Artificial Intelligence
Objective: The human hand is known to have excellent manipulation ability compared to other primate hands. Without the palm movements, the human hand would lose more than 40% of its functions. However, uncovering the constitution of palm movements is still a challenging problem involving kinesiology, physiology, and engineering science. Methods: By recording the palm joint angles during common grasping, gesturing, and manipulation tasks, we built a palm kinematic dataset.
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102 Views
This dataset investigates the suitability of different filters for Learning-Based Motion Magnification (LBMM) and examines the impact of filter parameters on output results. The study finds that the Butterworth filter produces satisfactory results, while the analysis of IIR filters is unsatisfactory due to computational and memory limitations. Additionally, the efficacy of IIR filters for image processing and the reliability of FIR filters are called into question.
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104 Views
With the increasing use of drones for surveillance and monitoring purposes, there is a growing need for reliable and efficient object detection algorithms that can detect and track objects in aerial images and videos. To develop and test such algorithms, datasets of aerial videos captured from drones are essential.
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956 Views
This data set contains three activities of laying up, passing, and shooting of nine professional basketball players, collected and processed by Yu Zhou, Chuanshi Xie, and Yufan Wang.
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140 ViewsThe dataset contains basketball activity data for nine varsity basketball players of professional skill levels. Each player wore a smart bracelet on their right wrist to record activity data during the event. The smart bracelet contains an accelerometer and gyroscope that collects acceleration and angular velocity information, and it has a sampling frequency of 50 Hz. The basketball activities of the players are laying up, passing and shooting, which are defined as shown in Table 1.
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70 Views
Soft robots are a promising area of research due to their potential use in various applications. Learning the kinematics of soft robots is crucial for their advancement and application. This dataset is designed to provide training data for the development of machine learning models that can learn the kinematics of soft robots with different actuation types. The dataset includes the positional data of three soft robots, specifically the simulated pneumatic soft robot, simulated tendon-driven soft robot, and real-world tendon-driven soft robot.
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40 Views
This dataset is used for TKGIN
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275 Views
A CNC adapter was utilized together with the software established as part of the GRBL project to operate the CNC adapter, and two data sets were produced for the physical model in order to build the linear and circular motion models. The parameters for motion quantity, motion duration, and feed rate are in the data set.
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317 Views
The codes of the physics-based non-local dual-domain network (PND-Net) for metal artifact reduction are submitted here.
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209 Views
For the UNB-GCN classification model, UNB as the input feature vector to the graph structure node, phenotypic information, such as gender, age and APOE genetic information, as the weight of the edges between nodes.
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94 Views