IEEE Signal Processing Letters最新文献_第4页

Greedy Capon Beamformer 贪心卡彭波束成形器

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-07 DOI: 10.1109/LSP.2024.3475351

Esa Ollila

引用次数: 0

On the Strong Convexity of PnP Regularization Using Linear Denoisers 论使用线性去oisers 的 PnP 正则化的强凸性

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-07 DOI: 10.1109/LSP.2024.3475913

Arghya Sinha;Kunal N. Chaudhury

引用次数: 0

Semantic Progressive Guidance Network for RGB-D Mirror Segmentation 用于 RGB-D 镜面分割的语义渐进引导网络

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-07 DOI: 10.1109/LSP.2024.3475357

Chao Li;Wujie Zhou;Xi Zhou;Weiqing Yan

引用次数: 0

Enhanced Dynamic Analysis for Malware Detection With Gradient Attack 利用梯度攻击加强恶意软件检测的动态分析

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-07 DOI: 10.1109/LSP.2024.3475354

Pei Yan;Shunquan Tan;Miaohui Wang;Jiwu Huang

{"title":"Enhanced Dynamic Analysis for Malware Detection With Gradient Attack","authors":"Pei Yan;Shunquan Tan;Miaohui Wang;Jiwu Huang","doi":"10.1109/LSP.2024.3475354","DOIUrl":"https://doi.org/10.1109/LSP.2024.3475354","url":null,"abstract":"Malware detection is an effective way to prevent the intrusion of malware into computer systems, and the API-based dynamic analysis method can effectively detect obfuscated and packaged malware. However, existing methods still suffer from limited detection accuracy and weak generalization. To address this issue, this paper presents a gradient attack-based malware dynamic analysis method. Through exerting adversarial noise into the embedding layer, the malware detection model can learn more robust representations of API sequences during training, achieving broader coverage of sample representations. The strategy of normalizing attack noise and recovering attacked representation is designed, which controls the strength of the gradient attack within a reasonable range and prevents a negative impact on the model's detection performance. The proposed method can be applied to existing API-based malware detection models to enhance their detection performance, indicating the strong generality of the proposed method. Experimental results on two benchmark datasets (\u0000<italic>i.e.</i>\u0000, \u0000<italic>Aliyun</i>\u0000 and \u0000<italic>Catak</i>\u0000) demonstrate the effectiveness of the proposed gradient attack method, which further improves the detection performance of the mainstream API-based models, with an average accuracy increase of 2.80% and 3.66% on these two datasets, respectively.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443102","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

MVP: One-Shot Object Pose Estimation by Matching With Visible Points MVP：通过与可见点匹配进行单次物体姿态估计

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-03 DOI: 10.1109/LSP.2024.3472492

Wentao Cheng;Minxing Luo

引用次数: 0

Oriented Object Detection Based on Adaptive Feature Learning and Enrichment 基于自适应特征学习和丰富的定向物体检测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-03 DOI: 10.1109/LSP.2024.3472490

Pei Li;Zhongjie Zhu;Yongqiang Bai;Yuer Wang;Lei Zhang

引用次数: 0

Multi-Scale Feature Fusion and Distribution Similarity Network for Few-Shot Automatic Modulation Classification 用于少镜头自动调制分类的多尺度特征融合与分布相似性网络

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-09-30 DOI: 10.1109/LSP.2024.3470762

Haoyue Tan;Zhenxi Zhang;Yu Li;Xiaoran Shi;Feng Zhou

{"title":"Multi-Scale Feature Fusion and Distribution Similarity Network for Few-Shot Automatic Modulation Classification","authors":"Haoyue Tan;Zhenxi Zhang;Yu Li;Xiaoran Shi;Feng Zhou","doi":"10.1109/LSP.2024.3470762","DOIUrl":"https://doi.org/10.1109/LSP.2024.3470762","url":null,"abstract":"Automatic modulation classification (AMC), as a key technology of cognitive radio, has become a focal point of research. However, most deep learning-based AMC methods require an extensive number of labeled signals to acquire a comprehensive understanding of modulation types, placing substantial pressure on signal acquisition and labeling. To solve this issue, we propose a few-shot AMC (FSAMC) method to facilitate rapid generalization and recognition with limited data, namely multi-scale feature fusion and distribution similarity network (MS2F-DS). Firstly, we design a multi-scale feature fusion (MS2F) model, which aims to extract features with varying fields of view and boost feature fusion, enabling the derivation of contextual information from the signal. Furthermore, we introduce a distribution similarity (DS) classifier to address the insufficient measurement of current similarity measurement functions by considering both micro and macro perspectives of vectors, further increasing intra-class compactness and inter-class separability. Finally, extensive experiments were conducted on 3-way 1, 3, and 5-shot FSAMC tasks using public datasets RML2016.10a and RML2016.10b, and the results demonstrated the effectiveness of our method.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524228","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

UWMamba: UnderWater Image Enhancement With State Space Model UWMamba：利用状态空间模型增强水下图像

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-09-30 DOI: 10.1109/LSP.2024.3470752

Guanhua An;Ao He;Yudong Wang;Jichang Guo

{"title":"UWMamba: UnderWater Image Enhancement With State Space Model","authors":"Guanhua An;Ao He;Yudong Wang;Jichang Guo","doi":"10.1109/LSP.2024.3470752","DOIUrl":"https://doi.org/10.1109/LSP.2024.3470752","url":null,"abstract":"Recently, state space models (SSM) with efficient design, i.e., Mamba, have shown great potential in modeling long-range dependencies with linear complexity. However, the pure SSM-based model yields sub-optimal underwater enhancement performance due to insufficient local details. Given the superiority of convolution in local perception, we propose a hybrid network, named UWMamba, which combines SSM and convolution for underwater image enhancement. We introduce a conv mamba layer (CML) as the foundation layer to combine the visual state space block (VSSB) with convolution. The convolution is used to capture local detailed features, while the VSSB is employed to capture long-range global features, which complement each other. Furthermore, considering underwater images suffer from severe and uneven degradation of spatial regions and color channels, we propose a Mamba Attention Fusion Module (MAFM), which fuses VSSB with an attention mechanism for better perception of channels and spatial regions. Extensive experiments on real-world underwater image datasets demonstrate the promising performance of our method in both objective metrics and subjective comparisons.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397083","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

Pedestrian Intrusion Detection in Railway Station Based on Mirror Translation Attention and Feature Pooling Enhancement 基于镜像平移注意和特征集合增强的火车站行人入侵检测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-09-30 DOI: 10.1109/LSP.2024.3471180

Zhufeng Jiang;Hui Wang;Guoliang Luo;Zizhu Fan;Lu Xu

{"title":"Pedestrian Intrusion Detection in Railway Station Based on Mirror Translation Attention and Feature Pooling Enhancement","authors":"Zhufeng Jiang;Hui Wang;Guoliang Luo;Zizhu Fan;Lu Xu","doi":"10.1109/LSP.2024.3471180","DOIUrl":"https://doi.org/10.1109/LSP.2024.3471180","url":null,"abstract":"Pedestrian intrusion detection is crucial to ensuring safe railway operation. Current pedestrian detection algorithms lack consideration for real-world railway scenarios, such as the reflective properties of screen doors and train windows, may mistakenly trigger pedestrian intrusion alerts. Scale variability and pedestrian overlap often lead to detection inaccuracy, making them inadequate for addressing the specific requirements of railway perimeter security. This letter introduces an innovative pedestrian detection algorithm that incorporates Mirror Translation Attention (MTA) and Feature Pooling Enhancement (FPE). MTA, including mirror flipping and offsetting the feature mapping, could significantly mitigate missed detection caused by reflective surfaces. Additionally, we introduce sparsity to the inputs of the self-attention, which significantly enhancing the model's inference speed. A multi-scale approach is adopted to accommodate the diversity in pedestrian sizes, while the FPE addresses occlusion issues across various scales. Compared to the advanced YOLOv8 model, the proposed method improves AP50 by 1.6% to 92.11% and reduces model parameters by 63.55% in our self-built railway pedestrian intrusion dataset.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397084","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

General Optimization Methods for YOLO Series Object Detection in Remote Sensing Images 遥感图像中 YOLO 系列物体检测的一般优化方法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-09-27 DOI: 10.1109/LSP.2024.3469787

Guozheng Nan;Yue Zhao;Chengxing Lin;Qiaolin Ye

{"title":"General Optimization Methods for YOLO Series Object Detection in Remote Sensing Images","authors":"Guozheng Nan;Yue Zhao;Chengxing Lin;Qiaolin Ye","doi":"10.1109/LSP.2024.3469787","DOIUrl":"https://doi.org/10.1109/LSP.2024.3469787","url":null,"abstract":"The You Only Look Once (YOLO) series of object detection algorithms has attracted considerable attention for its notable advantages in speed and accuracy, resulting in widespread applications in various real-world scenarios. However, achieving outstanding accuracy on remote sensing images with densely arranged small targets and complex backgrounds remains a challenging task. To address this issue, this letter proposes two easily integrated modules suitable for the YOLO architecture, namely global semantic information extraction (GSIE) and adaptive feature fusion (AFF). The GSIE module is designed to overcome the limitation of local information in traditional methods and facilitate global semantic information interaction by introducing multi-angle feature rotation to extend the receptive field. The AFF module effectively captures fine-grained features of objects by dynamically adjusting fusion weights, thereby reducing the loss of deep semantic information during feature transfer and fusion. The experimental results on the VEDAI and LEVIR remote sensing datasets demonstrate that when embedding these two modules into YOLO series algorithms that only use the small-scale detector, there is a significant improvement in performance while reducing computational complexity.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517857","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