Programa de visitas a laboratorios para fomento de la motivación de alumnos de nuevo ingreso en la titulación de Grado en Ingeniería Electrónica Industrial

Informe final del proyecto de innovación docente de la Universidad de Granada 22-42. Proyecto para realizar visitas acompañadas a laboratorios de electrónica dirigido a alumnos de nuevo ingreso del Grado en Ingeniería Electrónica Industrial

Medical data wrangling with sequential variational autoencoders

Medical data sets are usually corrupted by noise and missing data. These missing patterns are commonly assumed to be completely random, but in medical scenarios, the reality is that these patterns occur in bursts due to sensors that are off for some time or data collected in a misaligned uneven fashion, among other causes. This paper proposes to model medical data records with heterogeneous data types and bursty missing data using sequential variational autoencoders (VAEs). In particular, we propose a new methodology, the Shi-VAE, which extends the capabilities of VAEs to sequential streams of data with missing observations. We compare our model against state-of-theart solutions in an intensive care unit database (ICU) and a dataset of passive human monitoring. Furthermore, we find that standard error metrics such as RMSE are not conclusive enough to assess temporal models and include in our analysis the cross-correlation between the ground truth nd the imputed signal. We show that Shi-VAE achieves the best performance in terms of using both metrics, with lower computational complexity than the GP-VAE model, which is the state-of-the-art method for medical records.This work was supported in part by Spanish Government MCI under Grants TEC2017-92552-EXP and RTI2018-099655-B-100, in part by Comunidad de Madrid under Grants IND2017/TIC-7618, IND2018/TIC-9649, IND2020/TIC-17372, and Y2018/TCS-4705, in part by BBVA Foundation under the Deep-DARWiN Project, and in part by the European Union (FEDER) and the European Research Council (ERC) through the European Union's Horizon 2020 research and innovation program under Grant 714161

Influence of shielding arrangement on ECT sensors

This paper presents a full 3D study of a shielded ECT sensor. The spatial resolution and effective sensing field are obtained by means of Finite Element Method based simulations and are the compared to a conventional sensor's characteristics. An effective improvement was found in the sensitivity in the pipe cross-section, resulting in enhanced quality of the reconstructed image. The sensing field along the axis of the sensor also presents better behaviour for a shielded sensor.This job was financed for the Spanish Education and Science Ministry with a CICYT grant (reference DPI2002/04550/C07/04)

Readout circuit with improved sensitivity for contactless LC sensing tags

In this work we present a novel technique to estimate the resonance frequency of LC chipless tags (inductor-capacitor parallel circuit) with improved sensitivity and linearity. The developed reader measures the power consumption of a Colpitts oscillator during a frequency sweep. The readout circuit consists of a Colpitts oscillator with a coil antenna, varactor diodes to change the oscillator frequency, analog circuitry to measure the power consumption and a microcontroller to control the whole system and send the data to a PC via USB. When an LC tag is inductively coupled to the oscillator, without contact, a maximum power peak is found. As shown by an experimental calibration using an LC tag made on FR4 substrate, the frequency of this maximum is related to the resonance frequency. Both parameters, power consumption and resonance frequency, present an excellent linear dependence with a high correlation factor (R 2 = 0.995). Finally, a screen-printed LC tag has been fabricated and used as relative humidity sensor achieving a sensitivity of (−2.41 ± 0.21) kHz/% with an R 2 of 0.946

Portable multispectral imaging system based on Raspberry Pi

Purpose In this work, the authors aim to present a compact low-cost and portable spectral imaging system for general purposes. The developed system provides information that can be used for a fast in situ identification and classification of samples based on the analysis of captured images. The connectivity of the instrument allows a deeper analysis of the images in an external computer. Design/methodology/approach The wavelength selection of the system is carried out by light multiplexing through a light-emitting diode panel where eight wavelengths covering the spectrum from ultraviolet (UV) to near-infrared region (NIR) have been included. The image sensor used is a red green blue – infrared (RGB-IR) micro-camera controlled by a Raspberry Pi board where a basic image processing algorithm has been programmed. It allows the visualization in an integrated display of the reflectance and the histogram of the images at each wavelength, including UV and NIRs. Findings The prototype has been tested by analyzing several samples in a variety of applications such as detection of damaged, over-ripe and sprayed fruit, classification of different type of plastic materials and determination of properties of water. Originality/value The designed system presents some advantages as being non-expensive and portable in comparison to other multispectral imaging systems. The low-cost and size of the camera module connected to the Raspberry Pi provides a compact instrument for general purposes.Project CTQ2013-44545-R from the Ministry of Economy and Competitiveness (Spain)Junta de Andalucía (Proyecto de Excelencia P10-FQM-5974)European Regional Development Funds (ERDF

Adaptative ECT System Based on Reconfigurable Electronics

In this work we present a novel scheme for the design of electrical capacitance tomography systems that is based on the use of reconfigurable electronics. The objective of this strategy is to generate an adaptable and portable prototype for the processing electronics, i.e., an instrument suitable to be easily transported and applied to different ECT sensors and scenarios with no need of hardware redesign. In order to show the benefits of this approach, a prototype of the processing electronics for the readings of the inter-electrode capacitance values has been implemented using a Programmable System on Chip (PSoC) that allows configuring both analog and digital blocks included in the design. The result is a compact and portable instrument that can work with any ECT sensor up to 8 electrodes. The measurements are sent through a wireless Bluetooth link to an external smart-device such as smartphone, where the permittivity distribution is reconstructed using a custom-developed Android application.Junta de Andalucía (University Professor and Researcher Training Program – FPDI grant)EI BIOTiC under project MPTIC1

Las habilidades metacomprensivas dependen del tipo de texto: un análisis desde el Funcionamiento Diferencial de los Ítems.

Background: Metacomprehension skills determine an individual reader’s ability to judge their degree of learning and text comprehension and have considerable importance in their ability to learn from reading. Given that many comprehension processes are influenced by text characteristics, the aim of the present study was to analyze whether different types of text have significant impact on metacomprehension skills at two different points in primary education. Method: A total of 823 students (4th and 6th years of primary school, 9 to 11 years old) read three different texts (narrative, expository and discontinuous texts) taken from ECOM-PLEC.Pri, a standardized Spanish test for reading comprehension (León, Escudero, & Olmos, 2012). Students were classified by their metacomprehension skills. A Differential Item Functioning (DIF) analysis was conducted in order to analyze whether the underlying reading comprehension and metacomprehension processes differed across text types. Results: Results showed a considerable divergence of performance for reading narrative texts as opposed to expository and discontinuous texts. These differences were related to academic level. Conclusion: Text characteristics such as the type of text can have a great impact on metacomprehension skills and, consequently, on learning.Antecedentes: la metacomprensión supone la habilidad que uno mismo posee para juzgar su grado de aprendizaje y comprensión de un texto, adquiriendo una gran importancia en la comprensión lectora. Dado que los procesos de comprensión se encuentran infl uenciados por las características de los textos, el objetivo de este estudio fue analizar si diferentes tipos de texto afectan de manera signifi cativa a la habilidad metacomprensiva de estudiantes de distintos niveles de Educación Primaria. Método: un total de 823 estudiantes de 4º y 6º de Primaria (9 y 11 años) leyeron tres textos diferentes (narrativo, expositivo y discontinuo) tomados de la prueba estandarizada de comprensión lectora ECOMPLEC.Pri (León, Escudero, y Olmos, 2012). Los estudiantes fueron clasifi cados por su nivel de metacomprensión obtenido en la prueba. Un Análisis Diferencial del Ítem (DIF) se aplicó para analizar si los procesos de comprensión lectora y de metacomprensión difi eren entre tipos de texto y niveles académicos de los participantes. Resultados: los resultados mostraron una notable divergencia en el rendimiento metacognitivo del texto narrativo frente a los textos expositivo y discontinuo. Estas diferencias estaban relacionadas con el nivel académico. Conclusión: el tipo de texto puede tener un gran impacto en las habilidades de metacomprensión y, consecuentemente, en el aprendizaje de textosThis study was supported by Grant PSI2013-47219-P from the Ministry of Economic and Competitive (MINECO) of Spain, and European Union

Predicting emotional states using behavioral markers derived from passively sensed data: Data-driven machine learning approach

Background: Mental health disorders affect multiple aspects of patients’ lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient’s mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues. Objective: This study aims to present a machine learning–based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations. Methods: Passively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days’ worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient. Results: Probabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals’ overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days’ data. Conclusions: These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients’ mood states.This project has received funding from the European Union's Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie grant agreement number 813533. This work was partly supported by the Spanish government (Ministerio de Ciencia e Innovación) under grants TEC2017-92552-EXP and RTI2018-099655-B-100; the Comunidad de Madrid under grants IND2017/TIC-7618, IND2018/TIC-9649, IND2020/TIC-17372, and Y2018/TCS-4705; the BBVA Foundation under the Domain Alignment and Data Wrangling with Deep Generative Models (Deep-DARWiN) project; and the European Union (European Regional Development Fund and the European Research Council) through the European Union's Horizon 2020 Research and Innovation Program under grant 714161. The authors thank Enrique Baca-Garcia for providing demographic and clinical data and assisting in interpreting and summarizing the data