The pandemic of coronavirus Disease 2019 (COVID-19) caused enormous loss of life globally. 1-3 Case identification is critical. The reference method is using real-time reverse transcription PCR (rRT-PCR) assays, with limitations that may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that application of deep learning (DL) to the 3D CT images could help identify COVID-19 infections. Using the data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 patients. COVIDNet achieved an accuracy rate of 94.3% and an area under the curve (AUC) of 0.98. Application of DL to CT images may improve both the efficiency and capacity of case detection and long-term surveillance.
This paper presents an approach to determine the pose of a robot manipulator by using a single fixed camera. Conventionally, the pose determination is usually achieved by using the encoders to sense the joint angles, and then the pose of the end effector is obtained by using the direct kinematics of the manipulator. However, when the encoders or the manipulators are malfunctioning, the pose may not be accurately determined. This paper presents an approach based on machine vision, where a single camera is fixed away from the base of the manipulator. Besides, based on the kinematics of the manipulator and a calibrated camera, the pose of the manipulator can be determined. Furthermore, a graphical user interface is developed, which is convenient for users to operate the entire system. Two examples are demonstrated, and the estimated results are compared with those from the encoders. The proposed approach does not compete with the encoders. Instead, the approach can be treated as a backup method, which can provide a reference solution.INDEX TERMS Pose determination, robot manipulator, monocular vision.
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