Abstract
Innovative processes become available due to the high processing capacity of emergent infrastructures, such as cloud and ubiquitous computing and organizational infrastructures and applications. However, these intense computation processes are difficult to follow, where co-decision is required, for which the existence of disruptive visualization and collaboration tools that offer a visual tracing capacity with integrated decision supporting tools, are critical for its sustainable success. This project proposes: a) a set of immersive and disruptive visualization tools, supported by virtual and augmented reality, that enables a global perspective of any production agents; b) a data analytics tool to complement and assist the decision making; c) a resource federated network that allows the brokering and interaction between all existing resources; and d) a dynamic context-aware dashboard, to improve the overall productive process and contribute to intelligent manufacturing systems. The application domain addressed is Zero-Defects Diagnostics in manufacturing as well as in Industry 4.0 in general. © 2017 The Authors.

Abstract
Tomographic wavefront reconstruction is the main computational bottleneck to realize real-time correction for turbulence-induced wavefront aberrations in future laser-assisted tomographic adaptive-optics (AO) systems for ground-based giant segmented mirror telescopes because of its unprecedented number of degrees of freedom, N, i.e., the number of measurements from wavefront sensors. In this paper, we provide an efficient implementation of the minimum-mean-square error (MMSE) tomographic wavefront reconstruction, which is mainly useful for some classes of AO systems not requiring multi-conjugation, such as laser-tomographic AO, multi-object AO, and ground-layer AO systems, but is also applicable to multi-conjugate AO systems. This work expands that by Conan [Proc. SPIE 9148, 91480R (2014)] to the multi-wavefront tomographic case using natural and laser guide stars. The new implementation exploits the Toeplitz structure of covariance matrices used in an MMSE reconstructor, which leads to an overall ON log N real-time complexity compared with ON2 of the original implementation using straight vector-matrix multiplication. We show that the Toeplitz-based algorithm leads to 60 nm rms wavefront error improvement for the European Extremely Large Telescope laser-tomography AO system over a well-known sparse-based tomographic reconstruction; however, the number of iterations required for suitable performance is still beyond what a real-time system can accommodate to keep up with the time-varying turbulence.

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Adaptive optics (AO) restore the angular resolution of ground-based telescopes, but at the cost of delivering a time- and space-varying point spread function (PSF) with a complex shape. PSF knowledge is crucial for breaking existing limits on the measured accuracy of photometry and astrometry in science observations. In this paper, we concentrate our analyses of the anisoplanatism signature only on to the PSF. For large-field observations (20 arcmin) with single-conjugated AO, PSFs are strongly elongated due to anisoplanatism that manifests itself as three different terms for laser guide star (LGS) systems: angular, focal and tilt anisoplanatism. First, we propose a generalized model that relies on a point-wise decomposition of the phase and encompasses the non-stationarity of LGS systems. We demonstrate that it is more accurate and less computationally demanding than existing models: it agrees with end-to-end physical-optics simulations to within 0.1 per cent of PSF measurables, such as the Strehl ratio, FWHM and the fraction of variance unexplained (FVU). Secondly, we study off-axis PSF modelling with respect to the Cn2(h) profile (heights and fractional weights). For 10-mclass telescopes, PSF morphology is estimated at the 1 per cent level as long as we model the atmosphere with at least seven layers, whose heights and weights are known with precisions of 200 m and 10 per cent, respectively. As a verification test, we used the Canada's National Research Council - Herzberg NFIRAOS Optical Simulator (HeNOS) testbed data, featuring four lasers. We highlight the capability of retrieving off-axis PSF characteristics within 10 per cent of the FVU, which complies with the expected range from the sensitivity analysis. Our new off-axis PSF modelling method lays the groundwork for testing on-sky in the near future.