Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces
- DOI
- 10.3390/s24030899
- Published
- 2024-01-30
- Container
- Sensors
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/s24030899,
title = {Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces},
author = {Alberto Martín-Martín and Rubén Padial-Allué and Encarnación Castillo and Luis Parrilla and Ignacio Parellada-Serrano and Alejandro Morán and Antonio García},
year = {2024},
journal = {Sensors},
doi = {10.3390/s24030899},
url = {https://doi.org/10.3390/s24030899}
}RIS
TY - JOUR TI - Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces AU - Alberto Martín-Martín AU - Rubén Padial-Allué AU - Encarnación Castillo AU - Luis Parrilla AU - Ignacio Parellada-Serrano AU - Alejandro Morán AU - Antonio García PY - 2024 JO - Sensors DO - 10.3390/s24030899 UR - https://doi.org/10.3390/s24030899 ER -
APA
Martín-Martín, A., Padial-Allué, R., Castillo, E., Parrilla, L., Parellada-Serrano, I., Morán, A., & García, A. (2024). Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces. Sensors. https://doi.org/10.3390/s24030899
Source records
- crossref · retrieved 2026-09-25T06:06:36.506Z