MC

M.A. Coutino Minquez

46 records found

Revisiting matching pursuit

Beyond approximate submodularity

We study the problem of selecting a subset of vectors from a large set to obtain the best signal representation over a family of functions. Although greedy methods have been widely used to tackle this problem and many of those have been analyzed under the lens of (weak) submodula ...
The main focus of this paper is an active sensing application that involves selecting transmit and receive sensors to optimize the Cramér-Rao bound (CRB) on target parameters. Although the CRB is non-convex in the transmit and receive selection, we demonstrate that it is convex i ...
We consider the scenario of finding the transfer function of an aberrating layer in front of a receiving ultrasound (US) array, assuming a separate non-aberrated transmit source. We propose a method for blindly estimating this transfer function without exact knowledge of the ultr ...
Sensor selection is a useful method to help reduce computational, hardware, and power requirements while maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown t ...
One of the main challenges of graph filters is the stability of their design. While classical graph filters allow for a stable design using optimal polynomial approximation theory, generalized graph filters tend to suffer from the ill-conditioning of the involved system matrix. T ...
Doppler velocity estimation in pulse-Doppler radar is done by evaluating the target returns of bursts of pulses. While this provides convenience and accuracy, it requires multiple pulses. In adaptive and cognitive radar systems, the ability to adapt on consecutive pulses, instead ...
This work proposes an algorithmic framework to learn time-varying graphs from online data. The generality offered by the framework renders it model-independent, i.e., it can be theoretically analyzed in its abstract formulation and then instantiated under a variety of model-depen ...
Signal processing and machine learning algorithms for data sup-ported over graphs, require the knowledge of the graph topology. Unless this information is given by the physics of the problem (e.g., water supply networks, power grids), the topology has to be learned from data. Top ...
Graph sampling strategies require the signal to be relatively sparse in an alternative domain, e.g. bandlimitedness for reconstructing the signal. When such a condition is violated or its approximation demands a large bandwidth, the reconstruction often comes with unsatisfactory ...
A critical task in graph signal processing is to estimate the true signal from noisy observations over a subset of nodes, also known as the reconstruction problem. In this paper, we propose a node-adaptive regularization for graph signal reconstruction, which surmounts the conven ...

Advances in graph signal processing

Graph filtering and network identification

To the surprise of most of us, complexity in nature spawns from simplicity. No matter how simple a basic unit is, when many of them work together, the interactions among these units lead to complexity. This complexity is present in the spreading of diseases, where slightly differ ...
With the well-documented popularity of Frank Wolfe (FW) algorithms in machine learning tasks, the present paper establishes links between FW subproblems and the notion of momentum emerging in accelerated gradient methods (AGMs). On the one hand, these links reveal why momentum is ...
To deal with high-dimensional data, graph filters have shown their power in both graph signal processing and data science. However, graph filters process signals exploiting only pairwise interactions between the nodes, and they are not able to exploit more complicated topological ...
Topology identification is an important problem across many disciplines, since it reveals pairwise interactions among entities and can be used to interpret graph data. In many scenarios, however, this (unknown) topology is time-varying, rendering the problem even harder. In this ...
With an increasingly interconnected and digitized world, distributed signal processing and graph signal processing have been proposed to process its big amount of data. However, privacy has become one of the biggest challenges holding back the widespread adoption of these tools f ...
In this work, we explore the state-space formulation of network processes to recover the underlying network structure (local connections). To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology inference prob ...
The design of feasible trajectories to traverse the k-space for sampling in magnetic resonance imaging (MRI) is important while considering ways to reduce the scan time. Over the recent years, non-Cartesian trajectories have been observed to result in benign artifacts and being l ...
Data defined over a network have been successfully modelled by means of graph filters. However, although in many scenarios the connectivity of the network is known, e.g., smart grids, social networks, etc., the lack of well-defined interaction weights hinders the ability to model ...
Link prediction is one of the core problems in network and data science with widespread applications. While predicting pairwise nodal interactions (links) in network data has been investigated extensively, predicting higher-order interactions (higher-order links) is still not ful ...
We consider the scenario of finding the transfer function of an aberrating layer in front of an ultrasound array. We are interested in blindly estimating this transfer function without prior knowledge of the unknown ultrasound sources or ultrasound contrast image. The algorithm g ...