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.

Multi-Resolution Model Compression for Deep Neural Networks: A Variational Bayesian Approach
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 compression methods, the model structures for different resolutions are either predefined or uniformly controlled. This can lead to performance degradation as they fail to implement systematic compression to achieve the optimal ...
Source: IEEE Transactions on Signal Processing - April 2, 2024 Category: Biomedical Engineering Source Type: research

Set-Type Belief Propagation With Applications to Poisson Multi-Bernoulli SLAM
Belief propagation (BP) is a useful probabilistic inference algorithm for efficiently computing approximate marginal probability densities of random variables. However, in its standard form, BP is only applicable to the vector-type random variables with a fixed and known number of vector elements, while certain applications rely on random finite sets (RFSs) with an unknown number of vector elements. In this paper, we develop BP rules for factor graphs defined on sequences of RFSs where each RFS has an unknown number of elements, with the intention of deriving novel inference methods for RFSs. Furthermore, we show that vect...
Source: IEEE Transactions on Signal Processing - April 1, 2024 Category: Biomedical Engineering Source Type: research

Spatial Registration of Heterogeneous Sensors on Mobile Platforms
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 (active or passive) platforms, this paper proposes a moving heterogeneous sensor registration (MDSR) algorithm based on maximum likelihood estimation. The MDSR algorithm decouples the offset biases from the attitude biases and up...
Source: IEEE Transactions on Signal Processing - April 1, 2024 Category: Biomedical Engineering Source Type: research

DANSE: Data-Driven Non-Linear State Estimation of Model-Free Process in Unsupervised Learning Setup
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 Data-driven Nonlinear State Estimation method. DANSE provides a closed-form posterior of the state of the model-free process, given linear measurements of the state. In addition, it provides a closed-form posterior for forecasting. A data-driven recurrent neural network (RNN) is used in DANSE to provide the parameters of a prior of the state. The prior depends on the past m...
Source: IEEE Transactions on Signal Processing - March 29, 2024 Category: Biomedical Engineering Source Type: research

Ultimately Bounded State Estimation for Nonlinear Networked Systems With Constrained Average Bit Rate: A Buffer-Aided Strategy
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 signals when the communication channel is unavailable. A so-called average bit rate constraint is introduced to restrain the transmission rate of the communication channel with the aim of avoiding the communication burden. Th...
Source: IEEE Transactions on Signal Processing - March 28, 2024 Category: Biomedical Engineering Source Type: research

Samplet Basis Pursuit: Multiresolution Scattered Data Approximation With Sparsity Constraints
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 data sets. They provide similar properties as wavelets in terms of localization, multiresolution analysis, and data compression. By using the Riesz isometry, we embed samplets into reproducing kernel Hilbert spaces and discu...
Source: IEEE Transactions on Signal Processing - March 28, 2024 Category: Biomedical Engineering Source Type: research

A New Statistic for Testing Covariance Equality in High-Dimensional Gaussian Low-Rank Models
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 covariance matrices is proposed. In particular, we show that this statistic is consistent and with controlled type I error in the high-dimensional asymptotic regime where the sample sizes $N_{1},\dots,N_{L}$ of each time se...
Source: IEEE Transactions on Signal Processing - March 28, 2024 Category: Biomedical Engineering Source Type: research

Blind Graph Matching Using Graph Signals
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 first, a process that is highly sensitive to observation errors. In this paper, we tackle the blind graph matching problem with unknown underlying graphs directly using observations of graph signals, which are generated from ...
Source: IEEE Transactions on Signal Processing - March 28, 2024 Category: Biomedical Engineering Source Type: research

Optimal Bayesian Regression With Vector Autoregressive Data Dependency
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 $p=0$, which implies independent data. Our empirical results using both synthetic and real data show that the developed regressor can effectively be used in situations where the data are sequentially dependent. (Source: IEEE...
Source: IEEE Transactions on Signal Processing - March 27, 2024 Category: Biomedical Engineering Source Type: research

Multivariate Selfsimilarity: Multiscale Eigen-Structures for Selfsimilarity Parameter Estimation
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 rarely used in practice because of a lack of available reliable estimation procedures for the vector of selfsimilarity parameters. Building upon recent mathematical developments, the present work puts forth an efficient e...
Source: IEEE Transactions on Signal Processing - March 25, 2024 Category: Biomedical Engineering Source Type: research

Deep Unfolding Transformers for Sparse Recovery of Video
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 dictionary and are correlated over time. A corresponding optimization algorithm is derived and unfolded into a deep unfolding Transformer encoder architecture, coined DUST. To show its improved reconstruction quality and flexibil...
Source: IEEE Transactions on Signal Processing - March 25, 2024 Category: Biomedical Engineering Source Type: research

Sparse Modeling for Spectrometer Based on Band Measurement
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 combined with sparse-modeling-based post-processing to obtain the original high-resolution spectrum. BM is expected to improve the measurement signal-to-noise ratio because of the increase in the sample quantities reaching the de...
Source: IEEE Transactions on Signal Processing - March 25, 2024 Category: Biomedical Engineering Source Type: research

Tangent Bundle Convolutional Learning: From Manifolds to Cellular Sheaves and Back
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 filters admit a spectral representation that generalizes the ones of scalar manifold filters, graph filters and standard convolutional filters in continuous time. We then introduce a discretization procedure, both in the space a...
Source: IEEE Transactions on Signal Processing - March 20, 2024 Category: Biomedical Engineering Source Type: research

Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement Learning
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 non-parametric approach, where we use stochastic low-rank algorithms to estimate the VF matrix in an online and model-free fashion. Furthermore, as VFs tend to be multi-dimensional, we propose replacing the classical VF matr...
Source: IEEE Transactions on Signal Processing - March 20, 2024 Category: Biomedical Engineering Source Type: research

High Accuracy AUV-Aided Underwater Localization: Far-Field Information Fusion Perspective
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 solution is to increase the frame length to suppress noise and boost SNR. However, it is worth noting that the Doppler shift is actually changing over time, and the prolonged frame length will inevitably induce modeling error. T...
Source: IEEE Transactions on Signal Processing - March 19, 2024 Category: Biomedical Engineering Source Type: research