IEEE Signal Processing Letters最新文献_第8页

Sparse Non-Linear Vector Autoregressive Networks for Multivariate Time Series Anomaly Detection 稀疏非线性向量自回归网络用于多元时间序列异常检测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-18 DOI: 10.1109/LSP.2024.3520019

Mohammed Ayalew Belay;Adil Rasheed;Pierluigi Salvo Rossi

引用次数: 0

On the Limitations of the Bayesian Cramér-Rao Bound for Mixed-Resolution Data 关于混合分辨率数据贝叶斯cram<s:1> - rao界的局限性

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-18 DOI: 10.1109/LSP.2024.3519804

Yaniv Mazor;Itai E. Berman;Tirza Routtenberg

{"title":"On the Limitations of the Bayesian Cramér-Rao Bound for Mixed-Resolution Data","authors":"Yaniv Mazor;Itai E. Berman;Tirza Routtenberg","doi":"10.1109/LSP.2024.3519804","DOIUrl":"https://doi.org/10.1109/LSP.2024.3519804","url":null,"abstract":"In this paper, we consider Bayesian parameter estimation in systems incorporating both analog and 1-bit quantized measurements. We develop a tractable form of the Bayesian Cram\u0000<inline-formula><tex-math>$acute{text{e}}$</tex-math></inline-formula>\u0000r-Rao Bound (BCRB) tailored for the linear-Gaussian mixed-resolution scheme. We discuss the properties of the BCRB and examine its limitations as a system design tool. In addition, we present the partially-numeric minimum-mean-squared-error (MMSE) and linear MMSE (LMMSE) estimators with a general quantization threshold. In our simulations, the BCRB is compared with the mean-squared-errors (MSEs) of the estimators for channel estimation with mixed analog-to-digital converters. The results demonstrate that the BCRB is not a tight lower bound, and it fails to accurately capture the non-monotonic behavior of the estimators' MSEs versus signal-to-noise-ratio (SNR) and their behavior regarding different resource allocations. Consequently, while the BCRB provides some valuable insights on the quantization threshold, our results demonstrate that it is not suitable as a practical tool for system design in mixed-resolution settings.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"446-450"},"PeriodicalIF":3.2,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938331","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

FFSTIE: Video Restoration With Full-Frequency Spatio-Temporal Information Enhancement FFSTIE：基于全频率时空信息增强的视频恢复

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-18 DOI: 10.1109/LSP.2024.3519882

Liqun Lin;Jianhui Wang;Guangpeng Wei;Mingxing Wang;Ang Zhang

引用次数: 0

Radar Signal Sorting via Graph Convolutional Network and Semi-Supervised Learning 基于图卷积网络和半监督学习的雷达信号分类

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-18 DOI: 10.1109/LSP.2024.3519884

Ziying Li;Xiongjun Fu;Jian Dong;Min Xie

引用次数: 0

Calibration Matters: Prototype-Aware Diffusion for OCT Cervical Classification With Calibration 校正事项：有校正的OCT宫颈分类的原型感知扩散

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-18 DOI: 10.1109/LSP.2024.3520010

Yuxuan Xiong;Zhou Zhao;Yongchao Xu;Yan Zhang;Bo Du

{"title":"Calibration Matters: Prototype-Aware Diffusion for OCT Cervical Classification With Calibration","authors":"Yuxuan Xiong;Zhou Zhao;Yongchao Xu;Yan Zhang;Bo Du","doi":"10.1109/LSP.2024.3520010","DOIUrl":"https://doi.org/10.1109/LSP.2024.3520010","url":null,"abstract":"Cervical optical coherence tomography (OCT) imaging serves as an effective diagnostic tool, and the development of deep learning classification models for OCT has the potential to enhance diagnosis. However, the complex imaging patterns of OCT data, significant noise, and the substantial domain gap from multi-center data result in high uncertainty and low accuracy in classification networks. To address these challenges, we propose a Multi-scale Prototype-Guided Diffusion learning method (MPGD), which is constructed with the \u0000<bold>Multi-scale Feature Condition (MFC)</b>\u0000, \u0000<bold>Diffusion-based Classification Calibrator (DCC)</b>\u0000, and \u0000<bold>Multi-scale Prototype Bank (MPB)</b>\u0000 modules. Specifically, MFC provides initial classification based on multi-scale features, DCC calibrates MFC's classification results through a diffusion model, and MPB refines DCC's visual guidance using prototypes obtained from clustering. Extensive experiments demonstrate that MPGD outperforms widely-used competitors for cervical OCT image classification, showing excellent generalization performance.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"396-400"},"PeriodicalIF":3.2,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925369","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

Sparse Projection Matrix Approximation and Its Applications 稀疏投影矩阵逼近及其应用

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-17 DOI: 10.1109/LSP.2024.3519459

Zheng Zhai;Mingxin Wu;Jialu Xu;Xiaohui Li

引用次数: 0

How to Understand Generation of Dirac Weighted Combs in Signal Sampling Operation? 如何理解信号采样操作中狄拉克加权梳的产生？

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-17 DOI: 10.1109/LSP.2024.3519261

Andrzej Borys

引用次数: 0

Enhancing the Transferability of Adversarial Point Clouds by Initializing Transferable Adversarial Noise 通过初始化可转移的对抗噪声来增强对抗点云的可转移性

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-17 DOI: 10.1109/LSP.2024.3509335

Hai Chen;Shu Zhao;Yuanting Yan;Fulan Qian

{"title":"Enhancing the Transferability of Adversarial Point Clouds by Initializing Transferable Adversarial Noise","authors":"Hai Chen;Shu Zhao;Yuanting Yan;Fulan Qian","doi":"10.1109/LSP.2024.3509335","DOIUrl":"https://doi.org/10.1109/LSP.2024.3509335","url":null,"abstract":"One of the most popular methods for analyzing the robustness of 3D Deep Neural Networks (DNNs) is the transfer-based adversarial attack method, as it allows to analyze the robustness of an unknown model by generating an adversarial point cloud on an alternative model. However, the adversarial point clouds generated by current methods may overfit the surrogate models that generated them, thus limiting their performance in transfer attacks against different target 3D classifiers. To enhance the transferability of the adversarial point cloud, we propose in this letter an adversarial attack method by Initializing the Transferable Adversarial Noise, which named as \u0000<bold>ITAN</b>\u0000. Specifically, we pre-train on the training set a generator capable of generating the adversarial noise with transferability and diversity, and then the noise generated by the generator serves as the initial adversarial noise to be integrated into the iterations of the attack. Extensive experiments on well-recognized benchmark datasets demonstrate that the adversarial point clouds generated by the proposed ITAN could be effectively transferred across unknown 3D classifiers.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"201-205"},"PeriodicalIF":3.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844601","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

Cholesky-KalmanNet: Model-Based Deep Learning With Positive Definite Error Covariance Structure Cholesky-KalmanNet：基于模型的深度学习正定误差协方差结构

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-17 DOI: 10.1109/LSP.2024.3519265

Minhyeok Ko;Abdollah Shafieezadeh

{"title":"Cholesky-KalmanNet: Model-Based Deep Learning With Positive Definite Error Covariance Structure","authors":"Minhyeok Ko;Abdollah Shafieezadeh","doi":"10.1109/LSP.2024.3519265","DOIUrl":"https://doi.org/10.1109/LSP.2024.3519265","url":null,"abstract":"State estimation from noisy observations is crucial across various fields. Traditional methods such as Kalman, Extended Kalman, and Unscented Kalman Filter often struggle with nonlinearities, model inaccuracies, and high observation noise. This letter introduces Cholesky-KalmanNet (CKN), a model-based deep learning approach that considerably enhances state estimation by providing and enforcing transiently precise error covariance estimation. Specifically, the CKN embeds mathematical constraints associated with the positive definiteness of error covariance in a recurrent DNN architecture through the Cholesky decomposition. This architecture enhances statistical reliability and mitigates numerical instabilities. Furthermore, introducing a novel loss function that minimizes discrepancies between the estimated and empirical error covariance ensures a comprehensive minimization of estimation errors, accounting for interdependencies among state variables. Extensive evaluations on both synthetic and real-world datasets affirm CKN's superior performance vis-a-vis state estimation accuracy, robustness against system inaccuracies and observation noise, as well as stability across varying training data partitions, an essential feature for practical scenarios with suboptimal data conditions.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"326-330"},"PeriodicalIF":3.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890169","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

Parallel State Estimation for Systems With Integrated Measurements 集成测量系统的并行状态估计

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-17 DOI: 10.1109/LSP.2024.3519258

Fatemeh Yaghoobi;Simo Särkkä

引用次数: 0