ABSTRACT
Designing a senior-level course that involves problem-based learning, including project completion task, is laborious and challenging. A well-designed project motivates the students to be self-learners and prepares them for future industrial or academic endeavors. The COVID-19 pandemic brought many challenges when instructions were forced to move either online or to a remote teaching/learning environment. Due to this rapid transition, delivery modes in teaching and learning modalities faced disruption making course design more difficult. The senior level Flight Controls course AME - 4513 is designed with Unmanned Aerial Systems (UAS) related projects for the students to have a better understanding of UAS usage on various applications in support of Advanced Technological Education (ATE) program. The purpose of this paper is to present the UAS lab modules in a junior level robotics lab, AME - 4802, which preceded the Flight Controls course in the school of Aerospace and Mechanical Engineering at the University of Oklahoma. Successfully completing the course project requires independent research and involves numerical simulations of UAS. The Robotics Lab course focuses on hands-on projects of robotic systems with an emphasis on semi-autonomous mobile robots, including an UAS introduction module. • The UAS module in the Robotics Lab class is introduced in Spring 2020. Therefore, most of the students enrolled in the Spring 2020 Robotics Lab course have introductory knowledge about the UAS system when taking the Fall 2020 Flight Control course. In addition, Spring 2020 Robotics Lab was affected due to COVID-19. • The UAS module was not introduced in 2019 Spring Robotics lab. Thus, the students enrolled in Fall 2019 Flight Controls course did not have prior knowledge on the UAS system. • We thus present the implementation of UAS module in a junior level robotics lab which preceded the senior level Flight Controls course in following Fall semester, when the same instructor taught the course. © American Society for Engineering Education, 2022.
ABSTRACT
Healthcare 4.0 has changed dramatically over the last century. It is evolving daily, with physicians and researchers alike developing new tools and strategies. This study analyzes the impact of Industry 4.0 on healthcare (IHC) systems. Therefore, using PRISMA 2015, a systematic literature review was undertaken utilizing articles retrieved from Scopus and Web of Science. A bibliometric and qualitative assessment of 346 and 47 articles was performed. An IHC framework was developed considering the following components: scheduling problems, security issues, COVID-19, digital supply chain, blockchain technology, and artificial intelligence. The study found that during COVID-19, healthcare, and Industry4.0 fused and evolved together, addressing issues including data security, resource allocation, and data transparency. IHC enables a variety of technologies, including the internet of things (IoT), blockchain, big data, cloud computing, machine learning, and information and communication technologies (ICT), to track patient records and contribute to the reduction of social transmission of COVID-19. © 2022 The Author(s)
ABSTRACT
The recent spread of severe acute respiratory syndrome coronavirus 2 and its associated coronavirus disease has caused extensive public health concerns. University campuses are at higher risks since a lot of students are present inside the campus at a given point of time. Places where there are a lot of chances of spread of the infection in the campus include the entrance gate, canteen, library, photocopy center, seminar hall, etc. Strict actions must be taken against the violations of the covid-19 protocols which will ensure health safety and maintain hygiene in the campus. Doing this manually will be a tedious task. Owing to this problem, an attempt has been made to design a system to tackle the problem of following all the protocols and making everyone aware about the situation in the campus. This work proposes a system which will continuously monitor all these activities with the help of Computer Vision and Deep Learning. The collected CCTV cameras data has been checked in the real time mode using various object detection and object tracking models to identify and track the objects visible in the frame. This approach uses MobileNet and SSD Architecture along with the objection detection models to predict the desired output. Finally, based on the output the system checks for any violations and if encountered then it sends a text alert to the concerned authority. © 2021 IEEE.