Standards Research Data
This dataset is created with the usage of Galvanic Skin Response Sensor and Electrocardiogram sensor of MySignals Healthcare Toolkit. MySignals toolkit consists of the Arduino Uno board and different sensor ports. The sensors were connected to the different ports of the hardware kit which was controlled by Arduino SDK.
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The dataset contains:
- performance for random parameter values for the Embree datastructure on different scenes
- specific experiment data regarding the stability of triangle splitting, characterize by the angle of specific geometry
- partial tuning experiments, where parameters would be optimized while others would stay set
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Capacity measurement data for research project The Effectiveness of Charge Limiting and Partial Charge Limiting in Smartphones
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dateset of Research on Optimization for LogGP Data Transmission Evaluation Model
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dateset of Research on Optimization for LogGP Data Transmission Evaluation Model
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Database for a technological acceptance research
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This data is extracted SAV feature value from the raw reflectometry signal.
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Representative, normalized and flattened axial particle displacement fields of surface acoustic wave (SAW) propagating in in-vivo human skin at different sites used to generate Fig. 5 in Zhou's study "A Weighted Average Phase Velocity Inversion Model for Depth-Resolved Elasticity Evaluation in Human Skin In-Vivo".
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Normalized, flattened axial particle displacement fields of surface acoustic wave (SAW) propagating in multi-layered agar phantoms (three two-layer agar phantom and one three-layer agar phantom) used to generate Fig. 2 in Zhou's study "A Weighted Average Phase Velocity Inversion Model for Depth-Resolved Elasticity Evaluation in Human Skin In-Vivo".
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We present here one of the first studies that attempt to differentiate between genuine and acted emotional expressions, using EEG data. We present the first EEG dataset with recordings of subjects with genuine and fake emotional expressions. We build our experimental paradigm for classification of smiles; genuine smiles, fake/acted smiles and neutral expression. For the full details please refere to our paper entitled:
Discrimination of Genuine and Acted Emotional Expressions using EEG Signal and Machine Learning
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