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@@ -25,18 +25,17 @@ Thus, this research provides a [database](https://doi.org/10.5281/zenodo.8092621
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  ## Cite as
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  ```
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- @online{andres_j_florez_gonzalez_2023_8092850,
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- author = {Andres J. Florez-Gonzalez and
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- Carlos A. Viteri -Mera},
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- title = {{Fast Indoor Radio Propagation Prediction Using
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- Deep-Learning App}},
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- month = jun,
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- year = 2023,
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- publisher = {Zenodo},
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- version = {V1.0},
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- doi = {10.5281/zenodo.8092850},
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- url = {https://doi.org/10.5281/zenodo.8092850}
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- }
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  ```
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  ## Requirements
@@ -45,5 +44,4 @@ matplotlib==3.10.0
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  numpy==1.26.4
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  tensorflow==2.17.1
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  pillow==11.1.0
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- gradio=5.12.0
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- re==2.2.1
 
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  ## Cite as
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  ```
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+ @INPROCEEDINGS{10500989,
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+ author={Fl贸rez-Gonz谩lez, Andr茅s J. and Viteri-Mera, Carlos A. and Achicanoy-Mart铆nez, Wilson O.},
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+ booktitle={2024 18th European Conference on Antennas and Propagation (EuCAP)},
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+ title={Fast Indoor Radio Propagation Prediction using Deep Learning},
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+ year={2024},
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+ volume={},
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+ number={},
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+ pages={1-5},
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+ keywords={Deep learning;Indoor radio communication;Microprocessors;Wireless networks;Training data;Computer architecture;Software;Propagation;U-Net;radio map estimation;cell association estimation;WLAN},
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+ doi={10.23919/EuCAP60739.2024.10500989}
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+ }
 
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  ```
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  ## Requirements
 
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  numpy==1.26.4
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  tensorflow==2.17.1
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  pillow==11.1.0
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+ gradio=5.12.0