Digital Signal Processing最新文献_第7页

Physical layer security in RIS-aided communication systems: Secrecy performance analyses ris辅助通信系统中的物理层安全：保密性能分析

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-06-26 DOI: 10.1016/j.dsp.2025.105417

Mustafa Namdar , Arif Basgumus , Serdar Ozgur Ata , Sultan Aldirmaz-Colak

{"title":"Physical layer security in RIS-aided communication systems: Secrecy performance analyses","authors":"Mustafa Namdar , Arif Basgumus , Serdar Ozgur Ata , Sultan Aldirmaz-Colak","doi":"10.1016/j.dsp.2025.105417","DOIUrl":"10.1016/j.dsp.2025.105417","url":null,"abstract":"<div><div>In this work, the secrecy performance of a novel reconfigurable intelligent surface (RIS)-assisted wireless communication system is investigated in the presence of an eavesdropper. This study is realized to characterize the privacy performance for the case where a single antenna base station (BS) sends confidential information to a single antenna legitimate user via RIS with the existence of a single antenna eavesdropper in urban wireless networks. In particular, new average secrecy capacity and approximate secrecy capacity expressions are derived. The secrecy outage probability (SOP) scenario for a legitimate user receiver and single antenna eavesdropper is further examined. Finally, the asymptotic SOP analysis is also conducted to obtain insights for the considered system model. Additionally, the impact of imperfect channel state information, a crucial factor in practical RIS-aided systems, on the achievable secrecy performance is also considered. It is verified that the analytical results are consistent with the simulations, which confirm the accuracy of the derived closed-form equations. It can be observed from the extensive simulation results that increasing the number of RIS elements improves both the average secrecy capacity and the SOP performance.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105417"},"PeriodicalIF":2.9,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490162","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

Camouflaged object detection via dual domain fusion 基于双域融合的伪装目标检测

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-06-26 DOI: 10.1016/j.dsp.2025.105436

Chenxing Shen, Zhisheng Cui, Leheng Zhang, Miaohui Zhang

{"title":"Camouflaged object detection via dual domain fusion","authors":"Chenxing Shen, Zhisheng Cui, Leheng Zhang, Miaohui Zhang","doi":"10.1016/j.dsp.2025.105436","DOIUrl":"10.1016/j.dsp.2025.105436","url":null,"abstract":"<div><div>In camouflaged object detection (COD), the goal of accurately and completely segmenting foreground objects from the background on a per-pixel basis is pursued by various network architectures. Despite their specialized module designs tailored for camouflaged object detection, most of these approaches operate primarily within the RGB domain to locate camouflaged objects. Given the intrinsic characteristics of camouflaged objects, solely segmenting them within the RGB domain often encounters significant interference and challenges. To address this issue and leverage the advantages of detecting camouflaged objects from the frequency domain, this paper introduces a Dual-Domain Fusion Network (DDFNet). By integrating frequency domain features with RGB domain features, DDFNet exploits the complementary strengths of both domains, achieving precise localization and segmentation of camouflaged objects. DDFNet is composed of three main modules. The MHFM performs group fusion of backbone features to enhance the representation of these features. The GFGM, based on a dual-phase architecture, extracts the camouflaged target hidden in the RGB domain by operating in the frequency domain. It achieves this by leveraging the detailed information contained in high-frequency features and the spatial information in low-frequency features, thereby obtaining a frequency-domain feature representation of the camouflaged target, which supplements the RGB domain features. Finally, through GHIM, DDFNet performs complementary fusion of the frequency-domain features and the RGB-domain features, utilizing their respective advantages to achieve precise localization of the camouflaged target. Compared to other state-of-the-art methods in camouflaged object detection, DDFNet demonstrates superior performance. Additionally, we introduce polyp segmentation as a downstream task for the proposed network to showcase the ability of DDFNet in solving real-world problems.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105436"},"PeriodicalIF":2.9,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549188","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

Enhancing object detection with large kernel convolution and cross convolution 利用大核卷积和交叉卷积增强目标检测

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-06-25 DOI: 10.1016/j.dsp.2025.105433

Yaqian Li , Guoping Liu , Haibin Li , Wenming Zhang , Xiaoyang Shen

{"title":"Enhancing object detection with large kernel convolution and cross convolution","authors":"Yaqian Li , Guoping Liu , Haibin Li , Wenming Zhang , Xiaoyang Shen","doi":"10.1016/j.dsp.2025.105433","DOIUrl":"10.1016/j.dsp.2025.105433","url":null,"abstract":"<div><div>Existing object detection models often struggle with detecting small objects due to their limited ability to capture sufficient contextual information. In this paper, we introduce a lightweight object detection model that leverages large kernel convolution with attention (LKA) and a hierarchical feature fusion group (HFFG) to address this issue. The LKA module employs large kernel convolution to capture long-range dependencies and contextual information, combined with depthwise separate convolution to maintain a lightweight design. An incorporated attention mechanism further enables the modal to adaptively focus on key areas, thereby improving detection performance for small objects. The HFFG module, which integrates Cross Convolution Blocks, explores and retains structural information across different scales. By effectively extracting structural details, our model exhibits enhanced performance on object of various sizes. Extensive experiments on the VisDrone2019 and PASACAL VOC datasets demonstrate that our model achieves an outstanding mAP of 23.4 %, surpassing the baseline YOLOX-s model by +1.5 %. These results not only validate the effectiveness but also demonstrate its robustness and generalization capability.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105433"},"PeriodicalIF":2.9,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518631","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

The Cramer-Rao bound for missing samples scenario in Hermite transform domain Hermite变换域缺失样本场景的Cramer-Rao界

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-06-25 DOI: 10.1016/j.dsp.2025.105418

Djordje Stanković , Irena Orović

引用次数: 0

MCF-Net: a multi-scale context fusion network for real-time fabric defect detection MCF-Net：用于织物缺陷实时检测的多尺度上下文融合网络

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-06-23 DOI: 10.1016/j.dsp.2025.105425

Yayong Wang, Zhong Xiang, Weitao Wu, Qiang Wu

{"title":"MCF-Net: a multi-scale context fusion network for real-time fabric defect detection","authors":"Yayong Wang, Zhong Xiang, Weitao Wu, Qiang Wu","doi":"10.1016/j.dsp.2025.105425","DOIUrl":"10.1016/j.dsp.2025.105425","url":null,"abstract":"<div><div>Fabric defect detection plays a critical role in textile manufacturing to ensure product quality. However, challenges such as significant variations in defect sizes result in poor detection performance of existing models. To address these challenges, this paper proposes a Multi-scale Context Fusion Network (MCF-Net). First, The Multi-scale Context Diffusion Fusion Pyramid Network (MCD-FPN) was proposed, which reconfigures the traditional feature fusion path and designed a key component, the Multi-scale Context Aggregation Module (MCAM). MCAM takes multi-scale features extracted from the backbone network as input and establishes long-range dependencies between features and their surrounding background through a series of deep convolutional operations. The output of MCAM is then used to enhance both shallow and deep features, ensuring that each detection scale contains comprehensive multi-scale context information. Furthermore, to improve the network’s feature extraction capability, the Latent Feature Transformer (LFT) was proposed, which maps input features into a high-dimensional space and extracts depth features through high-dimensional information compression. Second, to improve the network’s multi-scale perception, the Local Cross Attention Mechanism (LCA) was proposed, which models the spatial information of the features to improve the network’s understanding of global context information. Experimental results on the self-built FD6052 dataset, as well as the publicly available TianChi and DAGM2007 datasets, demonstrate the effectiveness of MCF-Net, which outperforms existing methods in fabric defect detection. In addition, MCF-Net achieves an inference speed of 85.6 FPS, enabling real-time detection at industrial scale.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105425"},"PeriodicalIF":2.9,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481415","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 geometry and saliency driven network with adaptive label refinement for weakly supervised medical image segmentation 用于弱监督医学图像分割的自适应标签细化几何和显著性驱动网络

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-06-20 DOI: 10.1016/j.dsp.2025.105414

Jiwen Zhou , Wanyu Liu

{"title":"A geometry and saliency driven network with adaptive label refinement for weakly supervised medical image segmentation","authors":"Jiwen Zhou , Wanyu Liu","doi":"10.1016/j.dsp.2025.105414","DOIUrl":"10.1016/j.dsp.2025.105414","url":null,"abstract":"<div><div>Weakly supervised medical image segmentation is promising for its low annotation cost and strong performance, with bounding boxes offering notable advantages over image-level and scribble annotations. However, pseudo-labels generated from bounding boxes often suffer from boundary errors and high uncertainty in transition regions between the target and background, affecting segmentation quality. To overcome these challenges, a geometry and saliency driven weakly supervised segmentation network (GSR-Net) is proposed. The saliency optimization and spatial consistency learning module anchors the centers of segmentation targets, forming the basis for subsequent pseudo-label refinement and enhancing overall consistency. The geometry-guided dynamic feature focusing module uses bounding box geometry to create dynamic boundary weights, refining boundary representations and suppressing background interference. Based on the improved localization and refined boundaries, the dynamic propagation and refinement module iteratively optimizes pseudo labels in uncertain regions, further enhancing segmentation accuracy. Additionally, a random expansion and shrinkage strategy for bounding box annotations is introduced to evaluate the model under varied annotation conditions. Experiments on three representative medical image datasets, namely KiTS23, LiTS, and BraTS2021, demonstrate that GSR-Net significantly outperforms existing weakly supervised methods in segmentation accuracy (Dice) and boundary quality (95HD), exhibiting strong generalization in complex scenarios and under weakly supervised conditions.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105414"},"PeriodicalIF":2.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481417","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 novel pitch detection algorithm for noisy speech signal based on Radon transform and multi-frame correlation 基于Radon变换和多帧相关的噪声语音基音检测算法

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-06-20 DOI: 10.1016/j.dsp.2025.105415

Xianwu Zhang, Chenyang Liu, Jiashen Li

引用次数: 0

Edge real-time tracking and FPGA-based hardware implementation for infrared tiny object 红外微小目标边缘实时跟踪及fpga硬件实现

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-06-20 DOI: 10.1016/j.dsp.2025.105412

Yulian Li, Zhengwen Shen, Xu Wang, Xiao Yang, Jun Wang

{"title":"Edge real-time tracking and FPGA-based hardware implementation for infrared tiny object","authors":"Yulian Li, Zhengwen Shen, Xu Wang, Xiao Yang, Jun Wang","doi":"10.1016/j.dsp.2025.105412","DOIUrl":"10.1016/j.dsp.2025.105412","url":null,"abstract":"<div><div>Infrared tiny object tracking holds significant application value in video surveillance and air defense systems. Deep convolutional neural networks (DCNNs) have demonstrated impressive performance in object tracking over the past few years. However, the high complexity and computing power requirements of the DCNN model make it difficult to deploy on power-sensitive and resource-constrained edge devices. To address this issue, we designed and implemented an FPGA-based infrared tiny object tracker by a hardware-software co-optimization approach, meeting the requirements for accuracy, latency, and power consumption under limited resources. First, we propose a lightweight and hardware-friendly object tracking network, SiamITO-Tiny, effectively improving the tracking accuracy for infrared tiny objects. Second, we design a full-mapping hardware acceleration architecture, mainly comprising a layer-fusion-based convolutional accelerator, a parallel pipelined adder tree, and an efficient data caching scheme. This architecture increases computational parallelism and data access bandwidth through layer fusion, loop optimization, pipelining, and array partitioning, while simultaneously balancing resource consumption and latency, thereby effectively improving computational performance and energy efficiency. Finally, the SiamITO-Tiny network is deployed on the Xilinx All Programmable SoC (ZYNQ) platform ZCU104. Experiments show that our method achieves 45.45 FPS and has a tracking score of 0.9. The computational performance reaches 307.2 GOP/s. The energy efficiency is 30.03 GOP/s/W, which is 39 and 6.81 times higher than it is on the CPU and GPU platforms, respectively. Compared to other acceleration methods, the energy efficiency is improved by 1.4 to 9.3 times, confirming the superiority of the proposed approach in edge real-time tracking.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105412"},"PeriodicalIF":2.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481425","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

Compressive sensing networks based on attention mechanism reconfiguration 基于注意机制重构的压缩感知网络

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-06-20 DOI: 10.1016/j.dsp.2025.105413

Yuhui Gao , Jingyi Liu , Hao Peng , Shiqiang Chen

{"title":"Compressive sensing networks based on attention mechanism reconfiguration","authors":"Yuhui Gao , Jingyi Liu , Hao Peng , Shiqiang Chen","doi":"10.1016/j.dsp.2025.105413","DOIUrl":"10.1016/j.dsp.2025.105413","url":null,"abstract":"<div><div>The combination of deep learning and compressive sensing has brought new breakthroughs in the field of image and video processing, but how to design compressive sensing networks with good generalization ability and low computational complexity is still a great challenge. In this paper, we propose a multiscale compressive sensing network reconstructed based on the attention mechanism, where training a single model allows sampling and reconstruction of arbitrary sampling ratios. Initially, in the sampling phase, we employ multi-scale adaptive sampling within the wavelet domain. This method dynamically adjusts the sampling ratios of various image blocks to accommodate the varying complexities of different regions through a multi-scale mechanism, thereby enhancing data utilization. Next, we construct a deep reconstruction module based on the pyramid model, which realizes adaptive feature enhancement at different resolutions by applying the attention mechanism at different scales. We jointly optimize the sampling network and the reconstruction network, and the model obtained by training this network is able to adapt to arbitrary sampling ratios. Testing results across different datasets demonstrate that our proposed compressive sensing reconstruction network exhibits rapid operational speed while ensuring the high quality of image reconstruction.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"166 ","pages":"Article 105413"},"PeriodicalIF":2.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338891","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 visual detection method for maritime autonomous surface ships in port area navigation 海上自主水面舰艇港区航行轻量化视觉检测方法

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-06-20 DOI: 10.1016/j.dsp.2025.105422

Lai Wei, Yingjun Zhang, Bingqi Ding, WeiWei Li, Hongrui Lu

{"title":"A lightweight visual detection method for maritime autonomous surface ships in port area navigation","authors":"Lai Wei, Yingjun Zhang, Bingqi Ding, WeiWei Li, Hongrui Lu","doi":"10.1016/j.dsp.2025.105422","DOIUrl":"10.1016/j.dsp.2025.105422","url":null,"abstract":"<div><div>Accurate and real-time ship detection in complex port environments is critical for the safe navigation of intelligent ships. Compared to open waters, port areas feature narrow waterways, dense obstacles, and variable lighting, which impose stricter requirements on detection accuracy. Existing one stage detection models, while efficient, often suffer from excessive parameter size, high computational complexity, and insufficient optimization for port-specific challenges. Moreover, port ship image data is scarce, and traditional data augmentation methods are inadequate for generating effective training samples, resulting in poor model generalization. To address these issues, this paper proposes a lightweight visual detection model combining EAE-DCGAN and EAE-YOLO. By introducing Triplet Attention into DCGAN, EAE-DCGAN is proposed. It generates diverse port ship images to enrich the ship dataset. By integrating the LDHS Head, the Triplet Attention mechanism, and the Focal EIoU loss function, EAE-YOLO is proposed, which reduces model parameters and computational complexity while ensuring detection accuracy. Experimental results demonstrate that the proposed method achieves improved detection performance compared to YOLOv10n. Meanwhile, it reduces parameters by 21.74 %, FLOPs by 16.42 %, and model size by 25.58 %, while increasing FPS by 9.66 %. Real ship target detection results further validate the superiority of the proposed method.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"167 ","pages":"Article 105422"},"PeriodicalIF":2.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501535","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