ABSTRACT
COVID-19 has lately infected a big number of people worldwide. Medical service frameworks are strained as a result of the infection. The emergency unit, which is part of the medical services area, has experienced several challenges as a result of the low data quality offered by existing ICU clinical equipment. The Internet of Things has enhanced the capability for essential information mobility in medical services in the twenty-first century. Nonetheless, many of today's ideal models use IoT innovation to assess patients' well-being. As a result, executives lack understanding regarding the most effective method to apply such innovation to ICU clinical equipment. The IoT Based Paradigm for Medical Equipment Management Systems, a breakthrough IoT-based paradigm for successfully administering clinical hardware in ICUs, is introduced in this study. During the COVID-19 episode, IoT technology is used to boost the data stream between clinical hardware, executive frameworks, and ICUs, enabling the maximum level of openness and reasonableness in clinical equipment redistribution. IoT MEMS conceptual and functional features were painstakingly drawn. Using IoT MEMS expands the capacity and limits of emergency clinics, effectively easing COVID-19. It will also have a substantial impact on the nature of the data and will improve the partners' trust and transparency. © 2022 IEEE.
ABSTRACT
Coronaviruses are a group of viruses from the subfamily Orthocronavirinae in the Coronaviridae family and the order Nidovirales. In general, the transmission of this virus occurs through droplets or body fluids splashed on someone or objects around them within 1-2 meters' distance away through coughing and sneezing. Office buildings, cafes, and shopping areas are one of the clusters for spreading the coronavirus because the following places lack awareness of health protocols. This research will create a healthy and Smart Building system where this system consists of two devices from the following problems. The first device is a health procedures checker system where using an ultrasonic sensor as an automatic switch can provide a hand sanitizer as the first step to kill viruses. The GY609 sensor is used, which can measure the temperature of visitors without touching it. The second system is the Smart Switch;the doors and lights can control using voice using google assistant to minimize the doorknob's touch or the light switch. The healthy and Smart building test results are for the intelligent, healthy procedures system, 3-7 cm is the optimal distance from the hand to the sensor, 5cm is the distance from the hand to the temperature sensor, and the motor working time is 1 second. For the Smart switches, the recommended network system is 3G, 4G, or Wi-Fi to avoid excessive delays. © 2022 IEEE.
ABSTRACT
The system aims to direct the user to create their network system collaboratively for a case of Covid-19 health throughout a case study in a Web System and E-Commerce course. The framework of directed health learning is based on 7x2C content knowledge established criteria to assess the effectiveness and efficiency of the system. The self-design frame framework is plan-oriented and based on the concept of a plan and plans integrations and special relationships. The user is directed to break the system into plans and the design is to self-guide the user to build a system to comprehend, combat, coexist, cope, and trace COVID-19 with four layers of diagnostics, simulation, and pattern matching database. With the collaboration of users' systems, a fact from one user as output can be transferred to another user as an input in a circulation forming a general fact. Consequently, the transfer of learning from one system flows into another system resulting in a pattern to be found. Based on the pattern an algorithm will formulate to tackle a solution to COVID19. The implication of this study will be a guideline for others to initiate their own participant's system to find a pattern and formulate an algorithm for the pandemic. The idea of self-design and self-directed learning can be transferred to other fields of study covid-19 health. At present time, a parallel case study of goods and services on farming of Sunchoke plant has been directed with three plan themes of Grow, Eat, and Heal.) © 2021 IEEE.
ABSTRACT
In this paper, we present the technical design of a virtual CAN network system allowing engineers from different sites to work on a single CAN segment. The system was originally developed for interconnecting students in online university classes, due to COVID-19 pandemic – to learn together the principles of SAE J1939 and ISO 11783 technologies in guided programming exercises. The developed system is based on a centralized server located on the university network and multiple clients connecting to the central server. The protocol to tunnel CAN messages is based on TCP/IP. The unsecured CAN tunnel operates in a secured VPN tunnel. The system design leverages virtual CAN channels provided by two dongle manufactures: Kvaser and Vector – this driver level technology also allows easy access of PC software to the virtual CAN network without any physical CAN hardware. Within the university network, round trip times of under 50 ms were recorded between bus segments when all users were in Germany. © 2022, VDI Verlag GMBH. All rights reserved.
ABSTRACT
Using Deep Learning methods might be a proper answer to the need of the world for a fast, automatic solution for COVID-19 early-stage diagnosis. This article tries to take advantage of Convolutional Neural Network (CNN) systems for this purpose. Our proposed model is based on a CNN network and is trained based on the COUGHVID dataset. By implementing feature extraction using MFCC and using data augmentation methods, we tried to develop a fully functional model. The results show there were improvements compared to other state-of-the-art projects. Based on the metrics used in this work, we achieved an area under the curve of the receiver operating characteristics (AUC-ROC) of 0.94 on the task of COVID-19 classification. © 2022 IEEE.
ABSTRACT
As the COVID-19 becomes more prevalent worldwide, many countries take various measures to minimize the spread of infection. Since the main route of transmission of covid-19 is through the air, an analysis of transport, particularly global air traffic nodes, will provide a visual representation of the impact of covid-19 on the worldwide air transport industry. This paper uses publicly available aviation data to model the network and analyze the topology of the world aviation network before and after the COVID-19 outbreak in 2019 and 2020 to analyze the impact of the covid-19 epidemic on the world's aviation industry. First, we successfully visualized the significance of the change in the number of flight routes before and after the outbreak and the different distribution in each region by modeling the worldwide airline traffic network. Then, after a series of analyses and investigations. Second, we collected open-source data showing that the overall number of flights worldwide has been downward following the COVID-19 outbreak. Based on this information, we have chosen to conduct specific studies of countries and regions where there have been significant changes since the outbreak of covid-19, combined with reasonable hypotheses and analysis of local traffic control policies, and deduced that covid-19 had affected people's lives more from a policy rather than a medical perspective. Finally, we built up visual analysis images and tables to base our research using open-source aviation data sites such as open-fight. The results show that the analyzed aviation networks exhibit small-world characteristics, with the total number of flights not changing significantly due to the outbreak. However, the number of routes to the most crucial airport nodes worldwide decreases, and centrality diminishes, and the number of direct flights reductions and the increase of connecting flights. © 2021 IEEE.