Jae-Woo Kim

TL;DR: A novel deep convolutional neural network (DCNN) is proposed for learning a classification model from a massive amount of modulated signals, in which the network architecture has several convolutionals specialized to simultaneously capture the temporal intra-Signal correlations and the spatial inter-signal relations.

TL;DR: In this paper, a deep learning-based robust automatic modulation classification (AMC) method is proposed for cognitive radio networks, where the input size is extended as $4 \times N$ size by copying IQ components and concatenating in reverse order to improve the classification accuracy.

TL;DR: In this paper, a population pharmacokinetic (PK) modeling of size and maturation functions has been proposed for pediatric patients using clinical data from three different clinical studies, and a nonlinear mixed effect modeling method was employed, and to explore PK differences in pediatric patients, size with allometric and MMM with Michaelis-Menten type functions were evaluated.

TL;DR: A novel convolutional neural network architecture for AMC with bottleneck and asymmetric convolution structure are employed in the proposed model, which can reduce the computational complexity.

TL;DR: In this paper, the authors presented a test bed demonstration of NeuroEdge computing for face recognition using a novel neuromorphic chip-NM500, which offers scalability and consistent recognition time, which is required by real-time networked systems.