Digital Signal Processing最新文献_第4页

Comprehensive review of segment anything model across multiple domains 对跨多个领域的任何部分模型进行全面审查

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-07-05 DOI: 10.1016/j.dsp.2025.105459

Xin Wang , Taisen Duan , Ganxin Ouyang , Weifeng Hao , Lu Mu , Xuejun Zhang

引用次数: 0

GAVA: Spatial awareness in image captioning with geometric-aware visual attention 空间意识在图像字幕与几何意识视觉注意

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-07-05 DOI: 10.1016/j.dsp.2025.105435

Mohammad Alamgir Hossain , ZhongFu Ye , Md. Bipul Hossen , Md. Atiqur Rahman , Md Shohidul Islam , Md. Ibrahim Abdullah

{"title":"GAVA: Spatial awareness in image captioning with geometric-aware visual attention","authors":"Mohammad Alamgir Hossain , ZhongFu Ye , Md. Bipul Hossen , Md. Atiqur Rahman , Md Shohidul Islam , Md. Ibrahim Abdullah","doi":"10.1016/j.dsp.2025.105435","DOIUrl":"10.1016/j.dsp.2025.105435","url":null,"abstract":"<div><div>Image captioning models often face challenges in capturing spatial relationships, which are critical for generating accurate and contextually meaningful descriptions. In this work, we propose Geometric-Aware Visual Attention (GAVA), a novel attention mechanism that integrates spatial geometry—such as object positions, sizes, and aspect ratios—directly into the attention process. GAVA improves spatial reasoning by utilizing bilinear pooling to effectively combine visual and geometric features, leading to captions that are both descriptive and spatially coherent. The proposed GAVA mechanism enhances spatial reasoning by incorporating spatial geometry into the attention framework. Additionally, we present a unified feature extraction approach that exclusively extracts geometric information, forming a representation that captures complex spatial dependencies and results in more coherent and contextually accurate captions. We demonstrate the effectiveness of GAVA through experiments on the MS-COCO dataset, where it outperforms state-of-the-art models, achieving significant improvements in BLEU, CIDEr, and SPICE scores. These results underscore GAVA's ability to capture spatial accuracy and contextual relevance, establishing a new benchmark for spatially-aware image captioning. The code for GAVA is publicly available at <span><span>https://github.com/alamgirustc/GAVA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105435"},"PeriodicalIF":2.9,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Effective multiparameter estimation in SIMO-OTFS system for integrated sensing and communication via tensor analysis 基于张量分析的SIMO-OTFS传感通信系统多参数有效估计

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-07-05 DOI: 10.1016/j.dsp.2025.105437

Qiang Lv , Jianhe Du , Yuanzhi Chen , Rongzhen Chen , Xingwang Li

引用次数: 0

Secure-sensing balance beamforming with artificial-noise-aided for integrated sensing and backscatter communication system 集成传感和后向散射通信系统中人工噪声辅助的安全传感平衡波束形成

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-07-05 DOI: 10.1016/j.dsp.2025.105457

Hongyao Li , Siyang Xu , Yichen Tang

{"title":"Secure-sensing balance beamforming with artificial-noise-aided for integrated sensing and backscatter communication system","authors":"Hongyao Li , Siyang Xu , Yichen Tang","doi":"10.1016/j.dsp.2025.105457","DOIUrl":"10.1016/j.dsp.2025.105457","url":null,"abstract":"<div><div>The Integrated Sensing and Backscatter Communication (ISABC) system integrates communication and sensing by using passive tags to reflect signals, enabling simultaneous environmental data acquisition and data transmission at the base station. Balancing security with sensing performance is essential to ensure reliable communication and precise environmental sensing in critical applications. This paper investigates an ISABC system where a full-duplex base station (FD BS) communicates with a legitimate user and performs tag sensing under eavesdropping threats. We propose a weighted optimization problem to balance the minimization of the Cramér-Rao Bound (CRB) for base station sensing with the maximization of the secrecy rate (SR), while maintaining a specified signal-to-interference-plus-noise ratio (SINR). Additionally, to enhance security without degrading sensing performance, we introduce an orthogonalized noise signal to disrupt the eavesdropper. To overcome the inherent non-convexity of the optimization problem, we reformulate it as a convex problem using successive convex approximation (SCA) techniques, including Taylor series expansions and semidefinite relaxation. Numerical simulations validate the effectiveness of the proposed approach in achieving the balance between security and sensing performance. Trade-off analysis indicates the improvement of the secrecy rate is achieved at the cost of degraded sensing accuracy.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105457"},"PeriodicalIF":2.9,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Space-time adaptive processing based on Schatten p-norm minimization for airborne radar 基于Schatten p范数最小化的机载雷达空时自适应处理

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-07-05 DOI: 10.1016/j.dsp.2025.105439

Pengcheng Bai , Yi Gan , Yunxiu Yang , Qin Shu

{"title":"Space-time adaptive processing based on Schatten p-norm minimization for airborne radar","authors":"Pengcheng Bai , Yi Gan , Yunxiu Yang , Qin Shu","doi":"10.1016/j.dsp.2025.105439","DOIUrl":"10.1016/j.dsp.2025.105439","url":null,"abstract":"<div><div>In this paper, we consider the non-stationary clutter suppression for the airborne radar system under the Space-time adaptive processing (STAP) framework. In order to solve the off-grid problem caused by the discretization of angle-Doppler plane in sparse recovery based STAP (SR-STAP) methods and the performance degradation caused by the convex optimization of clutter rank function in atomic norm minimization based STAP (ANM-STAP) methods, we propose a novel STAP method based on Schatten <em>p</em>-norm minimization, termed as SpNM-STAP. In the proposed method, the Schatten <em>p</em>-norm, which can better induce low-rank, is utilized to construct the low-rank model for clutter covariance matrix (CCM). And we derive an efficient optimization algorithm for this model using the alternating direction method of multipliers (ADMM). This method is applicable to both single training sample model and multiple training samples model. Simulation results show that compared with the statistical STAP, SR-STAP and ANM-STAP methods, the proposed algorithm achieves more accurate CCM estimation and has better clutter suppression performance in the scenarios of both side-looking array and non-side-looking array.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105439"},"PeriodicalIF":2.9,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Einstein matrix-optimized multiscale frequency band fusion framework for speech denoising 基于爱因斯坦矩阵优化的语音去噪多尺度频带融合框架

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-07-04 DOI: 10.1016/j.dsp.2025.105442

Sinan Peng, Junfeng Shen

{"title":"Einstein matrix-optimized multiscale frequency band fusion framework for speech denoising","authors":"Sinan Peng, Junfeng Shen","doi":"10.1016/j.dsp.2025.105442","DOIUrl":"10.1016/j.dsp.2025.105442","url":null,"abstract":"<div><div>Frequency-domain speech enhancement plays a critical role in improving speech quality under complex noise conditions. However, current methods often suffer from limited feature granularity and constrained model complexity. To overcome these limitations, we propose a novel architecture, Einstein matrix-based Fast Fourier Transform with Multi-Band Frequency Feature fusion (EinFFT-MBFF). The model includes two key modules: a quadratic Fast Fourier Transform (FFT) module that projects signals into a high-dimensional “hyper-frequency” domain, where trainable matrix transformations extract fine-grained spectral features; and a multi-scale fusion mechanism that captures both global and local frequency characteristics via parallel sub-networks. To further improve robustness, a dynamic mixed data augmentation strategy is employed by diversifying noise-speech combinations during training. Experiments on DR-VCTK, FSD50K, and REVERB datasets show that under reverberant conditions, the model achieves relative improvements of 1.058% in Wideband Perceptual Evaluation of Speech Quality (WB-PESQ), 1.076% in Narrowband PESQ (NB-PESQ), and 8.15% in Short-Time Objective Intelligibility (STOI). In non-reverberant settings, improvements reach 0.96%, 0.989%, and 5.61%, respectively. Compared with state-of-the-art methods, EinFFT-MBFF achieves better noise suppression and more stable convergence, while preserving computational efficiency through the inherent cross-channel interaction capabilities of the Einstein matrix.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105442"},"PeriodicalIF":2.9,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Phase equalization using a hybrid cascade of causal and noncausal IIR allpass filters 相位均衡使用因果和非因果IIR全通滤波器的混合级联

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-07-04 DOI: 10.1016/j.dsp.2025.105455

Yuzhen Lyu, Ziyun Liu, Keyu Pan, Yong Shen

引用次数: 0

Straightforward tensorized anchor graph learning for multi-view clustering 多视图聚类的直接张紧锚图学习

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-07-04 DOI: 10.1016/j.dsp.2025.105443

Yulin Zhou , Changpeng Wang , Lizhen Ji , Jiangshe Zhang

{"title":"Straightforward tensorized anchor graph learning for multi-view clustering","authors":"Yulin Zhou , Changpeng Wang , Lizhen Ji , Jiangshe Zhang","doi":"10.1016/j.dsp.2025.105443","DOIUrl":"10.1016/j.dsp.2025.105443","url":null,"abstract":"<div><div>Even while graph-based multi-view clustering methods are quite effective in capturing the connection between data and clustering structures, the majority of them still exhibit the following limitations: (1) some methods fail to account for higher-order correlations and spatial structures between multi-view data; (2) random sampling and k-Means lead to unstable selection of anchors; (3) post-processing steps in many studies contribute to suboptimal clustering performance. To solve these issues, we propose a straightforward tensorized anchor graph learning method (STAGL) for multi-view clustering, which integrates the low-rank tensor learning and clustering into a unified framework. Specifically, we first employ a variance-based decorrelation strategy to select anchor points and construct an anchor graph for every view. Based on this, STAGL explores the similarities and spatial structures of each view by minimizing the tensor-adaptive log-determinant regularization. Additionally, we directly employ the anchor graphs to obtain the final clustering assignments by computing the distances between samples. Meanwhile, an adaptive strategy is incorporated to account for the varying importance of different views in the clustering process. Finally, we employed an efficient algorithm to solve this model, and comprehensive experiments on six datasets demonstrate the superior clustering performance of the proposed method.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105443"},"PeriodicalIF":2.9,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Multi-modal learning methods in medical imaging area: A survey 医学影像领域多模式学习方法综述

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-07-04 DOI: 10.1016/j.dsp.2025.105441

Yibo Sun, Weitong Chen, Zhe Sun

{"title":"Multi-modal learning methods in medical imaging area: A survey","authors":"Yibo Sun, Weitong Chen, Zhe Sun","doi":"10.1016/j.dsp.2025.105441","DOIUrl":"10.1016/j.dsp.2025.105441","url":null,"abstract":"<div><div>Multi-modal learning is an important branch in the field of deep learning area, which has been widely used for processing data from different media. The fusion of different modalities in natural images has shown significant results, but less attention has been paid to medical images of individual modalities due to data scarcity. The discussion of applications of multi-modal learning has raised great interest in the medical field, including general fusion methods, deep learning-based methods, and large language model-based methods. With the aim of describing the evolution of different models in the field of multi-modal medical imaging, this survey provides a thorough overview of representative methods and related applications. In this study, we first introduced the concept of modality and the development of multi-modal learning, then listed the commonly used medical modalities and fusion strategies. After that, we described the branches of multi-modal models in the medical imaging field in detail, along with various application scenarios and open datasets. We hope our survey will provide guidance for readers to understand typical models and the growing trend within the medical imaging domain.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105441"},"PeriodicalIF":2.9,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 lightweight underwater object detection with enhanced detail and edge-aware feature fusion 一种轻量级的水下目标检测，增强了细节和边缘感知特征融合

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-07-04 DOI: 10.1016/j.dsp.2025.105456

Chaolong Xu, Zhibin Xie

{"title":"A lightweight underwater object detection with enhanced detail and edge-aware feature fusion","authors":"Chaolong Xu, Zhibin Xie","doi":"10.1016/j.dsp.2025.105456","DOIUrl":"10.1016/j.dsp.2025.105456","url":null,"abstract":"<div><div>Underwater object detection often encounters challenges such as variable target scale, complex backgrounds, blurred object edges, and image distortion. In response to these challenges, a lightweight detection algorithm, EDFF-YOLO (Edge Detail Feature Fusion YOLO), is designed to enhance detection performance under these adverse conditions. To enhance the capability of the network to extract global features from images, a multi-scale residual enhancement module has been developed. This module captures and fuses a broader range of multi-scale contextual information. Secondly, a hybrid feature fusion module is proposed, which enhances the effectiveness of feature representation by using the hybrid local channel attention mechanism and element-wise operations to guide and fuse features. Then, a lightweight edge extraction block is designed to extract both edge and spatial information of the image, enriching feature diversity. Finally, the shared detail enhancement detection head is used to improve the ability of the detection head to capture details and to reduce the number of parameters and computational load of the algorithm. The experimental results reveal that the proposed algorithm outperforms the YOLOv8s baseline algorithm on the RUOD dataset. It demonstrates a reduction of 18 % and 30 % in both the number of parameters and computational load, respectively. Additionally, the [email protected] increases by 0.4 % to reach 88.1 %, surpassing the performance of other tested algorithms.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105456"},"PeriodicalIF":2.9,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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