Sorry, you need to enable JavaScript to visit this website.

We address the problem of filtering out localized time-frequency components in signals. The problem is formulatedas a minimization of a suitable quadratic form, that involves adata fidelity term on the short-time Fourier transform outsidethe support of the undesired component, and an energy pe-nalization term inside the support. The minimization yields alinear system whose solution can be expressed in closed formusing Gabor multipliers.

Categories:
61 Views

Many signal processing methods break the target signal into overlapping patches, process them separately, and then stitch them back to produce an output. How to merge the resulting patches at the overlaps is central to such methods. We propose a novel framework for this type of problem based on the idea that estimated patches should coincide at the overlaps (consensus), and develop an algorithm for solving the general problem. In particular, an efficient method for projecting patches onto the consensus constraint is presented.

Categories:
11 Views

This paper is concerned with the estimation of unknown drift functions of stochastic differential equations (SDEs) from observations of their sample paths. We propose to formulate this as a non-parametric Gaussian process regression problem and use an Itô-Taylor expansion for approximating the SDE. To address the computational complexity problem of Gaussian process regression, we cast the model in an equivalent state-space representation, such that (non-linear) Kalman filters and smoothers can be used.

Categories:
11 Views

In this paper, we present a new performance bound for uplink channel estimation (CE) accuracy in the Massive Multiple Input Multiple Output (MIMO) system. The proposed approach is based on noise power calculation after the CE unit in a multi-antenna receiver. Each time the impulse response of ideal channel estimation is decomposed into separate taps (beams) and cross-covariance matrix is calculated between them.

Categories:
11 Views

In this paper we investigate the problem of time of arrival estimation which occurs in many real-world applications, such as indoor localization or non-destructive testing via ultrasound or radar. A problem that is often overlooked when analyzing these systems is that in practice, we will typically not have exact information about the pulse shape. Therefore, there may be a mismatch between the parametric model that is assumed to derive and study the estimators versus the real model we find in practice.

Categories:
9 Views

The problem of row-sparse signal reconstruction for complex-valued data with outliers is investigated in this paper. First, we formulate the problem by taking advantage of a sparse weight matrix, which is used to down-weight the outliers. The formulated problem belongs to LASSO-type problems, and such problems can be efficiently solved via cyclic coordinate descent (CCD). We propose an extended CCD algorithm to solve the problem for complex-valued measurements, which requires careful characterization and derivation.

Categories:
34 Views

We study the inference of conditional independence graph from the partial Phase Locking Value (PLV) index of multivariate time series. A typical application is the inference of temporal functional connectivity from brain data. We extend the recently proposed time-varying graphical lasso to the measurement of partial locking values, yielding a sparse and temporally coherent dynamical graph that characterizes the evolution of the phase synchrony between each pair of signals.

Categories:
12 Views

This work considers blind methods for centrality estimation from graph signals. We model graph signals as the outcome of an unknown
low-pass graph filter excited with influences governed by a sparse sub-graph. This model is compatible with a number of data
generation process on graphs, including stock data and opinion dynamics. Based on the said graph signal model, we first prove that the

Categories:
7 Views

Pages