Během posledních třiceti let se adaptivní optika proměnila z vysoce sofistikované, nákladné a utajované technologie v běžnou součást pozemských teleskopů, mikroskopů, laserových systémů či nastupujících systémů pro satelitní komunikaci. Podívejme se zblízka na technologie, které byly pro její účely vyvinuty a stojí za jejím úspěchem., In the last thirty years adaptive optics have transformed from a highly sophisticated, expensive and secret technology into a common component of earth-bound telescopes, microscopes, laser systems or emerging systems for satellite communication. Let‘s take a closer look at these technologies which have been developed within this field and stands behind its success., Jan Pilař., and Obsahuje bibliografické odkazy
Adaptavní optické systémy se vyznačují schopností měnit své optické vlastnosti na požádání a v reálném čase. V tomto příspěvku jsou diskutovány základní prvky adaptivních optických systémů využívaných v astronomii ke kompenzaci vlivu atmosféry na zobrazení velkých pozemských teleskopů., Adaptive optical systems are those whose optical responses can be adjusted on demand, in real time. Here we discuss the basics of adaptive optical systems utilised in astronomy for compensation of aberrations due to atmospheric turbulence, which seriously impairs the performance of uncorrected large ground-based telescopes., Jaroslav Řeháček, Bohumil Stoklasa., and Obsahuje seznam literatury
After sigmoid activation function is replaced with piecewise linear activation function, the adding decaying self-feedback continuous Hopfield neural network (ADSCHNN) searching space changes to hyper-cube space, i.e. the simplified ADSCHNN is obtained. Then, convergence analysis is given for the simplified ADSCHNN in hyper-cube space. It is proved through convergence analysis that the ADSCHNN outperforms the continuous Hopfield neural network (CHNN), when they are applied to solve optimization problem. It is also proved that when extra self-feedback is negative, the ADSCHNN is more effective than the extra self-feedback is positive, when the ADSCHNN is applied to solve TSP.
In a case-cohort design, covariate histories are measured only on cases and a subcohort that is randomly selected from the entire cohort. This design has been widely used in large epidemiologic studies, especially when the exposures of interest are expensive to assemble for all the subjects. In this paper, we propose statistical procedures for analyzing case-cohort sampled current status data under the additive hazards model. Asymptotical properties of the proposed estimator are described and we suggest a resampling method to estimate the variances. Simulation studies show that the proposed method works well for finite sample sizes, and one data set is analyzed for illustrative purposes.