On distributed wavefront reconstruction for large-scale adaptive optics systems

The distributed-spline-based aberration reconstruction (D-SABRE) method is proposed for distributed wavefront reconstruction with applications to large-scale adaptive optics systems. D-SABRE decomposes the wavefront sensor domain into any number of partitions and solves a local wavefront reconstruction problem on each partition using multivariate splines. D-SABRE accuracy is within 1% of a global approach with a speedup that scales quadratically with the number of partitions. The D-SABRE is compared to the distributed cumulative reconstruction (CuRe-D) method in open-loop and closed-loop simulations using the YAO adaptive optics simulation tool. D-SABRE accuracy exceeds CuRe-D for low levels of decomposition, and D-SABRE proved to be more robust to variations in the loop gain.Control & SimulationTeam Raf Van de Pla

GPU implementation for spline-based wavefront reconstruction

This paper presents an adaptation of the distributed-spline-based aberration reconstruction method for Shack–Hartmann (SH) slope measurements to extremely large-scale adaptive optics systems and the execution on graphics processing units (GPUs). The introduction of a hierarchical multi-level scheme for the elimination of piston offsets between the locally computed wavefront (WF) estimates solves the piston error propagation observed for a large number of partitions with the original version. To obtain a fully distributed method for WF correction, the projection of the phase estimates is locally approximated and applied in a distributed fashion, providing stable results for low and medium actuator coupling. An implementation of the method with the parallel computing platform CUDA exploits the inherently distributed nature of the algorithm. With a standard off-the-shelf GPU, the computation of the adaptive optics correction updates is accomplished in under 1 ms for the benchmark case of a 200×200 SH array.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Numerics for Control & IdentificationControl & SimulationNumerical Analysi

Pines

Pinus is the most important genus within the Family Pinaceae and also within the gymnosperms by the number of species (109 species recognized by Farjon 2001) and by its contribution to forest ecosystems. All pine species are evergreen trees or shrubs. They are widely distributed in the northern hemisphere, from tropical areas to northern areas in America and Eurasia. Their natural range reaches the equator only in Southeast Asia. In Africa, natural occurrences are confined to the Mediterranean basin. Pines grow at various elevations from sea level (not usual in tropical areas) to highlands. Two main regions of diversity are recorded, the most important one in Central America (43 species found in Mexico) and a secondary one in China. Some species have a very wide natural range (e.g., P. ponderosa, P. sylvestris). Pines are adapted to a wide range of ecological conditions: from tropical (e.g., P. merkusii, P. kesiya, P. tropicalis), temperate (e.g., P. pungens, P. thunbergii), and subalpine (e.g., P. albicaulis, P. cembra) to boreal (e.g., P. pumila) climates (Richardson and Rundel 1998, Burdon 2002). They can grow in quite pure stands or in mixed forest with other conifers or broadleaved trees. Some species are especially adapted to forest fires, e.g., P. banksiana, in which fire is virtually essential for cone opening and seed dispersal. They can grow in arid conditions, on alluvial plain soils, on sandy soils, on rocky soils, or on marsh soils. Trees of some species can have a very long life as in P. longaeva (more than 3,000 years)