The DroneDetect dataset consists of 7 different models of popular Unmanned Aerial Systems (UAS) including the new DJI Mavic 2 Air S, DJI Mavic Pro, DJI Mavic Pro 2, DJI Inspire 2, DJI Mavic Mini, DJI Phantom 4 and the Parrot Disco. Recordings were collected using a Nuand BladeRF SDR and using open source software GNURadio. There are 4 subsets of data included in this dataset, the UAS signals in the presence of Bluetooth interference, in the presence of Wi-Fi signals, in the presence of both and with no interference.

Instructions: 

Sample rate: 60Mbits/s

Bandwidth: 28MHz

Centre Freq: 2.4375GHz

Each recording consists of 1.2 x 10^8 complex samples equating to 2 seconds recording time. Data is saved into ‘.dat’ files  and the complex data is saved as interleaved floats. ‘load_data.py’ is included for the data to be loaded into python and further split into smaller samples 20ms in length.

Files are categorised by interference, then by flight mode –

Switched on = ON

Hovering = HO

Flying = FY

Each file name uses an interference identifier, 00 for a clean signal, 01 for Bluetooth only, 10 for Wi-Fi only and 11 for Bluetooth and Wi-Fi interference concurrently. An example file name for Mavic Mini switched on in the presence of Bluetooth and Wi-Fi interference would be:

MIN + 11 + 00 + 00 = MIN_1100_00.dat

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The objective of this dataset is the fault diagnosis in diesel engines to assist the predictive maintenance, through the analysis of the variation of the pressure curves inside the cylinders and the torsional vibration response of the crankshaft. Hence a fault simulation model based on a zero-dimensional thermodynamic model was developed. The adopted feature vectors were chosen from the thermodynamic model and obtained from processing signals as pressure and temperature inside the cylinder, as well as, torsional vibration of the engine’s flywheel.

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988 Views

None

Instructions: 

None

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Supplementary Material for the paper "Binary Discrete Fourier Transform and its Inversion" by Howard W. Levinson and Vadim A. Markel, published in IEE Transactions on Signal Processing (2021)

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The Jackal UGV, from Clearpath Robotics, was used as the data collecting platform. This skid-steer four-wheel-drive vehicle comes with an onboard IMU, two DC motors with encoders that measure wheel angular speeds, and current sensors that measure motor current outputs. On each side of the robot, the front wheel and back wheel are jointed with a gearbox and so spin together at the same rate and direction. The IMU provided vehicle attitude measurements in terms of Euler angles, as well as linear acceleration and angular rate of the vehicle body in three Euclidean axes.

Instructions: 

Each HDF5 file contains four types of data entries: timestamps, signals, images, and labels. A .ipynb code example is included to demonstrate how to retrieve and format data appropriately. 

The .ipynb script requires h5py, numpy, and matplotlib libraries.

** If you plan to load the entire dataset into your memory, make sure your PC has >16 Gb RAM

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 The data set contains 152 measurements of room impulse responses for direction of arrival estimation, using a compact three-channel microphone array. Sources are placed at 10-degree intervals from -90 to 90 degrees in the azimuth plane at range 150 cm. There are also 5 off-grid measurement positions and 6 off-range positions - at ranges 1 m, 2 m, 2.5 m and 3 m. The measurements are performed in a furnished classroom, which is approximately rectangular and of dimensions 9 x 6 x 3 m. The reverberation time is 0.4 s.

Instructions: 

The data set contains: ·     Data: Room impulse responses are included in the file “RIRs_DTU.mat”·     Images: Pictures of the room and setup, included as *.jpg files.·     Documentation: A pdf file that contains the relevant info regarding the dataset.

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237 Views

This dataset consists of EEG data of 40 epileptic seizure patients (both male and female) of age from 4 to 80 years. The raw data was collected from Allengers VIRGO EEG machine at Medisys Hospitals, Hyderabad, India. The EEG electrodes were placed according to 10 – 20 International standard. The EEG data was recorded from 16 channels (FP2-F4, F4-C4, C4-P4, P4-O2, FP1-F3, F3-C3, C3-P3, P3-O1, FP2-F8, F8-T4, T4-T6, T6-O2, FP1-F7, F7-T3, T3-T5, and T5-O1) at 256 samples per second.

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2411 Views

Some 6G use cases include augmented reality and high-fidelity holograms, with this information flowing through the network. Hence, it is expected that 6G systems can feed machine learning algorithms with such context information to optimize communication performance. This paper focuses on the simulation of 6G MIMO systems that rely on a 3-D representation of the environment as captured by cameras and eventually other sensors. We present new and improved Raymobtime datasets, which consist of paired MIMO channels and multimodal data.

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159 Views

The presented data contain recordings of underwater acoustic transmissions collected from a field experiment whose goal was to characterize self-interference for in-band full-duplex underwater acoustic communications. The experiment was conducted in the Lake of Tuscaloosa in July 2019. A single transmission-receiving line was deployed off a boat that was moored in the center of the lake. The transmission-receiving line had one acoustic transmitter and eight hydrophone receivers.

Instructions: 

1) User Guide for In-Band Full-Duplex Underwater Acoustic Communication Measurements Self-interference.pdf

2) readme_BPSK.txt

3) readme_OFDM.txt

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160 Views

The channel path data (signal strength, delay, angle of departure and angle of arrival, etc. of each path for each Tx-Rx pair) calculated by a ray-tracing model in an industrial warehouse, via Wireless InSite
https://www.remcom.com/wireless-insite-em-propagation-software

For more details, please download the scripts and .zip to access the instructions and the data files that contain the path information.

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