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IEEE Transactions on Signal Processing最新文献

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Modeling Sparse Graph Sequences and Signals Using Generalized Graphons 使用广义图元对稀疏图序列和信号建模
IF 4.6 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2024-10-22 DOI: 10.1109/TSP.2024.3482350
Feng Ji;Xingchao Jian;Wee Peng Tay
{"title":"Modeling Sparse Graph Sequences and Signals Using Generalized Graphons","authors":"Feng Ji;Xingchao Jian;Wee Peng Tay","doi":"10.1109/TSP.2024.3482350","DOIUrl":"10.1109/TSP.2024.3482350","url":null,"abstract":"Graphons are limit objects of sequences of graphs and are used to analyze the behavior of large graphs. Recently, graphon signal processing has been developed to study signal processing on large graphs. A major limitation of this approach is that any sparse sequence of graphs inevitably converges to the zero graphon, rendering the resulting signal processing theory trivial and inadequate for sparse graph sequences. To overcome this limitation, we propose a new signal processing framework that leverages the concept of generalized graphons and introduces the stretched cut distance as a measure to compare these graphons. Our framework focuses on the sampling of graph sequences from generalized graphons and explores the convergence properties of associated operators, spectra, and signals. Our signal processing framework provides a comprehensive approach to analyzing and processing signals on graph sequences, even if they are sparse. Finally, we discuss the practical implications of our theory for real-world large networks through numerical experiments.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5048-5064"},"PeriodicalIF":4.6,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142487586","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
Towards Applicable Unsupervised Signal Denoising via Subsequence Splitting and Blind Spot Network 通过后续分裂和盲点网络实现适用的无监督信号去噪
IF 4.6 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2024-10-18 DOI: 10.1109/TSP.2024.3483453
Ziqi Wang;Zihan Cao;Julan Xie;Huiyong Li;Zishu He
{"title":"Towards Applicable Unsupervised Signal Denoising via Subsequence Splitting and Blind Spot Network","authors":"Ziqi Wang;Zihan Cao;Julan Xie;Huiyong Li;Zishu He","doi":"10.1109/TSP.2024.3483453","DOIUrl":"10.1109/TSP.2024.3483453","url":null,"abstract":"Denoising is a significant preprocessing process, garnering substantial attention across various signal-processing domains. Many traditional denoising methods assume signal stationary and adherence of noise to Gaussian distribution, thereby limiting their practical applicability. Despite significant advancements in machine learning and deep learning methods, machine learning-based (ML-based) approaches still require manual feature engineering and intricate parameter tuning, and deep learning-based (DL-based) methods, remain largely constrained by supervised denoising techniques. In this paper, we propose an unsupervised denoising approach that addresses the shortcomings of previous methods. Our proposed method uses subsequence splitting and blind spot network to adaptively learn the signal characteristics in different scenarios, so as to achieve the purpose of denoising. The experimental results show that our method performs satisfactorily on both single-sensor and array signal denoising problems under Gaussian white noise and Impulsive noise. Moreover, our method is also verified to be effective on some array signal processing problems of Direction of Arrival (DOA) estimation, Estimated Number of Sources, and Spatial Spectrum estimation. Finally, in the discussion experiments and generalization experiments, we demonstrate that our method performs well across a wide variety of array forms and degrees of signal correlation, and has good generalization. Our code will be released after possible acceptance.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4967-4982"},"PeriodicalIF":4.6,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449499","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
Gaussian-Cauchy Mixture Kernel Function Based Maximum Correntropy Kalman Filter for Linear Non-Gaussian Systems 基于高斯-考奇混杂核函数的线性非高斯系统最大熵卡尔曼滤波器
IF 5.4 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2024-10-16 DOI: 10.1109/tsp.2024.3479723
Quanbo Ge, Xuefei Bai, Pingliang Zeng
{"title":"Gaussian-Cauchy Mixture Kernel Function Based Maximum Correntropy Kalman Filter for Linear Non-Gaussian Systems","authors":"Quanbo Ge, Xuefei Bai, Pingliang Zeng","doi":"10.1109/tsp.2024.3479723","DOIUrl":"https://doi.org/10.1109/tsp.2024.3479723","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"101 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443805","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
Practical and Powerful Kernel-Based Change-Point Detection 实用而强大的基于内核的变化点检测
IF 4.6 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2024-10-14 DOI: 10.1109/TSP.2024.3479274
Hoseung Song;Hao Chen
{"title":"Practical and Powerful Kernel-Based Change-Point Detection","authors":"Hoseung Song;Hao Chen","doi":"10.1109/TSP.2024.3479274","DOIUrl":"10.1109/TSP.2024.3479274","url":null,"abstract":"Change-point analysis plays a significant role in various fields to reveal discrepancies in distribution in a sequence of observations. While a number of algorithms have been proposed for high-dimensional data, kernel-based methods have not been well explored due to difficulties in controlling false discoveries and mediocre performance. In this paper, we propose a new kernel-based framework that makes use of an important pattern of data in high dimensions to boost power. Analytic approximations to the significance of the new statistics are derived and fast tests based on the asymptotic results are proposed, offering easy off-the-shelf tools for large datasets. The new tests show superior performance for a wide range of alternatives when compared with other state-of-the-art methods. We illustrate these new approaches through an analysis of a phone-call network data. All proposed methods are implemented in an R package kerSeg.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5174-5186"},"PeriodicalIF":4.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440093","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
Joint Visibility Region Detection and Channel Estimation for XL-MIMO Systems via Alternating MAP 通过交替 MAP 实现 XL-MIMO 系统的联合可见性区域检测和信道估计
IF 4.6 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2024-10-14 DOI: 10.1109/TSP.2024.3479319
Wenkang Xu;An Liu;Min-jian Zhao;Giuseppe Caire
{"title":"Joint Visibility Region Detection and Channel Estimation for XL-MIMO Systems via Alternating MAP","authors":"Wenkang Xu;An Liu;Min-jian Zhao;Giuseppe Caire","doi":"10.1109/TSP.2024.3479319","DOIUrl":"10.1109/TSP.2024.3479319","url":null,"abstract":"We investigate a joint visibility region (VR) detection and channel estimation problem in extremely large-scale multiple-input-multiple-output (XL-MIMO) systems, where near-field propagation and spatial non-stationary effects exist. In this case, each scatterer can only see a subset of antennas, i.e., it has a certain VR over the antennas. Because of the spatial correlation among adjacent sub-arrays, VR of scatterers exhibits a two-dimensional (2D) clustered sparsity. We design a 2D Markov prior model to capture such a structured sparsity. Based on this, a novel alternating maximum a posteriori (MAP) framework is developed for high-accuracy VR detection and channel estimation. The alternating MAP framework consists of three basic modules: a channel estimation module, a VR detection module, and a grid update module. Specifically, the first module is a low-complexity inverse-free variational Bayesian inference (IF-VBI) algorithm that avoids the matrix inverse via minimizing a relaxed Kullback-Leibler (KL) divergence. The second module is a structured expectation propagation (EP) algorithm which has the ability to deal with complicated prior information. And the third module refines polar-domain grid parameters via gradient ascent. Simulations demonstrate the superiority of the proposed algorithm in both VR detection and channel estimation.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4827-4842"},"PeriodicalIF":4.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440092","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
Physically Architected Recurrent Neural Networks for Nonlinear Dynamical Loudspeaker Modeling 用于非线性动态扬声器建模的物理架构递归神经网络
IF 5.4 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2024-10-14 DOI: 10.1109/tsp.2024.3480321
Christian Gruber, Gerald Enzner, Rainer Martin
{"title":"Physically Architected Recurrent Neural Networks for Nonlinear Dynamical Loudspeaker Modeling","authors":"Christian Gruber, Gerald Enzner, Rainer Martin","doi":"10.1109/tsp.2024.3480321","DOIUrl":"https://doi.org/10.1109/tsp.2024.3480321","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"78 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440094","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
Ridge Detection for Nonstationary Multicomponent Signals With Time-Varying Wave-Shape Functions and its Applications 具有时变波形函数的非稳态多分量信号的脊检测及其应用
IF 4.6 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2024-10-08 DOI: 10.1109/TSP.2024.3476495
Yan-Wei Su;Gi-Ren Liu;Yuan-Chung Sheu;Hau-Tieng Wu
{"title":"Ridge Detection for Nonstationary Multicomponent Signals With Time-Varying Wave-Shape Functions and its Applications","authors":"Yan-Wei Su;Gi-Ren Liu;Yuan-Chung Sheu;Hau-Tieng Wu","doi":"10.1109/TSP.2024.3476495","DOIUrl":"10.1109/TSP.2024.3476495","url":null,"abstract":"We introduce a novel ridge detection algorithm for time-frequency (TF) analysis, particularly tailored for intricate nonstationary time series encompassing multiple non-sinusoidal oscillatory components. The algorithm is rooted in the distinctive geometric patterns that emerge in the TF domain due to such non-sinusoidal oscillations. We term this method \u0000<italic>shape-adaptive mode decomposition-based multiple harmonic ridge detection</i>\u0000 (\u0000<monospace>SAMD-MHRD</monospace>\u0000). A swift implementation is available when supplementary information is at hand. We demonstrate the practical utility of \u0000<monospace>SAMD-MHRD</monospace>\u0000 through its application to a real-world challenge. We employ it to devise a cutting-edge walking activity detection algorithm, leveraging accelerometer signals from an inertial measurement unit across diverse body locations of a moving subject.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4843-4854"},"PeriodicalIF":4.6,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142385521","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
Algorithms for Non-Negative Matrix Factorization on Noisy Data With Negative Values 带负值的噪声数据上的非负矩阵因式分解算法
IF 4.6 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2024-10-07 DOI: 10.1109/TSP.2024.3474530
Dylan Green;Stephen Bailey
{"title":"Algorithms for Non-Negative Matrix Factorization on Noisy Data With Negative Values","authors":"Dylan Green;Stephen Bailey","doi":"10.1109/TSP.2024.3474530","DOIUrl":"10.1109/TSP.2024.3474530","url":null,"abstract":"Non-negative matrix factorization (NMF) is a dimensionality reduction technique that has shown promise for analyzing noisy data, especially astronomical data. For these datasets, the observed data may contain negative values due to noise even when the true underlying physical signal is strictly positive. Prior NMF work has not treated negative data in a statistically consistent manner, which becomes problematic for low signal-to-noise data with many negative values. In this paper we present two algorithms, Shift-NMF and Nearly-NMF, that can handle both the noisiness of the input data and also any introduced negativity. Both of these algorithms use the negative data space without clipping or masking and recover non-negative signals without any introduced positive offset that occurs when clipping or masking negative data. We demonstrate this numerically on both simple and more realistic examples, and prove that both algorithms have monotonically decreasing update rules.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5187-5197"},"PeriodicalIF":4.6,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706600","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142384732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Privacy Preserving Semi-Decentralized Mean Estimation over Intermittently-Connected Networks 间歇连接网络上的隐私保护半分散均值估计
IF 5.4 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2024-10-04 DOI: 10.1109/tsp.2024.3473939
Rajarshi Saha, Mohamed Seif, Michal Yemini, Andrea J. Goldsmith, H. Vincent Poor
{"title":"Privacy Preserving Semi-Decentralized Mean Estimation over Intermittently-Connected Networks","authors":"Rajarshi Saha, Mohamed Seif, Michal Yemini, Andrea J. Goldsmith, H. Vincent Poor","doi":"10.1109/tsp.2024.3473939","DOIUrl":"https://doi.org/10.1109/tsp.2024.3473939","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"23 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377371","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
Decentralized Rank-Adaptive Matrix Factorization — Part I: Algorithm Development 分散式秩自适应矩阵因式分解 - 第一部分：算法开发
IF 5.4 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2024-10-04 DOI: 10.1109/tsp.2024.3465009
Yuchen Jiao, Yuantao Gu, Tsung-Hui Chang, Zhi-Quan Tom Luo
{"title":"Decentralized Rank-Adaptive Matrix Factorization — Part I: Algorithm Development","authors":"Yuchen Jiao, Yuantao Gu, Tsung-Hui Chang, Zhi-Quan Tom Luo","doi":"10.1109/tsp.2024.3465009","DOIUrl":"https://doi.org/10.1109/tsp.2024.3465009","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"17 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377370","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
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