Abstract
In this paper, a methodology to offer new potential of DR in real-time is presented. Since customers likely have extra possibilities for demand response (DR) participation in real-time, in addition to their scheduled potential in day-ahead, this method helps to provide balance in real-time market via DR aggregators. It can be vital once the stochastic variables of the network such as wind power generators (WPG) do not follow the forecasted production in real-time and have some distortions. Stochastic two-stage programming is applied to manage DR options, including load curtailment (LC), load shifting (LS), and load recovery (LR) in both day-ahead and real-time market. DR options in real-time are scheduled based on possible scenarios that reflect the behavior of wind power generation and are generated through Monte-Carlo simulation method. The merits of the method are demonstrated in a 6-bus case study, which shows a reduction in total operation cost.

Abstract

Abstract
In this work an acoustic tag detector was developed for the integration in a mobile robotic fish tracking architecture. The present paper presents both the developed system and preliminary results with particular emphasis of the developed solution with the tag manufacturer receiver. The work has been developed in the context of the MYTAG Portuguese RD project, addressing the study and characterisation of the European flounder migrations in the northern estuarine environments of Portugal. The detector is to be integrated in a tracking system using autonomous surface vehicles and fixed buoys. The main objective is to detect tags inserted surgically in flounders for the MYTAG project, while simultaneously identifying them. A detector solution is presented allowing for the detection and identification of V7 VEMCO tags and preliminary comparative results with the commercially available manufacturer receivers are also presented and discussed.

Abstract
This work presents the 2nd Cross-Spectrum Iris/Periocular Recognition Competition (Cross-Eyed2017). The main goal of the competition is to promote and evaluate advances in cross-spectrum iris and periocular recognition. This second edition registered an increase in the participation numbers ranging from academia to industry: five teams submitted twelve methods for the periocular task and five for the iris task. The benchmark dataset is an enlarged version of the dual-spectrum database containing both iris and periocular images synchronously captured from a distance and within a realistic indoor environment. The evaluation was performed on an undisclosed test-set. Methodology, tested algorithms, and obtained results are reported in this paper identifying the remaining challenges in path forward.

Abstract

Abstract
While deep learning has led to breakthroughs in many areas of computer science, its power has yet to be fully exploited in the area of adaptive optics (AO) and astronomy as a whole. In this paper we describe the first steps taken to apply deep, convolutional neural networks to the problem of wavefront reconstruction and prediction and demonstrate their feasibility of use in simulation. Our preliminary results show we are able to reconstruct wavefronts comparably well to current state of the art methods. We further demonstrate the ability to predict future wavefronts up to five simulation steps with under 1nm RMS wavefront error.