20 research outputs found

    Real-Time Sensor Networks and Systems for the Industrial IoT

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    The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected

    Convergence of packet communications over the evolved mobile networks; signal processing and protocol performance

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    In this thesis, the convergence of packet communications over the evolved mobile networks is studied. The Long Term Evolution (LTE) process is dominating the Third Generation Partnership Project (3GPP) in order to bring technologies to the markets in the spirit of continuous innovation. The global markets of mobile information services are growing towards the Mobile Information Society. The thesis begins with the principles and theories of the multiple-access transmission schemes, transmitter receiver techniques and signal processing algorithms. Next, packet communications and Internet protocols are referred from the IETF standards with the characteristics of mobile communications in the focus. The mobile network architecture and protocols bind together the evolved packet system of Internet communications to the radio access network technologies. Specifics of the traffic models are shortly visited for their statistical meaning in the radio performance analysis. Radio resource management algorithms and protocols, also procedures, are covered addressing their relevance for the system performance. Throughout these Chapters, the commonalities and differentiators of the WCDMA, WCDMA/HSPA and LTE are covered. The main outcome of the thesis is the performance analysis of the LTE technology beginning from the early discoveries to the analysis of various system features and finally converging to an extensive system analysis campaign. The system performance is analysed with the characteristics of voice over the Internet and best effort traffic of the Internet. These traffic classes represent the majority of the mobile traffic in the converged packet networks, and yet they are simple enough for a fair and generic analysis of technologies. The thesis consists of publications and inventions created by the author that proposed several improvements to the 3G technologies towards the LTE. In the system analysis, the LTE showed by the factor of at least 2.5 to 3 times higher system measures compared to the WCDMA/HSPA reference. The WCDMA/HSPA networks are currently available with over 400 million subscribers and showing increasing growth, in the meanwhile the first LTE roll-outs are scheduled to begin in 2010. Sophisticated 3G LTE mobile devices are expected to appear fluently for all consumer segments in the following years

    Human-Machine Interfaces using Distributed Sensing and Stimulation Systems

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    As the technology moves towards more natural human-machine interfaces (e.g. bionic limbs, teleoperation, virtual reality), it is necessary to develop a sensory feedback system in order to foster embodiment and achieve better immersion in the control system. Contemporary feedback interfaces presented in research use few sensors and stimulation units to feedback at most two discrete feedback variables (e.g. grasping force and aperture), whereas the human sense of touch relies on a distributed network of mechanoreceptors providing a wide bandwidth of information. To provide this type of feedback, it is necessary to develop a distributed sensing system that could extract a wide range of information during the interaction between the robot and the environment. In addition, a distributed feedback interface is needed to deliver such information to the user. This thesis proposes the development of a distributed sensing system (e-skin) to acquire tactile sensation, a first integration of distributed sensing system on a robotic hand, the development of a sensory feedback system that compromises the distributed sensing system and a distributed stimulation system, and finally the implementation of deep learning methods for the classification of tactile data. It\u2019s core focus addresses the development and testing of a sensory feedback system, based on the latest distributed sensing and stimulation techniques. To this end, the thesis is comprised of two introductory chapters that describe the state of art in the field, the objectives, and the used methodology and contributions; as well as six studies that tackled the development of human-machine interfaces

    Modelling and performance analysis of multigigabit serial interconnects using real number based analog verification methods

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    The increasing importance of multigigabit transceiver circuits in modern chip design calls for new methods of analyzing and integrating these challenging building blocks. This work presents a design and analysis framework basend on the SystemVerilog real number modeling ansatz. It further extends the simulation possibilities thus obtained by introducing additional higher level numeric modelling and evaluation methods to support multigigabit statistical link budgeting procedures based on the Peak Distortion Algorithm

    Improving Access and Mental Health for Youth Through Virtual Models of Care

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    The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

    The Impact of Digital Technologies on Public Health in Developed and Developing Countries

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    This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

    The Impact of Digital Technologies on Public Health in Developed and Developing Countries

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    This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

    Selected Papers from the 5th International Electronic Conference on Sensors and Applications

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    This Special Issue comprises selected papers from the proceedings of the 5th International Electronic Conference on Sensors and Applications, held on 15–30 November 2018, on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 5th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Papers which attracted the most interest on the web or that provided a particularly innovative contribution were selected for publication in this collection. These peer-reviewed papers are published with the aim of rapid and wide dissemination of research results, developments, and applications. We hope this conference series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications

    Machine learning for fast and accurate assessment of earthquake source parameters

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    Erdbeben gehören zu den zerstörerischsten Naturgefahren auf diesem Planeten. Obwohl Erdbeben seit Jahrtausenden dokumentiert sing, bleiben viele Fragen zu Erdbeben unbeantwortet. Eine Frage ist die Vorhersagbarkeit von Brüchen: Inwieweit ist es möglich, die endgültige Größe eines Bebens zu bestimmen, bevor der zugrundeliegende Bruchprozess endet? Diese Frage ist zentral für Frühwarnsysteme. Die bisherigen Forschungsergebnisse zur Vorhersagbarkeit von Brüchen sind widersprüchlich. Die Menge an verfügbaren Daten für Erdbebenforschung wächst exponentiell und hat den Tera- bis Petabyte-Bereich erreicht. Während viele klassische Methoden, basierend auf manuellen Datenauswertungen, hier ihre Grenzen erreichen, ermöglichen diese Datenmengen den Einsatz hochparametrischer Modelle und datengetriebener Analysen. Insbesondere ermöglichen sie den Einsatz von maschinellem Lernen und deep learning. Diese Doktorarbeit befasst sich mit der Entwicklung von Methoden des maschinellen Lernens zur Untersuchung zur Erbebenanalyse. Wir untersuchen zuerst die Kalibrierung einer hochpräzisen Magnitudenskala in einem post hoc Scenario. Nachfolgend befassen wir uns mit Echtzeitanalyse von Erdbeben mittels deep learning. Wir präsentieren TEAM, eine Methode zur Frühwarnung. Auf TEAM aufbauend entwickeln wir TEAM-LM zur Echtzeitschätzung von Lokation und Magnitude eines Erdbebens. Im letzten Schritt untersuchen wir die Vorhersagbarkeit von Brüchen mittels TEAM-LM anhand eines Datensatzes von teleseismischen P-Wellen-Ankünften. Dieser Analyse stellen wir eine Untersuchung von Quellfunktionen großer Erdbeben gegenüber. Unsere Untersuchung zeigt, dass die Brüche großer Beben erst vorhersagbar sind, nachdem die Hälfte des Bebens vergangen ist. Selbst dann können weitere Subbrüche nicht vorhergesagt werden. Nichtsdestotrotz zeigen die hier entwickelten Methoden, dass deep learning die Echtzeitanalyse von Erdbeben wesentlich verbessert.Earthquakes are among the largest and most destructive natural hazards known to humankind. While records of earthquakes date back millennia, many questions about their nature remain open. One question is termed rupture predictability: to what extent is it possible to foresee the final size of an earthquake while it is still ongoing? This question is integral to earthquake early warning systems. Still, research on this question so far has reached contradictory conclusions. The amount of data available for earthquake research has grown exponentially during the last decades reaching now tera- to petabyte scale. This wealth of data, while making manual inspection infeasible, allows for data-driven analysis and complex models with high numbers of parameters, including machine and deep learning techniques. In seismology, deep learning already led to considerable improvements upon previous methods for many analysis tasks, but the application is still in its infancy. In this thesis, we develop machine learning methods for the study of rupture predictability and earthquake early warning. We first study the calibration of a high-confidence magnitude scale in a post hoc scenario. Subsequently, we focus on real-time estimation models based on deep learning and build the TEAM model for early warning. Based on TEAM, we develop TEAM-LM, a model for real-time location and magnitude estimation. In the last step, we use TEAM-LM to study rupture predictability. We complement this analysis with results obtained from a deep learning model based on moment rate functions. Our analysis shows that earthquake ruptures are not predictable early on, but only after their peak moment release, after approximately half of their duration. Even then, potential further asperities can not be foreseen. While this thesis finds no rupture predictability, the methods developed within this work demonstrate how deep learning methods make a high-quality real-time assessment of earthquakes practically feasible
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