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Higher education degrees should be rigorously designed in terms of competencies, learning outcomes, and assessment formats to provide meaningful and valuable information for students and faculty. However, doing this with traditional methods is not an easy task, as current tools do not support adequate coordination between those responsible for degree design. This paper presents CORAMeval, a web-based tool that assists teaching staff, coordinators, and those managing degree programmes in assessing the quality of the competencies, learning outcomes, and assessment formats designed. This paper presents the tool developed and the results obtained after its implementation based on the evaluation carried out by a group of evaluators from six Spanish universities.

[EN] Atrial fibrillation (AF) is the most spread heart arrhythmia, whose mechanisms are not completely clear yet. Catheter ablation is a standard treatment, which isolates the area involved in the arrhythmia. Intracardiac electrograms (EGMs) are used to better understand the AF mechanisms and to find appropriate ablation sites. Bipolar EGMs (b-EGMs) are often employed, but their amplitude and shape depend on catheter orientation, limiting reliability. To avoid this uncertainty, an approach insensitive to catheter orientation, referred as Omnipolar EGM (OP-EGM) method, has been introduced, which uses an estimation of the electric field within a group of electrodes, referred as clique. In this work, we compare different mapping approaches based on b-EGMs and OPEGM signals in simulation including fibrosis, so to evaluate their ability to detect fibrosis and reproduce the spatial distribution of the voltage. Maps have been computed using two clique configurations (square and triangular), introducing or not a previous time alignment of the bEGMs. OP-EGM signals have been obtained by projecting the electric field along directions of its maximal excursion and its principal components. Results show that both cliques configurations present good performance, in terms of fibrosis detection and correlation with the reference voltage maps. In addition, the proposed alignment of b-EGMS improves maps based on OP-EGM signals, especially when square cliques are used., This work is part of a project that has received funding from the European Union¿s Horizon 2020 research and innovation program under the Marie Sk¿odowska-Curie grant agreement No 766082 (MY-ATRIA).

Among other variables, uncertainty and limitation of resources make real-life project portfolio management a complex activity. Simulation-optimization is considered an appropriate technique to face stochastic problems like this one. The main objective of this paper is to develop a hybrid model, which combines optimization with Monte Carlo simulation, to deal with stochastic project portfolio management. A series of computational experiments illustrate how these hybrid approach can include uncertainty into the model, and how this is an essential contribution for informed decision making. A relevant novelty is the inclusion of a sustainability dimension, which allows managers to select and prioritize projects not only based on their monetary profitability but also taking into account the associated environmental and/or social impact. This additional criterion can be necessary when evaluating projects in areas such as civil engineering, building and construction, or urban transformation.

It is well known that each year the amount of elderly people is increasing and the life expectancy is longer in EU countries. So, obviously, it is important to include new tools in the socio-sanitary system to maintain and improve the quality of life of the target group. The objective of the proposal described in this paper is to train cognitive abilities (memory, attention, spacial processing…) to promote independent living though a set of serious games (4). These video games serve as a tool to measure partially the cognitive skills of the users and helps to train and improve them. The tests were carried out with 100 people from 3 countries in different socio-cultural contexts. The first levels of each game are played by users several times with good scores, a fact that does not occur in the higher levels that are usually abandoned. However, the subjective feedback received is very promising as they feel they are having fun.

A Distributed Power Amplifier (DPA) with a tapered drain line is presented in this paper. The drain line impedance tapering technique allows to obtain a higher output power and efficiency compared to the conventional approach, whereas a constant drain line impedance avoids impedance changes in the power supply drive signal. The design was implemented using the D01GH/Si technology provided by the foundry OMMIC. The DPA achieves a P sat of 32 dBm and a flat gain over 14 dB in a frequency range that ranges from 1 to 8 GHz. Moreover, this circuit achieves a Power Added Efficiency (PAE) of 50%. Finally, the occupied area of the DPA is 2.2x1.2mm2 excluding pads.

Assessment of drug cardiatoxicity is crucial in the development of new compounds and is typically addressed by evaluating the blockade they cause in the potassium human ether-a-go-go related gene (hERG) channels. Our objective is to develop a classifier to determine the preference for binding to the different states of a drug. We created a set of 2600 virtual blockers with different affinities and kinetics to the conformational states of the channel divided into 13 classes. Simulations were carried out using three stimulation protocols that enhance the probabilities of the channel to occupy a certain state. Three measurements were taken for each of the simulations: $IC_{50}$ , the recovery constant of the $I_{Kr}$ potassium current and an estimation of the time required for the simulation to be stable. Therefore, we obtained 9 variables for each of the blockers studied. A two-step classifier was developed, trained and evaluated. First, $we$ used support vector machines on the $IC_{50}$ to separate the 13 classes into three groups with 4, 5 and 4 classes respectively. Secondly, we used neural networks on each group with all the variables to finally classify the blockers. The three classifiers obtained an overall accuracy on the test group of 90.83, 88.66 and 89.16% for each of the groups respectively.

The practical implementation of digital systems during the lab sessions of an undergraduate digital electronics course can be done using monolithic integrated circuits such as the TTL 7400 or CMOS 4000 series and breadboard wires to connect them into a protoboard, or using reconfigurable hardware devices such as the FPGAs. In the first approach, many students face difficulties in the wiring and testing processes due to the large number of wired connections that must be carried out, while in the second approach difficulties arise with the limited or restricted access that students have in order to use the software and hardware development tools needed to program the FPGAs. Frequently, in many schools of engineering and faculties, these tools are only available during the lab sessions and, after finishing each session, they are usually stored with no free access to them. Thus, students cannot practice freely to improve their digital electronics knowledge and skills. In order to try to overcome both problems, this work presents a low-cost development platform to design digital circuits on FPGAs that: 1) is easily replicable, 2) the number of wired connections is minimized, and 3) is based on the use of open-source software and hardware tools. Using such approach, students are able to work at home on the lab exercises before coming to the lab. Thus, during the lab sessions they can deepen the fundamental aspects of digital electronics instead of being focused on solving wiring mistakes.

This article focuses on the validation of the programming of the microcontroller S32K148, from NXP Semiconductors, through Rapid Prototyping using Matlab/Simulink. To do so, a motor control strategy (Field Oriented Control, FOC) is implemented using the Matlab’s toolbox (Model Based Design Toolbox) that allows this kind of programming. The algorithms developed by Simulink’s bocks diagrams, used to obtain the PWM signals, as well as the ones used to obtain the FOC strategy, are described in this paper. Furthermore, an optimized FOC is proposed replacing the encoder with an angle estimation by using the Hall Position Sensors integrated in the motor. Later on, the signals obtained with the control platform, are applied to a Brushless DC Motor in a prototype, and experimental tests are conducted. In addition, to improve the quality of the current demanded by the BLDC motor, a compensation strategy of the dominant harmonic is proposed. Experimental results validate this strategy.

[EN] This work uses a highly detailed computational model of human atria to investigate the effect of spatial gradient and smoothing of atrial wall thickness on inducibility and maintenance of atrial fibrillation (AF) episodes. An atrial model with homogeneous thickness (HO) was used as baseline for the generation of different atrial models including either a low (LG) or high thickness gradient between left/right atrial free wall and the other regions. Since the model with high spatial gradient presented non-natural sharp edges between regions, either 1 (HG1) or 2 (HG2) Laplacian smoothing iterations were applied. Arrhythmic episodes were initiated using a rapid pacing protocol and long-living rotors were detected and tracked over time. Thresholds optimised with receiver operating characteristic analysis were used to define high gradient/curvature regions. Greater spatial gradients increased the atrial model inducibility and unveiled additional regions vulnerable to maintain AF drivers. In the models with heterogeneous wall thickness (LG, HG2 and HG1), 73.5 ± 8.7% of the long living rotors were found in areas within 1.5 mm from nodes with high thickness gradient, and 85.0 ± 3.4% in areas around high endocardial curvature. These findings promote wall thickness gradient and endocardial curvature as measures of AF vulnerability, Research supported by the European Union¿s Horizon 2020 research and innovation programme under the Marie Sk¿odowska-Curie grant agreement No.766082 (MY-ATRIA project)

The implementation of new technologies for agriculture has gained relevance in the agricultural sector. In this sense, during the last decade, the use of aerial images has allowed to study the different factors related to production with a high level of detail, unlike traditional monitoring techniques, since these allow large areas to be analyzed in a short time. Aerial images are commonly obtained from satellite platforms because they have a low cost, but low spatial and temporal resolution limit their use in agricultural applications. For this reason, the use of unmanned aerial vehicles (UAV) has played a leading role in the study of agronomic variables of interest, because it allows obtaining information at any time and at a higher resolution. However, these images can offer more information that must be examined through a set of analysis techniques. An application that has been little explored corresponds to the population count, a factor that is determinant to obtain production estimates, but the difficulty to properly segment the rest of the information in the images has posed a challenge. For this reason, the following work presents a methodology based on spectral indices and digital image analysis to perform population counts in sunflower plants. Results indicate that it is possible to estimate the number of plants in the image with an error of 10%.

The aim of this study was to assess the applicability of VIS/NIR hyperspectral imaging in intelligently detecting postharvest quality of tomatoes. The postharvest quality of tomatoes was evaluated in terms of firmness, soluble solids content (SSC), pH and color (L*a*b*C*h). VNIR and NIR hyperspectral imaging (400-1700 nm) were used to build regression models using Partial Least Square (PLS). The effects of different preprocessing techniques, including moving weighted average smoothing, first- and second-derivative Savitzky Golay (S-G) and standard normal variate (SNV) on prediction performance were also evaluated. NIR spectra-based models performed the best at estimating the quality characteristics in tomatoes. Excellent prediction for SSC, firmness and pH (RPD > 3.0), and good prediction for color (RPD > 2.0) were obtained. Consequently, NIR spectra-based systems could be introduced in the manufacturing process of tomato industries as nondestructive method to monitor quality characteristics of tomatoes.

The continuous growth of the integration scale in CMOS circuits has derived in an increase in the memory systems capacity, but also in their fault rate. In this way, the probabilities of suffering Single Cell Upsets (SCUs) or Multiple Cell Upsets (MCUs) has thus raised. Traditionally, Error Correction Codes (ECCs) are used in memory systems to correct errors. However, when using ECCs, it is necessary to find a good balance between the redundancy of the code; the area, power consumption and delay overheads of the encoding and decoding circuits; and the error coverage achieved. In this work, we present two new low-redundant matrix ECCs that are able to correct different types of adjacent errors. Both codes have the same error coverage, but different levels of redundancy. In this way, we have been able to study the influence of these different levels of low redundancy in the area, power consumption and delay overheads. We have also compared our proposals to a well-known matrix code, in terms of overhead vs. coverage using a recently introduced metric. In all cases, our proposals get better scores.

The final product quality control is critical for any manufacturing process. In the case of steel products, there are different inspection methods that are able to classify the defects, but they usually require human intervention. In this context, a deep learning-based automatic defect classifier method for steel surfaces is proposed. The method combines some traditional Machine Learning techniques with a Convolutional Neural Network (CNN). Different experiments were carried out in order to obtain the best classifier parameter setup. To verily the robustness of the classifier some additional experiments were done, obtaining high classification rate against some sources of noise such as illumination changes or occlusions. The proposed method achieves a classification rate of 99.95% taking 0.019 seconds to classify a single image. The method is compared with seventeen related methods and outperforms them on a publicly available dataset, with six types of defects and 300 samples for each class. The source code of the proposed method is publicly available.

This paper presents the Virtual Open Operating System (vf-OS) Input / Output (IO) Toolkit Generator, which is a design tool to develop vf-OS IO components that interact with all kinds of manufacturing assets, either physical devices like Program Logic Controllers (PLCs), software applications like Enterprise Resource Planning Systems (ERPs) or legacy file formats like STEP. The vf-OS IO Toolkit Generator is based on software scaffolding, a code generation technique that allows a developer to create a working component to interact with a manufacturing asset from the vf-OS Platform without writing a line of code. As described in this paper, software scaffolding not only simplifies the development of interoperability components, but it also fosters system security and platform integration automation. Another contribution of this paper is to propose possible integrations between the IO Toolkit Generator and the vf-OS Security Command Centre in charge of platform security. Additionally, this paper describes how the concept can be extended to address other digital manufacturing platforms like Fi-Ware.

Industrial plants are distributed systems whose automation requires complex controllers. Networks of state machines model these controller systems but their syntheses remain a problem. In this paper, we approach it by combining extended finite state machines (EFSM) with state stacks. While EFSM match most design templates, extended finite state stack machines (EFS2M) accommodates to procedural agent architectures for modules at higher-abstraction levels of the controllers' networks. Results include a code generator from state machine diagrams and a simulator of a mobile robot for internal transportation whose controller stack includes EFSM and EFS2M.

A 4-channel Ka-band beamforming RFIC (BFIC) for millimeter wave (mmW) wireless power transfer to body area networks (BAN) is implemented in a 65nm CMOS process. The BFIC incorporates a traveling wave distribution network (TWDN) to reduce silicon area and lower cost. Measurements show ~18 dB of gain per channel, P 1dB of 4.5 dBm, 320 mW (80 mW/channel) consumption, in 2.25 mm2 of silicon area.

Analysis of back scattered field of a large array antenna is presented. The induced current on the ground plane is obtained employing the equivalence principle and Galerkin's Method of Moment (MoM). For fast convergence a progressive wave type of basis function is proposed. Results for the scattered field intensities are shown for three cases and it is found that a tapered excitation reduces back scattered field significantly.