205 research outputs found

    Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

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    Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-

    Integrated control of next generation power system

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    SHELDON Smart habitat for the elderly.

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    An insightful document concerning active and assisted living under different perspectives: Furniture and habitat, ICT solutions and Healthcare

    Advanced sensors technology survey

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    This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

    Smart Industry - Better Management

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    The ebook edition of this title is Open Access and freely available to read online. Smart industry requires better management. As industrial and production systems are future-proofed, becoming smart and interconnected through use of new manufacturing and product technologies, work is advancing on improving product needs, volume, timing, resource efficiency, and cost, optimally using supply chains. Presenting innovative, evidence-based, and cutting-edge case studies, with new conceptualizations and viewpoints on management, Smart Industry, Better Management explores concepts in product systems, use of cyber physical systems, digitization, interconnectivity, and new manufacturing and product technologies. Contributions to this volume highlight the high degree of flexibility in people management, production, including product needs, volume, timing, resource efficiency and cost in being able to finely adjust to customer needs and make full use of supply chains for value creation. Smart Industry, Better Management illustrates how industry can enabled by a more network-centric approach, making use of the value of information and the latest available proven manufacturing techniques

    Development of a low power reactive wireless chemical sensing network

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    A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

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    This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing

    A Survey of the European Security Market

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    This document synthesizes the results of the research made on the European security market. It deals with questions of interest regarding the provision of security goods and services for protecting society from terrorism and organised crime. It explores issues such as market revenues, demand and supply, industrial capabilities, technology, research and development, innovation, business strategies, competition as well as market structure, agents' conduct and economic performance. The research has been based upon desk analysis of open source information related to the security market. Economic theory and critical analysis has been applied to understand the gathered information, derive knowledge, point out key issues and assess trends and drivers that will likely shape the sector's future. The study is the outcome of the working package number 5 included in the research project A new Agenda for European Security Economics (EUSECON). This project with code number 218195 has been financed by the European Commission within the 7th European Research Framework Programme. The task has been performed by the company ISDEFE according to the scope and work plan described in the EUSECON proposal. The author wishes to express his appreciation to all the individuals that have provided input and valuable comments to this study, including anonymous referees. Any flaws or omissions contained in this document are solely the responsibility of the author.

    Deep Learning and parallelization of Meta-heuristic Methods for IoT Cloud

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    Healthcare 4.0 is one of the Fourth Industrial Revolution’s outcomes that make a big revolution in the medical field. Healthcare 4.0 came with more facilities advantages that improved the average life expectancy and reduced population mortality. This paradigm depends on intelligent medical devices (wearable devices, sensors), which are supposed to generate a massive amount of data that need to be analyzed and treated with appropriate data-driven algorithms powered by Artificial Intelligence such as machine learning and deep learning (DL). However, one of the most significant limits of DL techniques is the long time required for the training process. Meanwhile, the realtime application of DL techniques, especially in sensitive domains such as healthcare, is still an open question that needs to be treated. On the other hand, meta-heuristic achieved good results in optimizing machine learning models. The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT technologies are crucial in enhancing several real-life smart applications that can improve life quality. Cloud Computing has emerged as a key enabler for IoT applications because it provides scalable and on-demand, anytime, anywhere access to the computing resources. In this thesis, we are interested in improving the efficacity and performance of Computer-aided diagnosis systems in the medical field by decreasing the complexity of the model and increasing the quality of data. To accomplish this, three contributions have been proposed. First, we proposed a computer aid diagnosis system for neonatal seizures detection using metaheuristics and convolutional neural network (CNN) model to enhance the system’s performance by optimizing the CNN model. Secondly, we focused our interest on the covid-19 pandemic and proposed a computer-aided diagnosis system for its detection. In this contribution, we investigate Marine Predator Algorithm to optimize the configuration of the CNN model that will improve the system’s performance. In the third contribution, we aimed to improve the performance of the computer aid diagnosis system for covid-19. This contribution aims to discover the power of optimizing the data using different AI methods such as Principal Component Analysis (PCA), Discrete wavelet transform (DWT), and Teager Kaiser Energy Operator (TKEO). The proposed methods and the obtained results were validated with comparative studies using benchmark and public medical data
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