IEEE Transactions on Signal and Information Processing over Networks最新文献_第2页

Latent Graphical Models of Multivariate Count Time Series 多元计数时间序列的潜在图形模型

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-09-01 DOI: 10.1109/TSIPN.2025.3604659

V. Sathish;Debraj Chakraborty;Siuli Mukhopadhyay

{"title":"Latent Graphical Models of Multivariate Count Time Series","authors":"V. Sathish;Debraj Chakraborty;Siuli Mukhopadhyay","doi":"10.1109/TSIPN.2025.3604659","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3604659","url":null,"abstract":"Conventional mathematical models of infectious diseases frequently overlook the spatial spread of the disease concentrating only on local transmission. However, spatial propagation of various diseases have been noted between geographical regions mainly due to the movement of infectious individuals from one region to another. In this work, we propose generalized linear models to study the graph of dependencies between multiple infection count time series from neighbouring regions. Due to the inherent theoretical and computational difficulties in inferring traditional partial correlation and causality graphs for such multiple count time series data, weakened concepts of correlation and causality of appropriate latent variables are introduced to simplify computation. In order to estimate these latent graphs with tunable sparsity, a novel Monte Carlo expectation and maximization algorithm is used to iteratively maximize an appropriate regularized likelihood function, and asymptotic convergence is established. In addition to simulated data, the algorithm is applied on observed weekly dengue disease counts from each region of an Indian city. The interdependence of various regions in the proliferation of the disease is characterized by the edges of the inferred latent graphs. It is observed that some regions act as epicentres of dengue spread even though their disease counts are relatively low.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1163-1177"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secure Reduced-Dimensional Coding Scheme for Distributed Estimation With Communication Constraints 具有通信约束的分布式估计安全降维编码方案

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-08-28 DOI: 10.1109/TSIPN.2025.3603723

Longyu Li;Wen Yang;Yanfang Mo;Wenjie Ding;Jie Wang;Yang Tang

引用次数: 0

Distributed Optimization for Estimation and Communication Co-Design Under Bandwidth Constraints 带宽约束下估计与通信协同设计的分布式优化

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-08-28 DOI: 10.1109/TSIPN.2025.3603740

Peizhe Li;Cailian Chen;Shanying Zhu;Xinping Guan

{"title":"Distributed Optimization for Estimation and Communication Co-Design Under Bandwidth Constraints","authors":"Peizhe Li;Cailian Chen;Shanying Zhu;Xinping Guan","doi":"10.1109/TSIPN.2025.3603740","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3603740","url":null,"abstract":"In the Industrial Internet of Things (IIoT), multiple sensors are deployed in production sites to perform state estimation of large-scale physical systems, which is important to ensure the stable operation of the production process. Sensors can only transmit quantized local information composed of a finite number of bits, where more quantization bits improve estimation accuracy. However, ensuring the necessary data rate for such data transmission under bandwidth limitations requires larger transmission power, increasing the energy consumption of sensors. To address this trade-off, this paper considers the co-design of estimation and communication, where the data rate and transmission power are jointly allocated to minimize a weighted estimation-communication cost while satisfying the minimum data rate constraint. An <inline-formula><tex-math>$ell _{p}$</tex-math></inline-formula>-box alternating direction method of multipliers (ADMM) based distributed optimization method is designed to solve this mixed-integer nonlinear programming (MINLP) problem, and the global convergence of the proposed method is proved. Moreover, a distributed estimation algorithm is proposed to ensure the convergence of estimation errors with minimum data rates, and the balance of the ultimate bound and convergence rate of estimation errors can be achieved by tuning the estimation gain. A numerical case study in the hot rolling process shows the superiority of the proposed distributed optimization and estimation methods.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1112-1126"},"PeriodicalIF":3.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145051008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Variance-Constrained Distributed Filtering Under Limited Bit Rates for Time-Varying Systems 时变系统有限比特率下方差约束分布式滤波

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-08-22 DOI: 10.1109/TSIPN.2025.3600831

Yinghao Hong;Yun Chen;Xueyang Meng;Yunfei Guo

{"title":"Variance-Constrained Distributed Filtering Under Limited Bit Rates for Time-Varying Systems","authors":"Yinghao Hong;Yun Chen;Xueyang Meng;Yunfei Guo","doi":"10.1109/TSIPN.2025.3600831","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3600831","url":null,"abstract":"This article concentrates on the variance-constrained distributed filtering problem with the constraint of limited bit rates and imperfect measurements for nonlinear time-varying systems. The measurement outputs undergo the phenomena of sensor saturations and nonlinearities occurring in a random way. An encoding-decoding mechanism (EDM) is implemented to regulate the transmission procedures over shared communication network. The main purpose of this article is to formulate a suitable distributed filtering algorithm to enable the fulfillment of both stochastic <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> performance and variance constraint for the resultant filtering error system over a finite horizon. The sufficient conditions are initially established to satisfy the prescribed performance constraints, following which the proper filter parameters are derived by means of the solutions to a sequence of iterative matrix inequalities. Furthermore, based on the variance constraint analysis for filtering error, the genetic algorithm (GA) is utilized to optimize the bit rate allocation among every node by minimizing the value of triggered decoding error. Finally, the validity of the proposed distributed filtering scheme is testified by a numerical example.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1100-1111"},"PeriodicalIF":3.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NPD-SG: A Noise-Resistant Primal-Dual Stochastic Gradient Diffusion Algorithm Over Networks NPD-SG：一种网络上抗噪声的原对偶随机梯度扩散算法

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-08-20 DOI: 10.1109/TSIPN.2025.3600760

Jiacheng Wu;Zhengchun Zhou;Sheng Zhang;Hongyu Han

引用次数: 0

A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization 一种具有概率多八卦通信的近端梯度方法用于分散复合优化

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-08-20 DOI: 10.1109/TSIPN.2025.3600766

Luyao Guo;Luqing Wang;Xinli Shi;Jinde Cao

{"title":"A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization","authors":"Luyao Guo;Luqing Wang;Xinli Shi;Jinde Cao","doi":"10.1109/TSIPN.2025.3600766","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3600766","url":null,"abstract":"Decentralized optimization methods with local updates have recently gained attention for their provable ability to communication acceleration. In these methods, nodes perform several iterations of local computations between the communication rounds. Nevertheless, this capability is effective only when the network is sufficiently well-connected and the loss function is smooth. In this paper, we propose a communication-efficient method <inline-formula><tex-math>$textsc {MG-Skip}$</tex-math></inline-formula> with probabilistic local updates and multi-gossip communications for decentralized composite (smooth + nonsmooth) optimization, whose stepsize is independent of the number of local updates and the network topology. For any undirected and connected networks, <inline-formula><tex-math>$textsc {MG-Skip}$</tex-math></inline-formula> allows for the multi-gossip communications to be skipped in most iterations in the strongly convex setting, while its computation complexity is <inline-formula><tex-math>$mathcal {O}(kappa log frac {1}{epsilon })$</tex-math></inline-formula> and communication complexity is only <inline-formula><tex-math>$mathcal {O}(sqrt{frac {kappa }{(1-rho)}} log frac {1}{epsilon })$</tex-math></inline-formula>, where <inline-formula><tex-math>$kappa$</tex-math></inline-formula> is the condition number of the loss function, <inline-formula><tex-math>$rho$</tex-math></inline-formula> reflects the connectivity of the network topology, and <inline-formula><tex-math>$epsilon$</tex-math></inline-formula> is the target accuracy. The theoretical results indicate that <inline-formula><tex-math>$textsc {MG-Skip}$</tex-math></inline-formula> achieves provable communication acceleration, thereby validating the advantages of local updates in the nonsmooth setting.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1044-1057"},"PeriodicalIF":3.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust LOS Identification for Passive Multi-Target Localization in Multipath Obstructed Environments 多路径阻塞环境下被动多目标定位的鲁棒LOS识别

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-08-20 DOI: 10.1109/TSIPN.2025.3600826

Yifan Liang;Hongbin Li

{"title":"Robust LOS Identification for Passive Multi-Target Localization in Multipath Obstructed Environments","authors":"Yifan Liang;Hongbin Li","doi":"10.1109/TSIPN.2025.3600826","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3600826","url":null,"abstract":"This paper considers passive target localization using multiple spatially distributed sensors, each transmitting distinct waveforms to measure line-of-sight (LOS) and non-line-of-sight (NLOS) delays from the passive targets. Since LOS and NLOS measurements are not directly distinguishable, the problem is to identify the LOS measurements when certain sensors are blocked from some targets—without prior knowledge of which sensors or targets are affected—and the total number of targets present in the scene is unknown a priori. Leveraging the fact that targets can be categorized into different <italic>levels</i> according to the number of sensors obstructed from them, we propose a hierarchical type-based clustering algorithm (HiTCA), which employs a multi-level search strategy, with each designed to identify one specific level of targets. These searches can be performed in parallel across levels to efficiently identify targets with different extents of LOS blockage. Moreover, we exploit a <italic>spread</i> difference among the multi-level search results, which enables us to obtain a reliable inference of the total target number. Extensive computer simulations show that the proposed technique obtains superior performance compared to existing methods in multi-target multipath environments with blockage.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1030-1043"},"PeriodicalIF":3.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prescribed-Time Asynchronously Aperiodic Intermittent Dynamic Event-Triggered Control for Synchronization of Complex Networks 复杂网络同步的规定时间异步非周期间歇动态事件触发控制

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-08-18 DOI: 10.1109/TSIPN.2025.3599777

Xiaoqi Liu;Xiangyu Zuo;Tianrui Chen;Ju H. Park

{"title":"Prescribed-Time Asynchronously Aperiodic Intermittent Dynamic Event-Triggered Control for Synchronization of Complex Networks","authors":"Xiaoqi Liu;Xiangyu Zuo;Tianrui Chen;Ju H. Park","doi":"10.1109/TSIPN.2025.3599777","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3599777","url":null,"abstract":"This paper addresses the prescribed-time synchronization (PTS) problem of complex networks (CNs) under asynchronously aperiodic intermittent dynamic event-triggered control (AAIDE-TC). A novel asynchronous controller is designed by integrating the intermittent control (IC) scheme with the event-triggered control (E-TC) mechanism. By introducing a time-varying function into the controller, the networks’ convergence time can be constrained within any prescribed time. Furthermore, in the E-TC strategy, dynamic and exponential terms are introduced to extend the intervals between triggering events and numerical simulation verifies its effects in reducing control cost. The IC approach adopts an average control rate rather than the conventional minimal control rate, making the synchronization conditions of the networks met more easily. Additionally, a global Lyapunov function is established by adopting Kirchhoff’s Matrix Tree Theorem, thereby relaxing the requirements for coupling matrix. Consequently, a synchronization criterion of the CNs under AAIDE-TC is derived, and its accuracy and validity are verified through a numerical simulation of coupled single-link manipulators.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1151-1162"},"PeriodicalIF":3.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Statistical Mask Learning and Distributed Estimation Without Support Priors 联合统计掩码学习和无支持先验分布估计

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-08-15 DOI: 10.1109/TSIPN.2025.3599781

Mahdi Shamsi;Farokh Marvasti

{"title":"Joint Statistical Mask Learning and Distributed Estimation Without Support Priors","authors":"Mahdi Shamsi;Farokh Marvasti","doi":"10.1109/TSIPN.2025.3599781","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3599781","url":null,"abstract":"This paper addresses the problem of distributed estimation under partial observability, where nodes mustcollaboratively process masked or incomplete measurements to infer a global target vector. Such masking arises from sensing limitations, communication constraints, or privacy requirements. We propose a novel framework for <italic>distributed masked information learning</i>, extending the Diffusion Least Mean Squares (DLMS) algorithm to operate under node-specific observation masks. To enable effective cooperation, we develop a signal-flow-inspired combination strategy and a thresholding-based algorithm for support inference. This allows each node to identify observable components of the target signal and adaptively control the diffusion of local estimates. We analyze the convergence of the proposed method in terms of mean and energy, and derive conditions for optimal threshold selection based on mask estimation error. Simulation results across both time- and transform-domain sparsity scenarios show that our method achieves a 30-40 dB improvement in mean square deviation over standard DLMS, matching the performance of fully observable settings under realistic observability ratios. These results underscore the potential of mask-aware adaptation for robust and scalable signal processing over networks.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1127-1137"},"PeriodicalIF":3.0,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scalable Hypergraph Structure Learning With Diverse Smoothness Priors 具有不同平滑先验的可伸缩超图结构学习

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-08-15 DOI: 10.1109/TSIPN.2025.3599780

Benjamin T. Brown;Haoxiang Zhang;Daniel L. Lau;Gonzalo R. Arce

{"title":"Scalable Hypergraph Structure Learning With Diverse Smoothness Priors","authors":"Benjamin T. Brown;Haoxiang Zhang;Daniel L. Lau;Gonzalo R. Arce","doi":"10.1109/TSIPN.2025.3599780","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3599780","url":null,"abstract":"In graph signal processing, learning weighted connections between nodes from signals is a fundamental task when the underlying relationships are unknown. With the extension of graphs to hypergraphs, where edges can connect more than two nodes, graph learning methods have similarly been generalized to hypergraphs. However, the absence of a unified framework for calculating total variation has led to divergent definitions of smoothness and, consequently, differing approaches to hyperedge recovery. This challenge is confronted in this work through generalization of several previously proposed hypergraph total variations, allowing ease of substitution into a vector-based optimization. To this end, a novel hypergraph learning method is proposed that recovers a hypergraph topology from time-series signals using convex optimization based on a smoothness prior. This approach, designated Hypergraph Structure Learning with Smoothness (HSLS), addresses key limitations in prior works such as hyperedge selection and convergence issues. Additionally, a process is introduced that limits the span of the hyperedge search and maintains a valid hyperedge selection set, creating a scalable model. Experimental results demonstrate improved performance over state-of-the-art hypergraph inference methods. The method is empirically shown to be robust to total variation terms, biased towards global smoothness, and scalable to larger hypergraphs.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1072-1086"},"PeriodicalIF":3.0,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0