IEEE Transactions on Signal Processing最新文献_第8页

Denoising Piecewise Constant Nanopore Signals 分段常数纳米孔信号去噪

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-15 DOI: 10.1109/TSP.2025.3570417

Adrian Vidal;Emanuele Viterbo

{"title":"Denoising Piecewise Constant Nanopore Signals","authors":"Adrian Vidal;Emanuele Viterbo","doi":"10.1109/TSP.2025.3570417","DOIUrl":"10.1109/TSP.2025.3570417","url":null,"abstract":"Nanopore sequencing signals can be described as indirect noisy observations that reflect the instantaneous conductance of the nanopore channel as an analyte DNA molecule translocates through the pore in real time, with <inline-formula><tex-math>$delta$</tex-math></inline-formula> nucleotides (<inline-formula><tex-math>$delta$</tex-math></inline-formula>-mers) blocking the pore at any instant. The sequence of overlapping <inline-formula><tex-math>$delta$</tex-math></inline-formula>-mers along the ssDNA molecule are thus indirectly observed as a sequence of conductance levels (i.e., a <italic>signature) that is used to characterize its DNA sequence. In this paper, we denoise piecewise constant nanopore signals drawn from the same Gaussian-output, left-to-right hidden Markov model (HMM) and recover the unknown signature that is used to parameterize the HMM. We place a Gaussian prior on the signature and use importance sampling to approximate the minimum mean-square error estimate (MMSE) of the signature given the signals. To circumvent the difficulty of sampling from the true posterior, we construct a proposal distribution from which the joint segmentation of the observed signals can be efficiently sampled in <inline-formula><tex-math>$O(Mn^{2}k)$</tex-math></inline-formula> time, where <inline-formula><tex-math>$ M $</tex-math></inline-formula> is the number of signals, <inline-formula><tex-math>$ n $</tex-math></inline-formula> is the average duration of each signal, and <inline-formula><tex-math>$ k $</tex-math></inline-formula> is the length of the signature. Finally, we evaluate the performance of the algorithm using both simulated and experimental nanopore signals generated by Oxford Nanopore Technologies’ (ONT) R10.4.1 nanopore. The proposed method can be effective in constructing accurate <inline-formula><tex-math>$delta$</tex-math></inline-formula>-mer tables used to fully characterize all the <inline-formula><tex-math>$4^{delta}$</tex-math></inline-formula> states of any nanopore sequencer.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1993-2007"},"PeriodicalIF":4.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066008","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

Single-Timescale Multi-Sequence Stochastic Approximation Without Fixed Point Smoothness: Theories and Applications 无不动点平滑的单时间尺度多序列随机逼近：理论与应用

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-14 DOI: 10.1109/TSP.2025.3569665

Yue Huang;Zhaoxian Wu;Shiqian Ma;Qing Ling

{"title":"Single-Timescale Multi-Sequence Stochastic Approximation Without Fixed Point Smoothness: Theories and Applications","authors":"Yue Huang;Zhaoxian Wu;Shiqian Ma;Qing Ling","doi":"10.1109/TSP.2025.3569665","DOIUrl":"10.1109/TSP.2025.3569665","url":null,"abstract":"Stochastic approximation (SA) that involves multiple coupled sequences, also known as multiple-sequence SA (MSSA), finds diverse applications in the fields of signal processing and machine learning. However, existing theoretical understandings of MSSA are limited: the multi-timescale analysis implies a slow convergence rate, whereas the single-timescale analysis relies on a stringent fixed point smoothness assumption. In this paper, we establish tighter single-timescale analysis for MSSA, without assuming smoothness of the fixed points. Our theoretical findings reveal that, when all involved operators are strongly monotone, MSSA converges at a rate of <inline-formula><tex-math>$tilde{mathcal{O}}(K^{-1})$</tex-math></inline-formula>, where <inline-formula><tex-math>$K$</tex-math></inline-formula> denotes the total number of iterations. When all involved operators are strongly monotone except for the main one, MSSA converges at a rate of <inline-formula><tex-math>$O(K^{-frac{1}{2}})$</tex-math></inline-formula>. These rates align with the ones established for single-sequence SA. Applying our novel theoretical findings to bilevel optimization and communication-efficient distributed learning offers relaxed assumptions and/or simpler algorithms with perfor- mance guarantees, as validated by numerical experiments.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1939-1953"},"PeriodicalIF":4.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979756","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 Generalized Approach for Recovering Time Encoded Signals With Finite Rate of Innovation 有限创新率下恢复时间编码信号的一种广义方法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-14 DOI: 10.1109/TSP.2025.3567443

Dorian Florescu

{"title":"A Generalized Approach for Recovering Time Encoded Signals With Finite Rate of Innovation","authors":"Dorian Florescu","doi":"10.1109/TSP.2025.3567443","DOIUrl":"10.1109/TSP.2025.3567443","url":null,"abstract":"In this paper, we consider the problem of reconstructing a function <inline-formula><tex-math>$g(t)$</tex-math></inline-formula> from its direct time encoding machine (TEM) measurements in a general scenario in which the signal is represented as an infinite sum of weighted generic functions <inline-formula><tex-math>$varphi(t)$</tex-math></inline-formula> shifted in real time points. These functions belong to the class of signals with finite rate of innovation (FRI), which is more general than shift-invariant or bandlimited spaces, for which recovery guarantees were already introduced. For an FRI signal <inline-formula><tex-math>$g(t)$</tex-math></inline-formula>, recovery guarantees from their direct TEM samples were introduced for particular functions <inline-formula><tex-math>$varphi(t)$</tex-math></inline-formula> or functions <inline-formula><tex-math>$varphi(t)$</tex-math></inline-formula> with alias cancellation properties leading to <inline-formula><tex-math>$g(t)$</tex-math></inline-formula> being periodic and bandlimited. On the theoretical front, this work significantly increases the class of functions for which reconstruction is guaranteed, and provides a condition for perfect input recovery depending on the first two local derivatives of <inline-formula><tex-math>$varphi(t)$</tex-math></inline-formula>. We extend this result with reconstruction guarantees in the case of noise corrupted FRI signals. On the practical front, we validate the proposed method via numerical simulations with filters previously used in the literature, as well as filters that are not compatible with the existing results. In cases where the filter has an unknown mathematical function and is only measured, the proposed method streamlines the recovery process by bypassing the filter modelling stage. Additionally, we validate the proposed method using a TEM hardware implementation.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1862-1876"},"PeriodicalIF":4.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979640","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

Optimal TOA-Based Sensor-Anchor-Source Geometries and Estimation Bounds for Simultaneous Sensor and Source Localization 基于toa的最优传感器锚源几何和同时定位的估计界

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-14 DOI: 10.1109/TSP.2025.3545928

Sheng Xu;Bing Zhu;Xinyu Wu;Kutluyıl Doğançay

{"title":"Optimal TOA-Based Sensor-Anchor-Source Geometries and Estimation Bounds for Simultaneous Sensor and Source Localization","authors":"Sheng Xu;Bing Zhu;Xinyu Wu;Kutluyıl Doğançay","doi":"10.1109/TSP.2025.3545928","DOIUrl":"10.1109/TSP.2025.3545928","url":null,"abstract":"This paper focuses on optimal time-of-arrival (TOA) sensor placement for simultaneous sensor and source localization (SSSL) with the help of selected anchors at known positions in the environment. Firstly, the problem of sensor placement for SSSL is analyzed and formulated as an optimization task based on the approximate Cramér-Rao lower bound (CRLB), which is an approximation of the intractable true CRLB. Secondly, by minimizing the trace of the approximate CRLB, the optimal accuracy bounds for the estimated sensor and source positions are derived, which can serve as a useful evaluation metric for other studies. Thirdly, a systematic solution including both the analytical and algebraic methods is proposed to obtain the optimal sensor-anchor-source geometries for achieving the approximate bounds simultaneously. Significantly, the analytical sensor placement approach can quickly offer an optimal placement for some special cases, and the algebraic algorithm can provide a (sub-)optimal solution numerically for the general case. Furthermore, theoretical guidance for placing anchors in the localization area is provided. Finally, the theoretical findings and proposed algorithms are verified by computer simulations and experimental studies, demonstrating that the optimized sensor positions yield accurate performance. The results in this paper can be utilized as an evaluation tool and a performance improvement guidance for practical SSSL problems.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1727-1743"},"PeriodicalIF":4.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143635660","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

Parameter Estimation on Homogeneous Spaces 齐次空间的参数估计

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-13 DOI: 10.1109/TSP.2025.3567361

Shiraz Khan;Gregory S. Chirikjian

{"title":"Parameter Estimation on Homogeneous Spaces","authors":"Shiraz Khan;Gregory S. Chirikjian","doi":"10.1109/TSP.2025.3567361","DOIUrl":"10.1109/TSP.2025.3567361","url":null,"abstract":"The <italic>Fisher Information Metric (FIM) and the associated <italic>Cramér-Rao Bound (CRB) are fundamental tools in statistical signal processing, informing the efficient design of experiments and algorithms for estimating the underlying parameters. In this article, we investigate these concepts for the case where the parameters lie on a <italic>homogeneous space. Unlike the existing Fisher-Rao theory for general Riemannian manifolds, our focus is to leverage the group-theoretic structure of homogeneous spaces, which is often much easier to work with than their Riemannian structure. The FIM is characterized by identifying the homogeneous space with a coset space, the group-theoretic CRB and its corollaries are presented, and its relationship to the Riemannian CRB is clarified. The application of our theory is illustrated using two examples from engineering: (i) estimation of the pose of a robot and (ii) sensor network localization. In particular, these examples demonstrate that homogeneous spaces provide a natural framework for studying statistical models that are <italic>invariant with respect to a group of symmetries.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2110-2122"},"PeriodicalIF":4.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11002750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946129","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

Boosting RPCA by Prior Subspace 基于先验子空间的RPCA提升

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-13 DOI: 10.1109/TSP.2025.3569861

Lin Chen;Li Ge;Xue Jiang;Hongbin Li

{"title":"Boosting RPCA by Prior Subspace","authors":"Lin Chen;Li Ge;Xue Jiang;Hongbin Li","doi":"10.1109/TSP.2025.3569861","DOIUrl":"10.1109/TSP.2025.3569861","url":null,"abstract":"This paper introduces a novel method, boosting principal component analysis (BPCA), to address the challenge of extracting principal components in the presence of sparse outliers. Building on the traditional robust principal component analysis (RPCA) model, BPCA incorporates prior subspace information through a flexible weighting scheme, enhancing its robustness against the bias in prior subspaces. We develop a novel metric, based on QR decomposition, to assess the accuracy of a prior subspace, which facilitates the analysis of BPCA’s exact recovery feasibility. The exact recovery is achievable if the bias of the prior subspace meets a specific tolerance condition. We establish its recovery guarantee by introducing new incoherence conditions, which offer improved interpretability over existing conditions due to the boosting of prior subspaces. BPCA enjoys a more relaxed recovery bound than RPCA and traditional prior subspace-based methods, provided that the prior subspace is sufficiently accurate, though not necessarily perfect. The necessary level of accuracy for this relaxation is quantified, with an analysis using the convex geometry of the nuclear norm. Furthermore, the proposed BPCA model is scalable and successfully extended to three-dimensional scenes. Experimental results demonstrate the superior performance of BPCA over RPCA and traditional prior subspace-based methods in low-rank recovery. The code of the proposed methods is released at <uri>https://github.com/linchenee/BPCA-BTPCA</uri>.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2170-2186"},"PeriodicalIF":4.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946115","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

Asymptotic Matrix-Variate Bingham Distributions and Privacy-Preserving Principal Component Analysis 渐近矩阵变量Bingham分布与隐私保护主成分分析

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-13 DOI: 10.1109/TSP.2025.3569636

Lu Wei;Tianxi Ji

引用次数: 0

Greedy Selection for Heterogeneous Sensors 异构传感器的贪婪选择

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-12 DOI: 10.1109/TSP.2025.3549301

Kaushani Majumder;Sibi Raj B. Pillai;Satish Mulleti

{"title":"Greedy Selection for Heterogeneous Sensors","authors":"Kaushani Majumder;Sibi Raj B. Pillai;Satish Mulleti","doi":"10.1109/TSP.2025.3549301","DOIUrl":"10.1109/TSP.2025.3549301","url":null,"abstract":"Simultaneous operation of all sensors in a large-scale sensor network is power-consuming and computationally expensive. Hence, it is desirable to select fewer sensors. A greedy algorithm is widely used for sensor selection in homogeneous networks with a theoretical worst-case performance of <inline-formula><tex-math>$boldsymbol{(mathbf{1-1}/mathbf{e})mathbf{approx 63}}$</tex-math></inline-formula>% of the optimal performance when optimizing submodular metrics. For heterogeneous sensor networks (HSNs) comprising multiple sets of sensors, most of the existing sensor selection methods optimize the performance constrained by a budget on the total value of the selected sensors. However, in many applications, the number of sensors to select from each set is known apriori and solutions are not well-explored. For this problem, we propose a joint greedy heterogeneous sensor selection algorithm. Theoretically, we show that the worst-case performance of the proposed algorithm is bounded to <inline-formula><tex-math>$50$</tex-math></inline-formula>% of the optimum for submodular cost metrics. In the special case of HSNs with two sensor networks, the performance guarantee can be improved to <inline-formula><tex-math>$63$</tex-math></inline-formula>% when the number of sensors to select from one set is much smaller than the other. To validate our results experimentally, we propose a submodular metric based on the frame potential measure that considers both the correlation among the sensor measurements and their heterogeneity. We prove theoretical bounds for the mean squared error of the solution when this performance metric is used. We validate our results through simulation experiments considering both linear and non-linear measurement models corrupted by additive noise and quantization errors. Our experiments show that the proposed algorithm results in <inline-formula><tex-math>$4 {boldsymbol{mathbf{-}}} 10$</tex-math></inline-formula> dB lower error than existing methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1394-1409"},"PeriodicalIF":4.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608003","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

Recursive Estimation With Compensation Strategies Under Random Access Protocol and Deception Attacks 随机访问协议和欺骗攻击下补偿策略的递归估计

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-12 DOI: 10.1109/TSP.2025.3569348

Jiaxing Li;Raquel Caballero-Águila;Jun Hu;Josefa Linares-Pérez

引用次数: 0

Arithmetic Average Density Fusion—Part IV: Distributed Heterogeneous Fusion of RFS and LRFS Filters via Variational Approximation 算术平均密度融合-第四部分：通过变分逼近的RFS和LRFS滤波器的分布异质融合

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-11 DOI: 10.1109/TSP.2025.3550157

Tiancheng Li;Haozhe Liang;Guchong Li;Jesús García Herrero;Quan Pan

{"title":"Arithmetic Average Density Fusion—Part IV: Distributed Heterogeneous Fusion of RFS and LRFS Filters via Variational Approximation","authors":"Tiancheng Li;Haozhe Liang;Guchong Li;Jesús García Herrero;Quan Pan","doi":"10.1109/TSP.2025.3550157","DOIUrl":"10.1109/TSP.2025.3550157","url":null,"abstract":"This paper is the fourth part of a series of papers on the arithmetic average (AA) density fusion approach and its application for target tracking. In this paper, we address the intricate challenge of distributed heterogeneous multisensor multitarget tracking, where each inter-connected sensor operates a probability hypothesis density (PHD) filter, a multiple Bernoulli (MB) filter or a labeled MB (LMB) filter and they cooperate with each other via information fusion. Our recent work has proven that the existing linear fusion of these filters is all exactly built on averaging their respective unlabeled/labeled PHDs. Based on this finding, two PHD-AA fusion approaches are proposed via variational minimization of the upper bound of the Kullback-Leibler divergence between the local and multi-filter averaged PHDs subject to cardinality consensus based on the Gaussian mixture implementation, enabling heterogeneous filter cooperation. One focuses solely on fitting the weights of the local Gaussian components (L-GCs), while the other simultaneously fits all the parameters of the L-GCs at each sensor, both seeking average consensus on the unlabeled PHD, irrespective of the specific posterior form of the local filters. For the distributed peer-to-peer communication, both the classic consensus and flooding paradigms have been investigated. Simulations have demonstrated the effectiveness and flexibility of the proposed approaches in both homogeneous and heterogeneous scenarios.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1454-1469"},"PeriodicalIF":4.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599455","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