Introduccción

Women's Studies, Feminists and Gender Studies have raised relevant theoretical contributions to scientific knowledge, that are essential to understand the surrounding reality. Among the most important contributions is the consolidation of the gender category and feminist epistemology as a way for a more inclusive and less segregationist science. An essential analysis tool to understand the processes of inequality between men and women, and the development of a critical point of view that questions the traditional and androcentric perspective of scientific knowledge.Los Estudios de Género, Feministas y de las Mujeres han introducido importantes aportaciones teóricas al conocimiento científico sin las cuales sería imposible comprender la realidad que nos rodea. Entre las aportaciones más importantes se encuentra la consolidación de la categoría género y la epistemología feminista como vía para una ciencia más inclusiva y menos segregacionista. Una herramienta de análisis indispensable para comprender los procesos de desigualdad entre hombres y mujeres, y el desarrollo de un punto de vista crítico que cuestione la tradicional y androcéntrica forma de hacer ciencia

Personalised and Adjustable Interval Type-2 Fuzzy-Based PPG Quality Assessment for the Edge

Most of today's wearable technology provides seamless cardiac activity monitoring. Specifically, the vast majority employ Photoplethysmography (PPG) sensors to acquire blood volume pulse information, which is further analysed to extract useful and physiologically related features. Nevertheless, PPG-based signal reliability presents different challenges that strongly affect such data processing. This is mainly related to the fact of PPG morphological wave distortion due to motion artefacts, which can lead to erroneous interpretation of the extracted cardiac-related features. On this basis, in this paper, we propose a novel personalised and adjustable Interval Type-2 Fuzzy Logic System (IT2FLS) for assessing the quality of PPG signals. The proposed system employs a personalised approach to adapt the IT2FLS parameters to the unique characteristics of each individual's PPG signals.Additionally, the system provides adjustable levels of personalisation, allowing healthcare providers to adjust the system to meet specific requirements for different applications. The proposed system obtained up to 93.72\% for average accuracy during validation. The presented system has the potential to enable ultra-low complexity and real-time PPG quality assessment, improving the accuracy and reliability of PPG-based health monitoring systems at the edge

EMPATÍA-CM: protEcción integral de las víctimas de violencia de género Mediante comPutación AfecTIva multimodal

The EMPATIACM project begins in January 2019, executed by a multidisciplinary team formed by the Institute of Gender Studies in Universidad Carlos III of Madrid (UC3M-IEG, with research staff from several branches of the Social Sciences and Humanities) and by the UC3M-TEC group (formed in turn by research personnel from several branches of Engineering), with the  fundamental objective  of   understanding the reactions of victims of Gender-based Violence (GBV) to dangerous situations, generate mechanisms for automatic detection of these situations and study how to react in a comprehensive, coordinated and effective way to protect them in the best possible way. This objective is divided into six sub-objectives, which demonstrate the need and added value of the multidisciplinary approach. EMPATIA combines cyberphysical systems and affective computing proposing a comprehensive protocol that improves the protection of victims of gender violence with a solution capable of automatically, immediately and remotely warning of risk situations. Close to the end of the project, it is time to assess the work done and the results achieved, presenting the contributions in each of the sub-objectives raised in the project proposal.El proyecto EMPATIACM comienza en enero de 2019, a cargo de un equipo multidisciplinar formado por el Instituto de Estudios de Género de la Universidad Carlos III de Madrid (UC3M-IEG, con personal investigador de varias ramas de las Ciencias Sociales y las Humanidades) y por el grupo UC3M-TEC (formado a su vez por personal investigador de varias ramas de la Ingeniería), con el objetivo fundamental de entender las reacciones de las víctimas de la Violencia de Género (VG) ante situaciones de peligro, generar mecanismos de detección automática de estas situaciones y estudiar cómo reaccionar de forma integral, coordinada y eficaz para protegerlas de la mejor forma posible. Este objetivo se divide en seis subobjetivos, que demuestran la necesidad y valor añadido del enfoque multidisciplinar. EMPATIA aúna sistemas ciberfísicos y computación afectiva proponiendo un protocolo integral que mejora la protección de las víctimas de violencia de género con una solución capaz de avisar de forma automática, inmediata y remota de situaciones de riesgo. Cerca del final del proyecto, es el momento de hacer balance del trabajo realizado y los resultados alcanzados, presentando las aportaciones en cada uno de los subobjetivos planteados en la propuesta del proyecto

Extensive SEU impact analysis of a PIC microprocessor for selective hardening

In order to increase the robustness of a circuit against SEUs, fault injection is commonly used to locate weak areas. autonomous emulation is a very powerful tool to locate these areas by executing huge fault injection campaigns. In this work, fault injection has been extensively applied to a PIC18 microprocessor, while executing three different workloads. A 80 million fault campaign has been performed, and results show that a failure rate lower than 1% can be obtained by hardening a 24% of the circuit flip-flops, for the given applications

Solar Energy Harvesting to Improve Capabilities of Wearable Devices

The market of wearable devices has been growing over the past decades. Smart wearables are usually part of IoT (Internet of things) systems and include many functionalities such as physiological sensors, processing units and wireless communications, that are useful in fields like healthcare, activity tracking and sports, among others. The number of functions that wearables have are increasing all the time. This result in an increase in power consumption and more frequent recharges of the battery. A good option to solve this problem is using energy harvesting so that the energy available in the environment is used as a backup power source. In this paper, an energy harvesting system for solar energy with a flexible battery, a semi-flexible solar harvester module and a BLE (Bluetooth® Low Energy) microprocessor module is presented as a proof-of-concept for the future integration of solar energy harvesting in a real wearable smart device. The designed device was tested under different circumstances to estimate the increase in battery lifetime during common daily routines. For this purpose, a procedure for testing energy harvesting solutions, based on solar energy, in wearable devices has been proposed. The main result obtained is that the device could permanently work if the solar cells received a significant amount of direct sunlight for 6 h every day. Moreover, in real-life scenarios, the device was able to generate a minimum and a maximum power of 27.8 mW and 159.1 mW, respectively. For the wearable system selected, Bindi, the dynamic tests emulating daily routines has provided increases in the state of charge from 19% (winter cloudy days, 4 solar cells) to 53% (spring sunny days, 2 solar cells). Keywords: energy harvesting; internet of things; physiologicalThis research was funded by the Department of Research and Innovation of Madrid Regional Authority, in the EMPATIA-CM research project (reference Y2018/TCS-5046). This work has been partially supported by the European Union—NextGenerationEU, with the SAPIENTIAE4BINDI project “Proof of Concept” 2021. (Ref: PDC2021-121071-I00/AEI/10.13039/501100011033). This work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M26), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation)

Autonomous fault emulation: a new FPGA-based acceleration system for hardness evaluation

The appearance of nanometer technologies has produced a significant increase of integrated circuit sensitivity to radiation, making the occurrence of soft errors much more frequent, not only in applications working in harsh environments, like aerospace circuits, but also for applications working at the earth surface. Therefore, hardened circuits are currently demanded in many applications where fault tolerance was not a concern in the very near past. To this purpose, efficient hardness evaluation solutions are required to deal with the increasing size and complexity of modern VLSI circuits. In this paper, a very fast and cost effective solution for SEU sensitivity evaluation is presented. The proposed approach uses FPGA emulation in an autonomous manner to fully exploit the FPGA emulation speed. Three different techniques to implement it are proposed and analyzed. Experimental results show that the proposed Autonomous Emulation approach can reach execution rates higher than one million faults per second, providing a performance improvement of two orders of magnitude with respect to previous approaches. These rates give way to consider very large fault injection campaigns that were not possible in the past.This work was supported by the Directorate of Research of Madrid Community Government, Spain (Code 07/0052/2003 2) and by the European Commission and Spanish Government under MEDEA+ Project (PARACHUTE-2A701) and PROFIT Project (CIRCE-FIT-330100-2005-60)

SEU Sensitivity Comparison for Different Reprogrammable Technologies With Minority Check Block

In this work, a method is proposed for obtaining comparable measurements of the SEU sensitivity in reprogrammable devices that present different characteristics like internal architecture, technology, amount of available resources, etc. A specific minority checker is developed for reporting the presence of SEUs or MBUs which will help in this comparing task during dynamic tests.This work was supported in part by the Spanish Ministry of Science and Technology, code TEC2010-22095-C03-03. RENASER+ projec

Hardware Fault Injection

Hardware fault injection is the widely accepted approach to evaluate the behavior of a circuit in the presence of faults. Thus, it plays a key role in the design of robust circuits. This chapter presents a comprehensive review of hardware fault injection techniques, including physical and logical approaches. The implementation of effective fault injection systems is also analyzed. Particular emphasis is made on the recently developed emulation-based techniques, which can provide large flexibility along with unprecedented levels of performance. These capabilities provide a way to tackle reliability evaluation of complex circuits.Publicad

Fear Detection in Multimodal affective computing: Physiological Signals versus Catecholamine Concentration

Affective computing through physiological signals monitoring is currently a hot topic in the scientific literature, but also in the industry. Many wearable devices are being developed for health or wellness tracking during daily life or sports activity. Likewise, other applications are being proposed for the early detection of risk situations involving sexual or violent aggressions, with the identification of panic or fear emotions. The use of other sources of information, such as video or audio signals will make multimodal affective computing a more powerful tool for emotion classification, improving the detection capability. There are other biological elements that have not been explored yet and that could provide additional information to better disentangle negative emotions, such as fear or panic. Catecholamines are hormones produced by the adrenal glands, two small glands located above the kidneys. These hormones are released in the body in response to physical or emotional stress. The main catecholamines, namely adrenaline, noradrenaline and dopamine have been analysed, as well as four physiological variables: skin temperature, electrodermal activity, blood volume pulse (to calculate heart rate activity. i.e., beats per minute) and respiration rate. This work presents a comparison of the results provided by the analysis of physiological signals in reference to catecholamine, from an experimental task with 21 female volunteers receiving audiovisual stimuli through an immersive environment in virtual reality. Artificial intelligence algorithms for fear classification with physiological variables and plasma catecholamine concentration levels have been proposed and tested. The best results have been obtained with the features extracted from the physiological variables. Adding catecholamine’s maximum variation during the five minutes after the video clip visualization, as well as adding the five measurements (1-min interval) of these levels, are not providing better performance in the classifiers.This research has been supported by the Madrid Governement (Comunidad de Madrid, Spain) under the ARTEMISA-UC3M-CM research project (reference 2020/00048/001), the EMPATIACM research project (reference Y2018/TCS-5046) and the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M26), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation)

On the use of embedded debug features for permanent and transient fault resilience in microprocessors

Microprocessor-based systems are employed in an increasing number of applications where dependability is a major constraint. For this reason detecting faults arising during normal operation while introducing the least possible penalties is a main concern. Different forms of redundancy have been employed to ensure error-free behavior, while error detection mechanisms can be employed where some detection latency is tolerated. However, the high complexity and the low observability of microprocessors internal resources make the identification of adequate on-line error detection strategies a very challenging task, which can be tackled at circuit or system level. Concerning system-level strategies, a common limitation is in the mechanism used to monitor program execution and then detect errors as soon as possible, so as to reduce their impact on the application. In this work, an on-line error detection approach based on the reuse of available debugging infrastructures is proposed. The approach can be applied to different system architectures profiting from the debug trace port available in most of current microprocessors to observe possible misbehaviors. Two microprocessors have been used to study the applicability of the solution. LEON3 and ARM7TDMI. Results show that the presented fault detection technique enhances observability and thus error detection abilities in microprocessor-based systems without requiring modifications on the core architecture