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RF transmitter authentication based on front-end impairments
 Youssefová, Kristina ; Slanina, Martin (oponent) ; Maršálek, Roman (vedoucí práce)
This work focuses on classifying Radio Frequency transmitters depending on their Radiofrequency imperfections by using a machine learning algorithm. The thesis is divided into two parts – theoretical and practical. The theoretical part can be divided into three branches. In the first branch, an overview of the RF transmitter with direct conversion is given. In the second one, Possible imperfections in the radio front-end are studied. In the third one, some supervised machine learning algorithms were explained. A detailed explanation of the support vector machine and neural network algorithms is given. The practical part deals with the implementation of support vector machines and neural networks in the MATLAB program and the evaluation of results.
Úplný záznam
RF transmitter authentication based on front-end impairments
 Youssefová, Kristina ; Slanina, Martin (oponent) ; Maršálek, Roman (vedoucí práce)
This work focuses on classifying Radio Frequency transmitters depending on their Radiofrequency imperfections by using a machine learning algorithm. The thesis is divided into two parts – theoretical and practical. The theoretical part can be divided into three branches. In the first branch, an overview of the RF transmitter with direct conversion is given. In the second one, Possible imperfections in the radio front-end are studied. In the third one, some supervised machine learning algorithms were explained. A detailed explanation of the support vector machine and neural network algorithms is given. The practical part deals with the implementation of support vector machines and neural networks in the MATLAB program and the evaluation of results.
Úplný záznam

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