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Denoising Noisy Neural Networks: A Bayesian Approach With Compensation
This article studies a fundamental problem of NoisyNNs: how to reconstruct the DNN weights from their noisy manifestations. While prior works relied exclusively on the maximum likelihood (ML) estimation, this article puts forth a denoising approach to reconstruct DNNs with the aim of maximizing the inference accuracy of the reconstructed models. The superiority of our denoiser is rigorously proven in two small-scale problems, wherein we consider a quadratic neural network function and a shallow feedforward neural network, respectively. When applied to advanced learning tasks with modern DNN architectures, our denoiser exhi...
Source: IEEE Transactions on Signal Processing - July 14, 2023 Category: Biomedical Engineering Source Type: research

Bounded Simplex-Structured Matrix Factorization: Algorithms, Identifiability and Applications
In this article, we propose a new low-rank matrix factorization model dubbed bounded simplex-structured matrix factorization (BSSMF). Given an input matrix $X$ and a factorization rank $r$, BSSMF looks for a matrix $W$ with $r$ columns and a matrix $H$ with $r$ rows such that $X approx WH$ where the entries in each column of $W$ are bounded, that is, they belong to given intervals, and the columns of $H$ belong to the probability simplex, that is, $H$ is column stochastic. BSSMF generalizes nonnegative matrix factorization (NMF), and simplex-structured matrix factorization (SSMF). BSSMF is particularly well suited when the...
Source: IEEE Transactions on Signal Processing - July 14, 2023 Category: Biomedical Engineering Source Type: research

Efficient Estimation of Sensor Biases for the 3-D Asynchronous Multi-Sensor System
An important preliminary procedure in multi-sensor data fusion is sensor registration, and the key step in this procedure is to estimate sensor biases from their noisy measurements. There are generally two difficulties in this bias estimation problem: one is the unknown target states which serve as the nuisance variables in the estimation problem, and the other is the highly nonlinear coordinate transformation between the local and global coordinate systems of the sensors. In this article, we focus on the 3-dimensional asynchronous multi-sensor scenario and propose a weighted nonlinear least squares (NLS) formulation by as...
Source: IEEE Transactions on Signal Processing - July 11, 2023 Category: Biomedical Engineering Source Type: research

TRPAST: A Tunable and Robust Projection Approximation Subspace Tracking Method
In this article, the problem of estimating and tracking a subspace signal in the presence of non-Gaussian noise is addressed. In contrast to non-robust methods such as PAST, NIC, and NP3, which are based on restrictive noise models, we use the popular $epsilon -$contamination noise model employed in robust statistics with Gaussian density as the nominal model to estimate the subspace that the target signal lives in. Adopting a new robust measure borrowed from the information geometry, i.e., the parametric family of $alpha -$divergence, two subspace tracking methods are proposed. The first one tolerates deviations over the ...
Source: IEEE Transactions on Signal Processing - July 11, 2023 Category: Biomedical Engineering Source Type: research

Adaptive Filtering Algorithms for Set-Valued Observations—Symmetric Measurement Approach to Unlabeled and Anonymized Data
Suppose $\boldsymbol{L}$ simultaneous independent stochastic systems generate observations, where the observations from each system depend on the underlying model parameter of that system. The observations are unlabeled (anonymized), in the sense that an analyst does not know which observation came from which stochastic system. How can the analyst estimate the underlying model parameters of the $\boldsymbol{L}$ systems? Since the anonymized observations at each time are an unordered set of $\boldsymbol{L}$ measurements (rather than a vector), classical stochastic gradient algorithms cannot be directly used. By using symmet...
Source: IEEE Transactions on Signal Processing - July 10, 2023 Category: Biomedical Engineering Source Type: research

Greedy Sensor Selection: Leveraging Submodularity Based on Volume Ratio of Information Ellipsoid
This article focuses on greedy approaches to select the most informative $k$ sensors from $N$ candidates to maximize the Fisher information, i.e., the determinant of the Fisher information matrix (FIM), which indicates the volume of the information ellipsoid (VIE) constructed by the FIM. However, it is a critical issue for conventional greedy approaches to quantify the Fisher information properly when the FIM of the selected subset is rank-deficient in the first $(n-1)$ steps, where $n$ is the problem dimension. In this work, we propose a new metric, i.e., the Fisher information intensity (FII), to quantify the Fisher info...
Source: IEEE Transactions on Signal Processing - July 7, 2023 Category: Biomedical Engineering Source Type: research

Adaptive Range and Doppler Distributed Target Detection in Non-Gaussian Clutter
This article deals with the detection of range and Doppler distributed targets imbedded in non-Gaussian clutter. The clutter is modeled as a spherically invariant random process with unknown texture components and a covariance matrix structure. We also assume a set of secondary signal-free data is available to estimate the correlation properties of the clutter. Moreover, the target signal at each range cell is assumed to be a sum of returns from an unknown number of scattering centers (SCs) with unknown amplitudes and Doppler frequencies. A generalized likelihood ratio test based on adaptive Doppler steering matrix estimat...
Source: IEEE Transactions on Signal Processing - July 7, 2023 Category: Biomedical Engineering Source Type: research

Group Testing With Side Information via Generalized Approximate Message Passing
Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In a group testing setup, we are given $n$ samples, one per individual. Each individual is either infected or uninfected. These samples are arranged into $m < n$ pooled samples, where each pool is obtained by mixing a subset of the $n$ individual samples. Infected individuals are then identified using a group testing algorithm. In this article, we incorporate side information (SI) collected from contact tracing (CT) into nonadaptive/single-stage group testing algorithms. We generate different types of CT SI data by incorp...
Source: IEEE Transactions on Signal Processing - July 7, 2023 Category: Biomedical Engineering Source Type: research

On Efficient Parameter Estimation of Elementary Chirp Model
Elementary chirp signals can be found in various fields of science and engineering. We propose two computationally efficient algorithms based on the choice of two different initial estimators to estimate the parameters of the elementary chirp model. It is observed that the proposed efficient estimators are consistent; they have the identical asymptotic distribution as that of the least squares estimators and they are also less computationally intensive. We also propose sequential efficient procedures to estimate the parameters of the multi-component elementary chirp model. The asymptotic properties of the sequential effici...
Source: IEEE Transactions on Signal Processing - July 4, 2023 Category: Biomedical Engineering Source Type: research

Signal Accumulation Method for High-Speed Maneuvering Target Detection Using Airborne Coherent MIMO Radar
The airborne coherent multi-input multi-output (MIMO) radar benefiting from coherent integration (including intra-channel integration and inter-channel integration) of multi-channel echoes can obtain superior detection performance for high-speed maneuvering targets. However, due to the coupling motion characteristic between multiple airborne platforms and high-speed targets, the range walk (RW) and Doppler walk (DW) will occur within the intra-channel integration. Besides, the inter-channel signals also exist envelope and phase differences, which will lead to the difficulty in multi-channel integration. We make contributio...
Source: IEEE Transactions on Signal Processing - July 4, 2023 Category: Biomedical Engineering Source Type: research

Quantization for Decentralized Learning Under Subspace Constraints
In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained formulation includes consensus or single-task optimization as special cases, and allows for more general task relatedness models such as multitask smoothness and coupled optimization. In order to cope with communication constraints, we propose and study an adaptive decentralized strategy where the agents employ differential randomized quantizers to compress their estimates ...
Source: IEEE Transactions on Signal Processing - July 4, 2023 Category: Biomedical Engineering Source Type: research

Projection-Based Multiple Notch Filtering
A method for multi-frequency notch filtering is described. The approach is based on quadratic programming, which leads to projecting the signal vector onto the orthogonal complement of the subspace spanned by a set of sinusoidal basis vectors, each corresponding to a notch frequency. The projection matrix uses a weighted inner product whose weights are samples of a standard spectral analysis window. An overlap-add method suitable for block processing is also described. The projection-based notch filter (PNF) is compared with linear-phase FIR and IIR multiple notch filters and is found to have improved amplitude and phase d...
Source: IEEE Transactions on Signal Processing - July 4, 2023 Category: Biomedical Engineering Source Type: research

A Class of Bayesian Lower Bounds for Parameter Estimation Via Arbitrary Test-Point Transformation
In this article, a new class of global mean-squared-error (MSE) lower bound for Bayesian parameter estimation is derived. First, it is shown that under the non-Bayesian framework, the Hammersley-Chapman-Robbins (HCR) for the problem of single-source parameter estimation, is related to the corresponding ambiguity function. This result is achieved by judicious choice of signal test-point. This result implies that optimal shift test-points may be parameter-dependent, but this approach cannot be imitated in test-point based Bayesian bounds, where the test-points should be independent of the random parameters. Based on this obs...
Source: IEEE Transactions on Signal Processing - July 4, 2023 Category: Biomedical Engineering Source Type: research

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Source: IEEE Transactions on Signal Processing - July 4, 2023 Category: Biomedical Engineering Source Type: research

Enumeration for a Large Number of Sources Based on a Two-Step Difference Operation of Linear Shrinkage Coefficients
A novel and computationally efficient source enumeration algorithm is proposed for large-scale arrays with a small number of samples, by employing a two-step difference operation of linear shrinkage (LS) coefficients of sample covariance matrix (SCM) in large-dimensional scenarios. It is firstly proved that the difference between noise LS coefficients tends to zero and there exists a clear gap between the last signal LS coefficient ${hat{alpha } ^{(d - 1)}}$ and the first noise LS coefficient ${hat{alpha } ^{(d)}}$ in relatively high signal-to-noise ratio (SNR) cases for $m, nto infty$ and $m/nto cin (0,infty)$, where $m$,...
Source: IEEE Transactions on Signal Processing - June 30, 2023 Category: Biomedical Engineering Source Type: research