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Multi-dimensional Resource Management Scheme for Multiple Target Tracking under Dynamic Electromagnetic Environment IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-18 Peng Zhang, Junkun Yan, Wenqiang Pu, Hongwei Liu, Maria S. Greco
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Near-Field Wideband Secure Communications: An Analog Beamfocusing Approach IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-17 Yuchen Zhang, Haiyang Zhang, Sa Xiao, Wanbin Tang, Yonina C. Eldar
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6-DoF Location-and-Pose Estimation towards Integrated Visible Light Communication and Sensing: Algorithm Design and Performance Limits IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-16 Bingpeng Zhou, Xin Wang, Yuan Shen, Pingzhi Fan
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Matrix Approximation with Side Information: When Column Sampling is Enough IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-15 Jeongmin Chae, Praneeth Narayanamurthy, Selin Bac, Shaama Mallikarjun Sharada, Urbashi Mitra
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Source Enumeration Utilizing Adaptive Diagonal Loading and Linear Shrinkage Coefficients IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-11 Ye Tian, Zhicheng Zhang, Wei Liu, Hua Chen, Gang Wang
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Radar Waveform Design based on Target Pattern Separability via Fractional Programming IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-11 Jiahang Wang, Junli Liang, Zhiwei Cheng, Hing Cheung So, Shengqi Zhu, Jingwei Xu
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ROCS: Robust One-Bit Compressed Sensing with Application to Direction of Arrival IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-11 Xiao-Peng Li, Zhang-Lei Shi, Lei Huang, Anthony Man-Cho So, Hing Cheung So
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Properties and Structure of the Analytic Singular Value Decomposition IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-11 S. Weiss, I.K. Proudler, G. Barbarino, J. Pestana, J.G. McWhirter
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Adaptive Local Modularity Learning for Efficient Multilayer Graph Clustering IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-10 Danyang Wu, Penglei Wang, Junjie Liang, Jitao Lu, Jin Xu, Rong Wang, Feiping Nie
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BayGO: Decentralized Bayesian Learning and Information-Aware Graph Optimization Framework IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-10 Tamara AlShammari, Chathuranga Weeraddana, Mehdi Bennis
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Accounting for Vibration Noise in Stochastic Measurement Errors of Inertial Sensors IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-10 Mucyo Karemera, Lionel Voirol, Davide A. Cucci, Wenfei Chu, Roberto Molinari, Stéphane Guerrier
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Detection of Ghost Targets for Automotive Radar in the Presence of Multipath IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-09 Le Zheng, Jiamin Long, Marco Lops, Fan Liu, Xueyao Hu, Chuanhao Zhao
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Variance Reduced Random Relaxed Projection Method for Constrained Finite-sum Minimization Problems IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-09 Zhichun Yang, Fu-quan Xia, Kai Tu, Man-Chung Yue
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Neural Augmented Kalman Filtering with Bollinger Bands for Pairs Trading IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-08 Amit Milstein, Guy Revach, Haoran Deng, Hai Morgenstern, Nir Shlezinger
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Distributed Policy Gradient for Linear Quadratic Networked Control with Limited Communication Range IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-08 Yuzi Yan, Yuan Shen
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Non-uniform Array and Frequency Spacing for Regularization-free Gridless DOA IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-08 Yifan Wu, Michael B. Wakin, Peter Gerstoft
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Distributed Inference with Variational Message Passing in Gaussian Graphical Models: Trade-offs in Message Schedules and Convergence Conditions IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-08 Bin Li, Nan Wu, Yik-Chung Wu
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Adaptive Step-Size Methods for Compressed SGD with Memory Feedback IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-05 Adarsh M. Subramaniam, Akshayaa Magesh, Venugopal V. Veeravalli
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Multi-Resolution Model Compression for Deep Neural Networks: A Variational Bayesian Approach IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-02 Chengyu Xia, Huayan Guo, Haoyu Ma, Danny H. K. Tsang, Vincent K. N. Lau
The continuously growing size of deep neural networks (DNNs) has sparked a surge in research on model compression techniques. Among these techniques, multi-resolution model compression has emerged as a promising approach which can generate multiple DNN models with shared weights and different computational complexity (resolution) through a single training. However, in most existing multi-resolution
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Spatial Registration of Heterogeneous Sensors on Mobile Platforms IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-01 Yajun Zeng, Jun Wang, Shaoming Wei, Jinping Sun, Peng Lei, Yvon Savaria, Chi Zhang
Accurate georegistration is required in multi-sensor data fusion, since even minor biases in spatial registration can result in large errors in the converted target geolocation. This paper addresses the problem of estimating and correcting sensor biases in target geolocation. Aiming to solve the spatial registration problem in the case where heterogeneous measurements are provided by mobile sensor
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Set-Type Belief Propagation with Applications to Poisson Multi-Bernoulli SLAM IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-01 Hyowon Kim, Ángel F. García-Fernández, Yu Ge, Yuxuan Xia, Lennart Svensson, Henk Wymeersch
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DANSE: Data-Driven Non-Linear State Estimation of Model-Free Process in Unsupervised Learning Setup IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-29 Anubhab Ghosh, Antoine Honoré, Saikat Chatterjee
We address the tasks of Bayesian state estimation and forecasting for a model-free process in an unsupervised learning setup. For a model-free process, we do not have any a-priori knowledge of the process dynamics. In the article, we propose DANSE – a Da ta-driven N onlinear S tate E stimation method. DANSE provides a closed-form posterior of the state of the model-free process, given linear measurements
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A New Statistic for Testing Covariance Equality in High-Dimensional Gaussian Low-Rank Models IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-28 Rémi Beisson, Pascal Vallet, Audrey Giremus, Guillaume Ginolhac
In this paper, we consider the problem of testing equality of the covariance matrices of $L$ complex Gaussian multivariate time series of dimension $M$ . We study the special case where each of the $L$ covariance matrices is modeled as a rank $K$ perturbation of the identity matrix, corresponding to a signal plus noise model. A new test statistic based on the estimates of the eigenvalues of the different
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Ultimately Bounded State Estimation for Nonlinear Networked Systems With Constrained Average Bit Rate: A Buffer-Aided Strategy IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-28 Jie Sun, Bo Shen, Lei Zou
This article investigates the state estimation issue for a nonlinear networked system with network-based communication, where the measurement signals of the system are transmitted in an intermittent manner under the effects of unreliable communication. For the sake of enhancing the utilization efficiency of measurement signals, a buffer-aided strategy is employed here by storing historical measurement
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Blind Graph Matching Using Graph Signals IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-28 Hang Liu, Anna Scaglione, Hoi-To Wai
Classical graph matching aims to find a node correspondence between two unlabeled graphs of known topologies. This problem has a wide range of applications, from matching identities in social networks to identifying similar biological network functions across species. However, when the underlying graphs are unknown, the use of conventional graph matching methods requires inferring the graph topologies
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Samplet Basis Pursuit: Multiresolution Scattered Data Approximation With Sparsity Constraints IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-28 Davide Baroli, Helmut Harbrecht, Michael Multerer
We consider scattered data approximation in samplet coordinates with $\ell_{1}$ -regularization. The application of an $\ell_{1}$ -regularization term enforces sparsity of the coefficients with respect to the samplet basis. Samplets are wavelet-type signed measures, which are tailored to scattered data. Therefore, samplets enable the use of well-established multiresolution techniques on general scattered
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Optimal Bayesian Regression With Vector Autoregressive Data Dependency IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-27 Samira Reihanian, Edward R. Dougherty, Amin Zollanvari
In this study, we derive a closed-form analytic representation of the optimal Bayesian regression when the data are generated from $\text{VAR}(p)$ , which is a multidimensional vector autoregressive process of order $p$ . Given the covariance matrix of the underlying Gaussian white-noise process, the developed regressor reduces to the conventional optimal regressor for a non-informative prior and setting
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Topology Inference of Directed Graphs by Gaussian Processes with Sparsity Constraints IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-26 Chen Cui, Paolo Banelli, Petar M. Djurić
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Ziv-Zakai Bound for 2D-DOAs Estimation IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-25 Zongyu Zhang, Zhiguo Shi, Cunqi Shao, Jiming Chen, Maria Sabrina Greco, Fulvio Gini
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Sparse Modeling for Spectrometer Based on Band Measurement IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-25 Kyoya Uemura, Tomoyuki Obuchi, Toshiyuki Tanaka
In typical spectrometric measurement systems, a high-resolution spectrum is obtained directly via sequential observations with a narrow slit-like measurement window at the expense of sensitivity. In this paper, we propose a novel spectrometric method applicable to these typical spectrometric systems: a multiplexed low-resolution measurement with a wide measurement window, band measurement (BM), is
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Multivariate Selfsimilarity: Multiscale Eigen-Structures for Selfsimilarity Parameter Estimation IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-25 Charles-Gérard Lucas, Gustavo Didier, Herwig Wendt, Patrice Abry
Scale-free dynamics, formalized by selfsimilarity, provides a versatile paradigm massively and ubiquitously used to model temporal dynamics in real-world data. However, its practical use has mostly remained univariate so far. By contrast, modern applications often demand multivariate data analysis. Accordingly, models for multivariate selfsimilarity were recently proposed. Nevertheless, they have remained
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Deep Unfolding Transformers for Sparse Recovery of Video IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-25 Brent De Weerdt, Yonina C. Eldar, Nikos Deligiannis
Deep unfolding models are designed by unrolling an optimization algorithm into a deep learning network. By incorporating domain knowledge from the optimization algorithm, they have shown faster convergence and higher performance compared to the original algorithm. We design an optimization problem for sequential signal recovery, which incorporates that the signals have a sparse representation in a
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Tangent Bundle Convolutional Learning: From Manifolds to Cellular Sheaves and Back IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-20 Claudio Battiloro, Zhiyang Wang, Hans Riess, Paolo Di Lorenzo, Alejandro Ribeiro
In this work we introduce a convolution operation over the tangent bundle of Riemann manifolds in terms of exponentials of the Connection Laplacian operator. We define tangent bundle filters and tangent bundle neural networks (TNNs) based on this convolution operation, which are novel continuous architectures operating on tangent bundle signals, i.e. vector fields over the manifolds. Tangent bundle
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Channel estimation for mmWave using the convolutional beamspace approach IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-20 Po-Chih Chen, P. P. Vaidyanathan
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Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement Learning IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-20 Sergio Rozada, Santiago Paternain, Antonio G. Marques
Value function (VF) approximation is a central problem in reinforcement learning (RL). Classical non-parametric VF estimation suffers from the curse of dimensionality. As a result, parsimonious parametric models have been adopted to approximate VFs in high-dimensional spaces, with most efforts being focused on linear and neural network-based approaches. Differently, this paper puts forth a parsimonious
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High Accuracy AUV-Aided Underwater Localization: Far-Field Information Fusion Perspective IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-19 Ruoyu Su, Zijun Gong, Cheng Li, Shuai Han
An autonomous underwater vehicle (AUV) can be employed to estimate an underwater target's position using the Doppler shift measurement extracted from received signals. Conventionally, the received signals have to be divided into several short frames so that the Doppler shift is constant in each one. When the AUV is far away from the target, the signal to noise ratio (SNR) is quite low. An intuitive
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Stability to Deformations of Manifold Filters and Manifold Neural Networks IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-19 Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
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Projected Gradient Descent for Spectral Compressed Sensing via Symmetric Hankel Factorization IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-18 Jinsheng Li, Wei Cui, Xu Zhang
Current spectral compressed sensing methods via Hankel matrix completion employ symmetric factorization to demonstrate the low-rank property of the Hankel matrix. However, previous non-convex gradient methods only utilize asymmetric factorization to achieve spectral compressed sensing. In this paper, we propose a novel nonconvex projected gradient descent method for spectral compressed sensing via
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Learnable Filters for Geometric Scattering Modules IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-18 Alexander Tong, Frederik Wenkel, Dhananjay Bhaskar, Kincaid Macdonald, Jackson Grady, Michael Perlmutter, Smita Krishnaswamy, Guy Wolf
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Sensor Network Localization via Riemannian Conjugate Gradient and Rank Reduction IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-18 Yicheng Li, Xinghua Sun
This paper addresses the Sensor Network Localization (SNL) problem using received signal strength. The SNL is formulated as an Euclidean Distance Matrix Completion (EDMC) problem under the unit ball sample model. Using the Burer-Monteiro factorization type cost function, the EDMC is solved by Riemannian conjugate gradient with Hager-Zhang line search method on a quotient manifold. A “rank reduction”
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Deep Learning-Based Cooperative LiDAR Sensing for Improved Vehicle Positioning IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-14 Luca Barbieri, Bernardo Camajori Tedeschini, Mattia Brambilla, Monica Nicoli
Accurate positioning is known to be a fundamental requirement for the deployment of Connected Automated Vehicles (CAVs). To meet this need, a new emerging trend is represented by cooperative methods where vehicles fuse information coming from navigation and imaging sensors for joint positioning and environmental perception. In line with this trend, this paper proposes a novel data-driven cooperative
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Percentile Optimization in Wireless Networks—Part I: Power Control for Max-Min-Rate to Sum-Rate Maximization (and Everything in Between) IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-14 Ahmad Ali Khan, Raviraj S. Adve
Improving throughput for cell-edge users through coordinated resource allocation has been a long-standing driver of research in wireless cellular networks. While a variety of wireless resource management problems focus on sum utility, max-min utility and proportional fair utility, these formulations do not explicitly cater to cell-edge users and can, in fact, be disadvantageous to them. In this two-part
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Joint Design of Binary Probing Sequence Sets and Receive Filter Banks for MIMO PMCW Radar IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-14 Yutao Chen, Yuanbo Cheng, Ronghao Lin, Hing Cheung So, Jian Li
Joint design of binary probing sequence (BPS) sets and receive filter banks is critical to the performance of low-cost multiple-input multiple-output (MIMO) phase-modulated continuous wave (PMCW) radar for autonomous driving applications. Compared to the commonly used matched receive filter banks in MIMO PMCW radar, mismatched receive filter (MMRF) banks can attain much lower sidelobe levels due to
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Learning Spatiotemporal Graphical Models From Incomplete Observations IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-13 Amirhossein Javaheri, Arash Amini, Farokh Marvasti, Daniel P. Palomar
This paper investigates the problem of learning a graphical model from incomplete spatio-temporal measurements. Our purpose is to analyze a time-varying graph signal represented by an incomplete data matrix, the rows and columns of which correspond to spatial and temporal features/measurements of the signal, respectively. In contrast to the conventional approaches which utilize either a directed or
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UL-DL Duality for Cell-Free Massive MIMO With Per-AP Power and Information Constraints IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-13 Lorenzo Miretti, Renato Luís Garrido Cavalcante, Emil Björnson, Sławomir Stańczak
We derive a novel uplink-downlink duality principle for optimal joint precoding design under per-transmitter power and information constraints in fading channels. The information constraints model limited sharing of channel state information and data bearing signals across the transmitters. The main application is to cell-free networks, where each access point (AP) must typically satisfy an individual
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Message Passing Based Wireless Multipath SLAM With Continuous Measurements Correction IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-12 Jiawei Gao, Jiancun Fan, Shiyu Zhai, Gang Dai
The core of multipath-based simultaneous localization and mapping (SLAM) is to utilize the multipath propagation of signals to simultaneously achieve the estimation of the user and the surrounding environment's states. Existing multipath-based SLAM methods are Bayesian estimators that use the current-snapshot signal as input to update the states. However, the internal correlation of time-varying signals
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Augmented Multi-Subarray Dilated Nested Array With Enhanced Degrees of Freedom and Reduced Mutual Coupling IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-12 Hua Chen, Hongguang Lin, Wei Liu, Qing Wang, Qing Shen, Gang Wang
Sparse linear arrays (SLAs) can be designed in a systematic way, with the ability for underdetermined DOA estimation where a greater number of sources can be detected than that of sensors. In this paper, as the first stage, a new systematic design named multi-subarray dilated nested array (MDNA), whose difference co-array (DCA) can be proved to be hole-free, is firstly proposed by introducing a sparse
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Geometrically-Regularized Fast Independent Vector Extraction by Pure Majorization-Minimization IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-11 Rintaro Ikeshita, Tomohiro Nakatani
We propose computationally efficient algorithms for extracting a single source of interest (SOI) using geometrically-regularized independent vector extraction (GR-IVE). Conventional GR-IVE relies on a block majorization-minimization (block MM) algorithm, which successively optimizes each part (block) of the separation matrix based on the minimization of a surrogate function. We here extend the block
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On a Novel Time-Varying Up-Sampling Rate (TVUSR) Structure and Its Statistical Properties IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-11 Ayan Kumar Dutta, Shiv Dutt Joshi, Brejesh Lall
Several real-world signals exhibit semi-periodicity in that the period of repetition varies from pulse to pulse about a mean value instead of being constant. Some examples, including among others, are ECG signals, voiced phonemes in speech, carrier jitter in communication etc. In order to model/generate such signals, one can pass a train of discrete-time delta functions, having zeros between two ones
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A Tensor Based Varying-Coefficient Model for Multi-Modal Neuroimaging Data Analysis IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-11 Pratim Guha Niyogi, Martin A. Lindquist, Tapabrata Maiti
All neuroimaging modalities have their own strengths and limitations. A current trend is toward interdisciplinary approaches that use multiple imaging methods to overcome limitations of each method in isolation. At the same time neuroimaging data is increasingly being combined with other non-imaging modalities, such as behavioral and genetic data. The data structure of many of these modalities can
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Scattering and Gathering for Spatially Varying Blurs IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-11 Nicholas Chimitt, Xingguang Zhang, Yiheng Chi, Stanley H. Chan
A spatially varying blur kernel $h(\mathbf{x},\mathbf{u})$ is specified by an input coordinate $\mathbf{u} \mathbf{\in} \mathbb{R}^{2}$ and an output coordinate $\mathbf{x} \mathbf{\in} \mathbb{R}^{2}$ . For computational efficiency, we sometimes write $h(\mathbf{x},\mathbf{u})$ as a linear combination of spatially invariant basis functions. The associated pixelwise coefficients, however, can be indexed
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Chaotic Convergence of Newton's Method IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-11 Jont B. Allen
In 1680 Newton proposed an algorithm for finding roots of polynomials. His method has since evolved but the core concept remains intact. The convergence of Newton's Method has been widely challenged to be unstable or even chaotic. Here we briefly review this evolution, and consider the question of stable convergence. Newton's method may be applied to any complex analytic function, such as polynomials
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Decentralized Stochastic Optimization With Pairwise Constraints and Variance Reduction IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-08 Fei Han, Xuanyu Cao, Yi Gong
This paper focuses on minimizing the decentralized finite-sum optimization over a network, where each pair of neighboring agents is associated with a nonlinear proximity constraint. Additionally, each agent possesses a private convex cost that can be decomposed into an average of multiple constituent functions. The goal of the network is to collectively minimize the sum of individual costs while satisfying
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Hybrid Data-Induced Kalman Filtering Approach and Application in Beam Prediction and Tracking IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-08 Jianjun Zhang, Yongming Huang, Christos Masouros, Xiaohu You, Björn Ottersten
Beam prediction and tracking (BPT) are key technology for high-frequency communications. Typical techniques include Kalman filtering and Gaussian process regression (GPR). However, Kalman filter requires explicit models of system dynamics, which are challenging to obtain, especially for complicated environments. In contrast, as a data-driven approach, there is no need to derive the system dynamics
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Multisensor Multiobject Tracking with Improved Sampling Efficiency IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-07 Wenyu Zhang, Florian Meyer
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mPage: Probabilistic Gradient Estimator With Momentum for Non-Convex Optimization IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-06 Yuqing Liang, Hui Su, Jinlan Liu, Dongpo Xu
The probabilistic gradient estimator (PAGE) algorithm allows switching between vanilla SGD and variance-reduced methods in a flexible probabilistic manner. This motivates us to develop novel momentum-based algorithms for non-convex finite-sum problems. Specifically, we replace SGD with momentum acceleration in PAGE, and the momentum term is integrated in the inner and outer parts of the gradient estimator
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Bayesian Inference for Non-Linear Forward Model by Using a VAE-Based Neural Network Structure IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-06 Yechuan Zhang, Jian-Qing Zheng, Michael Chappell
In this paper, a Variational Autoencoder (VAE) based framework is introduced to solve parameter estimation problems for non-linear forward models. In particular, we focus on applications in the field of medical imaging where many thousands of model-based inference analyses might be required to populate a single parametric map. We adopt the concept from Variational Bayes (VB) of using an approximate
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Percentile Optimization in Wireless Networks—Part II: Beamforming for Cell-Edge Throughput Maximization IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-04 Ahmad Ali Khan, Raviraj S. Adve
Part I of this two-part paper focused on the formulation of percentile problems, complexity analysis, and development of power control algorithms via the quadratic fractional transform (QFT) and logarithmic fractional transform (LFT) for sum-least- $q^{\mathrm{th}}$ -percentile (SLqP) rate maximization problems. In this second part, we first tackle the significantly more challenging problems of optimizing
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Multi-Timescale Ensemble $Q$-Learning for Markov Decision Process Policy Optimization IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-04 Talha Bozkus, Urbashi Mitra
Reinforcement learning (RL) is a classical tool to solve network control or policy optimization problems in unknown environments. The original $Q$ -learning suffers from performance and complexity challenges across very large networks. Herein, a novel model-free ensemble reinforcement learning algorithm which adapts the classical $Q$ -learning is proposed to handle these challenges for networks which
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Optimal Transport Based Impulse Response Interpolation in the Presence of Calibration Errors IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-01 David Sundström, Filip Elvander, Andreas Jakobsson
Acoustic impulse responses (IRs) are widely used to model sound propagation between two points in space. Being a point-to-point description, IRs are generally estimated based on input-output pairs for source and sensor positions of interest. Alternatively, the IR at an arbitrary location in space may be constructed based on interpolation techniques, thus alleviating the need of densely sampling the