Multiple field-of-view MCAO for a Large Solar Telescope: LOST simulations

In the framework of a 4m class Solar Telescope we studied the performance of the MCAO using the LOST simulation package. In particular, in this work we focus on two different methods to reduce the time delay error which is particularly critical in solar adaptive optics: a) the optimization of the wavefront reconstruction by reordering the modal base on the basis of the Mutual Information and b) the possibility of forecasting the wavefront correction through different approaches. We evaluate these techniques underlining pros and cons of their usage in different control conditions by analyzing the results of the simulations and make some preliminary tests on real data.Comment: 10 pages, 5 figures to be published in Adaptive Optics Systems II (Proceedings Volume) Proceedings of SPI

Equalization-Based Digital Background Calibration Technique for Pipelined ADCs

In this paper, we present a digital background calibration technique for pipelined analog-to-digital converters (ADCs). In this scheme, the capacitor mismatch, residue gain error, and amplifier nonlinearity are measured and then corrected in digital domain. It is based on the error estimation with nonprecision calibration signals in foreground mode, and an adaptive linear prediction structure is used to convert the foreground scheme to the background one. The proposed foreground technique utilizes the LMS algorithm to estimate the error coefficients without needing high-accuracy calibration signals. Several simulation results in the context of a 12-b 100-MS/s pipelined ADC are provided to verify the usefulness of the proposed calibration technique. Circuit-level simulation results show that the ADC achieves 28-dB signal-to-noise and distortion ratio and 41-dB spurious-free dynamic range improvement, respectively, compared with the noncalibrated ADC

Perceptually-Driven Video Coding with the Daala Video Codec

The Daala project is a royalty-free video codec that attempts to compete with the best patent-encumbered codecs. Part of our strategy is to replace core tools of traditional video codecs with alternative approaches, many of them designed to take perceptual aspects into account, rather than optimizing for simple metrics like PSNR. This paper documents some of our experiences with these tools, which ones worked and which did not. We evaluate which tools are easy to integrate into a more traditional codec design, and show results in the context of the codec being developed by the Alliance for Open Media.Comment: 19 pages, Proceedings of SPIE Workshop on Applications of Digital Image Processing (ADIP), 201

Multivariate adaptive regression splines for estimating riverine constituent concentrations

Regression-based methods are commonly used for riverine constituent concentration/flux estimation, which is essential for guiding water quality protection practices and environmental decision making. This paper developed a multivariate adaptive regression splines model for estimating riverine constituent concentrations (MARS-EC). The process, interpretability and flexibility of the MARS-EC modelling approach, was demonstrated for total nitrogen in the Patuxent River, a major river input to Chesapeake Bay. Model accuracy and uncertainty of the MARS-EC approach was further analysed using nitrate plus nitrite datasets from eight tributary rivers to Chesapeake Bay. Results showed that the MARS-EC approach integrated the advantages of both parametric and nonparametric regression methods, and model accuracy was demonstrated to be superior to the traditionally used ESTIMATOR model. MARS-EC is flexible and allows consideration of auxiliary variables; the variables and interactions can be selected automatically. MARS-EC does not constrain concentration-predictor curves to be constant but rather is able to identify shifts in these curves from mathematical expressions and visual graphics. The MARS-EC approach provides an effective and complementary tool along with existing approaches for estimating riverine constituent concentrations

A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradation within an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed Computing (ICDCS 2017

Recursive estimation o dynamic models using cook's distance,with application to wind energy orecast

This article proposes an adaptive forgetting factor for the recursive estimation of time varying models.The proposed procedure is based on the Cook's distance of the new observation.It is proven that the proposed procedure encompasses the adaptive features of classic adaptive forgetting factors and,therefore,has a larger adaptability than its competitors.The proposed forgetting factor is applied to wind energy forecast,showing advantages with respect to alternative procedures

Ground-based adaptive optics coronagraphic performance under closed-loop predictive control

The discovery of the exoplanet Proxima b highlights the potential for the coming generation of giant segmented mirror telescopes (GSMTs) to characterize terrestrial --- potentially habitable --- planets orbiting nearby stars with direct imaging. This will require continued development and implementation of optimized adaptive optics systems feeding coronagraphs on the GSMTs. Such development should proceed with an understanding of the fundamental limits imposed by atmospheric turbulence. Here we seek to address this question with a semi-analytic framework for calculating the post-coronagraph contrast in a closed-loop AO system. We do this starting with the temporal power spectra of the Fourier basis calculated assuming frozen flow turbulence, and then apply closed-loop transfer functions. We include the benefits of a simple predictive controller, which we show could provide over a factor of 1400 gain in raw PSF contrast at 1 $\lambda/D$ on bright stars, and more than a factor of 30 gain on an I = 7.5 mag star such as Proxima. More sophisticated predictive control can be expected to improve this even further. Assuming a photon noise limited observing technique such as High Dispersion Coronagraphy, these gains in raw contrast will decrease integration times by the same large factors. Predictive control of atmospheric turbulence should therefore be seen as one of the key technologies which will enable ground-based telescopes to characterize terrrestrial planets.Comment: Accepted to JATI