Abstract. Ground-based astronomy utilizes modern telescopes to obtain information on the universe by analyzing recorded signals. Due to atmospheric turbulence, the reconstruction process requires solving a deconvolution problem with an unknown point spread function (PSF). The crucial step in PSF estimation is to obtain a high-resolution phase from low-resolution phase gradients, which is a challenging problem. In this paper, when multiple frames of low-resolution phase gradients are available, we introduce PhaseNet, a deep learning approach based on the Taylor frozen flow hypothesis. Our approach incorporates a data-driven residual regularization term, of which the gradient is parameterized by a network, into the Laplacian regularization based model. To solve the model, we unroll the Nesterov accelerated gradient algorithm so that the network can be efficiently and effectively trained. Finally, we evaluate the performance of PhaseNet under various atmospheric conditions and demonstrate its superiority over TV and Laplacian regularization based methods.

Adaptive optics is a commonly used technique to correct the phase distortions caused by the Earth's atmosphere to improve the image quality of the ground-based imaging systems. However, the observed images still suffer from the blur caused by the adaptive optics residual wavefront. In this paper, we propose a model for reconstructing the residual phase in high resolution from a sequence of deformable mirror data. Our model is based on the turbulence statistics and the Taylor frozen flow hypothesis with knowledge of the wind velocities in atmospheric turbulence layers. A tomography problem for the phase distortions from different altitudes is solved in order to get a high quality phase reconstruction. We also consider inexact tomography operators resulting from the uncertainty in the wind velocities. The wind velocities are estimated from the deformable mirror data and, additionally, by including them as unknowns in the objective function. We provide a theoretical analysis on the existence of a minimizer of the objective function. To solve the associated joint optimization problem, we use an alternating minimization method which results in a high resolution reconstruction algorithm with adaptive wind velocities. Numerical simulations are carried out to show the effectiveness of our approach.

While frameworks and application programming interfaces for virtual reality are commonplace today, designing scenarios for virtual environments still remains a tedious and time consuming task. We present a new authoring tool which combines scene assembly and visual programming in a desktop application with instant testing, tuning and planning in an immersive virtual environment. Two authors can work together, one with the desktop authoring application and the other in the immersive VR-simulation, to build a complete scenario.

The paper introduces the metadata management concept based on the object oriented paradigm for modeling and querying multidimensional OLAP data. In this model we apply the basic concepts of the object oriented model including object, class, and relationship between objects to describe objects of multidimensional data and the OLAP operations. In our approach the multidimensional OLAP data is modeled as a fact and a set of dimensions that are organized into class hierarchy.

This paper classifies a collection of semantic data models within a framework consisting of the following comparison-criteria: classification, aggregation, generalization and association (resp. cover aggregation).