-Net, a novel tumor-aware synthesis model that enhances tumor edge consistency through Specific Contrast extraction and Edge Consistency enhancement. Our model employs a Contrast-Specific Prototype Learning (CS-PL) method to extract contrast-specific prototype features and an Edge Consistency Contrast Learning (EC-CL) method to improve tumor edge pixel sampling and feature learning. This innovative approach ensures consistent and clear tumor edge depiction across different modalities, significantly improving multimodal MRI synthesis and tumor segmentation accuracy.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"2060-2064"},"PeriodicalIF":3.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117367","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

TF-CorrNet: Leveraging Spatial Correlation for Continuous Speech Separation tf - cornet：利用空间相关进行连续语音分离

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562819

Ui-Hyeop Shin;Bon Hyeok Ku;Hyung-Min Park

{"title":"TF-CorrNet: Leveraging Spatial Correlation for Continuous Speech Separation","authors":"Ui-Hyeop Shin;Bon Hyeok Ku;Hyung-Min Park","doi":"10.1109/LSP.2025.3562819","DOIUrl":"https://doi.org/10.1109/LSP.2025.3562819","url":null,"abstract":"In general, multi-channel source separation has utilized inter-microphone phase differences (IPDs) concatenated with magnitude information in time-frequency domain, or real and imaginary components stacked along the channel axis. However, the spatial information of a sound source is fundamentally contained in the “differences” between microphones, specifically in the correlation between them, while the power of each microphone also provides valuable information about the source spectrum, which is why the magnitude is also included. Therefore, we propose a network that directly leverages a correlation input with phase transform (PHAT)-<inline-formula><tex-math>$beta$</tex-math></inline-formula> to estimate the separation filter. In addition, the proposed TF-CorrNet processes the features alternately across time and frequency axes as a dual-path strategy in terms of spatial information. Furthermore, we add a spectral module to model source-related direct time-frequency patterns for improved speech separation. Experimental results demonstrate that the proposed TF-CorrNet effectively separates the speech sounds, showing high performance with a low computational cost in the LibriCSS dataset.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1875-1879"},"PeriodicalIF":3.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929827","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 RGBT Tracking via Multi-Path Mamba Fusion Network 基于多路径曼巴融合网络的高效rbt跟踪

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3563123

Fanghua Hong;Wanyu Wang;Andong Lu;Lei Liu;Qunjing Wang

{"title":"Efficient RGBT Tracking via Multi-Path Mamba Fusion Network","authors":"Fanghua Hong;Wanyu Wang;Andong Lu;Lei Liu;Qunjing Wang","doi":"10.1109/LSP.2025.3563123","DOIUrl":"https://doi.org/10.1109/LSP.2025.3563123","url":null,"abstract":"RGBT tracking aims to fully exploit the complementary advantages of visible and infrared modalities to achieve robust tracking, thus the design of multimodal fusion network is crucial. However, existing methods typically adopt CNNs or Transformer networks to construct the fusion network, which poses a challenge in achieving a balance between performance and efficiency. To overcome this issue, we introduce an innovative visual state space (VSS) model, represented by Mamba, for RGBT tracking. In particular, we design a novel multi-path Mamba fusion network that achieves robust multimodal fusion capability while maintaining a linear overhead. First, we design a multi-path Mamba layer to sufficiently fuse two modalities in both global and local perspectives. Second, to alleviate the issue of inadequate VSS modeling in the channel dimension, we introduce a simple yet effective channel swapping layer. Extensive experiments conducted on four public RGBT tracking datasets demonstrate that our method surpasses existing state-of-the-art trackers. Notably, our fusion method achieves higher tracking performance compared to the well-known Transformer-based fusion approach (TBSI), while also achieving 92.8% and 80.5% reductions in parameter count and computational cost, respectively.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1790-1794"},"PeriodicalIF":3.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924953","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

Performance Prediction of Hybrid Integration Detector for Radar Moderately Fluctuating Rayleigh Targets 雷达中波动瑞利目标混合积分探测器性能预测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562829

Hongying Zheng;Qilei Zhang;Yongsheng Zhang

引用次数: 0

Decentralized Smoothing ADMM for Quantile Regression With Non-Convex Sparse Penalties 非凸稀疏惩罚分位数回归的分散平滑ADMM

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562828

Reza Mirzaeifard;Diyako Ghaderyan;Stefan Werner

{"title":"Decentralized Smoothing ADMM for Quantile Regression With Non-Convex Sparse Penalties","authors":"Reza Mirzaeifard;Diyako Ghaderyan;Stefan Werner","doi":"10.1109/LSP.2025.3562828","DOIUrl":"https://doi.org/10.1109/LSP.2025.3562828","url":null,"abstract":"In the rapidly evolving internet-of-things (IoT) ecosystem, effective data analysis techniques are crucial for handling distributed data generated by sensors. Addressing the limitations of existing methods, such as the sub-gradient approach, which fails to distinguish between active and non-active coefficients effectively, this paper introduces the decentralized smoothing alternating direction method of multipliers (DSAD) for penalized quantile regression. Our method leverages non-convex sparse penalties like the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD), improving the identification and retention of significant predictors. DSAD incorporates a total variation norm within a smoothing ADMM framework, achieving consensus among distributed nodes and ensuring uniform model performance across disparate data sources. This approach overcomes traditional convergence challenges associated with non-convex penalties in decentralized settings. We present convergence proof and extensive simulation results to validate the effectiveness of the DSAD, demonstrating its superiority in achieving reliable convergence and enhancing estimation accuracy compared with prior methods.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1915-1919"},"PeriodicalIF":3.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937923","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

LYT-NET: Lightweight YUV Transformer-Based Network for Low-Light Image Enhancement LYT-NET：用于弱光图像增强的轻量级YUV变压器网络

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3563125

Alexandru Brateanu;Raul Balmez;Adrian Avram;Ciprian Orhei;Cosmin Ancuti

引用次数: 0

An Effective Yet Fast Early Stopping Metric for Deep Image Prior in Image Denoising 深度图像去噪中一种有效且快速的早期停止度量

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562948

Xiaohui Cheng;Shaoping Xu;Wuyong Tao

引用次数: 0