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We present a distributed (non-Bayesian) learning algorithm for the problem of parameter estimation with Gaussian noise. The algorithm is expressed as explicit updates on the parameters of the Gaussian beliefs (i.e. means and precision). We show a convergence rate of O(1/k) with the constant term depending on the number of agents and the topology of the network. Moreover, we show almost sure convergence...
State-of-the-art narrowband noise cancellation techniques utilise the generalised eigenvalue decomposition (GEVD) for multi-channel Wiener filtering, which can be applied to independent frequency bins in order to achieve broadband processing. Here we investigate the extension of the GEVD to broadband, polynomial matrices, akin to strategies that have already been developed by McWhirter et. al on the...
In this paper, the estimation of a narrowband time-varying channel under the practical assumptions of finite block length and finite transmission bandwidth is investigated. It is shown that the signal, after passing through a time-varying narrowband channel reveals a particular parametric low-rank structure that can be represented as a bilinear form. To estimate the channel, we propose two structured...
Among various sparse array techniques, co-prime array is found to be more attractive because its higher DoF with a smaller number of sensing elements. However, because of its specific physical non-uniform linear structure, it would be inconvenient and costly to implement in the presence of varying detection scenarios. In this paper, we propose to exploit the current ULA to dynamically formulate a...
Performing statistical inference on massive data sets may not be computationally feasible using the conventional statistical inference methodology. In particular, there is a need for methods that are scalable to large volume and variability of data. Moreover, veracity of the inference is crucial. Hence, there is a need to produce quantitative information on the statistical correctness of parameter...
Analog-to-information converters and Compressed Sampling (CS) sensor front-ends try to only extract the relevant, information-bearing elements of an incoming data stream. Information extraction and recognition tasks can run directly on the compressed data stream without needing full signal reconstruction. The accuracy of the extracted information or classification is strongly determined by the front-end...
This work introduces an algorithm for localization of the seizure onset zone (SOZ) of epileptic patients based on electrocorticography (ECoG) recordings. The algorithm represents the set of electrodes using a directed graph in which nodes correspond to recording electrodes, while the edge weights are the pair-wise causal influence. This causal influence is quantified by estimating the pair-wise directed...
The generalized linear model (GLM), where a random vector x is observed through a noisy, possibly nonlinear, function of a linear transform output z = Ax, arises in a range of applications such as robust regression, binary classification, quantized compressed sensing, phase retrieval, photon-limited imaging, and inference from neural spike trains. When A is large and i.i.d. Gaussian, the generalized...
The recently developed super-resolution framework by Candes enables direction-of-arrival (DOA) estimation from a sparse spatial power spectrum in the continuous domain with infinite precision in the noise-free case. By means of atomic norm minimization (ANM), the discretization of the spatial domain is no longer required, which overcomes the basis mismatch problem in conventional sparse signal recovery...
In this work, we consider distributed adaptive learning over multitask mean-square-error (MSE) networks where each agent is interested in estimating its own parameter vector, also called task, and where the tasks at neighboring agents are related according to a set of linear equality constraints. We assume that each agent knows its own cost function of its vector and the set of constraints involving...
In this paper, we consider the problem of estimating the principal subspace of data in decentralized sensing systems with resource constraints, where the sensors only transmit a single bit to the fusion center to minimize communication costs. In particular, the data covariance matrix is modeled as a low-rank Toeplitz positive semidefinite (PSD) matrix, which arises in applications such as array signal...
This work investigates the impact of imperfect statistical information in the uplink of massive MIMO systems. In particular, we first show why covariance information is needed and then propose two schemes for covariance matrix estimation. A lower bound on the spectral efficiency (SE) of any combining scheme is derived, under imperfect covariance knowledge, and a closed-form expression is computed...
Many physical phenomena across several applications can be described by partial differential equations (PDEs). In these applications, sensors collect sparse samples of the resulting phenomena with the aim of detecting its cause/source, using some intelligent data analysis tools on the samples. These problems are commonly referred to as inverse source problems. This work presents a novel framework...
While most literature in compressive sensing mostly concentrates on recovering a sparse signal from a reduced number of measurements, parameter estimation problems have recently been studied under this acquisition framework. In this paper, we focus on the problem of direction-of-arrival (DOA) estimation from compressive measurements taken at each antenna in a receiver array. In contrast with the common...
Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C (τ), as opposed to standard methods that decompose...
Fast and accurate respiratory rate (RR) estimation from photoplethysmography (PPG) signal is still a challenging problem. In this paper, we propose a real-time algorithm for RR estimation from PPG signal using sparse signal reconstruction (SSR) based on orthogonal matching pursuit (OMP). This algorithm greatly reduces the computational complexity of the original sparse signal reconstruction and respiratory...
This paper considers the problem of DOA estimation of correlated sources using sparse arrays, where the number of sources can exceed the number of sensors. Depending on the magnitude of the cross correlation terms, our algorithm either treats them as additive noise (small correlation) or estimates them jointly with the DOAs (large correlation). In the latter case, the problem is cast as an equivalent...
In this paper, we introduce a novel framework for semi-parametric estimation of an unknown number of signals, each parametrized by a group of components. Via a reformulation of the covariance fitting criteria, we formulate a convex optimization problem over a grid of candidate representations, promoting solutions with only a few active groups. Utilizing the covariance fitting allows for a hyperparameter-free...
We consider the problem of recovering an image using block compressed sensing (BCS). Traditional BCS algorithms recovers each image block independently and utilizes post-processing methods for removing the blocking artifacts. In contrast, we propose an image recovery method free of post-processing, where we utilize a lapped transform (LT) for the sparse representation of the image in order to reduce...
There has been growing interest in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, which would likely employ hybrid analog-digital precoding with large-scale analog arrays deployed at wide bandwidths. Primary challenges here are how to efficiently estimate the large-dimensional frequency-selective channels and customize the wideband hybrid analog-digital precoders and combiners...
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