Abstract
The theoretical potential for renewable energy resources (RES) to meet the global demands of energy is generally high and the ambitions for introducing RES into energy systems are growing worldwide, which also can contribute to global climate change mitigation if it is produced in a sustainable manner. To address these issues, more and more governments are implementing various programs and energy policies to accelerate the deployment of RES. The aforementioned two reasons lead to an urgent need to add new generating capacity or reduce consumption during peak periods, or both. The first option for power generation is to use RES which can inject electric energy to the grid while avoiding greenhouse gas emissions. However, the capacities of RES are not enough to supply all the required power from the side of the load. Facts that are leading to the proposal of original ways to reduce the use of energy in many sectors, namely in commercial, residential, and industrial sectors, in order to reduce the total energy costs of the consumer, to reduce the energy demand specially during on-peak hours and the greenhouse gas emissions while safeguarding end-user preferences. The aim of this paper is to determine the impact of model predictive control (MPC) on energy savings of residential households. Furthermore, the value and impact of generated power by local power sources, such as roof-top-solar, will be determined during off-peak, mid-peak, and on-peak, providing simulations during 24 hours in a house.

Abstract
In this article we present a smart textile system for the continuous monitoring of cardiorespiratory signals, produced and integrated with an industrial embroidery unit. The design of a T-shirt system, having embedded textile sensors and interconnects and custom designed circuit for data collection and Bluetooth transmission is presented. The performance of skin-contact textile electrodes, having distinctive electrical characteristics and surface morphologies, was characterized by measurements of signal to noise ratio, under dry and moisture conditions. The influence of the electrodes size and the wear resistance were addressed. Results of an electrocardiogram acquisition with a subject wearing the T-shirt and display on a smartphone are also shown. The presented smart textile systems exhibit good performance and versatility for custom demand production.

Abstract
Raven is a Multi-Object Adaptive Optics science demonstrator which has been used on-sky at Subaru telescope from May 2014 to July 2015. Raven has been developed at the University of Victoria AO Lab, in partnership with NRC, NAOJ and Tohoku University. Raven includes three open loop WFSs, a central laser guide star WFS, and two science pick-off arms feeding light to the Subaru IRCS spectrograph. Raven supports different AO modes: SCAO, open-loop GLAO and MOAO. This paper gives an overview of the instrument design, compares the on-sky performance of the different AO modes and presents some of the science results achieved with MOAO.

Abstract

Abstract
Premiums for renewable energy are being reduced as a consequence of the world economic crisis. This paper models the trading of the energy generated by a photovoltaic generator. The problem is solved through stochastic mixed integer linear programming where the objective function aims at maximizing the profit of selling the photovoltaic production in the day-ahead market. The model is tested without any premium and market and imbalance market prices are forecasted using AR, MA and ARIMA models while photovoltaic production is simulated using Montecarlo method. The model is tested for a case study where the behaviour of the offer, imbalances, incomes and costs is analyzed.

Abstract
Fingerprint liveness detection methods have been developed as an attempt to overcome the vulnerability of fingerprint biometric systems to spoofing attacks. Traditional approaches have been quite optimistic about the behavior of the intruder assuming the use of a previously known material. This assumption has led to the use of supervised techniques to estimate the performance of the methods, using both live and spoof samples to train the predictive models and evaluate each type of fake samples individually. Additionally, the background was often included in the sample representation, completely distorting the decision process. Therefore, we propose that an automatic segmentation step should be performed to isolate the fingerprint from the background and truly decide on the liveness of the fingerprint and not on the characteristics of the background. Also, we argue that one cannot aim to model the fake samples completely since the material used by the intruder is unknown beforehand. We approach the design by modeling the distribution of the live samples and predicting as fake the samples very unlikely according to that model. Our experiments compare the performance of the supervised approaches with the semi-supervised ones that rely solely on the live samples. The results obtained differ from the ones obtained by the more standard approaches which reinforces our conviction that the results in the literature are misleadingly estimating the true vulnerability of the biometric system.