Machine Learning

This synthetic dataset is generated using Matlab automotive driving toolbox to simulate a 77GHz FMCW millimeter-wave radar sensing in the road scenario. Especially for the Doppler ambiguity case, when the object vehicles move within or out of the unambiguous detecable velocity range. The dataset contains in total 20 recordings with the duration of 1 second each. Both time-division modulation (TDM) and binary phase modulation (BPM) data are provided. Each recording consists of complex ADC raw data and complex range-Doppler map, together with the ground-truth range and velocity.

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Biometric management and that to which uses face, is indeed a very challenging work and requires a dedicated dataset which imbibes in it variations in pose, emotion and even occlusions. The Current work aims at delivering a dataset for training and testing purposes.SJB Face dataset is one such Indian face image dataset, which can be used to recognize faces. SJB Face dataset contains face images which were collected from digital camera. The face dataset collected has certain conditions such as different pose, Expressions, face partially occluded and with a uniform attire.

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This work presents a dataset based on multiple metrics namely KQIs, which provide the E2E conditions of different services. Particularly, the dataset considers video streaming and cloud gaming (CG) services. 

 

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Future wireless networks must incorporate awareness, adaptability, and intelligence as fundamental building elements in order to meet the wide range of requirements of the next-generation communication systems. Wireless sensing techniques can be used to gather awareness from the radio signals present in the surroundings. However, threats from hostile attackers, such as jamming, eavesdropping, and manipulation, are also present along with this. This paper describes in detail an RF-jamming detection test-bed and provides experimentally measured data.

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This dataset provides wireless measurements from two industrial testbeds: iV2V (industrial Vehicle-to-Vehicle) and iV2I+ (industrial Vehicular-to-Infrastructure plus sensor).

iV2V covers 10h of sidelink communication scenarios between 3 Automated Guided Vehicles (AGVs), while iV2I+ was conducted for around 16h at an industrial site where an autonomous cleaning robot is connected to a private cellular network.

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The dataset provides data to develop wireless sensing applications -- namely activity recognition, people identification and people counting -- leveraging Wi-Fi devices. Human movements cause modifications to the multi-path propagation of Wi-Fi signals. Such modifications reflect on the channel frequency response and, in turn, wireless sensing can be performed by analyzing the channel state information (CSI) of the Wi-Fi channel when the person/people move within the propagation environment.

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A real-world radio frequency (RF) fingerprinting dataset for commercial off-the-shelf (COTS) Bluetooth and WiFi emitters under challenging testbed setups is presented in this dataset. The chipsets within the devices (2 laptops and 8 commercial chips) are WiFi-Bluetooth combo transceivers. The emissions are captured with a National Instruments Ettus USRP X300 radio outfitted with a UBX160 daughterboard and a VERT2450 antenna. The receiver is tuned to record a 66.67 MHz bandwidth of the spectrum centered at the 2.414 GHz frequency.

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Synthetic Digitally Modulated Signal Datasets for Automatic Modulation Classification contain CSPB.ML.2018 and CSPB.ML.2022, two high-quality communication signal datasets with eight modulation types: BPSK, QPSK, 8-PSK, pi/4-DQPSK, MSK, 16-QAM, 64-QAM, and 256-QAM. There are 14,000 signals of each modulation type in each dataset for a total of 112,000 signals per dataset. The two datasets are useful for signal processing testing, neural network (NN) training, initial NN testing, and out-of-distribution NN testing as signal generation parameters differ between the two datasets.

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Opening up of the CBRS band for the secondary users' transmissions poses challenges in the protection of incumbent radar users from co-channel interference. The use of Machine Learning algorithms for addressing these challenges requires representative real-world datasets.This dataset contains overlapping radar and LTE signals captures over-the-air in the shared CBRS band using an experimental testbed composed of software defined radios in RF anechoic chamber.

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