The plenoptic camera has the capability of generating images from as many different viewpoints as microlenses building its array, therefore it is possible to extract several images of the telescope aperture as viewed from those different viewpoints, and after processing them, estimate the incoming atmospheric wavefront. A customized plenoptic camera has been installed at the focus of the Vacuum Tower Telescope at the Teide Observatory in order to assess the feasibility of the plenoptic device as wavefront sensor for solar telescopes. While conventional sensors struggle when measuring phase in wide field-of-view and further from the optical axis, the plenoptic camera gets advantage of imaging extended objects as it could be the solar surface. Telescope results derived from sensing the atmospheric turbulence in a solar scenario by integrating the plenoptic camera in a real science environment are presented and the viability of this type of device as wavefront sensor for solar adaptive optics is hereby demonstrated.

Astronomical images taken from large ground-based telescopes requires techniques as Adaptive Optics in order to improve their spatial resolution. In this work are presented computational results from a modified curvature sensor, the Tomographic Pupil Image Wavefront Sensor (TPI-WFS), which measures the turbulence of the atmosphere, expressed in terms of an expansion over Zernike polynomials. Convolutional Neural Networks (CNN) are presented as an alternative to the TPI-WFS reconstruction. This technique is a machine learning model of the family of Artificial Neural Networks (ANN), which are widely known for its performance as modeling and prediction technique in complex systems. Results obtained from the reconstruction of the networks are compared with the TPI-WFS reconstruction by estimating errors and optical measurements (Root Mean Square error, Mean Structural Similarity and Strehl ratio).In general, CNN trained as reconstructor showed slightly better performance than the conventional reconstruction in TPI-WFS for most of the turbulent profiles, but it made significant improvements for higher turbulent profiles that have the lowest r0 values.

This paper describes the development and implementation of a six-pointed Unmanned Aerial Vehicle [UAV] prototype, designed for finding lost people in hard to access areas, using Arduino MultiWii platform. A platform capable of performing a stable flight to identify people through an on-board camera and an image processing algorithm was developed. Although the use of UAV represents a low cost and quick response –in terms of displacement– solution, capable to prevent or reduce the number of deaths of lost people in away places, also represents a technological challenge, since the recognition of objects from an aerial view is difficult, due to the distance of the UAV to the objective, the UAV’s position and its constant movement. The solution proposed implements an aerial device that performs the image capture, wireless transmission and image processing while it is in a controlled and stable flight.

Accurate measurement of non-common path aberrations (NCPAs) is an important step to be undertaken correctly when operating a real adaptive optics system. NCPAs are defined as the aberrations which are present at the science image but cannot be seen by the wavefront sensor (WFS), basically due to the different placements along the optical path. Compensating these aberrations is required to obtain the best image at the science detector from the resolution point of view. Obtaining the best resolution image, available by a particular optical system having a deformable mirror in the optical path, can be also considered an interesting problem from an abstract point of view, because it can simultaneously compensate for the NCPA and provide the best initial setup for the DM actuators, independent of the accuracy or calibration of the WFS. Phase Diversity (PD) is a very commonly used method of measuring the NCPAs, based in the analysis of two images taken at different focus positions, or just one defocused image. There are also Focal plane Sharpening (FPS) methods, which only deal with the science image, trying to minimize the width of the Point Spread Function by blindly actuating on the DM. The method described in this contribution, Noise Weighted Image Width Minimization (NWIWM), lies in the latter FPS group, and has been developed and tested for the AOLI, EDIFSE and GTCAO projects being developed at Institute of Astrophysics of the Canary Islands (IAC). It is based on the signal to noise analysis of a function describing the width of the PSF with respect to the DM actuations, both zonally and modally, in order to select the actuators or modes to be used during the minimization. A complete description of the algorithm is included, together with simulation results and practical examples obtained within the above mentioned projects.

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