*.csv (zip); *.mat; *.h5

Non-invasive monitoring and surveillance methods of blood glucose measurement can provide ease of use and simplicity for different individuals while reducing the risks and damages of invasive methods. The non-invasive method based on photoplethysmography (PPG) signal is one of the innovative methods on this topic which numerous studies have been conducted by research centers and various companies. However, due to various reasons, the reviewed dataset was not available and no standard dataset has been published on this topic.

The dataset consists of 4-channeled EOG data recorded in two environments. First category of data were recorded from 21 poeple using driving simulator (1976 samples). The second category of data were recorded from 30 people in real-road conditions (390 samples).

All the signals were acquired with JINS MEME ES_R smart glasses equipped with 3-point EOG sensor. Sampling frequency is 200 Hz.

RRN-ATR-Net is a radar echo data set containing the micro-Doppler signatures (m-Ds) of different aerial targets, with a total sample size of 1200. It is available for researchers interested in this field but having difficulty collecting data. The data set is acquired using Texas Instruments' AWR1642BOOST radar sensor and the DCA1000EVM high-speed data acquisition card. The target types subject to acquisition include quadrotor (Phantom3s), fixed-wing (Cessna182), helicopter (T-REX450), and bionic bird (Gogo Bird1020).

This is our generated 3890 3D skin vessel tissue models which could be used for medical image analysis such as classification, segmentation, reconstruction and quantitative medical image analysis.