Sensors

A mobile sensor can be described as a kind of smart technology that can capture minor or major changes in an environment and can respond by performing a particular task. The scope of the dataset is for forensic purposes that will help segregate day-to-day activities from criminal actions. Smartphones supplied with sensors can be utilised for monitoring and recording simple daily activities such as walking, climbing stairs, eating and more. For the generation of this dataset, we have collected data for 13 classes of daily life activities, which has been done by a single individual.

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This peach tree disease detection dataset is a multimodal, multi-angle dataset which was constructed for monitoring the growth of peach trees, including stress analysis and prediction. An orchard of peach trees is considered in the area of Thessaly, where 889 peach trees were recorded in a full crop season starting from Jul. 2021 to Sep. 2022. The dataset includes a) aerial / Unmanned Aerial Vehicle (UAV) images, b) ground RGB images/photos, and c) ground multispectral images/photos.

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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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The Research Paper "Detection of Bicep Form Using Myoware and Machine Learning" based on the novel dataset has been recently accepted in September 2022 and is being published in SCOPUS Indexed SPRINGER Book Series “Lecture Notes in Networks and Systems”

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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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FEA-RDC-simulation data and prototype data

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The Bluetooth 5.1 Core Specification brought Angle of Arrival (AoA) based Indoor Localization to the Bluetooth Standard. This feature is usually referred to as Bluetooth Direction Finding.Technically, the Direction Finding is done by adding a so-called Constant Tone Extension (CTE) to a Bluetooth packet. During this CTE, either transmitter or receiver can switch through several antennas of an antenna array while IQ data, i.e. amplitude and phase information, is sampled on the receiving side. Using these IQ samples, the direction(s) of the incident wavefronts can be estimated.

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