Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs

A Computational Approach for the Understanding of Stochastic Resonance Phenomena in the Human Auditory System

Stochastic resonance (SR) is a nonlinear phenomenon by which the introduction of noise in a system causes a counterintuitive increase in levels of detection performance of a signal. SR has been extensively studied in different physical and biological systems, including the human auditory system (HAS), where a positive role for noise has been recognized both at the level of peripheral auditory system (PAS) and central nervous system (CNS). This dualism regarding the mechanistic underpinnings of the RS phenomenon in the HAS is confirmed by discrepancies among different experimental studies and reflects on a disagreement about how this phenomenon can be exploited for the improvement of prosthesis and aids devoted to hypoacusic people. HAS is one of the human body’s most complex sensory system. On the other hand, SR involves system nonlinearities. Then, the characterization of SR in the HAS is very challenging and many efforts are being made to characterize this mechanism as a whole. Current computational modelling tools make possible to investigate the phenomena separately in the CNS and in the PAS, then simplifying the analysis of the involved mechanisms. In this work we present a computational model of PAS supporting SR, that shows improved detection of sounds when input noise is added. As preparatory step, we provided a test signal to the system, at the edge of the hearing threshold. As next step, we repeated the experiment adding background noise at different intensities. We found an increase of relative spike count in the frequency bands of the test signal when input noise is added, confirming that the maximum value is obtained under a specific range of added noise, whereas further increase in noise intensity only degrades signal detection or information content

a computational approach for the understanding of stochastic resonance phenomena in the human auditory system

Stochastic resonance (SR) is a nonlinear phenomenon by which the introduction of noise in a system causes a counterintuitive increase in levels of detection performance of a signal. SR has been extensively studied in different physical and biological systems, including the human auditory system (HAS), where a positive role for noise has been recognized both at the level of peripheral auditory system (PAS) and central nervous system (CNS). This dualism regarding the mechanistic underpinnings of the RS phenomenon in the HAS is confirmed by discrepancies among different experimental studies and reflects on a disagreement about how this phenomenon can be exploited for the improvement of prosthesis and aids devoted to hypoacusic people. HAS is one of the human body's most complex sensory system. On the other hand, SR involves system nonlinearities. Then, the characterization of SR in the HAS is very challenging and many efforts are being made to characterize this mechanism as a whole. Current computational modelling tools make possible to investigate the phenomena separately in the CNS and in the PAS, then simplifying the analysis of the involved mechanisms. In this work we present a computational model of PAS supporting SR, that shows improved detection of sounds when input noise is added. As preparatory step, we provided a test signal to the system, at the edge of the hearing threshold. As next step, we repeated the experiment adding background noise at different intensities. We found an increase of relative spike count in the frequency bands of the test signal when input noise is added, confirming that the maximum value is obtained under a specific range of added noise, whereas further increase in noise intensity only degrades signal detection or information content

A gamification framework demonstrating a complete cycle of vehicle driver performance evaluation

Training through a gamified environment motivates the users in achieving optimal outcome and reduces the complexity of learning by adding factor of entertainment in it. The deployment of serious games in automotive industry is a major leap in technological grounds, as it\u2019s a best way to inculcate safe driving patterns to reduce the fatalities and enhance resource usage which includes car accessories and fuel. The Ph.D. thesis represents Gamification platform aimed to Green Mobility and Safe Driving

Human Action Recognition and Monitoring in Ambient Assisted Living Environments

Population ageing is set to become one of the most significant challenges of the 21st century, with implications for almost all sectors of society. Especially in developed countries, governments should immediately implement policies and solutions to facilitate the needs of an increasingly older population. Ambient Intelligence (AmI) and in particular the area of Ambient Assisted Living (AAL) offer a feasible response, allowing the creation of human-centric smart environments that are sensitive and responsive to the needs and behaviours of the user. In such a scenario, understand what a human being is doing, if and how he/she is interacting with specific objects, or whether abnormal situations are occurring is critical. This thesis is focused on two related research areas of AAL: the development of innovative vision-based techniques for human action recognition and the remote monitoring of users behaviour in smart environments. The former topic is addressed through different approaches based on data extracted from RGB-D sensors. A first algorithm exploiting skeleton joints orientations is proposed. This approach is extended through a multi-modal strategy that includes the RGB channel to define a number of temporal images, capable of describing the time evolution of actions. Finally, the concept of template co-updating concerning action recognition is introduced. Indeed, exploiting different data categories (e.g., skeleton and RGB information) improve the effectiveness of template updating through co-updating techniques. The action recognition algorithms have been evaluated on CAD-60 and CAD-120, achieving results comparable with the state-of-the-art. Moreover, due to the lack of datasets including skeleton joints orientations, a new benchmark named Office Activity Dataset has been internally acquired and released. Regarding the second topic addressed, the goal is to provide a detailed implementation strategy concerning a generic Internet of Things monitoring platform that could be used for checking users' behaviour in AmI/AAL contexts

Contributions to Context-Aware Smart Healthcare: A Security and Privacy Perspective

Les tecnologies de la informació i la comunicació han canviat les nostres vides de manera irreversible. La indústria sanitària, una de les indústries més grans i de major creixement, està dedicant molts esforços per adoptar les últimes tecnologies en la pràctica mèdica diària. Per tant, no és sorprenent que els paradigmes sanitaris estiguin en constant evolució cercant serveis més eficients, eficaços i sostenibles. En aquest context, el potencial de la computació ubiqua mitjançant telèfons intel·ligents, rellotges intel·ligents i altres dispositius IoT ha esdevingut fonamental per recopilar grans volums de dades, especialment relacionats amb l'estat de salut i la ubicació de les persones. Les millores en les capacitats de detecció juntament amb l'aparició de xarxes de telecomunicacions d'alta velocitat han facilitat la implementació d'entorns sensibles al context, com les cases i les ciutats intel·ligents, capaços d'adaptar-se a les necessitats dels ciutadans. La interacció entre la computació ubiqua i els entorns sensibles al context va obrir la porta al paradigma de la salut intel·ligent, centrat en la prestació de serveis de salut personalitzats i de valor afegit mitjançant l'explotació de grans quantitats de dades sanitàries, de mobilitat i contextuals. No obstant, la gestió de dades sanitàries, des de la seva recollida fins a la seva anàlisi, planteja una sèrie de problemes desafiants a causa del seu caràcter altament confidencial. Aquesta tesi té per objectiu abordar diversos reptes de seguretat i privadesa dins del paradigma de la salut intel·ligent. Els resultats d'aquesta tesi pretenen ajudar a la comunitat científica a millorar la seguretat dels entorns intel·ligents del futur, així com la privadesa dels ciutadans respecte a les seves dades personals i sanitàries.Las tecnologías de la información y la comunicación han cambiado nuestras vidas de forma irreversible. La industria sanitaria, una de las industrias más grandes y de mayor crecimiento, está dedicando muchos esfuerzos por adoptar las últimas tecnologías en la práctica médica diaria. Por tanto, no es sorprendente que los paradigmas sanitarios estén en constante evolución en busca de servicios más eficientes, eficaces y sostenibles. En este contexto, el potencial de la computación ubicua mediante teléfonos inteligentes, relojes inteligentes, dispositivos wearables y otros dispositivos IoT ha sido fundamental para recopilar grandes volúmenes de datos, especialmente relacionados con el estado de salud y la localización de las personas. Las mejoras en las capacidades de detección junto con la aparición de redes de telecomunicaciones de alta velocidad han facilitado la implementación de entornos sensibles al contexto, como las casas y las ciudades inteligentes, capaces de adaptarse a las necesidades de los ciudadanos. La interacción entre la computación ubicua y los entornos sensibles al contexto abrió la puerta al paradigma de la salud inteligente, centrado en la prestación de servicios de salud personalizados y de valor añadido mediante la explotación significativa de grandes cantidades de datos sanitarios, de movilidad y contextuales. No obstante, la gestión de datos sanitarios, desde su recogida hasta su análisis, plantea una serie de cuestiones desafiantes debido a su naturaleza altamente confidencial. Esta tesis tiene por objetivo abordar varios retos de seguridad y privacidad dentro del paradigma de la salud inteligente. Los resultados de esta tesis pretenden ayudar a la comunidad científica a mejorar la seguridad de los entornos inteligentes del futuro, así como la privacidad de los ciudadanos con respecto a sus datos personales y sanitarios.Information and communication technologies have irreversibly changed our lives. The healthcare industry, one of the world’s largest and fastest-growing industries, is dedicating many efforts in adopting the latest technologies into daily medical practice. It is not therefore surprising that healthcare paradigms are constantly evolving seeking for more efficient, effective and sustainable services. In this context, the potential of ubiquitous computing through smartphones, smartwatches, wearables and IoT devices has become fundamental to collect large volumes of data, including people's health status and people’s location. The enhanced sensing capabilities together with the emergence of high-speed telecommunication networks have facilitated the implementation of context-aware environments, such as smart homes and smart cities, able to adapt themselves to the citizens needs. The interplay between ubiquitous computing and context-aware environments opened the door to the so-called smart health paradigm, focused on the provision of added-value personalised health services by meaningfully exploiting vast amounts of health, mobility and contextual data. However, the management of health data, from their gathering to their analysis, arises a number of challenging issues due to their highly confidential nature. In particular, this dissertation addresses several security and privacy challenges within the smart health paradigm. The results of this dissertation are intended to help the research community to enhance the security of the intelligent environments of the future as well as the privacy of the citizens regarding their personal and health data

A Novel Data-Driven Fault Tree Methodology for Fault Diagnosis and Prognosis

RÉSUMÉ : La thèse développe une nouvelle méthodologie de diagnostic et de pronostic de défauts dans un système complexe, nommée Interpretable logic tree analysis (ILTA), qui combine les techniques d’extraction de connaissances à partir des bases de données « knowledge discovery in database (KDD) » et l’analyse d’arbre de défaut « fault tree analysis (FTA) ». La méthodologie capitalise les avantages des deux techniques pour appréhender la problématique de diagnostic et de pronostic de défauts. Bien que les arbres de défauts offrent des modèles interprétables pour déterminer les causes possibles à l’origine d’un défaut, leur utilisation pour le diagnostic de défauts dans un système industriel est limitée, en raison de la nécessité de faire appel à des connaissances expertes pour décrire les relations de cause-à-effet entre les processus internes du système. Cependant, il sera intéressant d’exploiter la puissance d’analyse des arbres de défaut mais construit à partir des connaissances explicites et non biaisées extraites directement des bases de données sur la causalité des fautes. Par conséquent, la méthodologie ILTA fonctionne de manière analogue à la logique du modèle d'analyse d'arbre de défaut (FTA) mais avec une implication minimale des experts. Cette approche de modélisation doit rejoindre la logique des experts pour représenter la structure hiérarchique des défauts dans un système complexe. La méthodologie ILTA est appliquée à la gestion des risques de défaillance en fournissant deux modèles d'arborescence avancés interprétables à plusieurs niveaux (MILTA) et au cours du temps (ITCA). Le modèle MILTA est conçu pour accomplir la tâche de diagnostic de défaillance dans les systèmes complexes. Il est capable de décomposer un défaut complexe et de modéliser graphiquement sa structure de causalité dans un arbre à plusieurs niveaux. Par conséquent, un expert est en mesure de visualiser l’influence des relations hiérarchiques de cause à effet menant à la défaillance principale. De plus, quantifier ces causes en attribuant des probabilités aide à comprendre leur contribution dans l’occurrence de la défaillance du système. Le modèle ITCA est conçu pour réaliser la tâche de pronostic de défaillance dans les systèmes complexes. Basé sur une répartition des données au cours du temps, le modèle ITCA capture l’effet du vieillissement du système à travers de l’évolution de la structure de causalité des fautes. Ainsi, il décrit les changements de causalité résultant de la détérioration et du vieillissement au cours de la vie du système.----------ABSTRACT : The thesis develops a new methodology for diagnosis and prognosis of faults in a complex system, called Interpretable logic tree analysis (ILTA), which combines knowledge extraction techniques from knowledge discovery in databases (KDD) and the fault tree analysis (FTA). The methodology combined the advantages of the both techniques for understanding the problem of diagnosis and prognosis of faults. Although fault trees provide interpretable models for determining the possible causes of a fault, its use for fault diagnosis in an industrial system is limited, due to the need for expert knowledge to describe cause-and-effect relationships between internal system processes. However, it will be interesting to exploit the analytical power of fault trees but built from explicit and unbiased knowledge extracted directly from databases on the causality of faults. Therefore, the ILTA methodology works analogously to the logic of the fault tree analysis model (FTA) but with minimal involvement of experts. This modeling approach joins the logic of experts to represent the hierarchical structure of faults in a complex system. The ILTA methodology is applied to failure risk management by providing two interpretable advanced logic models: a multi-level tree (MILTA) and a multilevel tree over time (ITCA). The MILTA model is designed to accomplish the task of diagnosing failure in complex systems. It is able to decompose a complex defect and graphically model its causal structure in a tree on several levels. As a result, an expert is able to visualize the influence of hierarchical cause and effect relationships leading to the main failure. In addition, quantifying these causes by assigning probabilities helps to understand their contribution to the occurrence of system failure. The second model is a logical tree interpretable in time (ITCA), designed to perform the task of prognosis of failure in complex systems. Based on a distribution of data over time, the ITCA model captures the effect of the aging of the system through the evolution of the fault causation structure. Thus, it describes the causal changes resulting from deterioration and aging over the life of the system