Abstract

Abstract
In this paper, we describe Fourier-based wave-front sensors (WFSs) as linear integral operators, characterized by their kernel. In the first part, we derive the dependency of this quantity with respect to the WFS’s optical parameters: pupil geometry, filtering mask, and tip/tilt modulation. In the second part, we focus the study on the special case of convolutional kernels. The assumptions required to be in such a regime are described. We then show that these convolutional kernels allow to drastically simplify the WFS model by summarizing its behavior in a concise and comprehensive quantity called the WFS impulse response. We explain in particular how it allows to compute the sensor’s sensitivity with respect to spatial frequencies. Such an approach therefore provides a fast diagnostic tool to compare and optimize Fourier-based WFSs. In the third part, we develop the impact of the residual phases on the sensor’s impulse response, and show that the convolutional model remains valid. Finally, a section dedicated to the pyramid WFS concludes this work and illustrates how the slope maps are easily handled by the convolutional model.

Abstract
Central serous chorioretinopathy is a retinal disease in which there is a leak of fluid into the subretinal space resulting in mild to moderate loss of visual acuity. Sequences of images from a fluorescein angiography exam are most of the times used for analyzing these leaks. This work presents a diagnostic aid method to detect and characterize the progression of fluid area along the exam, in order to provide a second opinion and increase the focus and the speed of analysis of the ophthalmologists. The method is based on a comparative approach by image subtraction between the late and early frames. The obtained segmentation results are quite promising with an average Dice coefficient of 0.801 +/- 0.106 for the training set and 0.774 +/- 0.106 for the test set.

Abstract
The herein proposed research project brings together the area of the multi-agent recommender systems and the IoT and aims to study the extent to which a context-based multi-agent recommender system can contribute to improving efficiency in the evacuation of buildings under a fire emergency, recommending the most adequate and efficient evacuation routes in real time. © Springer Nature Switzerland AG 2019.

Abstract

Abstract
An alternative approach to compute the signal to noise ratio of analogue to digital converters based on the computation of the cross-correlation coefficient of the captured response is proposed here. It is shown, after simulation and experimental results, that this approach allows obtaining good accuracy results with the added advantages of not requiring coherent sampling and high purity sine wave stimuli.