*.csv (zip); *.mat; *.h5
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.
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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).
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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.
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The dataset contains:
1. We conducted a A 24-hour recording of ADS-B signals at DAB on 1090 MHz with USRP B210 (8 MHz sample rate). In total, we got the signals from more than 130 aircraft.
2. An enhanced gr-adsb, in which each message's digital baseband (I/Q) signals and metadata (flight information) are recorded simultaneously. The output file path can be specified in the property panel of the ADS-B decoder submodule.
3. Our GnuRadio flow for signal reception.
4. Matlab code of the paper, wireless device identification using the zero-bias neural network.
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