Artificial Intelligence
The data in this file include not only original data which is not preprocessed, but also training data to train our deep learning : bucket tip trajectory planning AI.
These csv files includes pressure, position, and angle of bucket tip, and current time, which were measured in real time excavation. Also, the point cloud is captured before each excavation.
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Biometric management and that to which uses face, is indeed a very challenging work and requires a dedicated dataset which imbibes in it variations in pose, emotion and even occlusions. The Current work aims at delivering a dataset for training and testing purposes.SJB Face dataset is one such Indian face image dataset, which can be used to recognize faces. SJB Face dataset contains face images which were collected from digital camera. The face dataset collected has certain conditions such as different pose, Expressions, face partially occluded and with a uniform attire.
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The dataset contains recorded and synthesized ASMR audio clips, as well as their corresponding psychological survey results, see details in the linked paper
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For academic purposes, we are happy to release our datasets. This dataset is in support of my research paper 'TOW-IDS: Intrusion Detection System based on Three Overlapped Wavelets in Automotive Ethernet'. If you want to use our dataset for your experiment, please cite our paper.
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Evolving from the well-known ray-tracing dataset DeepMIMO, the DeepVerse 6G dataset additionally provides multi-modal sensing data generated from various emulators. These emulators provide the wireless, radar, LiDAR, vision and position data. With a parametric generator, the DeepVerse dataset can be customized by the user for various communication and sensing applications.
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This data set provides complex-baseband samples of vehicle-to-vehicle communication (V2V) radios in the presence of primitive jamming signals. Up to 600 vehicles are simulated in this dataset using commerical IEEE 802.11p radios. Supporting example code found on CodeOcean can be used for developing novel physical layer based jamming detection and mitigation strategies using machine learning for ad hoc vehicular networks.
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Beam management is a challenging task for millimeter wave (mmWave) and sub-terahertz communication systems, especially in scenarios with highly-mobile users. Leveraging external sensing modalities such as vision, LiDAR, radar, position, or a combination of them, to address this beam management challenge has recently attracted increasing interest from both academia and industry. This is mainly motivated by the dependency of the beam direction decision on the user location and the geometry of the surrounding environment---information that can be acquired from the sensory data.
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This work presents a dataset based on multiple metrics namely KQIs, which provide the E2E conditions of different services. Particularly, the dataset considers video streaming and cloud gaming (CG) services.
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This dataset provides wireless measurements from two industrial testbeds: iV2V (industrial Vehicle-to-Vehicle) and iV2I+ (industrial Vehicular-to-Infrastructure plus sensor).
iV2V covers 10h of sidelink communication scenarios between 3 Automated Guided Vehicles (AGVs), while iV2I+ was conducted for around 16h at an industrial site where an autonomous cleaning robot is connected to a private cellular network.
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The complete description of the dataset can be found at: https://arxiv.org/abs/2305.03170
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