PORTO @ Archivio Istituzionale della Ricerca

ROSA BRUSIN, ANN MARGARETH
 Distribuzione geografica
Salva come PNG Salva come JPEG
Continente #
NA - Nord America 498
EU - Europa 315
AS - Asia 25
SA - Sud America 4
AF - Africa 1
OC - Oceania 1
Totale 844
Salva come PNG Salva come JPEG
Nazione #
US - Stati Uniti d'America 490
IT - Italia 139
GB - Regno Unito 117
NL - Olanda 16
IE - Irlanda 14
IR - Iran 11
SE - Svezia 9
FR - Francia 7
CA - Canada 6
HK - Hong Kong 5
FI - Finlandia 4
DE - Germania 3
EC - Ecuador 3
UZ - Uzbekistan 3
CN - Cina 2
DK - Danimarca 2
JP - Giappone 2
MX - Messico 2
UA - Ucraina 2
AU - Australia 1
BR - Brasile 1
CZ - Repubblica Ceca 1
EG - Egitto 1
IN - India 1
NO - Norvegia 1
TR - Turchia 1
Totale 844
Salva come PNG Salva come JPEG
Città #
Ashburn 364
Southend 115
Torino 60
Turin 24
Chandler 14
Dublin 14
Huskvarna 8
Mashhad 7
Ann Arbor 6
Palo Alto 6
Toronto 6
Council Bluffs 5
Milan 5
Bruino 4
Central 4
Pars 4
Guayaquil 3
Houston 3
Leawood 3
Norwalk 3
Tashkent 3
Buffalo 2
Chicago 2
Helsinki 2
Naaldwijk 2
Pozzuolo Martesana 2
Querétaro City 2
University Park 2
Woodbridge 2
Akishima 1
Ancona 1
Andover 1
Antalya 1
Bengaluru 1
Caselle Torinese 1
Castelnuovo Scrivia 1
Cremona 1
Deventer 1
Düsseldorf 1
Fairfield 1
Fremont 1
Fucecchio 1
Fuzhou 1
Genoa 1
Hartford 1
Kongens Lyngby 1
Kvetnice 1
Lappeenranta 1
Magenta 1
Milpitas 1
New York 1
Nokia 1
Nuremberg 1
Orange 1
Padova 1
Reggio Nell'emilia 1
Rivoli 1
Rome 1
Saluzzo 1
San Donato Milanese 1
Savigliano 1
Shubra al Khaymah 1
Stavanger 1
Trofarello 1
Wuhan 1
Totale 713
Salva come PNG Salva come JPEG
Nome #
Machine Learning Applications to Optical Communication Systems, file e384c434-eb7c-d4b2-e053-9f05fe0a1d67 412
Enhanced resilience towards ROADM-induced optical filtering using subcarrier multiplexing and optimized bit and power loading, file e384c431-6b6e-d4b2-e053-9f05fe0a1d67 75
An ultra-fast method for gain and noise prediction of Raman amplifiers, file e384c431-a22c-d4b2-e053-9f05fe0a1d67 35
Load aware Raman gain profile prediction in dynamic multi-band optical networks, file e384c433-ab30-d4b2-e053-9f05fe0a1d67 26
Advancing classical and quantum communication systems with machine learning, file e384c433-b950-d4b2-e053-9f05fe0a1d67 26
Experimental demonstration of arbitrary Raman gain-profile designs using machine learning, file e384c433-bfb3-d4b2-e053-9f05fe0a1d67 25
Introducing Load Aware Neural Networks for Accurate Predictions of Raman Amplifiers, file e384c433-fb73-d4b2-e053-9f05fe0a1d67 23
Ultra-Long-Haul WDM Transmission in a Reduced InterModal Interference NANF Hollow-Core Fiber, file e384c434-329d-d4b2-e053-9f05fe0a1d67 23
Multi-Band Programmable Gain Raman Amplifier, file e384c433-eafa-d4b2-e053-9f05fe0a1d67 21
Experimental Characterization of Raman Amplifier Optimization through Inverse System Design, file e384c434-0114-d4b2-e053-9f05fe0a1d67 21
Generalization Properties of Machine Learning-based Raman Models, file e384c433-a647-d4b2-e053-9f05fe0a1d67 18
Optimization of a Hybrid EDFA-Raman C+L Band Amplifier through Neural-Network Models, file e384c433-a645-d4b2-e053-9f05fe0a1d67 16
Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning, file e384c433-e36f-d4b2-e053-9f05fe0a1d67 14
Machine Learning for Power Profiles Prediction in Presence of Inter-channel Stimulated Raman Scattering, file e384c434-0fbc-d4b2-e053-9f05fe0a1d67 14
Real Time Closed-Form Model for Nonlinearity Assessment of Fibre Optic Links with Lumped Loss, file e384c434-605e-d4b2-e053-9f05fe0a1d67 11
Inverse System Design Using Machine Learning: The Raman Amplifier Case, file e384c432-5336-d4b2-e053-9f05fe0a1d67 10
Machine Learning Applications to Optical Communication Systems, file e384c434-e265-d4b2-e053-9f05fe0a1d67 10
An ultra-fast method for gain and noise prediction of Raman amplifiers, file e384c431-c78d-d4b2-e053-9f05fe0a1d67 8
Inverse System Design Using Machine Learning: The Raman Amplifier Case, file e384c432-60fc-d4b2-e053-9f05fe0a1d67 7
Machine learning applied to inverse systems design, file e384c434-ca46-d4b2-e053-9f05fe0a1d67 6
Optimization of Raman amplifiers using machine learning, file 13fec304-e5fe-4805-8ced-996dcf20c1e8 4
Machine Learning for Power Profiles Prediction in Presence of Inter-channel Stimulated Raman Scattering, file e384c434-4e88-d4b2-e053-9f05fe0a1d67 4
Optimization of Raman amplifiers using machine learning, file 635ca345-893f-4073-a768-01f2b7e3a99d 3
Machine learning enabled Raman amplifiers, file adec78d8-e51e-4717-a321-6814d2dc8e48 3
Inverse System Design Using Machine Learning: The Raman Amplifier Case, file e384c432-3de0-d4b2-e053-9f05fe0a1d67 3
Load aware Raman gain profile prediction in dynamic multi-band optical networks, file e384c433-b94f-d4b2-e053-9f05fe0a1d67 3
Ultra-Long-Haul WDM Transmission in a Reduced InterModal Interference NANF Hollow-Core Fiber, file e384c433-d6b6-d4b2-e053-9f05fe0a1d67 3
Introducing Load Aware Neural Networks for Accurate Predictions of Raman Amplifiers, file e384c433-f4c4-d4b2-e053-9f05fe0a1d67 3
Fiber-Agnostic Machine Learning-Based Raman Amplifier Models, file 20cf4341-c029-4768-9546-6fb6a887050d 2
Machine learning enabled Raman amplifiers, file 57388c9b-2b0b-461c-93a8-860377e89221 2
Multi-band programmable gain Raman amplifier for high-capacity optical networks, file 89ba5ca4-71e9-4916-aee8-5e6266add403 2
Experimental Characterization of Raman Amplifier Optimization through Inverse System Design, file e384c433-ab2f-d4b2-e053-9f05fe0a1d67 2
Advancing classical and quantum communication systems with machine learning, file e384c433-b951-d4b2-e053-9f05fe0a1d67 2
Experimental demonstration of arbitrary Raman gain-profile designs using machine learning, file e384c433-bfb4-d4b2-e053-9f05fe0a1d67 2
Multi-Band Programmable Gain Raman Amplifier, file e384c433-d034-d4b2-e053-9f05fe0a1d67 2
Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning, file e384c434-0113-d4b2-e053-9f05fe0a1d67 2
Multi-band programmable gain Raman amplifier for high-capacity optical networks, file e3c4f3e9-367d-403f-b91a-ef44c5185ca9 2
Maximizing 425G SWDM VCSEL-MMF Systems Reach Through Variable Rate per λ, file 12f31e1c-a5e4-40a8-9691-360821895d1e 1
ML-Based Spectral Power Profiles Prediction in Presence of ISRS for Ultra-Wideband Transmission, file 223a9157-8090-49d7-ba77-6fb6efc00742 1
Fiber-Agnostic Machine Learning-Based Raman Amplifier Models, file 602e6d1e-fbd8-4b47-a5ab-e9a5fdc52707 1
ML-Based Spectral Power Profiles Prediction in Presence of ISRS for Ultra-Wideband Transmission, file 89058b28-011b-4e7f-a26e-a51068f959d6 1
Optimization of a Hybrid EDFA-Raman C+L Band Amplifier through Neural-Network Models, file e384c433-a646-d4b2-e053-9f05fe0a1d67 1
Generalization Properties of Machine Learning-based Raman Models, file e384c433-a648-d4b2-e053-9f05fe0a1d67 1
Real Time Closed-Form Model for Nonlinearity Assessment of Fibre Optic Links with Lumped Loss, file e384c434-3bcd-d4b2-e053-9f05fe0a1d67 1
Machine learning applied to inverse systems design, file e384c434-e1b0-d4b2-e053-9f05fe0a1d67 1
Maximizing 425G SWDM VCSEL-MMF Systems Reach Through Variable Rate per λ, file f3576fa7-463f-4c55-b873-53a3777afe32 1
Totale 854
Salva come PNG Salva come JPEG
Categoria #
all - tutte 1.797
article - articoli 417
book - libri 0
conference - conferenze 552
curatela - curatele 0
other - altro 0
patent - brevetti 0
selected - selezionate 0
volume - volumi 0
Totale 2.766
Salva come PNG Salva come JPEG


Totale Lug Ago Sett Ott Nov Dic Gen Feb Mar Apr Mag Giu
2019/202014 0 0 0 0 0 1 3 8 2 0 0 0
2020/202176 1 3 2 5 1 1 0 1 4 5 43 10
2021/2022264 14 11 15 13 15 9 15 28 20 32 55 37
2022/2023101 8 4 6 8 6 29 9 8 16 2 5 0
2023/2024399 0 0 0 0 0 2 382 4 2 2 7 0
Totale 854