… We propose a radio fingerprinting approach based on deep learning CNN architecture to
train using I/Q sequence examples. Our design enables learning features embedded in the sig…

… Deep learning with a convolutional neural network (CNN) is gaining attention recently for its
… in a learning process with this technique, and feature extraction in advance of the learning …

… Deep learning has become an area of interest to the researchers in the past few years.
Convolutional Neural Network (CNN) is a deep learning … traditional machine learning approaches…

… Deep learning literature and software implementations use the terms convolution and cross-…
to the input, and the output of the convolution operation is higher when the input is similar to …

… of learning. Having multiple hidden layers stacked upon each-other is commonly called deep
learning. … This paper has outlined the basic concepts of Convolutional Neural Networks, …

… most representative neural networks in the field of deep learning. Computer vision based
on CNN has enabled people to accomplish what had been considered impossible in the past …

… learning methods, ie, artificial neural network (ANN), support vector machines (SVM) and
random forest (RF), and different deep-learning convolution neural … , deep-learning can …

… 1D convolutional neural network (CNN), a deep learning … The modelling paradigm includes
applying two convolutional … We demonstrate that the convolutional network is suitable for …

… employing deep neural networks, especially convolutional neural net… deep learning models
while at least 20% of papers at IEEE Conference on CVPR 2014 were related to deep neural …

… dozen or more convo lution layers, and deep learning refers to methodologies for training
these systems to automatically learn their functional pa rameters using data representative of a …