IEEE Signal Processing Letters最新文献_第6页

Hardware-Decoder-Friendly High Throughput String Prediction for SCC Implemented in AVS3

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-25 DOI: 10.1109/LSP.2025.3545284

Liping Zhao;Zhuge Yan;Zongda Wu;Jiangda Wang;Tao Lin

引用次数: 0

OSFusion: A One-Stream Infrared and Visible Image Fusion Framework

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-25 DOI: 10.1109/LSP.2025.3545293

Shengjia An;Zhi Li;Shaorong Zhang;Yongjun Wang;Bineng Zhong

{"title":"OSFusion: A One-Stream Infrared and Visible Image Fusion Framework","authors":"Shengjia An;Zhi Li;Shaorong Zhang;Yongjun Wang;Bineng Zhong","doi":"10.1109/LSP.2025.3545293","DOIUrl":"https://doi.org/10.1109/LSP.2025.3545293","url":null,"abstract":"The current popular two-stream two-stage image fusion framework extracts features of infrared and visible images separately and then performs feature fusion. The extracted features lack interaction between the source images and have limited cross-modal complementary capability. To address these issues, we propose a novel one-stream infrared and visible image fusion (OSFusion) framework that connects a source image pair to achieve bidirectional information flow. In this way, the fused features with cross-modal complementary information can be dynamically extracted by mutual guidance. To further improve the inference efficiency and obtain high-quality fused images, a feature extraction and fusion module (FEFM) is proposed based on Transformer structure. The combination of feature extraction and feature fusion is realized by using it. Since there is no need for an extra feature interaction module and the implementation is highly parallel, the speed of image fusion is extremely fast. Benefiting from the one-stream structure and FEFM, OSFusion achieves promising infrared and visible image fusion performance on MSRS, M3FD, and RoadScene datasets. Besides, our method achieves a good balance in the trade-off between performance and complexity, and also shows a faster convergence trend.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1086-1090"},"PeriodicalIF":3.2,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629599","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

Learning Robust Representations by Autoencoders With Dynamical Implicit Mapping

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-24 DOI: 10.1109/LSP.2025.3544969

Jianda Zeng;Weili Jiang;Zhang Yi;Yong-Guo Shi;Jianyong Wang

引用次数: 0

Segment Anything for Visual Bird Sound Denoising

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-24 DOI: 10.1109/LSP.2025.3545005

Chenxi Zhou;Tianjiao Wan;Kele Xu;Peng Qiao;Yong Dou

{"title":"Segment Anything for Visual Bird Sound Denoising","authors":"Chenxi Zhou;Tianjiao Wan;Kele Xu;Peng Qiao;Yong Dou","doi":"10.1109/LSP.2025.3545005","DOIUrl":"https://doi.org/10.1109/LSP.2025.3545005","url":null,"abstract":"Current audio denoising methods perform well with synthetic noise but struggle with complex natural noise, especially for bird sounds, which contain natural environmental sounds such as wind and rain, making it challenging to extract clean bird sounds. This issue becomes more pronounced with short and faint bird sounds, where existing methods are less effective. In this paper, we introduce <bold>BudSAM</b>, a novel audio denoising model that incorporates the <bold>Segment Anything Model (SAM)</b>, originally designed for image segmentation task, into the field of visual bird sound denoising. By treating audio denoising as a segmentation task, BudSAM utilizes SAM's powerful segmentation capabilities and we incorporates BCE and Dice losses to enhance the model's ability to segment weak signals, effectively isolating the clean bird sounds that are often masked by background noise. Our method is evaluated on the BirdSoundsDenoising dataset, achieving a 4.0% improvement in IoU and a 0.77 dB increase in SDR compared to state-of-the-art methods. To the best knowledge of the authors, BudSAM marks the first attempt which employs SAM in audio denoising task, offering a promising direction for future research and real-world bird sound processing tasks.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1076-1080"},"PeriodicalIF":3.2,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629596","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

Optimizing a-DCF for Spoofing-Robust Speaker Verification

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-24 DOI: 10.1109/LSP.2025.3545290

Oğuzhan Kurnaz;Jagabandhu Mishra;Tomi H. Kinnunen;Cemal Hanilçi

引用次数: 0

A 3D Reconstruction and Relocalization Method for Humanoid Welding Robots

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-24 DOI: 10.1109/LSP.2025.3544967

Peng Chi;Zhenmin Wang;Haipeng Liao;Ting Li;Qin Zhang

{"title":"A 3D Reconstruction and Relocalization Method for Humanoid Welding Robots","authors":"Peng Chi;Zhenmin Wang;Haipeng Liao;Ting Li;Qin Zhang","doi":"10.1109/LSP.2025.3544967","DOIUrl":"https://doi.org/10.1109/LSP.2025.3544967","url":null,"abstract":"Welding robots represent pivotal equipment in intelligent welding for manufacturing and maintenance. Presently, most welding robots are stationary single-arm units, exhibiting limited flexibility and efficiency, thereby compromising welding quality and productivity. Consequently, there is an urgent need to develop a new generation of humanoid welding robots (HWR) endowed with autonomous mobility and dual-arm collaborative capabilities. Key to this advancement are pose estimation and three-dimensional (3D) reconstruction methods, which traditionally focus on mapping and navigating unfamiliar environments, often struggling to adapt to the routine welding and maintenance scenes of large-scale equipment. This paper introduces a novel approach to 3D reconstruction and relocalization tailored for HWR, facilitating rapid localization of welding areas and transmission of point cloud maps. Initially, a vision-based 3D reconstruction system is proposed, encompassing pose estimation, 3D reconstruction, and target detection, enabling self-localization and precise targeting for HWR. Subsequently, a novel method for 3D point cloud map segmentation based on 2D features and 3D point clouds matching is introduced to expedite the transmission of point cloud maps. Finally, a relocalization and point cloud map updating method grounded in prior knowledge is proposed, facilitating seamless welding operations by HWR in routine maintenance scenes. The effectiveness and superiority of the proposed methodology are validated through comparative tests with existing methods using actual HWR.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1071-1075"},"PeriodicalIF":3.2,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627838","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

IR-Pro: Baking Probes to Model Indirect Illumination for Inverse Rendering of Scenes

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-24 DOI: 10.1109/LSP.2025.3545291

Zhihao Liang;Qi Zhang;Yirui Guan;Ying Feng;Kui Jia

引用次数: 0

Paradoxical Role of Adversarial Attacks: Enabling Crosslinguistic Attacks and Information Hiding in Multilingual Speech Recognition

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-24 DOI: 10.1109/LSP.2025.3545276

Wenjie Zhang;Zhihua Xia;Bin Ma;Diqun Yan

{"title":"Paradoxical Role of Adversarial Attacks: Enabling Crosslinguistic Attacks and Information Hiding in Multilingual Speech Recognition","authors":"Wenjie Zhang;Zhihua Xia;Bin Ma;Diqun Yan","doi":"10.1109/LSP.2025.3545276","DOIUrl":"https://doi.org/10.1109/LSP.2025.3545276","url":null,"abstract":"With the rise of automatic speech recognition (ASR) research and practical applications, enabling adversarial attacks on ASR systems via subtle perturbations has become a priority. Most prior research has focused on single-language, single-model ASR systems. However, multilingual ASR systems hold opportunities for crosslinguistic attacks and covert message transmission. This letter introduces a new approach for crosslinguistic adversarial attacks in multilingual ASR, focusing on information hiding. For example, in military settings, adversarial examples applied to eavesdropping devices can encode messages detectable only by friendly devices, leaving adversaries, even with identical methods, unable to access them. This letter examines multilingual ASR system properties and introduces a crosslinguistic adversarial example with minimal perturbation, allowing friendly classifiers to extract hidden information while being undetectable by hostile classifiers. The experimental results on 5 models and 5 datasets show that the proposed method achieves a success rate of over 90% and an SNR close to 40 dB.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1046-1050"},"PeriodicalIF":3.2,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594315","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 Two-Stage Imaging Framework Combining CNN and Physics-Informed Neural Networks for Full- Inverse Tomography: A Case Study in Electrical Impedance Tomography (EIT)

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-24 DOI: 10.1109/LSP.2025.3545306

Xuanxuan Yang;Yangming Zhang;Haofeng Chen;Gang Ma;Xiaojie Wang

引用次数: 0

Improved Low-Complexity Sparse Bayesian Learning With Embedded Bayesian Threshold

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-21 DOI: 10.1109/LSP.2025.3544536

Yifei Yang;Tengfei Qi;Qianli Wang;Pengcheng Zhu;Xiong Deng

引用次数: 0