Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

Alberto Martín-Martín, Rubén Padial-Allué, Encarnación Castillo, Luis Parrilla, Ignacio Parellada-Serrano, Alejandro Morán, Antonio García

Open source

DOI
10.3390/s24030899
Published
2024-01-30
Container
Sensors
Publisher
MDPI AG
Open access
unknown

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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

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