IEEE Signal Processing Letters最新文献

Robust Joint Design of MIMO Radar Waveform and Filter via Structured Covariance Matrix 基于结构化协方差矩阵的MIMO雷达波形与滤波器鲁棒联合设计

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-06-19 DOI: 10.1109/LSP.2025.3581145

Guohao Sun;Yingkui Zhang;Zhaoke Ning;Yuandong Ji;Zhiquan Ding

引用次数: 0

Efficient Detection of QIM-Based VoIP Steganography Using Adjacent Frame Integration and Multi-Codeword Priority Attention 基于相邻帧集成和多码字优先注意的基于qim的VoIP隐写有效检测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-06-17 DOI: 10.1109/LSP.2025.3580496

Cheng Zhang;Yue Yan;Shujuan Jiang;Zhong Chen

{"title":"Efficient Detection of QIM-Based VoIP Steganography Using Adjacent Frame Integration and Multi-Codeword Priority Attention","authors":"Cheng Zhang;Yue Yan;Shujuan Jiang;Zhong Chen","doi":"10.1109/LSP.2025.3580496","DOIUrl":"https://doi.org/10.1109/LSP.2025.3580496","url":null,"abstract":"With the growing volume of VoIP traffic, many steganography algorithms exploit VoIP speech as a carrier, posing a threat to cybersecurity. Among them, quantization index modulation (QIM)-based VoIP steganography has demonstrated excellent stealth, making detection difficult. In recent years, more studies have focused on developing feasible QIM-based VoIP steganalysis methods for detecting QIM-based VoIP steganography. Previous studies have mostly focused on improving detection performance while neglecting efficiency, resulting in insufficient research on lightweight models. In online detection scenarios, detection efficiency is crucial. On the one hand, the long inference time of large models can delay warnings. On the other hand, the high computational requirements of these models make them difficult to deploy on remote devices, which reduces their practical value. In this letter, we propose a simple yet efficient model named EQVS (efficient QIM-based VoIP steganalysis network) for detecting QIM-based VoIP steganography. In EQVS, the fold and unfold operations are redesigned based on the characteristics of VoIP speech samples and the requirements of the QIM-based VoIP steganalysis task, to avoid disrupting correlation features. Then, multi-codeword priority attention mechanism, inspired by the multi-query attention and retention mechanisms, redefines the calculation procedure for the query, key, and value matrices, as well as the normalization and softmax operations, to further reduce computational resource consumption in a single attention head. Experimental results demonstrate that EQVS outperforms other state-of-the-art models in both detection performance and efficiency.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"2534-2538"},"PeriodicalIF":3.2,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524399","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

Frequency Increment Optimization With FDA-MIMO Radar for Target Localization 基于频率增量优化的FDA-MIMO雷达目标定位

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-06-16 DOI: 10.1109/LSP.2025.3580319

Lan Lan;Kunkun Li;Jingwei Xu;Guisheng Liao;Hing Cheung So

{"title":"Frequency Increment Optimization With FDA-MIMO Radar for Target Localization","authors":"Lan Lan;Kunkun Li;Jingwei Xu;Guisheng Liao;Hing Cheung So","doi":"10.1109/LSP.2025.3580319","DOIUrl":"https://doi.org/10.1109/LSP.2025.3580319","url":null,"abstract":"This letter presents an optimization approach for frequency increments tailored to Frequency Diverse Array (FDA)-Multiple-Input Multiple-Output (MIMO) radar for target localization. We start to formulate the problem as minimizing the Cramér-Rao Bounds (CRBs) for both range and angle estimation, subject to practical constraints on the frequency increments. To facilitate optimization, the objective function is mathematically transformed, which results in a maximization problem, leveraging its inherent non-negativity of both the numerator and denominator. To address the resultant non-convex and NP-hard optimization problem, a Minorization-Maximization (MM)-Maximum Block Improvement (MBI) algorithm is devised by partitioning the frequency increment vector into distinct blocks, allowing for alternating maximization. In particular, each frequency increment is refined with the MM algorithm, while holding the others fixed, and only the block yielding the maximum objective increment is updated within each iteration. Simulation results are provided to demonstrate the excellent target localization of our proposed approach.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"2529-2533"},"PeriodicalIF":3.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524398","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

Optimal Analysis of Consensus Algorithms for $r$-Nearest Ring Networks $r$-最近环网络一致性算法的最优分析

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-06-12 DOI: 10.1109/LSP.2025.3578956

V Sateeshkrishna Dhuli;Said Kouachi;Stefan Werner

{"title":"Optimal Analysis of Consensus Algorithms for $r$-Nearest Ring Networks","authors":"V Sateeshkrishna Dhuli;Said Kouachi;Stefan Werner","doi":"10.1109/LSP.2025.3578956","DOIUrl":"https://doi.org/10.1109/LSP.2025.3578956","url":null,"abstract":"Analyzing consensus algorithms within the context of the <inline-formula><tex-math>$r$</tex-math></inline-formula>-nearest ring networks is critical for understanding the efficiency and reliability of large-scale distributed networks. The special properties of the <inline-formula><tex-math>$r$</tex-math></inline-formula>-nearest neighbor ring offer multiple communication paths, accelerate convergence, and improve the robustness of consensus algorithms. However, this increased connectivity also introduces significant complexity in evaluating the performance of consensus algorithms, since key metrics are typically defined in terms of Laplacian eigenvalues. Especially, estimating the largest eigenvalue of the Laplacian matrix remains a major challenge for the <inline-formula><tex-math>$r$</tex-math></inline-formula>-nearest neighbor ring networks. We reformulate the maximization of Laplacian eigenvalue as a minimization of the Dirichlet kernel problem. Firstly, we prove that the first and last lobes of the Dirichlet kernel are the deepest using the shift approach. Next, we apply local smoothness analysis and integer rounding arguments to demonstrate that there is at least one discrete sample to achieve a global minimum in that lobe. This study presents a rigorous analysis to precisely locate and compute the largest eigenvalue, resulting in exact analysis for key performance metrics, including convergence time, first-order network coherence, second-order network coherence, and maximum communication delay, with reduced computational complexity. In addition, our findings illustrate the effect of <inline-formula><tex-math>$r$</tex-math></inline-formula> in improving the performance of consensus algorithms in large-scale networks.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"2494-2498"},"PeriodicalIF":3.2,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519419","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

Blind Image Super-Resolution With Efficient Network Design Using Frequency Domain Information 基于频域信息的盲图像超分辨率高效网络设计

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-06-11 DOI: 10.1109/LSP.2025.3578957

Sunwoo Cho;Nam Ik Cho

{"title":"Blind Image Super-Resolution With Efficient Network Design Using Frequency Domain Information","authors":"Sunwoo Cho;Nam Ik Cho","doi":"10.1109/LSP.2025.3578957","DOIUrl":"https://doi.org/10.1109/LSP.2025.3578957","url":null,"abstract":"Blind Image Super-Resolution (BSR) tackles the challenge of enhancing image resolution that has been degraded by unknown kernels. Although existing BSR models have achieved remarkable results, they often demand significant computational resources to manage various degradation kernels. Recent studies have utilized large models with parameter counts ranging from 4 M to 20 M, resulting in computational costs exceeding 200 G Multi-Adds. In this paper, we introduce a blind super-resolution method, which is the first lightweight non-iterative approach for BSR. By leveraging the connection between degradation kernel shapes and the frequency-domain characteristics of low-resolution images, we simplify the kernel estimation process, thereby reducing the overall model complexity. Additionally, we employ a constrained least squares approach to refine the low-resolution image using the estimated kernel, which serves as the input to the hierarchical Transformer blocks. Our approach delivers competitive performance while requiring only 1.7 M parameters and 2 G Multi-Adds. Experimental results demonstrate that our method achieves comparable results with significantly smaller network size.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"2524-2528"},"PeriodicalIF":3.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524404","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

SAFT: Learning Scale-Aware Inter-Series Correlations for Time Series Forecasting 学习尺度感知的序列间相关性用于时间序列预测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-06-11 DOI: 10.1109/LSP.2025.3578914

Xin-Yi Li;Yu-Bin Yang

引用次数: 0

RPCC: Rectified Pearson Correlation Coefficient for Radiance Fields Optimization RPCC：校正Pearson相关系数的辐射场优化

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-06-11 DOI: 10.1109/LSP.2025.3578913

Jun Peng;Chunyi Chen

引用次数: 0

DCMNet: A Supervised Learning Framework for Radar Signal Modulation Recognition 雷达信号调制识别的监督学习框架

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-06-09 DOI: 10.1109/LSP.2025.3578289

Kaige Hou;Xiaolin Du;Guolong Cui;Xiaolong Chen;Jibin Zheng

引用次数: 0

Dynamic-Clustering-Based Color Quantization for Electrophoretic Display 基于动态聚类的电泳显示颜色量化

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-06-09 DOI: 10.1109/LSP.2025.3577928

Tingyu Cheng;Xiaoyan Zhao;Gongning Yang;Wei Yuan;Tiesong Zhao

引用次数: 0

Vision-Language Model Priors-Driven State Space Model for Infrared-Visible Image Fusion 红外-可见图像融合的视觉语言模型-先验驱动状态空间模型

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-06-09 DOI: 10.1109/LSP.2025.3578250

Rongjin Zhuang;Yingying Wang;Xiaotong Tu;Yue Huang;Xinghao Ding

{"title":"Vision-Language Model Priors-Driven State Space Model for Infrared-Visible Image Fusion","authors":"Rongjin Zhuang;Yingying Wang;Xiaotong Tu;Yue Huang;Xinghao Ding","doi":"10.1109/LSP.2025.3578250","DOIUrl":"https://doi.org/10.1109/LSP.2025.3578250","url":null,"abstract":"Infrared and visible image fusion (IVIF) aims to effectively integrate complementary information from both infrared and visible modalities, enabling a more comprehensive understanding of the scene and improving downstream semantic tasks. Recent advancements in Mamba have shown remarkable performance in image fusion, owing to its linear complexity and global receptive fields. However, leveraging Vision-Language Model (VLM) priors to drive Mamba for modality-specific feature extraction and using them as constraints to enhance fusion results has not been fully explored. To address this gap, we introduce VLMPD-Mamba, a Vision-Language Model Priors-Driven Mamba framework for IVIF. Initially, we employ the VLM to adaptively generate modality-specific textual descriptions, which enhance image quality and highlight critical target information. Next, we present Text-Controlled Mamba (TCM), which integrates textual priors from the VLM to facilitate effective modality-specific feature extraction. Furthermore, we design the Cross-modality Fusion Mamba (CFM) to fuse features from different modalities, utilizing VLM priors as constraints to enhance fusion outcomes while preserving salient targets with rich details. In addition, to promote effective cross modality feature interactions, we introduce a novel bi-modal interaction scanning strategy within the CFM. Extensive experiments on various datasets for IVIF, as well as downstream visual tasks, demonstrate the superiority of our approach over state-of-the-art (SOTA) image fusion algorithms.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"2514-2518"},"PeriodicalIF":3.2,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524401","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