Abstract
The determination of the optical point spread function (PSF) or a model thereof is one key step in the estimation of key astronomical quantities for most science cases. Yet it has proven quite challenging due to crowding or the total absence of point-sources in the field or variability (time, angle and wavelength). For Adaptive-optics (AO) assisted observations, alternative techniques exist such as PSF reconstruction (PSF-R) which relies on the AO control loop data. Our goal is to provide a standardized nomenclature and categorization of the techniques that use focal-plane data (numerical extraction, parametric model-fitting), recovery from telemetry (reconstruction, analytic or Monte Carlo modeling) or both jointly combined (hybrid, deconvolution) in an attempt to gain insight into the advantages and shortcomings of such techniques. Applicability to AO systems on Giant Segmented Mirror Telescopes (GSMT) is our main motivation.

Abstract

Abstract
Classical Weighted Least Squares (WLS) State estimation (SE) in power systems is known for not performing well in the presence of Gross Errors (GE). The alternative using Correntropy proved to be appealing in dealing with outliers. Now, a novel SE method, generalized correntropy interior point method (GCIP) is being proposed, taking advantage of the properties of the Generalized Correntropy and of the Interior Point Method (IPM) as solver. This paper discusses how the choice of different central path neighborhoods, an essential concept in IPM, is critical in the identification of gross errors. The simulation results indicate that a one-sided infinity norm neighborhood successfully identifies outliers in the SE problem, making GCIP a competitive method. © 2019 IEEE.

Abstract
Optical microbubble resonators are among the highest sensitivity optical sensors. In the context of its application in the detection of water micro contaminants, in portable systems, their interrogation must be made by tracking the resonant wavelength peak position with the highest accuracy possible, at a reasonable cost. In this work different laser sources and scanning methods were tested and compared, aiming the development of a portable prototype. Each tunable laser source, was evaluated using a C2H2 Gas cell, which provided an absolute wavelength reference. Light transmitted through the cell was recorded using a photodetector and a software controlled feedback loop, enabling locking into selected reference peaks. Three distinct scanning methods were tested and compared for each laser source: large and short-range laser scanning and external waveform dithering, from which minimum standard deviations of 20, 0.18, and 0.07 pm, were obtained, respectively.

Abstract
Model-Based Testing (MBT) relies on models of a System Under Test (SUT) to derive test cases for said system. While Finite State Machine (FSM), workflow, etc. are widely used to derive test cases for WIMP applications (i.e. applications depending on 2D widgets such as menus and icons), these notations lack the expressive power to describe the interaction techniques and behaviors found in post-WIMP applications. In this paper, we aim at demonstrating that thanks to ICO, a formal notation for describing interactive systems, it is possible to generate test cases that go beyond the state of the art by addressing the MBT of advanced interaction techniques in post-WIMP applications. © Springer Nature Switzerland AG 2020.

Abstract
In the past years, INESC TEC has been working on using ns-3 to reduce the gap between Simulation and Experimentation. Two major contributions resulted from our work: 1) the Fast Prototyping development process, where the same ns-3 protocol model is used in a real experiment; 2) the Trace-based Simulation (TS) approach, where traces of radio link qualities and position of nodes from past experiments are injected into ns-3 to achieve repeatable and reproducible experiments. In this paper we present ns-3 NEXT, our vision for ns-3 to enable simulation and experimentation using the same platform. We envision ns-3 as the platform that can automatically learn from past experiments and improve its accuracy to a point where simulated resources can seamlessly replace real resources. At that point, ns-3 can either replace a real testbed accurately (Offline Experimentation) or add functionality and scale to testbeds (Augmented Experimentation). Towards this vision, we discuss the current limitations and propose a plan to overcome them collectively within the ns-3 community. © 2019 ACM.