Artificial Intelligence

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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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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538 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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The 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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This dataset is used for TKGIN

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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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The codes of the physics-based non-local dual-domain network (PND-Net) for metal artifact reduction are submitted here.

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177 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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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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18 Views

The historical dataset of the meteorological variables was recorded by the Mexican National Water Council (Comisión Nacional del Agua, CONAGUA) ground station located at the city of Mérida, Yucatán, Mexico; the following variables were found: temperature (T), vapor pressure (P), and relative humidity (H). The range dates of the records were from January 1, 2000 to September 30, 2018, where there is a daily record of temperatures with minimum, maximum, and average units, resulting in nine readings provided for each day.

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