Microwave Surveillance based on Ghost Imaging and Distributed Antennas

In this letter, we proposed a ghost imaging (GI) and distributed antennas based microwave surveillance scheme. By analyzing its imaging resolution and sampling requirement, the potential of employing microwave GI to achieve high-quality surveillance performance with low system complexity has been demonstrated. The theoretical analysis and effectiveness of the proposed microwave surveillance method are also validated via simulations.Comment: 4 pages, 11 figures, submitted for possible journal publicatio

FRNET: Flattened Residual Network for Infant MRI Skull Stripping

Skull stripping for brain MR images is a basic segmentation task. Although many methods have been proposed, most of them focused mainly on the adult MR images. Skull stripping for infant MR images is more challenging due to the small size and dynamic intensity changes of brain tissues during the early ages. In this paper, we propose a novel CNN based framework to robustly extract brain region from infant MR image without any human assistance. Specifically, we propose a simplified but more robust flattened residual network architecture (FRnet). We also introduce a new boundary loss function to highlight ambiguous and low contrast regions between brain and non-brain regions. To make the whole framework more robust to MR images with different imaging quality, we further introduce an artifact simulator for data augmentation. We have trained and tested our proposed framework on a large dataset (N=343), covering newborns to 48-month-olds, and obtained performance better than the state-of-the-art methods in all age groups.Comment: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI