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1,772 results for “sensors”
Datasets for assessment of damage in flat panel and final demonstrator with UoI's sensor and PPI-LT approach
<p>Datasets acquired during experimental assessment of damage in the composite flat panel and final demonstrator by the team of the University of Ioannina, in the context of project CompInnova: An Advanced Methodology for the Inspection and Quantification of Damage on Aerospace Composites and Metals using an Innovative Approach (H2020 FETOPEN, Grant Agreement No. 665238). The PPI-LT approach and dedicated IRT sensor developed within CompInnova were used for recording the data. Data are thermograms in image format (jpg, png).<br> <br> The data were used in deliverables D7.2 & D.7.3.</p> <p> </p>
Dataset for assessment of damage in composites and laminates with UoI's sensor and PPI-LT approach
<p>Datasets acquired during experimental assessment of damage in composites and laminates by the team of the University of Ioannina, in the context of project CompInnova: An Advanced Methodology for the Inspection and Quantification of Damage on Aerospace Composites and Metals using an Innovative Approach (H2020 FETOPEN, Grant Agreement No. 665238). The PPI-LT approach and dedicated IRT sensor developed within CompInnova were used for recording the data. Data are thermograms in image format (jpg, png).</p> <p>The data were used in deliverables D3.3 & D.7.1</p> <p> </p> <p> </p>
Acoustic transfer function data for source and sensor placement
<p>Acoustic transfer function (ATF) data for the codes of source and sensor placement in sound field control. </p> <p>https://github.com/sh01k/SourceSensorPlacementSFC</p> <p>The ATF data in the 2D acoustic field was generated by the finite element method using FreeFem++ (<a href="https://freefem.org/">https://freefem.org/</a>).</p>
MINDS-Libras Dataset (RGB-D sensor data)
<p>Brazilian Sign Language (Libras) data set with 20 signs for sign language and gesture recognition benchmark:<br> <br> - Acontecer (To happen)<br> - Aluno (Student)<br> - Amarelo (Yellow)<br> - América (America)<br> - Aproveitar (To enjoy)<br> - Bala (Candy)<br> - Banco (Bank)<br> - Banheiro (Bathroom)<br> - Barulho (Noise)<br> - Cinco (Five)<br> - Conhecer (To know)<br> - Espelho (Mirror)<br> - Esquina (Corner)<br> - Filho (Son)<br> - Maçã (Apple)<br> - Medo (Fear)<br> - Ruim (Bad)<br> - Sapo (Frog)<br> - Vacina (Vaccine)<br> - Vontade (Will)<br> <br> Each one of the signs was recorded 5 times by 12 signers, using a Chroma Key background. Among the signers are men and women with basic to advanced knowledge in Libras. </p> <p>The RGB-D sensor (kinect v2) available the RGB videos (1920 x 1080) and depth videos (640 x 480) in "mp4" format, and the body points and face data in "txt" file.</p> <ul> <li>The body file has seven different information (Position X, Y and Z; Orientation X, Y and Z; TrackingState; LeftHandState; RightHandState; ColorPosition X and Y; and DepthPosition X and Y) about the 25 points: (1) Spine Base, (2) Spine Mid, (3) Neck, (4) Head, (5) Shoulder Left, (6) Elbow Left, (7) Wrist Left, (8) Hand Left, (9) Shoulder Right, (10) Elbow Right, (11) Wrist Right, (12) Hand Right, (13) Hip Left, (14) Knee Left, (15) Ankle Left, (16) Foot Left, (17) Hip Right, (18) Knee Right, (19) Ankle Right, (20) Foot Right, (21) Spine Shoulder, (22) Hand Tip Left, (23) Thumb Left, (24) Hand Tip Right and (25) Thumb Right. There are 13 lines (or data) for each frame. This order is repeated sequentially up to 1950 lines (13 lines $\times$ 150 frames), representing the sign video.</li> </ul> <p> </p> <ul> <li>Regarding to the face data, the same organisation was adopted. In this case, we have seven information (FaceBox, FaceRotation, HeadPivot, AnimationUnit, FaceModel X, Y and Z; ColorFaceModel X and Y; and DepthFaceModel X and Y), describing 11 data, distributed in 1650 (11 lines $\times$ 150 frames) lines in the ``.txt'' file.</li> </ul> <p>(Former name: Libras-20)</p>
Human activities with videos, inertial units and ambient sensors
Worldwide demographic projections point to a progressively older population. This fact has fostered research on Ambient Assisted Living, which includes developments on smart homes and social robots. To endow such environments with truly autonomous behaviours, algorithms must extract semantically meaningful information from whichever sensor data is available. Human activity recognition is one of the most active fields of research within this context. Proposed approaches vary according to the input modality and the environments considered. Different from others, this paper addresses the problem of recognising heterogeneous activities of daily living centred in home environments considering simultaneously data from videos, wearable IMUs and ambient sensors. For this, two contributions are presented. The first is the creation of the Heriot-Watt University/University of Sao Paulo (HWU-USP) activities dataset, which was recorded at the Robotic Assisted Living Testbed at Heriot-Watt University. This dataset differs from other multimodal datasets due to the fact that it consists of daily living activities with either periodical patterns or long-term dependencies, which are captured in a very rich and heterogeneous sensing environment. In particular, this dataset combines data from a humanoid robot's RGBD (RGB + depth) camera, with inertial sensors from wearable devices, and ambient sensors from a smart home. The second contribution is the proposal of a Deep Learning (DL) framework, which provides multimodal activity recognition based on videos, inertial sensors and ambient sensors from the smart home, on their own or fused to each other. The classification DL framework has also validated on our dataset and on the University of Texas at Dallas Multimodal Human Activities Dataset (UTD-MHAD), a widely used benchmark for activity recognition based on videos and inertial sensors, providing a comparative analysis between the results on the two datasets considered. Results demonstrate that the introduction of data from ambient sensors expressively improved the accuracy results.
The Multi-Radar Multi-Sensor (MRMS) and the Stage IV rainfall products, and the aggregate forecast statistics for the three real case studies
<p>This is a data repository in support of the article "Impact of Assimilating High-Resolution Atmospheric Motion Vectors on Convective Scale Short-Term Forecasts. Part II: Assimilation Experiments of GOES-16 Satellite Derived Winds" submitted to AGU <em>J. of Advances in Modeling of Earth Systems. </em></p> <p>The data set consists of</p> <ul> <li>The Multi-Radar Multi-Sensor (MRMS) and the Stage IV rainfall products used for validation in the three real case studies.</li> <li>The aggregate forecast statistics for composite reflectivity and APCP for the three real case studies are contained in the zipped files.</li> </ul>
Listening and watching: do camera traps or acoustic sensors more efficiently detect wild chimpanzees in an open habitat?
<p>1. With one million animal species at risk of extinction, there is an urgent need to regularly monitor threatened species. However, in practice this is challenging, especially with wide-ranging, elusive and cryptic species or those that occur at low density.<br> 2. Here we compare two non-invasive methods, passive acoustic monitoring (n=12) and camera trapping (n=53), to detect chimpanzees (Pan troglodytes) in a savanna-woodland mosaic habitat at the Issa Valley, Tanzania. With occupancy modelling we evaluate the efficacy of each method, using the estimated number of sampling days needed to establish chimpanzee absence with 95% probability, as our measure of efficacy.<br> 3. Passive acoustic monitoring was more efficient than camera trapping in detecting wild chimpanzees. Detectability varied over seasons, likely due to social and ecological factors that influence party size and vocalization rate. The acoustic method can infer chimpanzee absence with less than ten days of recordings in the field during the late dry season, the period of highest detectability, which was five times faster than the visual method.<br> 4. Synthesis and applications: Despite some technical limitations, we demonstrate that passive acoustic monitoring is a powerful tool for species monitoring. Its applicability in evaluating presence/absence, especially but not exclusively for loud call species, such as cetaceans, elephants, gibbons or chimpanzees provides a more efficient way of monitoring populations and inform conservation plans to mediate species-loss.</p>
Data for Secure communication in IP-based wireless sensor networks via a trusted gateway publication
<p>This archive file contains the raw data obtained from Contiki sensor nodes during Cooja experiments in the folders e2e, terminate, terminate_1st and plaintext.</p> <p>The archive accompagnies the IEEE ISSNIP 2015 publication titled "Secure communication in IP-based wireless sensor networks via a trusted gateway" by Floris Van den Abeele, Tom Vandewinckele, Jeroen Hoebeke, Ingrid Moerman and Piet Demeester.</p> <p><br /> Also included is the data_parser python script that converts the raw data into CSV files that are parseable by R. The script contains the definitions of the contents of the raw data files.<br /> Finally, the R scripts that use the CSV files to generate the plots from the paper are also included.</p>
NDVI datasets related with the article "A comparative study of cross-product NDVI dynamics in the Kilimanjaro region - a matter of sensor, degradation calibration, and significance"
<p>AHVRR GIMMS and MODIS NDVI input datasets and main results on seasonal and long-term NDVI dynamics related with the manuscript "A comparative study of cross-product NDVI dynamics in the Kilimanjaro region - a matter of sensor, degradation calibration, and significance" recently published in Remote Sensing 8(2), 159, doi: 10.3390/rs8020159.</p>
Ultimaker 2 Sensor Data
<p>This research data set includes accelerometer sensory data collected during a print job on Ultimaker 2 3D printer. We used ViFDAQ which is a mobile and wireless data-acquisition system developed at the VIRTUAL VEHICLE to capture the data. Sensors were installed on printer head (x,y,z achses) and on printing plate (x,y,z achses). During the print job, we conducted a series of events (e.g. vibrations on the printer plate, table ... ), to influence the quality of the printed object. We then applied a series of machine learning algorithms in order to detect the disturbances. A research paper is in production.</p> <p>ViFDAQ Info: http://www.v2c2.at/produkte/vifdaq/</p>
Typical Sensor Defects Dataset
<p>Thirteen datasets of sensor values, with one dataset without sensor defects (data_standard.csv). All other datasets are based on the dataset without a defect, with the values of Temp_Sensor_2 modified to simulate different sensor defects:</p> <ul> <li>Sensor Drift: 1‰/hour (data_drift_0_001.csv), 2.5‰/hour (data_drift_0_0025.csv), 5‰/hour (data_drift_0_005.csv)</li> <li>Sensor Offset: 1°C Offset (data_offset_1.csv), 2°Offset (data_offset_2.csv), 5°Offset (data_offset_5.csv)</li> <li>Sensor Peaks: 1 Peak/Minute (data_peak_1.csv), 2 Peaks/Minute (data_peak_2.csv), 5 Peaks/Minute (data_peak_5.csv), 10 Peaks/Minute(data_peak_10.csv)</li> <li>Sensor Noise: 10 dB SNR (data_noise_10dB.csv), 0 dB SNR (data_noise_0dB.csv)</li> </ul> <p>The datasets are given as comma-separated values in text files. The first column in each file holds time stamps, while the following columns hold the sensor values. The first entry in every column gives the name of the sensor. All datasets are zipped into one file (data.zip).</p> <p>Additionally attached is configuration data (Configuration.pdf) for the sensor fusion approach that was used to classify the datasets.</p> <p>For more information please contact the uploader.</p>
Sensor solutions for an energy-efficient and user-centered heating system
<p>Corresponding dataset for the article "Sensor solutions for an energy-efficient and user-centered heating system" published in the Journal of Sensors and Sensor Systems Special Issue "Sensors and Measurement Systems 2016".</p>
Dataset of Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion
<p>MATLAB Dataset for the paper. </p> <p>Paper Abstract:</p> <p>Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).</p>
Automotive Sensor Data. An Example Dataset from the AEGIS Big Data Project
<p>This is an example research data dataset for the automotive demonstrator within the "AEGIS - Advanced Big Data Value Chain for Public Safety and Personal Security" big data project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732189. The time series data has been collected by using a BeagleBone single plate computer which has been developed at VIF to collect data for driving analytics. The BeagleBoard can be connected to the OBD2 interface of a vehicle to capture data from CAN bus and has been additionally equipped with further sensors (GPS, gyroscope, acceleration). The data in this research dataset was collected during 35 different trips conducted by one driver driving one vehicle in the Graz area in Austria.</p>
Preliminary Data 9 sensor plate
<p>Preliminary Data 9 sensor plate. Before Impact and initial investigations.</p> <p>Data sheet and plate dims. attached</p> <p>Layup [0,45,-45,90]2s, </p> <p>914 TS 5 34</p> <p> </p>
Miniature multihole airflow sensor for lightweight aircraft over wide speed and angular range
<p>This repository contains:</p> <ul> <li>the data collected for the paper "Miniature multihole airflow sensor for lightweight aircraft over wide speed and angular range"</li> <li>the python code to extract the wind tunnel and flight data to reproduce the plots in the paper</li> <li>the exact polynomials used for the calibration</li> <li>the 3D object files of the airflow sensor</li> <li>the schematics of the PCB</li> <li>the video of the flown manoeuvres (available on youtube: https://youtu.be/U3nR1v3fbZg)</li> </ul> <p>The data and the plots are included in the repository, but can be reproduced by running the python scripts in the following order:</p> <ol> <li>Code/reduce_data.py</li> <li>Code/plot_averages.py</li> <li>Code/read_tunnel.py</li> <li>Code/Regress.py</li> <li>Code/Validation_plot.py</li> </ol>
Data for A miniaturized magnetic field sensor based on nitrogen-vacancy centers
<p>Here, data sets as plotted in the preprint "A miniaturized magnetic field sensor based on nitrogen-vacancy centers" are uploaded. <br><br>The zip file Data_Zenodo_v3.zip contains a folder for each figure and one additional table supporting the findings in the preprint/publication. Folders of figures containing only images include the images in the preprint/publication as .png and .svg or .pdf data. Folders of figures containing plots include the plot as .png and .pdf aswell as the data points and fit parameters collected in a .xlsx, .csv or .h5 file.</p>
Grid-type transparent conductive thin films of carbon nanotubes as capacitive touch sensors
<p>This dataset contains the measurement data for figures (graphs) published in journal article:</p><p>Grid-type transparent conductive thin films of carbon nanotubes as capacitive touch sensors</p><p>by Ronja Valasma, Eva Bozo, Olli Pitkänen, Topias Järvinen, Aron Dombovari, Melinda Mohl, Gabriela Simone Lorite, Janos Kiss, Zoltan Konya and Krisztian Kordas</p><p>Published 11 May 2020 • © 2020 The Author(s). Published by IOP Publishing Ltd</p><p>Nanotechnology, Volume 31, Number 30</p><p>Citation: Ronja Valasma et al 2020 Nanotechnology 31 305303</p><p>DOI 10.1088/1361-6528/ab8590</p>
mRI: multi-modal 3d human pose estimation dataset using mmwave, rgb-d, and inertial sensors
<p>The ability to estimate 3D human body pose and movement, also known as human pose estimation~(HPE), enables many applications for home-based health monitoring, such as remote rehabilitation training. Several possible solutions have emerged using sensors ranging from RGB cameras, depth sensors, millimeter-Wave (mmWave) radars, and wearable inertial sensors. Despite previous efforts on datasets and benchmarks for HPE, few datasets exploit multiple modalities and focus on home-based health monitoring.</p> <p>To bridge this gap, we present <em>mRI</em>, a multi-modal 3D human pose estimation dataset with mmWave, RGB-D, and Inertial Sensors. Our dataset consists of over 5 million frames from 20 subjects performing rehabilitation exercises and supports the benchmarks of HPE and action detection. We perform extensive experiments using our dataset and delineate the strength of each modality.</p> <p>We hope that the release of <em>mRI</em> can catalyze the research in pose estimation, multi-modal learning, and action understanding, and more importantly, facilitate the applications of home-based health monitoring.</p>
Dataset related to the publication E. Fasci et al., Sensors and Actuators: A. Physical 362 (2023) 114632 (https://doi.org/10.1016/j.sna.2023.114632)
<p>file <i>Allan analysis.xlsx</i>: Allan-Werle deviation analysis of the measured ring down times. From these data a minimum detectable absorption coefficient of 6.5x10^-12 cm^-1 can be inferred, being the ring-down time under vacuum conditions about 127.5 us.</p><p>file <i>Water mole fraction VS time.xlsx</i> : Time development of the CRDS measured water mole fraction with a N_2 flow rate of 2 l/min. The gas pressure was set to the constant value of 2666 Pa. Each data point was retrieved from the recording of a full absorption spectrum of the H_2O 3_(2,2)→2_(2,1) transition, belonging to nu_1 + nu_3 vibrational band.</p><p>This work was done within the project PROMETH2O (EMPIR 20IND06), which received funding from the EMPIR programme cofinanced by the Participating States and from the European Union's Horizon 2020 research and innovation programme. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.