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

Instructions: 
  • The groups refer to groups of batteries charged with the same charging method
    • Group 1: Full Charge Limiting
    • Group 2: Partial Charge Limiting
    • Group 3: Conventional Charging
  • The first line of each file states the voltage value for 1023
  • Lines with a t following the arrow are voltage measurement
  • The number after t represents the battery number in a group
  • The first voltage field is discharging voltage
  • The second voltage field is the open circuit voltage
  • The batteries discharge at a constant 500 mA
  • When undervoltage is reached, a line appears with "Stopping discharge"
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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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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".

 

Instructions: 

There are 3 ".mat" files in this ".zip" file. One for palm, one for forearm and the other for back of hand. There are matrix/vectors of the flattened structure image, flattened SAW axial displacement fields, the reconstructed phase velocity dispersion curve and the estimated shear wave velocities of each layer. The phase velocity dispersion curve was reconstructed based on the flattened SAW axial displacement fields through a 2D FFT analysis (described in detail in Zhou's paper "High‐intensity‐focused ultrasound and phase‐sensitive optical coherence tomography for high resolution surface acoustic wave elastography"). The estimated shear wave velocities of each layer were obtained by fitting the reconstructed phase velocity dispersion curve into the proposed WAPV inversion model. 

Here give the descriptions of each variable in each file:

  • Str - flattened structure image
  • vzStr - vector of axial distance for structure image in the unit of mm
  • vxStr - vector of lateral distance for structure image  in the unit of mm
  • Thk_epidermis - the thickness of the epidermis layer in the unit of mm
  • Thk_dermis - the thickness of the dermis layer in the unit of mm
  • Phframe - flattened SAW axial displacement field extracted from the sample surface
  • vxPhframe - vector of lateral distance for Phframe in the unit of mm
  • vtPhframe - vector of time for Phframe in the unit of s
  • Freq - vector of frequency in the unit of kHz
  • Cr - vector of reconstructed phase velocity dispersion curve 
  • Freqfit - vector of the frequency for Crfit in the unit of kHz
  • Crfit - vector of phase velocity curve predicted by WAPV inversion model
  • Cs_epi - estimated shear wave velocity of the epidermis layer
  • Cs_der - estimated shear wave velocity of the dermis layer
  • Cs_hypo - estimated shear wave velocity of the hypodermis layer
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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".

Instructions: 

There are 9 ".mat" files in this ".zip" file. Among them, 5 files are the results of three two-layer agar phantoms, and the remaining 4 files are the results of one three-layer agar phantom. In each ".mat" file, there are matrix/vectors of the flattened structure image, flattened SAW axial displacement fields, the reconstructed phase velocity dispersion curve and the estimated shear wave velocities of each layer. The phase velocity dispersion curve was reconstructed based on the flattened SAW axial displacement fields through a 2D FFT analysis (described in detail in Zhou's paper "High‐intensity‐focused ultrasound and phase‐sensitive optical coherence tomography for high resolution surface acoustic wave elastography"). The estimated shear wave velocities of each layer were obtained by fitting the reconstructed phase velocity dispersion curve into the proposed WAPV inversion model. 

Here give the descriptions of each variable in each file:

  • Str - flattened structure image
  • vzStr - vector of axial distance for structure image in the unit of mm
  • vxStr - vector of lateral distance for structure image  in the unit of mm
  • Thk_top - the thickness of the top layer in the unit of mm
  • Phframe - flattened SAW axial displacement field extracted from the sample surface
  • vxPhframe - vector of lateral distance for Phframe in the unit of mm
  • vtPhframe - vector of time for Phframe in the unit of s
  • Freq - vector of frequency in the unit of kHz
  • Cr - vector of reconstructed phase velocity dispersion curve 
  • Freqfit - vector of the frequency for Crfit in the unit of kHz
  • Crfit - vector of phase velocity curve predicted by WAPV inversion model
  • Csfit_bottom - estimated shear wave velocity of the bottom layer
  • Csfit_top - estimated shear wave velocity of the top layer
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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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