Search for: All records

The COVID-19 pandemic has highlighted the importance of diagnosis and monitoring as early and accurately as possible. However, the reverse-transcription polymerase chain reaction (RT-PCR) test results in two issues: (1) protracted turnaround time from sample collection to testing result and (2) compromised test accuracy, as low as 67%, due to when the test is administered and due to how the samples are collected, handled, and delivered to the lab to conduct the RT-PCR test. Thus, we present ComputeCOVID19+, our computed tomography-based framework to improve the testing speed and accuracy of COVID-19 (plus its variants) via a deep learning-based network for CT image enhancement called DDnet. To demonstrate its speed and accuracy, we evaluate ComputeCOVID19+ across many sources of computed tomography (CT) images and on many heterogeneous platforms, including multi-core CPU, many-core GPU, and even FPGA. Our results show that ComputeCOVID19+ can significantly shorten the turnaround time from days to minutes and improve the testing accuracy to 91%. 

3D Printing (3DP), also known as Additive Manufacturing (AM) is the latest production technology. Its popularity in fabricating functional parts in all fields is growing day by day. The range of 3D printed products is limitless, including glass frames to hearing aids. It is thus important to train educators and students regarding this cutting-edge technology so that they become familiar with the functionality and implementation of it in several courses, laboratories, and projects. This paper reports several novel developments which have been implemented in the past few years, including details of these unique practices and feedback received from the educators and students.