IEEE Signal Processing Letters最新文献_第3页

A RNN for Temporal Consistency in Low-Light Videos Enhanced by Single-Frame Methods 用单帧方法增强低照度视频中时间一致性的 RNN

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-08 DOI: 10.1109/LSP.2024.3475969

Claudio Rota;Marco Buzzelli;Simone Bianco;Raimondo Schettini

引用次数: 0

Prototypical Metric Segment Anything Model for Data-Free Few-Shot Semantic Segmentation 用于无数据少镜头语义分割的原型度量分割 Anything 模型

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-08 DOI: 10.1109/LSP.2024.3476208

Zhiyu Jiang;Ye Yuan;Yuan Yuan

{"title":"Prototypical Metric Segment Anything Model for Data-Free Few-Shot Semantic Segmentation","authors":"Zhiyu Jiang;Ye Yuan;Yuan Yuan","doi":"10.1109/LSP.2024.3476208","DOIUrl":"https://doi.org/10.1109/LSP.2024.3476208","url":null,"abstract":"Few-shot semantic segmentation (FSS) is crucial for image interpretation, yet it is constrained by requirements for extensive base data and a narrow focus on foreground-background differentiation. This work introduces Data-free Few-shot Semantic Segmentation (DFSS), a task that requires limited labeled images and forgoes the need for extensive base data, allowing for comprehensive image segmentation. The proposed method utilizes the Segment Anything Model (SAM) for its generalization capabilities. The Prototypical Metric Segment Anything Model is introduced, featuring an initial segmentation phase followed by prototype matching, effectively addressing the learning challenges posed by limited data. To enhance discrimination in multi-class segmentation, the Supervised Prototypical Contrastive Loss (SPCL) is designed to refine prototype features, ensuring intra-class cohesion and inter-class separation. To further accommodate intra-class variability, the Adaptive Prototype Update (APU) strategy dynamically refines prototypes, adapting the model to class heterogeneity. The method's effectiveness is demonstrated through superior performance over existing techniques on the DFSS task, marking a significant advancement in UAV image segmentation.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438589","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

RS-2-BP: A Unified Deep Learning Framework for Deriving EIT-Based Breathing Patterns From Respiratory Sounds RS-2-BP：从呼吸声中得出基于 EIT 的呼吸模式的统一深度学习框架

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-08 DOI: 10.1109/LSP.2024.3475358

Arka Roy;Udit Satija

{"title":"RS-2-BP: A Unified Deep Learning Framework for Deriving EIT-Based Breathing Patterns From Respiratory Sounds","authors":"Arka Roy;Udit Satija","doi":"10.1109/LSP.2024.3475358","DOIUrl":"https://doi.org/10.1109/LSP.2024.3475358","url":null,"abstract":"Respiratory disorders have become the third largest cause of death worldwide, which can be assessed by one of the two key diagnostic modalities: breathing patterns (BPs) or the airflow signals, and respiratory sounds (RSs). In recent years, few studies have been conducted on finding correlation between these two modalities which indicate the structural flaws of lungs under disease condition. In this letter, we propose ‘RS-2-BP’: a unified deep learning framework for deriving the electrical impedance tomography-based airflow signals from respiratory sounds using a hybrid neural network architecture, namely ReSTL, that comprises cascaded standard and residual shrinkage convolution blocks, followed by feature refined transformer encoders and long-short term memory (LSTM) units. The proposed framework is extensively evaluated using the publicly available BRACETS dataset. Experimental results suggest that our ReSTL can accurately derive the BPs from RSs with an average mean absolute error of \u0000<inline-formula><tex-math>$0.024pm 0.011, ,0.436pm 0.120, ,0.020pm 0.011,,0.134pm 0.068$</tex-math></inline-formula>\u0000, and \u0000<inline-formula><tex-math>$0.031pm 0.019$</tex-math></inline-formula>\u0000, respectively for five different tasks. Furthermore, these derived BPs can be used for extracting different respiratory vitals, identifying disease conditions efficiently, and retrieving salient breathing cycle information from the RSs.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434635","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

MUSIC Based Multipath Delay Estimation Method in the Fractional Domain for OFDM-LFM 基于 MUSIC 的分数域 OFDM-LFM 多径延迟估计方法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-07 DOI: 10.1109/LSP.2024.3475356

Jiaojiao Liu;Erdi Chen;Nan Sun;Biyun Ma

引用次数: 0

Kendall's Tau Based Spectrum Sensing for Cognitive Radio in the Presence of Laplace Noise 基于 Kendall's Tau 的认知无线电拉普拉斯噪声下的频谱传感

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-07 DOI: 10.1109/LSP.2024.3475916

Yongjian Huang;Huadong Lai;Jisheng Dai;Weichao Xu

引用次数: 0

Optimizing Subband Adaptive Filters for Resilience Against Unanticipated Signal Truncation 优化子带自适应滤波器以抵御意外信号截断

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-07 DOI: 10.1109/LSP.2024.3475349

Yuhong Wang;Xu Zhou;Zongsheng Zheng

引用次数: 0

Automated Audio Data Augmentation Network Using Bi-Level Optimization for Sound Event Localization and Detection 利用双级优化实现声音事件定位和检测的自动音频数据增强网络

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-07 DOI: 10.1109/LSP.2024.3475350

Wenjie Zhang;Peng Yu;Jun Yin;Xiaoheng Jiang;Mingliang Xu

{"title":"Automated Audio Data Augmentation Network Using Bi-Level Optimization for Sound Event Localization and Detection","authors":"Wenjie Zhang;Peng Yu;Jun Yin;Xiaoheng Jiang;Mingliang Xu","doi":"10.1109/LSP.2024.3475350","DOIUrl":"https://doi.org/10.1109/LSP.2024.3475350","url":null,"abstract":"In sound event localization and detection (SELD), traditional methods often treat localization and detection algorithms separately from data augmentation. During the model training process, the strategy for data augmentation is typically implemented in a non-learnable manner. Existing audio data augmentation strategies struggle to find optimal parameter solutions for data augmentation that can be effectively applied to SELD systems. To address this challenge, we introduce an innovative network-based strategy, termed the Automated Audio Data Augmentation (AADA) network. This strategy employs bi-level optimization to synergistically integrate audio data augmentation techniques with SELD tasks. In the AADA network, the lower-level SELD task serves as a constraint for the higher-level data augmentation process. The audio data augmentation parameters are adaptively optimized by utilizing the transfer of intermediate feature information from the SELD tasks, thus obtaining optimal parameters for these tasks. Evaluation of our approach on the Sony-TAU Realistic Spatial Soundscapes 2023 dataset achieves a SELD score of 0.4801, significantly surpassing the performance metrics of all traditional data augmentation strategies for SELD.","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":"142434568","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 Momentum Accelerated Algorithm for ReLU-Based Nonlinear Matrix Decomposition 基于 ReLU 的非线性矩阵分解的动量加速算法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-07 DOI: 10.1109/LSP.2024.3475910

Qingsong Wang;Chunfeng Cui;Deren Han

引用次数: 0

Understanding Correlated Information Diffusion: From a Graphical Evolutionary Game Perspective 理解相关信息扩散：从图形进化博弈的视角出发

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-07 DOI: 10.1109/LSP.2024.3475353

Hong Hu;Zhuoqun Li;H. Vicky Zhao

{"title":"Understanding Correlated Information Diffusion: From a Graphical Evolutionary Game Perspective","authors":"Hong Hu;Zhuoqun Li;H. Vicky Zhao","doi":"10.1109/LSP.2024.3475353","DOIUrl":"https://doi.org/10.1109/LSP.2024.3475353","url":null,"abstract":"In online social networks, millions of connected intelligent individuals actively interact with each other, which not only facilitates opinion sharing but also offers the platform to spread detrimental gossips and rumors. Therefore, it is of crucial importance to better understand how the avalanche of information propagates over social networks and affects our social life and economy. However, most model-based works on information diffusion either consider the spreading of one single message or assume that different information spreads independently. In this letter, we investigate how correlated information spreads together and jointly influences users' decisions from a graphical evolutionary game perspective. We model the multi-source information diffusion process, analyze the impact of information's correlation and time delay on the evolutionary dynamics and the evolutionary stable states (ESS). Simulation results on synthetic networks and Facebook real-world networks are consistent with our analytical results. This investigation offers important insights to the understanding and management of multi-source information diffusion.","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":"142443067","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

Random Tensor Analysis: Outlier Detection and Sample-Size Determination 随机张量分析：离群点检测和样本大小确定

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-07 DOI: 10.1109/LSP.2024.3475909

Shih Yu Chang;Hsiao-Chun Wu

引用次数: 0