Zobrazeno 1 - 10
of 137
pro vyhledávání: '"Alessandro Chiuso"'
Autor:
Danilo Benozzo, Giorgia Baron, Ludovico Coletta, Alessandro Chiuso, Alessandro Gozzi, Alessandra Bertoldo
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract Exploring how the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the major goals of modern neuroscience. At the macroscale level, no one-to-one correspondence between structura
Externí odkaz:
https://doaj.org/article/669e9d81c8f54a68b7f0aff3473292ed
Autor:
Karan Kabbur Hanumanthappa Manjunatha, Giorgia Baron, Danilo Benozzo, Erica Silvestri, Maurizio Corbetta, Alessandro Chiuso, Alessandra Bertoldo, Samir Suweis, Michele Allegra
Publikováno v:
PLoS Computational Biology, Vol 20, Iss 1, p e1011274 (2024)
The network control theory framework holds great potential to inform neurostimulation experiments aimed at inducing desired activity states in the brain. However, the current applicability of the framework is limited by inappropriate modeling of brai
Externí odkaz:
https://doaj.org/article/f62169b5bdc74fa6922e1c82b42b7602
Publikováno v:
Sensors, Vol 23, Iss 1, p 262 (2022)
The paper reports a machine learning approach for estimating the phase in a distributed acoustic sensor implemented using optical frequency domain reflectometry, with enhanced robustness at the fading points. A neural network configuration was traine
Externí odkaz:
https://doaj.org/article/a161201e797c4961ae6e925ae2fd649f
Autor:
Giulia Prando, Mattia Zorzi, Alessandra Bertoldo, Maurizio Corbetta, Marco Zorzi, Alessandro Chiuso
Publikováno v:
NeuroImage, Vol 208, Iss , Pp 116367- (2020)
Contemporary neuroscience has embraced network science and dynamical systems to study the complex and self-organized structure of the human brain. Despite the developments in non-invasive neuroimaging techniques, a full understanding of the directed
Externí odkaz:
https://doaj.org/article/ec89a8224d004b1a83f1cb8d9916be7c
This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel
Autor:
Daniele Orsuti, Cristian Antonelli, Alessandro Chiuso, Marco Santagiustina, Antonio Mecozzi, Andrea Galtarossa, Luca Palmieri
Publikováno v:
Journal of Lightwave Technology. 41:578-592
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4fcb37ab90b1364056d4b4998eaa5032
https://doi.org/10.1109/jlt.2022.3219639
https://doi.org/10.1109/jlt.2022.3219639
Publikováno v:
Sensors; Volume 23; Issue 1; Pages: 262
The paper reports a machine learning approach for estimating the phase in a distributed acoustic sensor implemented using optical frequency domain reflectometry, with enhanced robustness at the fading points. A neural network configuration was traine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e02d73561558b17bdc4ba1762b93c15
http://cds.cern.ch/record/2848106
http://cds.cern.ch/record/2848106
The paper describes the use of machine learning to enhance the extraction of phase information from a distributed acoustic sensor based on optical frequency domain reflectometry. RMSE reduction by about 5 dB is reported.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2ffd9876b564b07d0a5dd86a786158f5
https://hdl.handle.net/11577/3471521
https://hdl.handle.net/11577/3471521
Publikováno v:
Regularized System Identification ISBN: 9783030958596
Adopting a quadratic loss, the performance of an estimator can be measured in terms of its mean squared error which decomposes into a variance and a bias component. This introductory chapter contains two linear regression examples which describe the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f70d35c3bc2d29bf6aeafbc40f73111f
https://doi.org/10.1007/978-3-030-95860-2_1
https://doi.org/10.1007/978-3-030-95860-2_1
Publikováno v:
Regularized System Identification ISBN: 9783030958596
In this chapter we review some basic ideas for nonlinear system identification. This is a complex area with a vast and rich literature. One reason for the richness is that very many parameterizations of the unknown system have been suggested, each wi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::42d7091cbe4aee5a9f7f293fa1c65b9b
https://doi.org/10.1007/978-3-030-95860-2_8
https://doi.org/10.1007/978-3-030-95860-2_8