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
Metaheuristics are very useful to achieve good solutions in reasonable execution times. Sometimes they even obtain optimal solutions. However, to achieve near-optimal solutions, the appropriate tuning of parameters is required. This paper presents a Racing based learning module proposal for an autonomous parameter tuning of Metaheuristics. After a literature review on Metaheuristics parameter tuning and Racing approaches, the learning module is presented. A computational study for the resolution of the Scheduling problem is also presented. Comparing the preliminary obtained results with previous published results allow to conclude about the effectiveness and efficiency of this proposal.

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.

Abstract
Nonstationary oscillations are ubiquitous in music and speech, ranging from the fast transients in the attack of musical instruments and consonants to amplitude and frequency modulations in expressive variations present in vibrato and prosodic contours. Modeling nonstationary oscillations with sinusoids remains one of the most challenging problems in signal processing because the fit also depends on the nature of the underlying sinusoidal model. For example, frequency modulated sinusoids are more appropriate to model vibrato than fast transitions. In this paper, we propose to model nonstationary oscillations with adaptive sinusoids from the extended adaptive quasi-harmonic model (eaQHM).We generated synthetic nonstationary sinusoids with different amplitude and frequency modulations and compared the modeling performance of adaptive sinusoids estimated with eaQHM, exponentially damped sinusoids estimated with ESPRIT, and log-linear-amplitude quadratic-phase sinusoids estimated with frequency reassignment. The adaptive sinusoids from eaQHM outperformed frequency reassignment for all nonstationary sinusoids tested and presented performance comparable to exponentially damped sinusoids.