Object Classification and Segmentation Based on Deep Learning Using Underwater Mapping Data

This paper presents a fast and accurate classification method for underwater objects using underwater mapping data obtained by a small Autonomous Underwater Vehicle (AUV) and autonomous surface vehicle (ASV). For the mapping data, in addition to underwater acoustic reflection intensity images, water depth data, point cloud data and backscattering reflection intensity data are employed. We propose the automatic classification and semantic segmentation method on deep learning using a convolutional neural network (CNN) and PointNet++. In order to verify the effectiveness of the present method, we applied it to the measured several underwater mapping data

ZnSTe coherently grown onto GaP substrates by molecular beam epitaxy using ZnS buffer layers

ZnS1-xTex epitaxial layers with x ~ 0.06, nearly lattice-matched to GaP substrates, have been grown by molecular beam epitaxy. Direct growth of the layers onto the substrates results in poor crystal quality, showing no sign of coherent growth. This seems to be due to alloy composition deviation at the initial stage of the growth. To avoid the problem, a thin coherent ZnS buffer layer has been inserted at the ZnSTe/GaP interface. With the buffer layers, coherent growth of ZnSTe layers is achieved and the crystal quality has been improved