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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.
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Optimizing neural network architecture for EEG processing using evolutionary algorithms
Pijáčková, Kristýna ; Maršálek, Roman (oponent) ; Götthans, Tomáš (vedoucí práce)
This thesis deals with an optimization of neural network hyperparameters for EEG signal processing using evolutionary algorithms. The incorporation of evolutionary optimization can reduce reliance on human intuition and empirical knowledge when designing neural network and can thus make the process design more effective. In this work, a genetic algorithm was proposed that is suitable for hyperparameters optimization as well as neural architecture search. These methods were compared to a benchmark model designed by an engineer with expertise in iEEG processing. Data used in this work are classified into four categories and come from St. Anne's University Hospital (SAUH) and Mayo Clinic (MAYO) and were recorded on drug-resistant epileptic patients undergoing pre-surgical examination. The results of the neural architecture search method were comparable with the benchmark model. The hyperparameter optimization improved the F1 score over the original, empirically designed, model from 0.9076 to 0.9673 for the SAUH data and 0.9222 to 0.9400 for the Mayo Clinic data. The increased scores were mainly due to the increased accuracy of the classification of pathological events and noise, which may have further positive implications in applications of this model in seizure and noise detectors.
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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.
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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.
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