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.

A Log-Linear Nonparametric Online Changepoint Detection Algorithm Based on Functional Pruning
Online changepoint detection aims to detect anomalies and changes in real time within high frequency data streams, sometimes with limited available computational resources. This is an important task that is rooted in many real-world applications including, but not limited to, cybersecurity, medicine and astrophysics. While fast and efficient online algorithms have been recently introduced, these rely on parametric assumptions which are often violated in practical applications. Motivated by data streams from the telecommunications sector, we build a flexible nonparametric approach to detect a change in the distribution of a...
Source: IEEE Transactions on Signal Processing - December 19, 2023 Category: Biomedical Engineering Source Type: research

Asymmetric Beampattern Synthesis for Rectangular Planar Array via Window Function Design
In this paper, we propose a window function design to synthesize beampattern for uniform/sparse rectangular planar array (RPA) with strict constraints on array gain loss to ensure the performance of target detection/tracking, parameter estimation, etc. The array structure under consideration in this work is generated by the Kronecker product of two uniform/sparse linear arrays, thus referred to as RPA-K. In addition to the common symmetric beampatterns, the proposed method is able to synthesize asymmetric beampatterns to enhance interference/clutter suppression in practical scenarios, such as airborne phased array radar (A...
Source: IEEE Transactions on Signal Processing - December 19, 2023 Category: Biomedical Engineering Source Type: research

NeuralPUMA: Learning to Phase Unwrap Through Differentiable Graph Cuts
Deep learning solutions have recently demonstrated remarkable performance in phase unwrapping by approaching the problem as a semantic segmentation task. However, these solutions lack explainability and robustness to unseen conditions, and they often need a large amount of data for training. By contrast, traditional phase unwrapping algorithms, such as PUMA, rely on principled pipelines that estimate the phase through optimization solvers, despite often failing under severe noise conditions. In this work, we show how to exploit the benefits of both approaches by proposing a way to combine deep neural networks with iterativ...
Source: IEEE Transactions on Signal Processing - December 19, 2023 Category: Biomedical Engineering Source Type: research

Fast and Robust Sparsity-Aware Block Diagonal Representation
The block diagonal structure of an affinity matrix is a commonly desired property in cluster analysis because it represents clusters of feature vectors by non-zero coefficients that are concentrated in blocks. However, recovering a block diagonal affinity matrix is challenging in real-world applications, in which the data may be subject to outliers and heavy-tailed noise that obscure the hidden cluster structure. To address this issue, we first analyze the effect of different fundamental outlier types in graph-based cluster analysis. A key idea that simplifies the analysis is to introduce a vector that represents a block d...
Source: IEEE Transactions on Signal Processing - December 19, 2023 Category: Biomedical Engineering Source Type: research

Bayesian Data Fusion With Shared Priors
The integration of data and knowledge from several sources is known as data fusion. When data is only available in a distributed fashion or when different sensors are used to infer a quantity of interest, data fusion becomes essential. In Bayesian settings, a priori information of the unknown quantities is available and, possibly, present among the different distributed estimators. When the local estimates are fused, the prior knowledge used to construct several local posteriors might be overused unless the fusion node accounts for this and corrects it. In this paper, we analyze the effects of shared priors in Bayesian dat...
Source: IEEE Transactions on Signal Processing - December 19, 2023 Category: Biomedical Engineering Source Type: research

Ensembled Seizure Detection Based on Small Training Samples
This paper proposes an interpretable ensembled seizure detection procedure using electroencephalography (EEG) data, which integrates data driven features and clinical knowledge while being robust against artifacts interference. The procedure is built on the spatially constrained independent component analysis supplemented by a knowledge enhanced sparse representation of seizure waveforms to extract seizure intensity and waveform features. Additionally, a multiple change point detection algorithm is implemented to overcome EEG signal's non-stationarity and to facilitate temporal feature aggregation. The selected features ar...
Source: IEEE Transactions on Signal Processing - December 18, 2023 Category: Biomedical Engineering Source Type: research

An Amplitude Activation Function-Based Model for Behavioral Modeling of Nonlinear Systems
Nonlinear models with a linear-in-coefficients property, i.e., the property that the model output is linear with respect to model coefficients, are highly valuable for behavioral modeling of nonlinear systems. One major reason is that well-established estimation methods used in linear systems (such as the least-squares estimation) are suitable for model identification. To date, however, nonlinear models with a linear-in-coefficients property have been limited almost exclusively to polynomial function-based models. This paper explores the use of nonlinear functions other than the polynomial function to construct a nonlinear...
Source: IEEE Transactions on Signal Processing - December 15, 2023 Category: Biomedical Engineering Source Type: research

Learning High-Dimensional Differential Graphs From Multiattribute Data
We consider the problem of estimating differences in two Gaussian graphical models (GGMs) which are known to have similar structure. The GGM structure is encoded in its precision (inverse covariance) matrix. In many applications one is interested in estimating the difference in two precision matrices to characterize underlying changes in conditional dependencies of two sets of data. Existing methods for differential graph estimation are based on single-attribute (SA) models where one associates a scalar random variable with each node. In multi-attribute (MA) graphical models, each node represents a random vector. In this p...
Source: IEEE Transactions on Signal Processing - December 15, 2023 Category: Biomedical Engineering Source Type: research

Over-the-Air Multisensor Collaboration for Resource Efficient Joint Detection
We develop a resource-efficient framework for collaborative decision-making over distributed sensor networks by proposing a novel over-the-air soft information aggregation. We exploit the natural superposition of wireless transmissions to enable sensors to utilize over-the-air computation to approximate the sufficient statistic for optimum detection over a shared channel. By designing practical transmission and receiver processing in over-the-air computation, the decision-making fusion center can wirelessly obtain a good approximation of the aggregate log-likelihood ratio computed over all observed data with low distortion...
Source: IEEE Transactions on Signal Processing - December 15, 2023 Category: Biomedical Engineering Source Type: research

Analog Product Coding for Over-the-Air Aggregation Over Burst-Sparse Interference Multiple-Access Channels
Over-the-Air aggregation (OTA) is a promising technology for Internet-of-Things (IoT) applications, but it can be vulnerable to burst interference from co-channel non-cooperative IoT communications. In this paper, we propose a robust OTA framework for IoT-OTA applications that mitigates burst sparse interference using analog product code. We first propose a hierarchical likelihood model and a Markov-based hierarchical prior model to capture the structural properties in the received coded observations and the statistics of the burst-sparse interference, respectively. Then, we design a low-complexity Bayesian decoder using o...
Source: IEEE Transactions on Signal Processing - December 14, 2023 Category: Biomedical Engineering Source Type: research

Auxiliary Diffusion Strategy Against Link Noises Over Distributed Networks
Recently, the compressive diffusion strategies have been proposed to reduce the communication load of adaptive networks in the absence of link noises. In this paper, we are the first to study the compressive diffusion double normalized least mean square (CD${}^{2}$-NLMS) algorithm in the presence of noisy communication links. By means of the single update global weight error model, the transient and steady-state results of the CD${}^{2}$-NLMS algorithm are formulated analytically. One of the main findings is that the impact of link noises is well suppressed by use of small step-sizes and randomized projection vectors with ...
Source: IEEE Transactions on Signal Processing - December 14, 2023 Category: Biomedical Engineering Source Type: research

MAP Estimation of Graph Signals
In this paper, we consider the problem of recovering random graph signals from nonlinear measurements. We formulate the maximum a-posteriori probability (MAP) estimator, which results in a nonconvex optimization problem. Conventional iterative methods for minimizing nonconvex problems are sensitive to the initialization, have high computational complexity, and do not utilize the underlying graph structure behind the data. In this paper we propose three new estimators that are based on the Gauss-Newton method: 1) the elementwise graph-frequency-domain MAP (eGFD-MAP) estimator; 2) the sample graph signal processing MAP (sGSP...
Source: IEEE Transactions on Signal Processing - December 12, 2023 Category: Biomedical Engineering Source Type: research

Byzantine-Robust Distributed Online Learning: Taming Adversarial Participants in An Adversarial Environment
This paper studies distributed online learning under Byzantine attacks. The performance of an online learning algorithm is often characterized by (adversarial) regret, which evaluates the quality of one-step-ahead decision-making when an environment incurs adversarial losses, and a sublinear regret bound is preferred. But we prove that, even with a class of state-of-the-art robust aggregation rules, in an adversarial environment and in the presence of Byzantine participants, distributed online gradient descent can only achieve a linear adversarial regret bound, which is tight. This is the inevitable consequence of Byzantin...
Source: IEEE Transactions on Signal Processing - December 12, 2023 Category: Biomedical Engineering Source Type: research

Accelerated Griffin-Lim Algorithm: A Fast and Provably Converging Numerical Method for Phase Retrieval
The recovery of a signal from the magnitudes of its transformation, like the Fourier transform, is known as the phase retrieval problem and is of big relevance in various fields of engineering and applied physics. In this paper, we present a fast inertial/momentum based algorithm for the phase retrieval problem. Our method can be seen as an extended algorithm of the Fast Griffin-Lim Algorithm, a method originally designed for phase retrieval in acoustics. The new numerical algorithm can be applied to a more general framework than acoustics, and as a main result of this paper, we prove a convergence guarantee of the new sch...
Source: IEEE Transactions on Signal Processing - December 12, 2023 Category: Biomedical Engineering Source Type: research

Quasi-Closed-Form Algorithms for Spherical Angle-of-Arrival Source Localization
The ground curvature cannot be neglected when locating the source that generates long-range propagation signals, so the traditional angle-of-arrival (AOA) source localization evolves into spherical AOA source localization. For this problem, a quasi-closed-form algorithm, the spherical weighted pseudolinear estimator (SWPLE), is proposed in this paper. The analysis of the SWPLE reveals that the algorithm has bias problems. To mitigate the bias of the SWPLE, we propose an algorithm based on biased term estimation, the bias compensated spherical weighted pseudolinear estimator (BCSWPLE). The instrumental variable (IV) method ...
Source: IEEE Transactions on Signal Processing - December 8, 2023 Category: Biomedical Engineering Source Type: research