Zobrazeno 1 - 10
of 72
pro vyhledávání: '"Chiara Ravazzi"'
Publikováno v:
IEEE Access, Vol 11, Pp 6018-6044 (2023)
This paper presents a review of the literature on network traffic prediction, while also serving as a tutorial to the topic. We examine works based on autoregressive moving average models, like ARMA, ARIMA and SARIMA, as well as works based on Artifi
Externí odkaz:
https://doaj.org/article/5681fc26643e440aa56f2f126366fa9f
Publikováno v:
IEEE Access, Vol 11, Pp 227-248 (2023)
Advanced large-scale environmental monitoring systems relying on the emerging aerial/terrestrial technologies of wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), and mobile crowdsensing, impose strong requirements on the reliability
Externí odkaz:
https://doaj.org/article/bb2f96251c1448a2962a45e8fdac39cf
Publikováno v:
PLoS ONE, Vol 15, Iss 9, p e0238481 (2020)
Inspired by the increasing attention of the scientific community towards the understanding of human relationships and actions in social sciences, in this paper we address the problem of inferring from voting data the hidden influence on individuals f
Externí odkaz:
https://doaj.org/article/051cff3466514183952c5d6d22a7d01c
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2018, Iss 1, Pp 1-18 (2018)
Abstract The aim of this paper is to develop strategies to estimate the sparsity degree of a signal from compressive projections, without the burden of recovery. We consider both the noise-free and the noisy settings, and we show how to extend the pr
Externí odkaz:
https://doaj.org/article/73c45a70c2cb4757b02d0ba143b3138e
Autor:
Chiara Ravazzi, Enrico Magli
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2018, Iss 1, Pp 1-26 (2018)
Abstract In this paper, we propose a new method for support detection and estimation of sparse and approximately sparse signals from compressed measurements. Using a double Laplace mixture model as the parametric representation of the signal coeffici
Externí odkaz:
https://doaj.org/article/6a66cd43fc3646afbcee0316c26d92fa
Publikováno v:
IEEE Access. 11:227-248
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0bd81cf8d546d86e0db925e15feb24a8
https://doi.org/10.1109/access.2022.3231909
https://doi.org/10.1109/access.2022.3231909
Publikováno v:
IEEE Transactions on Control of Network Systems. 9:1666-1678
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e20d85de3ff2e44a66b9bdbce3247ab4
https://doi.org/10.1109/tcns.2022.3164969
https://doi.org/10.1109/tcns.2022.3164969
Publikováno v:
Transportation Research Part A: Policy and Practice, 171:103651. Elsevier
For a real “green deal” to take place, it is important that technological achievements in the realm of green mobility solutions are paired with novel sustainable and energy efficient mobility models, smart enough to answer the multifaceted needs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d065cae917a0c9f9ced9ff3f83f1559d
https://doi.org/10.1016/j.tra.2023.103651
https://doi.org/10.1016/j.tra.2023.103651
Publikováno v:
IEEE Control Systems Letters. 6:2960-2965
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4935c86a40bed1d2dbdf40b1fe684455
https://doi.org/10.1109/lcsys.2022.3182291
https://doi.org/10.1109/lcsys.2022.3182291
Publikováno v:
IEEE control systems 41 (2021): 61–103. doi:10.1109/MCS.2021.3092810
info:cnr-pdr/source/autori:Ravazzi C.; Dabbene F.; Lagoa C.; Proskurnikov A.V./titolo:Learning Hidden Influences in Large-Scale Dynamical Social Networks: A Data-Driven Sparsity-Based Approach, in Memory of Roberto Tempo/doi:10.1109%2FMCS.2021.3092810/rivista:IEEE control systems/anno:2021/pagina_da:61/pagina_a:103/intervallo_pagine:61–103/volume:41
info:cnr-pdr/source/autori:Ravazzi C.; Dabbene F.; Lagoa C.; Proskurnikov A.V./titolo:Learning Hidden Influences in Large-Scale Dynamical Social Networks: A Data-Driven Sparsity-Based Approach, in Memory of Roberto Tempo/doi:10.1109%2FMCS.2021.3092810/rivista:IEEE control systems/anno:2021/pagina_da:61/pagina_a:103/intervallo_pagine:61–103/volume:41
The processes of information diffusion across social networks (for example, the spread of opinions and the formation of beliefs) are attracting substantial interest in disciplines ranging from behavioral sciences to mathematics and engineering (see "
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cbf99feb4d5a671c5435df4c33e59481
https://doi.org/10.1109/mcs.2021.3092810
https://doi.org/10.1109/mcs.2021.3092810