IEEE Transactions on Signal Processing 
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This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.Virtual Array Interpolation for 2-D DOA and Polarization Estimation Using Coprime EMVS Array via Tensor Nuclear Norm Minimization
In this article, we develop an interpolation-based algorithm for two-dimensional (2-D) direction-of-arrival (DOA) and polarization estimation with coprime electromagnetic vector-sensor (EMVS) array. First of all, we derive the tensor form coarray output of coprime EMVS array, and perform virtual array interpolation on the output components of the difference coarray. Subsequently, we construct a low-rank third-order augmented tensor using the interpolated uniform linear array output, and derive two important properties for this low-rank tensor in the Fourier domain. Based on these properties, we reconstruct a noise-free thi...
Source: IEEE Transactions on Signal Processing - September 29, 2023 Category: Biomedical Engineering Source Type: research
One-Bit Channel Estimation for IRS-Aided Millimeter-Wave Massive MU-MISO System
This article considers the uplink channel estimation for IRS-aided multiuser massive multi-input single-output (MISO) communications with one-bit ADCs at the base station (BS). The use of one-bit ADC is impelled by the low-cost and power efficient implementation of massive antennas techniques. However, the passiveness of IRS and the lack of signal level information after one-bit quantization make the IRS channel estimation challenging. To tackle this problem, we exploit the structured sparsity of the user-IRS-BS cascaded channels and develop three channel estimators, each of which utilizes the structured sparsity at differ...
Source: IEEE Transactions on Signal Processing - September 29, 2023 Category: Biomedical Engineering Source Type: research
Two-Stage Channel Estimation for RIS-Aided Multiuser mmWave Systems With Reduced Error Propagation and Pilot Overhead
In this paper, we propose a novel two-stage uplink channel estimation strategy with reduced pilot overhead and error propagation for reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) multiple-antenna systems. The base station (BS) and the RIS are equipped with a uniform linear array (ULA) and a uniform planar array (UPA), respectively. Specifically, in Stage I, by carefully designing the RIS phase shift matrix and introducing a matching matrix, all users jointly transmit pilot signals for the estimation of the correlation factors between different paths of the common RIS-base station (...
Source: IEEE Transactions on Signal Processing - September 25, 2023 Category: Biomedical Engineering Source Type: research
Neural Enhanced Belief Propagation for Multiobject Tracking
Algorithmic solutions for multi-object tracking (MOT) are a key enabler for applications in autonomous navigation and applied ocean sciences. State-of-the-art MOT methods fully rely on a statistical model and typically use preprocessed sensor data as measurements. In particular, measurements are produced by a detector that extracts potential object locations from the raw sensor data collected at discrete time steps. This preparatory processing step reduces data flow and computational complexity but may result in a loss of information. State-of-the-art Bayesian MOT methods that are based on belief propagation (BP) systemati...
Source: IEEE Transactions on Signal Processing - September 22, 2023 Category: Biomedical Engineering Source Type: research
Unlimited Sampling of Bandpass Signals: Computational Demodulation via Undersampling
Bandpass signals are an important sub-class of bandlimited signals that naturally arise in a number of application areas but their high-frequency content poses an acquisition challenge. Consequently, “Bandpass Sampling Theory” has been investigated and applied in the literature. In this article, we consider the problem of modulo sampling of bandpass signals with the main goal of sampling and recovery of high dynamic range inputs. Our work is inspired by the Unlimited Sensing Framework (USF). In the USF, the modulo operation folds high dynamic range inputs into low dynamic range, modulo samples. This fundamentally avoid...
Source: IEEE Transactions on Signal Processing - September 22, 2023 Category: Biomedical Engineering Source Type: research
Graphon Pooling for Reducing Dimensionality of Signals and Convolutional Operators on Graphs
We present three methods that exploit the induced graphon representation of graphs and graph signals on partitions of $\mathbf{[0,1]^{2}}$ in the graphon space. As a result we derive low dimensional representations of the convolutional operators, while a dimensionality reduction of the signals is achieved by simple local interpolation of functions in $\boldsymbol{L^{2}}\mathbf{([0,1])}$. We prove that those low dimensional representations constitute a convergent sequence of graphs and graph signals, respectively. The methods proposed and the theoretical guarantees that we provide show that the reduced graphs and signals in...
Source: IEEE Transactions on Signal Processing - September 22, 2023 Category: Biomedical Engineering Source Type: research
Differential Private Discrete Noise-Adding Mechanism: Conditions, Properties, and Optimization
This study addresses this gap by examining the primary differential privacy conditions and properties for general discrete random mechanisms, and investigating the trade-off between data privacy and data utility. We establish sufficient and necessary conditions for discrete $\boldsymbol{\epsilon}$-differential privacy and sufficient conditions for discrete $(\boldsymbol{\epsilon},\boldsymbol{\delta})$-differential privacy, with closed-form expressions for differential privacy parameters. These conditions can be applied to evaluate the differential privacy properties of discrete noise-adding mechanisms with various types of...
Source: IEEE Transactions on Signal Processing - September 22, 2023 Category: Biomedical Engineering Source Type: research
Enforcing Privacy in Distributed Learning With Performance Guarantees
We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the popular additive random perturbation scheme degrades performance because it is not well-tuned to the graph structure. For this reason, we exploit two alternative graph-homomorphic constructions and show that they improve performance while guaranteeing privacy. Moreover, contrary to most earlier studies, the gradient of the risks is not assumed to be bounded (a condition that rarely holds in practice; e.g., quadratic risk). We avoid this condition an...
Source: IEEE Transactions on Signal Processing - September 22, 2023 Category: Biomedical Engineering Source Type: research
Super-Resolution With Binary Priors: Theory and Algorithms
The problem of super-resolution is concerned with the reconstruction of temporally/spatially localized events (or spikes) from samples of their convolution with a low-pass filter. Distinct from prior works which exploit sparsity in appropriate domains in order to solve the resulting ill-posed problem, this paper explores the role of binary priors in super-resolution, where the spike (or source) amplitudes are assumed to be binary-valued. Our study is inspired by the problem of neural spike deconvolution, but also applies to other applications such as symbol detection in hybrid millimeter wave communication systems. This pa...
Source: IEEE Transactions on Signal Processing - September 20, 2023 Category: Biomedical Engineering Source Type: research
Quasi-Periodic Gaussian Process Modeling of Pseudo-Periodic Signals
Pseudo-periodic signals are frequently encountered in modern scientific and engineering applications. Most current signal modeling methods focus on strictly periodic signals, and they may fail to account for both the within- and between-period correlations of pseudo-periodic signals, which could lower the modeling and prediction accuracy. To address this issue, we develop a novel quasi-periodic Gaussian process method for signals collected at grids. It can well model the within- and between-period correlations and has an easy-to-interpret structure that can quantify the magnitude of cycle oscillations in pseudo-periodic si...
Source: IEEE Transactions on Signal Processing - September 20, 2023 Category: Biomedical Engineering Source Type: research
A Proximal-Proximal Majorization-Minimization Algorithm for Nonconvex Rank Regression Problems
In this paper, we introduce a proximal-proximal majorization-minimization (PPMM) algorithm for nonconvex rank regression problems. The basic idea of the algorithm is to apply the proximal majorization-minimization algorithm to solve the nonconvex problem with the inner subproblems solved by a sparse semismooth Newton (SSN) method based proximal point algorithm (PPA). It deserves mentioning that we adopt the sequential regularization technique and design an implementable stopping criterion to overcome the singular difficulty of the inner subproblem. Especially for the stopping criterion, it plays a very important role for t...
Source: IEEE Transactions on Signal Processing - September 20, 2023 Category: Biomedical Engineering Source Type: research
Lazy Queries Can Reduce Variance in Zeroth-Order Optimization
A major challenge of applying zeroth-order (ZO) methods is the high query complexity, especially when queries are costly. We propose a novel gradient estimation technique for ZO methods based on adaptive lazy queries that we term as LAZO. Unlike the classic one-point or two-point gradient estimation methods, LAZO develops two alternative ways to check the usefulness of old queries from previous iterations, and then adaptively reuses them to construct the low-variance gradient estimates. We rigorously establish that through judiciously reusing the old queries, LAZO can reduce the variance of stochastic gradient estimates so...
Source: IEEE Transactions on Signal Processing - September 20, 2023 Category: Biomedical Engineering Source Type: research
Communication-Efficient Federated Learning: A Variance-Reduced Stochastic Approach With Adaptive Sparsification
Federated learning (FL) is an emerging distributed machine learning paradigm that aims to realize model training without gathering the data from data sources to a central processing unit. A traditional FL framework consists of a central server as well as a number of computing devices (aka workers). Training a model under the FL framework usually consumes a massive amount of communication resources because the server and devices should frequently communicate with each other. To alleviate the communication burden, we, in this paper, propose to adaptively sparsify the gradient vector which is transmitted to the server by each...
Source: IEEE Transactions on Signal Processing - September 20, 2023 Category: Biomedical Engineering Source Type: research
L${}_{text{2}}$min${}^{text{2/2s}}$: Efficient Linear Reconstruction Filter for Incremental Delta-Sigma ADCs
While it becomes more challenging to improve the energy efficiency of incremental delta-sigma data converters (IDCs) from the analog circuit design perspective, we propose two novel linear reconstruction filters for IDCs to enhance their performance in a digital way, including the L${}_{mathbf{2}}$min${}^{mathbf{2}}$ filter and its symmetric version, the L${}_{mathbf{2}}$min${}^{mathbf{2s}}$ filter. Compared to the classical linear reconstruction filters, such as the cascade-of-integrators (CoI) and cascaded integrator-comb (CIC) filter (an implementation of sinc filter), the proposed filters can achieve efficient quantiza...
Source: IEEE Transactions on Signal Processing - September 19, 2023 Category: Biomedical Engineering Source Type: research
Nonparametric and Regularized Dynamical Wasserstein Barycenters for Sequential Observations
We consider probabilistic models for sequential observations which exhibit gradual transitions among a finite number of states. We are particularly motivated by applications such as human activity analysis where observed accelerometer time series contains segments representing distinct activities, which we call pure states, as well as periods characterized by continuous transition among these pure states. To capture this transitory behavior, the dynamical Wasserstein barycenter (DWB) model of (Cheng et al., 2021) associates with each pure state a data-generating distribution and models the continuous transitions among thes...
Source: IEEE Transactions on Signal Processing - September 19, 2023 Category: Biomedical Engineering Source Type: research
