Searched for: author%3A%22Coutino%2C+Mario%22
(1 - 20 of 30)

Pages

List view
Grid view
document
Kokke, C.A. (author), Coutino, Mario (author), Anitori, Laura (author), Heusdens, R. (author), Leus, G.J.T. (author)
Sensor selection is a useful method to help reduce data throughput, as well as computational, power, and hardware requirements, while still maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown source models. In this work, we...
conference paper 2023
document
Kokke, C.A. (author), Coutiño, Mario (author), Heusdens, R. (author), Leus, G.J.T. (author), Anitori, Laura (author)
Integrated sidelobe level is a useful measure to quantify robustness of a waveform-filter pair to unknown range clutter and multiple closely located targets. Sidelobe suppression on receive will incur a loss in the signal to noise ratio after pulse compression. We derive a pulse compression filter that has the greatest integrated sidelobe...
conference paper 2023
document
Focante, E. (author), Martin, Lilian De (author), Coutino, Mario (author)
In recent years, convolutional neural networks (CNNs) have been increasingly used for classifying radar micro-Doppler signatures of various targets. However, obtaining large amounts of data for efficient CNN training in defence and surveillance scenarios can be challenging. Therefore, designing techniques that maximize the use of available...
conference paper 2023
document
Kokke, C.A. (author), Coutino, Mario (author), Heusdens, R. (author), Leus, G.J.T. (author)
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 target directions. We propose to tackle the sensor selection problem...
conference paper 2023
document
He, Y. (author), Coutino, Mario (author), Isufi, E. (author), Leus, G.J.T. (author)
In this work, we focus on partitioning dynamic graphs with two types of nodes (bi-colored), though not necessarily bipartite graphs. They commonly appear in communication network applications, e.g., one color being base stations, the other users, and the dynamic process being the varying connection status between base stations and moving...
conference paper 2022
document
Kokke, C.A. (author), Coutino, Mario (author), Heusdens, R. (author), Leus, G.J.T. (author), Anitori, L. (author)
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 of bursts, brings potential performance benefits. Hence, with...
conference paper 2022
document
Leus, G.J.T. (author), Yang, Maosheng (author), Coutino, Mario (author), Isufi, E. (author)
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 structures. Graph Volterra models, on the other hand, are also...
conference paper 2021
document
Natali, A. (author), Coutino, Mario (author), Isufi, E. (author), Leus, G.J.T. (author)
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. Topology identification is a challenging task, as the problem is often...
conference paper 2021
document
Zhang, Kaiwen (author), Coutino, Mario (author), Isufi, E. (author)
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 results even with large samples. In this paper, we propose an...
conference paper 2021
document
Natali, A. (author), Isufi, E. (author), Coutino, Mario (author), Leus, G.J.T. (author)
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 paper, we focus on a time-varying version of the structural...
conference paper 2021
document
Coutino, Mario (author), Isufi, E. (author), Maehara, T. (author), Leus, G.J.T. (author)
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 problem. This approach provides a unified view of the traditional...
conference paper 2020
document
Li, Qiongxiu (author), Coutino, Mario (author), Leus, G.J.T. (author), Christensen, M. Graesboll (author)
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 for processing sensitive data. As a step towards a solution, we...
conference paper 2020
document
Coutino, Mario (author), Karanikolas, Georgios V (author), Leus, G.J.T. (author), Giannakis, Georgios B. (author)
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 fully understood. Several approaches have been advocated to predict...
conference paper 2020
document
Kazaz, T. (author), Coutino, Mario (author), Janssen, G.J.M. (author), van der Veen, A.J. (author)
In this paper, we focus on the problem of blind joint calibration of multiband transceivers and time-delay (TD) estimation of multipath channels. We show that this problem can be formulated as a particular case of covariance matching. Although this problem is severely ill-posed, prior information about radio-frequency chain distortions and...
conference paper 2020
document
Yang, Qiuling (author), Coutino, Mario (author), Wang, Gang (author), Giannakis, Georgios B. (author), Leus, G.J.T. (author)
To perform any meaningful optimization task, distribution grid operators need to know the topology of their grids. Although power grid topology identification and verification has been recently studied, discovering instantaneous interplay among subsets of buses, also known as higher-order interactions in recent literature, has not yet been...
conference paper 2020
document
Yang, M. (author), Coutino, Mario (author), Isufi, E. (author), Leus, G.J.T. (author)
While regularization on graphs has been successful for signal reconstruction, strategies for controlling the bias-variance trade-off of such methods have not been completely explored. In this work, we put forth a node varying regularizer for graph signal reconstruction and develop a minmax approach to design the vector of regularization...
conference paper 2020
document
van der Meulen, P.Q. (author), Coutino, Mario (author), Kruizinga, P. (author), Bosch, J.G. (author), Leus, G.J.T. (author)
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 gives an exact solution if the matrix representing the aberration...
conference paper 2020
document
Natali, A. (author), Coutino, Mario (author), Leus, G.J.T. (author)
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 the observed networked data using graph filters. Therefore, in...
conference paper 2020
document
Kazaz, T. (author), Coutino, Mario (author), Janssen, G.J.M. (author), Leus, G.J.T. (author), van der Veen, A.J. (author)
Synchronization and ranging in internet of things (IoT) networks are challenging due to the narrowband nature of signals used for communication between IoT nodes. Recently, several estimators for range estimation using phase difference of arrival (PDoA) measurements of narrowband signals have been proposed. However, these estimators are based...
conference paper 2019
document
Coutino, Mario (author), Isufi, E. (author), Maehara, Takanori (author), Leus, G.J.T. (author)
In this work, we explore the limits of finite-time distributed consensus through the intersection of graph filters and matrix function theory. We focus on algorithms capable to compute the consensus exactly through filtering operations over a graph, and that have been proven to converge in finite time. In this context, we show that there...
conference paper 2019
Searched for: author%3A%22Coutino%2C+Mario%22
(1 - 20 of 30)

Pages

List view
Grid view