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Using the PVIFS-02 whole-sky imagers, we collected 500,000 independent cloud images from 2021 to 2023, captured in a southern city and a northern city in China. The cloud images collected in southern China are clear, with obvious cloud edges. In contrast, the cloud images from northern China appear relatively blurred. This difference is attributed to the geographical characteristics of northern China, where regions are frequently affected by sand and dust, leading to a certain degree of image blurring. It brings challenges to cloud detection and classification.
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Activities of daily living (ADLs) monitoring is essenitial in elderly field as it provides daily activities information for caregivers. In human daily life, different activities occur in various locations and generate distinct sounds and body movement. We can combine this kind of information to recognize the activities.
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We prepared a new dataset (Cov-Pneum) for X-ray images by processing and merging three well-known publicly available datasets from Kaggle [1]–[4]. This dataset includes a total of 21,272 CXR images of COVID-19 (COVID-19 virus-infected lung), pneumonia (pneumonia-infected lung), and normal (clear lung) and consisting of images 4296, 5824, and 11152, respectively. We applied image scaling and preprocessing operations to enhance the quality of these CXR images.
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The C3I Synthetic Human Dataset provides 48 female and 84 male synthetic 3D humans in fbx format generated from iClone 7 Character creator “Realistic Human 100” toolkit with variations in ethnicity, gender, race, age, and clothing. For each of these, it further provides the full-body model with five different facial expressions – Neutral, Angry, Sad, Happy, and Scared. Along with the body models, it also open-sources a data generation pipeline written in python to bring those models into a 3D Computer Graphics tool called Blender.
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