Sensors

In this paper, we design and present a testbed implementation and the resulting dataset for modulation recognition from real-world imperfect scans. We describe our efforts to build a testbed of heterogeneous spectrum sensors (low-cost RTL-SDR and mid-cost USRP) and a controlled transmitter in order to facilitate real-world data collection for modulation recognition from partial and biased scans.
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Traditional magnetic tracking approaches based on mathematical models and optimization algorithms are computationally intensive, depend on initial guesses, and do not guarantee convergence to a global optimum. Although fully-supervised data-driven deep learning can solve the above issues, the demand for a comprehensive dataset hampers its applicability in magnetic tracking. Thus, we propose an annular magnet pose estimation network (called AMagPoseNet) based on dual-domain few-shot learning from a prior mathematical model, which consists of two sub-networks: PoseNet and CaliNet.
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This dataset is in support of my research paper - Short Circuit Analysis of 666 Wh Li-Ion NMC
Faults and datasets can be copied to submit in fire cause investigation reports or thesis. The simulation is run for 20 hours (72000 seconds) of simulation time for each fault of 100 faults.
PrePrint : (Make sure you have read Caution.)
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For academic purposes, we are happy to release our datasets. This dataset is in support of my research paper 'TOW-IDS: Intrusion Detection System based on Three Overlapped Wavelets in Automotive Ethernet'. If you want to use our dataset for your experiment, please cite our paper.
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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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All the 500 sets of original data used in theoretical fittings.
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This dataset contains information about published papers on how biological signals (ECG, EEG, EDA and MG + eye-tracking) are being used and collected in the field of video games. This dataset reflects the information published including the choice of signals, the devices used to collect them (e.g., wearables), the purposes for which they are collected, and the main results reported from their use.
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