# KristynaPijackova/Radio-Modulation-Recognition-Networks

<https://repo.masters-masters.com/repos/KristynaPijackova/Radio-Modulation-Recognition-Networks>

[Accueil](https://repo.masters-masters.com/) › [KristynaPijackova](https://repo.masters-masters.com/owners/KristynaPijackova) › [Radio-Modulation-Recognition-Networks](https://repo.masters-masters.com/repos/KristynaPijackova/Radio-Modulation-Recognition-Networks)

KristynaPijackova/Radio-Modulation-Recognition-Networks, dépôt open source à 113 étoiles sur GitHub. Carte Prototype de RepoMasters. Langage : Jupyter Notebook.

| Champ | Valeur |
| --- | --- |
| Propriétaire | [KristynaPijackova](https://repo.masters-masters.com/owners/KristynaPijackova) |
| Étoiles | 113 |
| Forks | 25 |
| Langage | [Jupyter Notebook](https://repo.masters-masters.com/languages/jupyter-notebook) |
| Licence | MIT |
| Création | 2020 |
| Sujets | modulation-classification, modulation-recognition, radio-modulation-classification, deepsig, rf-signals, radio-modulation-recognition |
| Rang | [Prototype (100 à 249 ★)](https://repo.masters-masters.com/ranks/rush) |
| Voir sur GitHub | [KristynaPijackova/Radio-Modulation-Recognition-Networks](https://github.com/KristynaPijackova/Radio-Modulation-Recognition-Networks) |

## Fiche du dépôt

Radio modulation recognition with CNN, CLDNN, CGDNN and MCTransformer architectures. Best results were achieved with the CGDNN architecture, which has roughly 50,000 parameters, and the final model has a memory footprint of 636kB. More details can be found in my bachelor thesis linked in the readme…

Rang des dépôts qui ont 100 à 249 ★ sur GitHub. Probabilité publiée par carte tirée : 70 %. Rareté : Commune.

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Données : GitHub · Illustration dessinée par RepoMasters à partir de l’identifiant du dépôt et de la couleur de son langage.