Abstract
Computationally efficient wave-front reconstruction techniques for astronomical adaptive-optics (AO) systems have seen great development in the past decade. Algorithms developed in the spatial-frequency (Fourier) domain have gathered much attention, especially for high-contrast imaging systems. In this paper we present the Wiener filter (resulting in the maximization of the Strehl ratio) and further develop formulae for the anti-aliasing (AA) Wiener filter that optimally takes into account high-order wave-front terms folded in-band during the sensing (i.e., discrete sampling) process. We employ a continuous spatial-frequency representation for the forward measurement operators and derive the Wiener filter when aliasing is explicitly taken into account. We further investigate and compare to classical estimates using least-squares filters the reconstructed wave-front, measurement noise, and aliasing propagation coefficients as a function of the system order. Regarding high-contrast systems, we provide achievable performance results as a function of an ensemble of forward models for the Shack-Hartmann wave-front sensor (using sparse and nonsparse representations) and compute point-spread-function raw intensities. We find that for a 32 × 32 single-conjugated AOs system the aliasing propagation coefficient is roughly 60% of the least-squares filters, whereas the noise propagation is around 80%. Contrast improvements of factors of up to 2 are achievable across the field in the H band. For current and next-generation high-contrast imagers, despite better aliasing mitigation, AA Wiener filtering cannot be used as a standalone method and must therefore be used in combination with optical spatial filters deployed before image formation actually takes place. © 2014 Optical Society of America.

Abstract

Abstract
Distributed systems rely in communication networks, typically a bus, in order to exchange messages and fulfill their goals. However, message transmission is subject to interferences that ultimately can lead to message corruption. In systems where a high-reliability is sought, error recovery mechanisms can be deployed in order to give the required reliability level, and this can be done in the spatial or temporal domain. In the scope of the FTT paradigm, and applied to the FTT-CAN protocol, the authors have previously presented a time domain recovery method using message retransmissions controlled by a server. In this article we assess the impact of different scheduling policies for the server, presenting a qualitative evaluation of the alternatives, complemented by a simulation study, in order to verify their advantages and weak points.

Abstract
Dysfunctions of the autonomic nervous system in critically ill patients with Acute Brain Injury (ABI) lead to changes in Heart Rate Variability (HRV) which appear to be particularly marked in patients subsequently declared in Brain Death (BD). HRV series are non-stationary, exhibit long memory in the mean and time-varying conditional variance (volatility), characteristics that are well modeled by AutoRegressive Fractionally Integrated Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. The long memory is estimated by the parameter d of the ARFIMA-GARCH model, whilst the time-varying conditional variance parameters, u and v characterize, respectively, the short-range and the persistence in the conditional variance. In this work, the ARFIMA-GARCH approach is applied to HRV series of 15 pediatric patients with ABI admitted in a pediatric intensive care unit, 5 of which has BD confirmed and 9 patients survived. The long memory and time-varying conditional variance parameters estimated by ARFIMA-GARCH modeling significantly differ between groups and seem able to contribute to characterize disease severity in children with ABI.

Abstract
This paper proposes a novel method to characterize the availability of household loads to participate in demand response programmes, as well as detailed mathematical models to characterize households loads. The availability of the households results from the flexibility of their controllable loads to increase/reduce consumption. This flexibility is calculated taking into account the comfort levels predefined by the customers and the technical restrictions of the controllable loads. The proposed method was evaluated through a management algorithm developed to perform demand control actions in quasi-real-time, according to the objectives of the distribution system operator or energy aggregator and the availability of the household loads. A scenario with a single household located in a semi-urban area is used to illustrate the application of the algorithm and validate the proposed method.

Abstract
Research datasets in the so-called "long-tail of science" are easily lost after their primary use. Support for preservation, if available, is hard to fit in the research agenda. Our previous work has provided evidence that dataset creators are motivated to spend time on data description, especially if this also facilitates data exchange within a group or a project. This activity should take place early in the data generation process, when it can be regarded as an actual part of data creation. We present the first prototype of the Dendro platform, designed to help researchers use concepts from domain-specific ontologies to collaboratively describe and share datasets within their groups. Unlike existing solutions, ontologies are used at the core of the data storage and querying layer, enabling users to establish meaningful domain-specific links between data, for any domain. The platform is currently being tested with research groups from the University of Porto.