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Uiara Moura
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2020 – today
- 2022
- [c14]Ali Cem, Siqi Yan, Uiara Celine de Moura, Yunhong Ding, Darko Zibar, Francesco Da Ros:
Comparison of Models for Training Optical Matrix Multipliers in Neuromorphic PICs. OFC 2022: 1-3 - [c13]Uiara C. de Moura
, Francesco Da Ros
Machine learning applied to inverse systems design. ONDM 2022: 1-3 - [c12]Uiara C. de Moura
Online versus Offline Optimization Methods for Raman Amplifier Optimization. OECC/PSC 2022: 1-4 - [i4]Metodi Plamenov Yankov, Francesco Da Ros, Uiara Celine de Moura, Andrea Carena, Darko Zibar:
Flexible Raman Amplifier Optimization Based on Machine Learning-aided Physical Stimulated Raman Scattering Model. CoRR abs/2206.07650 (2022) - 2021
- [c11]Uiara C. de Moura, Darko Zibar, A. Margareth Rosa Brusin, Andrea Carena, Francesco Da Ros:
Generalization Properties of Machine Learning-based Raman Models. OFC 2021: 1-3 - [c10]Francesco Da Ros, Uiara C. de Moura, Ruben S. Luis, Georg Rademacher, Benjamin J. Puttnam, Ann Margareth Rosa Brusin, Andrea Carena, Yoshinari Awaji, Hideaki Furukawa, Darko Zibar:
Optimization of a Hybrid EDFA-Raman C+L Band Amplifier through Neural-Network Models. OFC 2021: 1-3 - [i3]Ali Cem, Siqi Yan, Uiara Celine de Moura, Yunhong Ding, Darko Zibar, Francesco Da Ros:
Comparison of Models for Training Optical Matrix Multipliers in Neuromorphic PICs. CoRR abs/2111.14787 (2021) - 2020
- [c9]Francesco Da Ros
Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices. ECOC 2020: 1-4 - [c8]Metodi P. Yankov, Uiara Celine de Moura
Power Evolution Prediction and Optimization in a Multi-span System Based on Component-wise System Modeling. ECOC 2020: 1-4 - [c7]A. Margareth Rosa Brusin, Uiara C. de Moura, Andrea D'Amico, Vittorio Curri, Darko Zibar, Andrea Carena:
Load Aware Raman Gain Profile Prediction in Dynamic Multi-Band Optical Networks. OFC 2020: 1-3 - [c6]Uiara C. de Moura, Francesco Da Ros, A. Margareth Rosa Brusin, Andrea Carena, Darko Zibar:
Experimental Demonstration of Arbitrary Raman Gain-Profile Designs using Machine Learning. OFC 2020: 1-3 - [c5]Darko Zibar, Uiara C. de Moura, Hou-Man Chin, A. Margareth Rosa Brusin, Nitin Jain, Francesco Da Ros, Sebastian Kleis, Christian G. Schaeffer, Tobias Gehring, Ulrik L. Andersen, Andrea Carena:
Advancing Classical and Quantum Communication Systems with Machine Learning. OFC 2020: 1-3 - [i2]Francesco Da Ros, Uiara Celine de Moura, Metodi P. Yankov:
Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices. CoRR abs/2009.05326 (2020) - [i1]Metodi P. Yankov, Uiara Celine de Moura, Francesco Da Ros:
Power Evolution Prediction and Optimization in a Multi-span System Based on Component-wise System Modeling. CoRR abs/2009.05348 (2020)
2010 – 2019
- 2017
- [j1]Erick de A. Barboza
Local and global approaches for the adaptive control of a cascade of amplifiers. Photonic Netw. Commun. 33(2): 194-207 (2017) - 2016
- [c4]Miquel Garrich
Pioneering hardware modeling and software design for optical infrastructure in the Autonomous Network project. ONDM 2016: 1-6 - 2015
- [c3]Uiara Moura
SDN-enabled EDFA gain adjustment cognitive methodology for dynamic optical networks. ECOC 2015: 1-3 - [c2]Yue Fei, Andrea Fumagalli, Miquel Garrich
Estimating EDFA output power with an efficient numerical modeling framework. ICC 2015: 5222-5227 - 2013
- [c1]Juliano Oliveira, Antonio Caballero, Eduardo Magalhães, Uiara Moura, Robert Borkowski, Giovanni Curiel, Alberto T. Hirata, Luis Henrique Hecker de Carvalho, Edson Porto da Silva, Darko Zibar, Jose Maranhao, Idelfonso Tafur Monroy, Julio C. R. F. Oliveira:
Demonstration of EDFA cognitive gain control via GMPLS for mixed modulation formats in heterogeneous optical networks. OFC/NFOEC 2013: 1-3
Coauthor Index
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