A high precision profilometer based on vertical scanning microscopic interferometry

A profilometer used for 3 dimension measurement of micro-surface topography is presented. The instrument is based on the vertical scanning microscopic interferometry （VSMI）. A Linnik type interference microscope is used and the interferograms which present changes of surface profile are recorded with a CCD camera. A developed nano-positioning work stage with an integrated optical grating displacement measuring system realizes the precise vertical scanning motion during profile measurement. By a white-light phase shifting algorithm of arbitrary step, frames of interferograms are processed by a computer to rebuild and evaluate the measured profile. Because of the specialty of VSMI, the profilometer is suitable for both smooth and rough surface measurement. It can also be used to measure curved surfaces, dimension of micro electro mechanical systems （MEMS）, etc. The vertical resolution of the profilometer is 0.5 nm, and lateral resolution 0.5 μm.

Cardinality Estimator:Processing SQL with a Vertical Scanning Convolutional Neural Network

Although the popular database systems perform well on query optimization,they still face poor query execution plans when the join operations across multiple tables are complex.Bad execution planning usually results in bad cardinality estimations.The cardinality estimation models in traditional databases cannot provide high-quality estimation,because they are not capable of capturing the correlation between multiple tables in an effective fashion.Recently,the state-of-the-art learning-based cardinality estimation is estimated to work better than the traditional empirical methods.Basically,they used deep neural networks to compute the relationships and correlations of tables.In this paper,we propose a vertical scanning convolutional neural network(abbreviated as VSCNN)to capture the relationships between words in the word vector in order to generate a feature map.The proposed learning-based cardinality estimator converts Structured Query Language(SQL)queries from a sentence to a word vector and we encode table names in the one-hot encoding method and the samples into bitmaps,separately,and then merge them to obtain enough semantic information from data samples.In particular,the feature map obtained by VSCNN contains semantic information including tables,joins,and predicates about SQL queries.Importantly,in order to improve the accuracy of cardinality estimation,we propose the negative sampling method for training the word vector by gradient descent from the base table and compress it into a bitmap.Extensive experiments are conducted and the results show that the estimation quality of q-error of the proposed vertical scanning convolutional neural network based model is reduced by at least 14.6%when compared with the estimators in traditional databases.

Computer generated hologram from full-parallax 3D image data captured by scanning vertical camera array(Invited Paper)

Full-parallax light-field is captured by a small-scale 3D image scanning system and applied to holographic display. A vertical camera array is scanned horizontally to capture full-parallax imagery, and the vertical views between cameras are interpolated by depth image-based rendering technique. An improved technique for depth estimation reduces the estimation error and high-density light-field is obtained. The captured data is employed for the calculation of computer hologram using ray-sampling plane. This technique enables high-resolution display even in deep 3D scene although a hologram is calculated from ray information, and thus it makes use of the important advantage of holographic 3D display.