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Národní úložiště šedé literatury Nalezeno 3 záznamů.  Hledání trvalo 0.00 vteřin. 
Radio Modulation Recognition Networks
 Pijáčková, Kristýna ; Maršálek, Roman (oponent) ; Götthans, Tomáš (vedoucí práce)
The bachelor thesis is focused on radio modulation classification with a deep learning approach. There are four deep learning architectures presented in the thesis. Three of them use convolutional and recurrent neural networks, and the fourth uses a transformer architecture. The final number of parameters of each model was considered during the design phase, as it can have a big impact on a memory footprint of a deployed model. The architectures were written in Keras, which is a software library, which provides a Python interface for neural networks. The results of the architectures were additionally compared to results from other research papers on this topic.
Úplný záznam
Evaluation Of Cnn And Cldnn Architectures On Radio Modulation Datasets
 Pijáčková, Kristýna
This paper presents an evaluation of deep learning architectures designed for modulationrecognition. The evaluation inspects, whether the architectures behave in the same way as they didon the dataset they were designed on. The architectures are trained and tested on two different radiomodulation datasets. This results in proposing additional binary classification as a method to reducemisclassification of QAM modulation types in one of the datasets.
Úplný záznam
Radio Modulation Recognition Networks
 Pijáčková, Kristýna ; Maršálek, Roman (oponent) ; Götthans, Tomáš (vedoucí práce)
The bachelor thesis is focused on radio modulation classification with a deep learning approach. There are four deep learning architectures presented in the thesis. Three of them use convolutional and recurrent neural networks, and the fourth uses a transformer architecture. The final number of parameters of each model was considered during the design phase, as it can have a big impact on a memory footprint of a deployed model. The architectures were written in Keras, which is a software library, which provides a Python interface for neural networks. The results of the architectures were additionally compared to results from other research papers on this topic.
Úplný záznam

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