Yin Zhang

TL;DR: A novel data-depended Doppler centroid estimation method that can significantly provide reliable estimation accuracy under low echo signal to noise ratio, independent of conditions that strictly required by conventional methods is proposed.

TL;DR: In order to detect cerebral microbleed more efficiently, a novel computer-aided detection method based on susceptibility-weighted imaging based on sparse autoencoder was developed and a deep neural network was established using the learned features.

TL;DR: The results of the comparative experiments show that the outpatient quantity prediction is not a simple time-series problem, and it contains a variety of nonlinear influencing factors, so the multi-dimensional prediction model including air quality feature is better than others, and the experimental results prove that air quality indicators play an important role in the prediction of respiratory clinic outpatient visits.

TL;DR: Two Integer Linear Programming models are proposed to solve the fog planning issue under the integrated Cloud-Fog (iCloudFog) framework and show that efficiently planned fogs can help to reduce the planning overhead while satisfying diverse IoT task requirements.

TL;DR: A novel forward-looking signal model is established as the product of the measurement matrix and the cross-range target distribution, which is more accurate than the conventional convolution model, and the widely-used sparse regularization is considered as the penalty term to recover the target distribution.