##VGG19 model for Keras
This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
Authors: Nicolas Clement, Katsuhiko Nishiguchi, Akira Fujiwara & Dominique Vuillaume
Low frequency noise gives additional informations to DC current in nanodevices such as estimation of number/position of defects in nanotransistors [1-14] and inelastic resonant energy levels in molecular devices [15-16]. It provides also the charge sensitivity limit [1,5] for sensors and memories. However, low frequency noise measurements remain marginal for device electrical characterization which is probably due to additional efforts required for non specialists to measure and analyse such signals. This is particularly true for nanodevices where current level is low and noise more difficult to measure. Here, we detail a protocol including experimental setup optimization, developed automation softwares and data analysis. An example of low frequency noise study of a silicon nanowire transistor is shown.