Xiaoyan Wei

We proposed a new three-dimensional (3D) convolutional neural network (CNN) structure, whose inputs are multi-channel Electroencephalogram (EEG) data. Expand

This study describes the LRCN network for analyzing EEG data to predict epileptic seizures, thereby enabling the implementation of an early warning system that detects epileptic seizure before they occur in clinical applications. Expand

We propose a new kernel named Chebyshev kernel which determines a nonlinear mapping with orthogonal features. Expand

We develop a high performance, spatiotemporal statistical analysis system (Geostatistics-Hadoop) to support analyses of the First National Geographic Condition Data Acquisition database of China. Expand

A dynamic brain network for epilepsy patients when they were awake, asleep and in seizure, analyzing the changing topology indexes. Expand

We develop a novel transfer learning strategy to address problems of small or insufficient data. Expand

An efficient optimization approach for evaluating Top-k query on uncertain data based on dominant relationship analysis . Expand

We propose WPU-net that redesigns the architecture and weighted loss of U-Net that forces the network to integrate information from adjacent slices and pays more attention to the topology in boundary detection task. Expand

We present an approach to rapidly create microscopic images with pixel-level labels from material 3d simulated models, which simulate the growth of polycrystal. Expand