57 citations · 225 across the 22 of their papers we have counts for
23 papers
Model Proficiency in Centralized Multi-Agent Systems: A Performance Study
Anna Guerra, Francesco Guidi, Pau Closas +2
Autonomous agents are increasingly deployed in dynamic environments where their ability to perform a given task depends on both individual and team-level proficiency. While profici…
Continuously Optimizing Radar Placement with Model Predictive Path Integrals
Michael Potter, Shuo Tang, Paul Ghanem +8
Continuously optimizing sensor placement is essential for precise target localization in various military and civilian applications. While information theory has shown promise in o…
Robust Interference Mitigation techniques for Direct Position Estimation
Haoqing Li, Shuo Tang, Peng Wu +1
Global Navigation Satellite System (GNSS) is pervasive in navigation and positioning applications, where precise position and time referencing estimations are required. Conventiona…
Bayesian and Neural Inference on LSTM-based Object Recognition from Tactile and Kinesthetic Information
Francisco Pastor, Jorge García-González, Juan M. Gandarias +4
Recent advances in the field of intelligent robotic manipulation pursue providing robotic hands with touch sensitivity. Haptic perception encompasses the sensing modalities encount…
Jammer classification with Federated Learning
Peng Wu, Helena Calatrava, Tales Imbiriba +1
Jamming signals can jeopardize the operation of GNSS receivers until denying its operation. Given their ubiquity, jamming mitigation and localization techniques are of crucial impo…
NEUROPULS: NEUROmorphic energy-efficient secure accelerators based on Phase change materials aUgmented siLicon photonicS
Fabio Pavanello, Cedric Marchand, Ian O'Connor +34
This special session paper introduces the Horizon Europe NEUROPULS project, which targets the development of secure and energy-efficient RISC-V interfaced neuromorphic accelerators…
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks
Ricardo Augusto Borsoi, Tales Imbiriba, Pau Closas
Multitemporal hyperspectral unmixing (MTHU) is a fundamental tool in the analysis of hyperspectral image sequences. It reveals the dynamical evolution of the materials (endmembers)…
On Parametric Misspecified Bayesian Cramér-Rao bound: An application to linear Gaussian systems
Shuo Tang, Gerald LaMountain, Tales Imbiriba +1
A lower bound is an important tool for predicting the performance that an estimator can achieve under a particular statistical model. Bayesian bounds are a kind of such bounds whic…
Online Fusion of Multi-resolution Multispectral Images with Weakly Supervised Temporal Dynamics
Haoqing Li, Bhavya Duvvuri, Ricardo Borsoi +4
Real-time satellite imaging has a central role in monitoring, detecting and estimating the intensity of key natural phenomena such as floods, earthquakes, etc. One important constr…
Recursive classification of satellite imaging time-series: An application to land cover mapping
Helena Calatrava, Bhavya Duvvuri, Haoqing Li +5
Despite the extensive body of literature focused on remote sensing applications for land cover mapping and the availability of high-resolution satellite imagery, methods for contin…
Jamming Source Localization Using Augmented Physics-Based Model
Andrea Nardin, Tales Imbiriba, Pau Closas
Monitoring interferences to satellite-based navigation systems is of paramount importance in order to reliably operate critical infrastructures, navigation systems, and a variety o…
Bayesian data fusion with shared priors
Peng Wu, Tales Imbiriba, Victor Elvira +1
The integration of data and knowledge from several sources is known as data fusion. When data is only available in a distributed fashion or when different sensors are used to infer…
Sample generation for the spin-fermion model using neural networks
Georgios Stratis, Phillip Weinberg, Tales Imbiriba +2
Quantum Monte-Carlo simulations of hybrid quantum-classical models such as the double exchange Hamiltonian require calculating the density of states of the quantum degrees of freed…
Neural Network-based OFDM Receiver for Resource Constrained IoT Devices
Nasim Soltani, Hai Cheng, Mauro Belgiovine +10
Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are used for communication links in many current and emerging Internet of Things (IoT) applications, including the…
Online multi-resolution fusion of space-borne multispectral images
Haoqing Li, Bhavia Duvviri, Ricardo Borsoi +4
Satellite imaging has a central role in monitoring, detecting and estimating the intensity of key natural phenomena. One important feature of satellite images is the trade-off betw…
Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering
Tales Imbiriba, Ahmet Demirkaya, Jindřich Duník +3
In this paper we present a hybrid neural network augmented physics-based modeling (APBM) framework for Bayesian nonlinear latent space estimation. The proposed APBM strategy allows…
Personalized Federated Learning over non-IID Data for Indoor Localization
Peng Wu, Tales Imbiriba, Junha Park +2
Localization and tracking of objects using data-driven methods is a popular topic due to the complexity in characterizing the physics of wireless channel propagation models. In the…
SemperFi: A Spoofer Eliminating GPS Receiver for UAVs
Harshad Sathaye, Gerald LaMountain, Pau Closas +1
It is well-known that GPS is vulnerable to signal spoofing attacks. Although several spoofing detection techniques exist, they are incapable of mitigation and recovery from stealth…
Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing
Haoqing Li, Ricardo Augusto Borsoi, Tales Imbiriba +3
Autoencoder (AEC) networks have recently emerged as a promising approach to perform unsupervised hyperspectral unmixing (HU) by associating the latent representations with the abun…
Importance Gaussian Quadrature
Víctor Elvira, Luca Martino, Pau Closas
Importance sampling (IS) and numerical integration methods are usually employed for approximating moments of complicated target distributions. In its basic procedure, the IS method…
Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing
Ricardo Augusto Borsoi, Tales Imbiriba, Pau Closas +2
The recent evolution of hyperspectral imaging technology and the proliferation of new emerging applications presses for the processing of multiple temporal hyperspectral images. In…
Mean Square Error bounds for parameter estimation under model misspecification
Adrià Gusi-Amigó, Pau Closas, Luc Vandendorpe
In parameter estimation, assumptions about the model are typically considered which allow us to build optimal estimation methods under many statistical senses. However, it is usual…
Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density
Pau Closas, Antoni Guillamon
This paper deals with the problem of inferring the signals and parameters that cause neural activity to occur. The ultimate challenge being to unveil brain's connectivity, here we…