20,181 research outputs found
Multiple field-of-view MCAO for a Large Solar Telescope: LOST simulations
In the framework of a 4m class Solar Telescope we studied the performance of
the MCAO using the LOST simulation package. In particular, in this work we
focus on two different methods to reduce the time delay error which is
particularly critical in solar adaptive optics: a) the optimization of the
wavefront reconstruction by reordering the modal base on the basis of the
Mutual Information and b) the possibility of forecasting the wavefront
correction through different approaches. We evaluate these techniques
underlining pros and cons of their usage in different control conditions by
analyzing the results of the simulations and make some preliminary tests on
real data.Comment: 10 pages, 5 figures to be published in Adaptive Optics Systems II
(Proceedings Volume) Proceedings of SPI
Real-valued feature selection for process approximation and prediction
The selection of features for classification, clustering and approximation is an important task in pattern recognition, data mining and soft computing. For real-valued features, this contribution shows how feature selection for a high number of features can be implemented using mutual in-formation. Especially, the common problem for mutual information computation of computing joint probabilities for many dimensions using only a few samples is treated by using the Rènyi mutual information of order two as computational base. For this, the Grassberger-Takens corre-lation integral is used which was developed for estimating probability densities in chaos theory. Additionally, an adaptive procedure for computing the hypercube size is introduced and for real world applications, the treatment of missing values is included. The computation procedure is accelerated by exploiting the ranking of the set of real feature values especially for the example of time series. As example, a small blackbox-glassbox example shows how the relevant features and their time lags are determined in the time series even if the input feature time series determine nonlinearly the output. A more realistic example from chemical industry shows that this enables a better ap-proximation of the input-output mapping than the best neural network approach developed for an international contest. By the computationally efficient implementation, mutual information becomes an attractive tool for feature selection even for a high number of real-valued features
Speckle statistics in adaptive optics images at visible wavelengths
Residual speckles in adaptive optics (AO) images represent a well-known
limitation on the achievement of the contrast needed for faint source
detection. Speckles in AO imagery can be the result of either residual
atmospheric aberrations, not corrected by the AO, or slowly evolving
aberrations induced by the optical system. We take advantage of the high
temporal cadence (1 ms) of the data acquired by the System for Coronagraphy
with High-order Adaptive Optics from R to K bands-VIS forerunner experiment at
the Large Binocular Telescope to characterize the AO residual speckles at
visible wavelengths. An accurate knowledge of the speckle pattern and its
dynamics is of paramount importance for the application of methods aimed at
their mitigation. By means of both an automatic identification software and
information theory, we study the main statistical properties of AO residuals
and their dynamics. We therefore provide a speckle characterization that can be
incorporated into numerical simulations to increase their realism and to
optimize the performances of both real-time and postprocessing techniques aimed
at the reduction of the speckle noise
- …