IEEE Transactions on Signal Processing 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.

FedFM: Anchor-Based Feature Matching for Data Heterogeneity in Federated Learning
One of the key challenges in federated learning (FL) is local data distribution heterogeneity across clients, which may cause inconsistent feature spaces across clients. To address this issue, we propose Federated Feature Matching (FedFM), which guides each client's features to match shared category-wise anchors (landmarks in feature space). This method attempts to mitigate the negative effects of data heterogeneity in FL by aligning each client's feature space. We tackle the challenge of varying objective functions in theoretical analysis and provide convergence guarantee for FedFM. In FedFM, to mitigate the phenomenon of...
Source: IEEE Transactions on Signal Processing - November 21, 2023 Category: Biomedical Engineering Source Type: research

RTSNet: Learning to Smooth in Partially Known State-Space Models
The smoothing task is core to many signal-processing applications. A widely popular smoother is the RTS algorithm, which achieves minimal mean-squared error recovery with low complexity for linear Gaussianstate-space (SS) models, yet is limited in systems that are only partially known, as well as nonlinear and non-Gaussian. In this work, we propose RTSNet, a highly efficient model-based and data-driven smoothing algorithm suitable for partially known SS models. RTSNet integrates dedicated trainable models into the flow of the classical RTS smoother, while iteratively refining its sequence estimate via deep unfolding method...
Source: IEEE Transactions on Signal Processing - November 17, 2023 Category: Biomedical Engineering Source Type: research

Convex Quaternion Optimization for Signal Processing: Theory and Applications
We present several discriminant theorems for convex quaternion functions analogous to their complex counterparts. We also provide several discriminant criteria for strongly convex functions by the theorems of convex quaternion functions. Furthermore, we prove that the quaternion Newton method can converge in one step for positive definite quadratic quaternion functions and provide two applications in quaternion signal processing. These results provide a solid theoretical foundation for convex quaternion optimization and open avenues for further developments in quaternion signal processing applications. (Source: IEEE Transa...
Source: IEEE Transactions on Signal Processing - November 17, 2023 Category: Biomedical Engineering Source Type: research

Actor-Critic Methods for IRS Design in Correlated Channel Environments: A Closer Look Into the Neural Tangent Kernel of the Critic
The article studies the design of an Intelligent Reflecting Surface (IRS) in order to support a Multiple-Input-Single-Output (MISO) communication system operating in a mobile, spatiotemporally correlated channel environment. The design objective is to maximize the expected sum of Signal-to-Noise Ratio (SNR) at the receiver over an infinite time horizon. The problem formulation gives rise to a Markov Decision Process (MDP). We propose an actor-critic algorithm for continuous control that accounts for both channel correlations and destination motion by constructing the state of the Reinforcement Learning algorithm to include...
Source: IEEE Transactions on Signal Processing - November 14, 2023 Category: Biomedical Engineering Source Type: research

Orthogonal AMP for Problems With Multiple Measurement Vectors and/or Multiple Transforms
We present an example of a MIMO relay system with correlated source data and signal clipping, which can be effectively modelled as a joint MMV-MT system. While existing methods encounter challenges when applied to this example, OAMP offers an efficient solution with excellent performance. (Source: IEEE Transactions on Signal Processing)
Source: IEEE Transactions on Signal Processing - November 10, 2023 Category: Biomedical Engineering Source Type: research

Hypothesis Test Procedures for Detecting Leakage Signals in Water Pipeline Channels
We design statistical hypothesis tests for performing leak detection in water pipeline channels. By applying an appropriate model for signal propagation, we show that the detection problem becomes one of distinguishing signal from noise, with the noise being described by a multivariate Gaussian distribution with unknown covariance matrix. We first design a test procedure based on the generalized likelihood ratio test, which we show through simulations to offer appreciable leak detection performance gain over conventional approaches designed in an analogous context (for radar detection). Our proposed method requires estimat...
Source: IEEE Transactions on Signal Processing - November 9, 2023 Category: Biomedical Engineering Source Type: research

A Trainable Approach to Zero-Delay Smoothing Spline Interpolation
The task of reconstructing smooth signals from streamed data in the form of signal samples arises in various applications. This work addresses such a task subject to a zero-delay response; that is, the smooth signal must be reconstructed sequentially as soon as a data sample is available and without having access to subsequent data. State-of-the-art approaches solve this problem by interpolating consecutive data samples using splines. Here, each interpolation step yields a piece that ensures a smooth signal reconstruction while minimizing a cost metric, typically a weighted sum between the squared residual and a derivative...
Source: IEEE Transactions on Signal Processing - November 9, 2023 Category: Biomedical Engineering Source Type: research

Super-Resolution With Sparse Arrays: A Nonasymptotic Analysis of Spatiotemporal Trade-Offs
Sparse arrays have emerged as a popular alternative to the conventional uniform linear array (ULA) due to the enhanced degrees of freedom (DOF) and superior resolution offered by them. In the passive setting, these advantages are realized by leveraging correlation between the received signals at different sensors. This has led to the belief that sparse arrays require a large number of temporal measurements to reliably estimate parameters of interest from these correlations, and therefore they may not be preferred in the sample-starved regime. In this paper, we debunk this myth by performing a rigorous non-asymptotic analys...
Source: IEEE Transactions on Signal Processing - November 9, 2023 Category: Biomedical Engineering Source Type: research

On-the-Fly Communication-and-Computing for Distributed Tensor Decomposition Over MIMO Channels
Distributed tensor decomposition (DTD) is a fundamental data-analytics technique that extracts latent important properties from high-dimensional multi-attribute datasets distributed over edge devices. Conventionally its wireless implementation follows a one-shot approach that first computes local results at devices using local data and then aggregates them to a server with communication-efficient techniques such as over-the-air computation (AirComp) for global computation. Such implementation is confronted with the issues of limited storage-and-computation capacities and link interruption, which motivates us to propose a f...
Source: IEEE Transactions on Signal Processing - November 9, 2023 Category: Biomedical Engineering Source Type: research

Covariance Matrix Recovery From One-Bit Data With Non-Zero Quantization Thresholds: Algorithm and Performance Analysis
Covariance matrix recovery is a topic of great significance in the field of one-bit signal processing and has numerous practical applications. Despite its importance, the conventional arcsine law with zero threshold is incapable of recovering the diagonal elements of the covariance matrix. To address this limitation, recent studies have proposed the use of non-zero clipping thresholds. However, the relationship between the estimation error and the sampling threshold is not yet known. In this article, we undertake an analysis of the mean squared error by computing the Fisher information matrix for a given threshold. Our res...
Source: IEEE Transactions on Signal Processing - November 9, 2023 Category: Biomedical Engineering Source Type: research

Nonasymptotic Pointwise and Worst-Case Bounds for Classical Spectrum Estimators
Spectrum estimation is a fundamental methodology in the analysis of time-series data, with applications including medicine, speech analysis, and control design. The asymptotic theory of spectrum estimation is well-understood, but the theory is limited when the number of samples is fixed and finite. This paper gives non-asymptotic error bounds for a broad class of spectral estimators, both pointwise (at specific frequencies) and in the worst case over all frequencies. The general method is used to derive error bounds for the classical Blackman-Tukey, Bartlett, and Welch estimators. In particular, these are first non-asympto...
Source: IEEE Transactions on Signal Processing - November 8, 2023 Category: Biomedical Engineering Source Type: research

Learning Graph ARMA Processes From Time-Vertex Spectra
In this study, we propose an algorithm for computing graph autoregressive moving average (graph ARMA) processes based on learning the joint time-vertex power spectral density of the process from its incomplete realizations for the task of signal interpolation. Our solution relies on first roughly estimating the joint spectrum of the process from partially observed realizations and then refining this estimate by projecting it onto the spectrum manifold of the graph ARMA process through convex relaxations. The initially missing signal values are then estimated based on the learnt model. Experimental results show that the pro...
Source: IEEE Transactions on Signal Processing - November 7, 2023 Category: Biomedical Engineering Source Type: research

A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse Problems
For solving linear inverse problems, particularly of the type that appears in tomographic imaging and compressive sensing, this paper develops two new approaches. The first approach is an iterative algorithm that minimizes a regularized least squares objective function where the regularization is based on a compound Gaussian prior distribution. The compound Gaussian prior subsumes many of the commonly used priors in image reconstruction, including those of sparsity-based approaches. The developed iterative algorithm gives rise to the paper's second new approach, which is a deep neural network that corresponds to an “unro...
Source: IEEE Transactions on Signal Processing - November 7, 2023 Category: Biomedical Engineering Source Type: research

Robust Near-Optimal Arm Identification With Strongly-Adaptive Adversaries
In this work, we study the best arm identification problem in the adversarial multi-armed bandits framework. We define a strongly-adaptive adversarial model in this framework, based on strongly-adaptive adversaries in security and distributed systems. On the negative side, we show the increased strength of the adversarial model by proving that it is impossible for any best-arm identification algorithm to return an arm with rank $\boldsymbol{\leq}\left\lfloor\frac{\boldsymbol{\epsilon}\boldsymbol{K}}{ \mathbf{1}\boldsymbol{+}\boldsymbol{\epsilon}_{\mathbf{0}}}\right\rfloor$, where $K$ is the number of arms, $\epsilon$ is th...
Source: IEEE Transactions on Signal Processing - November 7, 2023 Category: Biomedical Engineering Source Type: research

On Linear Convergence of ADMM for Decentralized Quantile Regression
The alternating direction method of multipliers (ADMM) is a natural method of choice for distributed parameter learning. For smooth and strongly convex consensus optimization problems, it has been shown that ADMM and some of its variants enjoy linear convergence in the distributed setting, much like in the traditional non-distributed setting. The optimization problem associated with parameter estimation in quantile regression is neither smooth nor strongly convex (although is convex) and thus it seems can only have sublinear convergence at best. Although this insinuates slow convergence, we show that, if the local sample s...
Source: IEEE Transactions on Signal Processing - November 7, 2023 Category: Biomedical Engineering Source Type: research