IEEE Signal Processing Letters最新文献_第6页

Reinforcing Localization Credibility Through Convex Optimization 通过凸优化增强定位可信度

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-22 DOI: 10.1109/LSP.2025.3601835

Slavisa Tomic;Marko Beko;Yakubu Tsado;Bamidele Adebisi;Abiola Oladipo

引用次数: 0

Matrix-Based Signal Reconstruction and Narrowband Interference Suppression in DSSS Systems DSSS系统中基于矩阵的信号重构与窄带干扰抑制

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-22 DOI: 10.1109/LSP.2025.3601850

Yuan Liu;Zhenguo Liu;Xuanang Mao;Chuxin Tang

引用次数: 0

CTrans-SegDiff: CTransfomer-Based Diffusion Model for Ultrasound Image Segmentation CTrans-SegDiff：基于ctransform的超声图像分割扩散模型

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-22 DOI: 10.1109/LSP.2025.3601987

Yuzhu Cao;Jizhao Liu;Liping Wang;Jing Lian

引用次数: 0

Dual-Brain EEG Decoding for Target Detection via Joint Learning in Shared and Private Spaces 基于共享空间和私有空间联合学习的双脑EEG解码目标检测

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-22 DOI: 10.1109/LSP.2025.3601978

Bingfeng He;Li Zhu;Junhua Li;Andrzej Cichocki;Wanzeng Kong

{"title":"Dual-Brain EEG Decoding for Target Detection via Joint Learning in Shared and Private Spaces","authors":"Bingfeng He;Li Zhu;Junhua Li;Andrzej Cichocki;Wanzeng Kong","doi":"10.1109/LSP.2025.3601978","DOIUrl":"https://doi.org/10.1109/LSP.2025.3601978","url":null,"abstract":"Hyperscanning enables simultaneous electroencephalography (EEG) recording from multiple individuals, facilitating collaborative brain activity to reduce individual biases and enhance the reliability of decision-making. The decoding of such collaborative paradigm tasks has traditionally relied solely on simple fusion methods based on each individual brain activity, without incorporating cross-brain coupling information. Inspired by social interaction studies on enhanced inter-brain synchrony in collaborative tasks using hyperscanning, we propose a joint learning framework for dual-brain target detection that integrates a shared space construction module and shared feature-guided module. The shared space construction module incorporates brain-to-brain coupling analysis to identify cross-brain synchrony, and further integrates shared and private features through a multi-head fusion mechanism for joint representation learning in shared feature-guided module. Experimental results show an average 10% improvement in balanced accuracy across 12 participant groups compared to traditional single-brain approaches, with some groups achieving up to a 5% gain over state-of-the-art (SOTA) methods. Notably, higher-performing groups exhibit stronger inter-brain coupling and more synchronized target-related responses. These findings advance the development of collaborative brain-computer interface (BCI) systems for more robust and effective target detection.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"3500-3504"},"PeriodicalIF":3.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078640","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 Simple Yet Robust Nonlinear Function for Low-Light Image Enhancement Task 一种用于弱光图像增强的简单而鲁棒的非线性函数

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-22 DOI: 10.1109/LSP.2025.3602001

A. Razafindratovolahy;Y. Rao

引用次数: 0

Spectrally Sharpened Two Channel Graph Filter Bank and Its Polyphase Structure 谱锐化双通道图滤波器组及其多相结构

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-21 DOI: 10.1109/LSP.2025.3601560

David B. Tay

引用次数: 0

Correntropy-Induced Hypergraph Spectral Clustering With Discrete Optimization 具有离散优化的相关熵诱导超图谱聚类

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-21 DOI: 10.1109/LSP.2025.3601523

Jiaqi Nie;Ben Yang;Zhiyuan Xue;Xuetao Zhang;Fei Wang

{"title":"Correntropy-Induced Hypergraph Spectral Clustering With Discrete Optimization","authors":"Jiaqi Nie;Ben Yang;Zhiyuan Xue;Xuetao Zhang;Fei Wang","doi":"10.1109/LSP.2025.3601523","DOIUrl":"https://doi.org/10.1109/LSP.2025.3601523","url":null,"abstract":"Hypergraph clustering has garnered considerable attention in complex learning tasks due to its powerful capacity for modeling high-order relationships among samples. Nevertheless, existing methods encounter two fundamental challenges: 1) The need for an additional discretization step following low-dimensional spectral embedding, which introduces a suboptimal mismatch between continuous embeddings and discrete cluster assignments, thereby impairing clustering performance; and 2) the susceptibility to diverse and complex noise are commonly present in real-world scenarios, which significantly compromises clustering robustness. To address these issues, we propose a novel correntropy-induced hypergraph spectral clustering (CIHSC) model. Different from current spectral clustering methods, CIHSC integrates a correntropy-based framework to enable direct discrete spectral decomposition on hypergraphs, eliminating the need for post discretization and thereby enhancing clustering fidelity and robustness. To effectively address the non-convex optimization arising from the correntropy-induced objective, we develop a half-quadratic optimization strategy tailored to the CIHSC model. Extensive experiments conducted on both real-world and noise-contaminated datasets demonstrate that CIHSC consistently outperforms state-of-the-art clustering methods in terms of performance and robustness.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"3280-3284"},"PeriodicalIF":3.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909103","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

Prompt-Based Cross-Modal Feature Alignment for Weakly Supervised IFER 基于提示的弱监督IFER跨模态特征对齐

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-21 DOI: 10.1109/LSP.2025.3601513

Hanqin Shi;Xiaofeng Kang;Jiaxiang Wang;Aihua Zheng;Wenjuan Cheng

引用次数: 0

Hybrid Cooperative Relative Localization for Urban Vehicles Based on Vehicle-to-Vehicle Communication 基于车对车通信的城市车辆混合协同相对定位

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-21 DOI: 10.1109/LSP.2025.3601515

Qijie Li;Zhi Xiong;Chenfa Shi;Tianxv Wu;Jun Xiong

引用次数: 0

Continual NeRF-Based 3D ISAR Imaging With Multilevel Distillation 基于连续nerf的多级蒸馏三维ISAR成像

IF 3.9 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-08-21 DOI: 10.1109/LSP.2025.3601535

Jianqiang Xu;Yulai Cong;Junyuan Deng;Fei Zeng;Mingcheng Dai

{"title":"Continual NeRF-Based 3D ISAR Imaging With Multilevel Distillation","authors":"Jianqiang Xu;Yulai Cong;Junyuan Deng;Fei Zeng;Mingcheng Dai","doi":"10.1109/LSP.2025.3601535","DOIUrl":"https://doi.org/10.1109/LSP.2025.3601535","url":null,"abstract":"In 3D inverse synthetic aperture radar (ISAR) imaging of space targets, radar neural radiance fields (i.e., ISAR-NeRF) is an important research direction. However, a significant yet unexplored problem is its deployment in continual learning scenarios, where gradual 3D imaging is expected since target ISAR images often emerge sequentially. To address this issue, this letter proposes a new continual 3D ISAR imaging method, named CL-ISAR-NeRF. Specifically, CL-ISAR-NeRF leverages a multilevel distillation mechanism to simultaneously replay pixel, field, and feature-levels information, to alleviate the forgetting of previously learned knowledge. In addition, an efficient memory selection strategy is designed to enrich the diversity of line-of-sight (LOS) when selecting replayed data, which improves imaging performance and further enhances the stability and plasticity of the method. In order to evaluate the proposed method in continual learning settings, we design a realistic simulation scenario in which the trajectories of space targets are calculated by the Simplified General Perturbations-4 (SGP4) model. The comparative experiments with classic continual learning methods demonstrate the superior performance and robustness of CL-ISAR-NeRF.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"3495-3499"},"PeriodicalIF":3.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078643","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