Flor Ortiz
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| Department | SigCom | ||||
| Adresse postale |
Université du Luxembourg 29, avenue JF Kennedy L-1855 Luxembourg |
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| Bureau sur le campus | JFK Building, E03-313 | ||||
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| Téléphone | (+352) 46 66 44 9968 | ||||
Flor G. Ortiz-Gomez received the B.S. degree in telecommunications engineering and the M.S. degree in electrical engineering-telecommunications from the Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico, in 2015 and 2016, respectively. Flor received her PhD degree in telecommunications engineering from the Universidad Politécnica de Madrid (UPM) in September 2021, Madrid, Spain. Her PhD thesis focused on the optimization of the design of very high throughput satellite systems, especially on the optimization of the cost per Gbps in orbit and on the management of radio resources for flexible payloads using different Machine Learning techniques, especially those based on Reinforcement Learning and Supervised Learning. During her PhD (in 2019), she performed a research period at the University of Bologna, in Bologna, Italy. And she has recently joined as a research associate at the interdisciplinary center for security, reliability and trust (SnT) at the University of Luxembourg. Her research interests focus on the implementation of innovative Machine Learning techniques for operations in satellite communication systems.
Last updated on: lundi 22 août 2022
2023
Onboard Processing in Satellite Communications Using AI Accelerators; ; ; ; ; ; ; ;
in Aerospace (2023), 10(2),
2022
Method of Rain Attenuation Prediction Based on Long–Short Term Memory Network; ; ; ; ;
in Neural Processing Letters (2022)
Radio Regulation Compliance of NGSO Constellations’ Interference towards GSO Ground Stations; ; ; ; ;
in IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, 12–15 September 2022, Virtual Conference (2022, September)
Enhanced Communications on Satellite-Based IoT Systems to Support Maritime Transportation Services; ; ;
in Sensors (2022), 22(17),
Towards the Application of Neuromorphic Computing to Satellite Communications; ; ; ; ; ; ; ;
in Towards the Application of Neuromorphic Computing to Satellite Communications (2022, October)
Unsupervised Learning for User Scheduling in Multibeam Precoded GEO Satellite Systems; ;
Scientific Conference (2022, June 09)
Machine Learning for Radio Resource Management in Multibeam GEO Satellite Systems; ; ; ; ; ; ;
in Electronics (2022), 11(7), 992
Cooperative Multi-Agent Deep Reinforcement Learning for Resource Management in Full Flexible VHTS Systems; ; ; ; ;
in IEEE Transactions on Cognitive Communications and Networking (2022), 8(1),
2021
Optimization in VHTS Satellite System Design with Irregular Beam Coverage for Non-Uniform Traffic Distribution; ; ;
in Remote Sensing (2021), 13(13),
Convolutional Neural Networks for Flexible Payload Management in VHTS Systems; ; ; ; ;
in IEEE Systems Journal (2021), 15(3), 4675-4686
Supervised machine learning for power and bandwidth management in very high throughput satellite systems; ; ; ; ;
in International Journal of Satellite Communications and Networking (2021)
2020
Laboratory Tests of ATSC M/H Commercial Receivers Performance on SFN Networks; ; ; ; ;
in IEEE Latin America Transactions (2020), 18(10),
Forward Link Optimization for the Design of VHTS Satellite Networks; ; ; ;
in Electronics (2020), 9(3),
