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1,772 results for “sensors”
Data for OFDVDnet: A sensor fusion approach for video denoising in fluorescence guided surgery
<p>Many applications in machine vision and medical imaging require the capture of images from a scene with very low radiance, which may result in very noisy images and videos. An important example of such an application is the imaging of fluorescently-labeled tissue in fluorescence-guided surgery. Medical imaging systems, especially when intended to be used in surgery, are designed to operate in well-lit environments and use optical filters, time division, or other strategies that allow the simultaneous capture of low radiance fluorescence video and a well-lit visible light video of the scene. This work demonstrates video denoising can be dramatically improved by utilizing deep learning together with motion and textural cues from the noise-free video.</p>
Dataset for Diamond-coated quartz crystal microbalance sensors: Challenges in high yield production and enhanced detection of ethanol and sars-cov-2 proteins
<p>The data set to paper: </p> <p>Name: Diamond-coated quartz crystal microbalance challenges in mass production and enhanced detection of ethanol and sars-cov-2 proteins</p> <p>Authors: Tibor Izsák1*, Marian Varga1, Michal Kočí2,3, Ondrej Szabó2, Katarína Aubrechtová Dragounová2, Gabriel Vanko2, Miroslav Gál4, Jana Korčeková5, Michaela Hornychová 4, Alexandra Poturnayová5, Alexander Kromka2*</p> <p>Affiliations: 1 Department of Microelectronics and Sensors, Institute of Electrical Engineering, Slovak Academy of Sciences, Dúbravská Cesta 9, Bratislava, 841 04, Slovak Republic<br> 2 Department of Semiconductors, Institute of Physics of the Czech Academy of Sciences, Cukrovarnicka 10/112, Prague 6 162 00, Czech Republic<br> 3 Department of Microelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, Prague 6, 166 27, Czech Republic<br> 4 Faculty of Chemical and Food Technology, Slovak University of Technology, Bratislava, Slovak Republic<br> 5 Center of Biosciences, Institute of Molecular Physiology and Genetics, Slovak Academy of Sciences, Bratislava, Slovak Republic<br> *corresponding author: tibor.izsak@savba.sk</p> <p>Data manager: Kristýna Dostálová: dostalovak@fzu.cz</p> <p>Date of collection: 1. 5. 2023 - 31. 7. 2024</p> <p>Description: Figure 1: Photos of QCM substrates oriented horizontally or vertically on the substrate holder in the deposition chamber (left) and during the diamond CVD process with ignited plasma (right).<br> Figure 2: a) 3D model of the measurement setup and b) photograph of the open gas chamber with embedded QCM sample.<br> Figure 3: Photo of the a) measurement setup and b) disassembled flow cell with V-Dia-QCM. c) Side view photo of the assembled flow cell in the measurement setup.<br> Figure 4: a) SEM images revealing surface morphology and b) corresponding Raman spectra of Dia-QCM and Dia-Si substrates horizontally or vertically oriented on the substrate holder and corresponding optical photos. There is also the Raman spectrum of the bare QCM (Au-QCM) sample before the diamond deposition.<br> Figure 5: a) Raman spectra and b) SEM images depicting surface morphology of porous diamond film grown on Si (H-PorDia-Si) and QCM (H-PorDia-QCM) substrate. The inset in Fig. 5a represents the optical photo of diamond-coated QCM. Note: ‘H-’ in sample names means horizontally loaded samples.<br> Figure 6: The response delta fR of diamond-coated QCM sensors horizontally and vertically oriented, i.e., single-sided and double-sided diamond-coated QCMs, when applying periodic switching (at 3-minute intervals) of ethanol vapour (E) with various concentrations (from 10 ppm to 100 ppm) and synthetic air (Air).<br> Figure 7: a) First resonant frequency shift (delta fR) of individual QCM sensors and b) mean values of delta fR with corresponding error bars for each QCM sensor group dependent on ethanol concentration.<br> Figure 8: a) The changes of the resonant frequency, delta fR, after the addition of neutravidin (NA) dissolved in water, biotinylated 1C aptamers (1C APT) dissolved in PBS with MgCl2, and 50 pg/mL S-RBD protein in PBS. The addition of neutravidin, aptamers, proteins, and surface washings by water (H2O) or buffer (PBS) are highlighted by arrows. b) Zoom in on the highlighted area in Fig. 8a.<br> Figure 9: Decrease of the resonant frequency, fR, at various S-RBD protein concentrations. The comparison of the sensitivity of diamond and gold QCM surfaces on which S-RBD was determined is indicated in the graph legend.</p>
Phase optimization of thermally actuated piezoresistive resonant MEMS cantilever sensors (Data)
<p>Origin projects, figures and COMSOL simulation used for the article "Phase optimization of thermally actuated piezoresistive resonant MEMS cantilever sensors", published in <em>Journal of Sensors and Sensor Systems </em>on 14 Jan 2019.</p>
Enhancement of real-time resonance tracking in electro-thermally actuated cantilever sensor with optimized phase characteristic (Data)
<p>Origin projects and figures used for the article "Enhancement of real-time resonance tracking in electro-thermally actuated cantilever sensor with optimized phase characteristic", published in the proceedings of the 29th Micromechanics and Microsystems Europe Workshop; 26.08.2018 to 29.08.2018; Smolenice Castle, Slovakia.</p>
Enhancement of unsteady frequency responses of electro-thermal resonance MEMS cantilever sensors (Data)
<p>Origin projects and figures used for the article "Enhancement of unsteady frequency responses of electro-thermal resonance MEMS cantilever sensors", published in the proceedings of the 30th Micromechanics and Microsystems Europe Workshop; 18.08.2019 to 20.08.2019; Wolfson College, Oxford, United Kingdom.</p>
AI4EU Robotics Pilot: Vibration sensor measurements in a robotic pump
<p>The robotic pump demonstrator represents a hydraulic pump that can be mounted on an industrial robot, for example, to pump liquid paint for spray painting. On this pump, one accelerometer is mounted for vibration monitoring and recording.</p> <p>The pump can be controlled in terms of speed (rotations per minute, rpm), affecting the throughput of paint and the pressure in and out of the pump.</p> <p>The dataset consists of 380 million measurements of several sensor data of the pump system in 1-second intervals over two months in 2020. The data is split by the recording date over 33 files.</p>
AI4EU Robotics Pilot: Vibration sensor measurements in a robotic wrist
<p>The robotic wrist demonstrator represents a mechanical wrist with three axes that can hold tools, e.g. for spray painting in combination with a pump. On this robotic wrist, two accelerometers are mounted for vibration monitoring and recording: one in the movable front part of the wrist and one in the shaft. The wrist can be controlled through the torque or the designated position of each axis’ motor.</p> <p>The dataset consists of 1.8 billion measurements of several sensor data of the robotic wrist in 1-second intervals over six months in 2020. The data is split by the recording date over 98 files.</p>
A Sensorized Soft Pneumatic Actuator Fabricated with Extrusion-Based Additive Manufacturing
<p>Soft pneumatic actuators with a channel network (pneu-net) based on thermoplastic elastomers are compatible with fused deposition modeling (FDM). However, conventional filament-based fused deposition modeling (FDM) printers are not well suited for thermoplastic elastomers with a shore hardness (Sh < 70A). Therefore, in this study, a pellet-based FDM printer was used to print pneumatic actuators with a shore hardness of Sh18A. Additionally, the method allowed the in situ integration of soft piezoresistive sensing elements during the fabrication. The integrated piezoresistive elements were based on conductive composites made of three different styrene-ethylene-butylene-styrene (SEBS) thermoplastic elastomers, each with a carbon black (CB) filler with a ratio of 1:1. The best sensor behavior was achieved by the SEBS material with a shore hardness of Sh50A. The dynamic and quasi-static sensor behavior were investigated on SEBS strips with integrated piezoresistive sensor composite material, and the results were compared with TPU strips from a previous study. Finally, the piezoresistive composite was used for the FDM printing of soft pneumatic actuators with a shore hardness of 18 A. It is worth mentioning that 3 h were needed for the fabrication of the soft pneumatic actuator with an integrated strain sensing element. In comparison to classical mold casting method, this is faster, since curing post-processing is not required and will help the industrialization of pneumatic actuator-based soft robotics</p>
Data supplement to: Quality control of image sensors using gaseous tritium light sources
<p>In the article "Quality Control of Image Sensors using Gaseous Tritium Light Sources" (<a href="https://doi.org/10.1098/rsta.2021.0130)">https://doi.org/10.1098/rsta.2021.0130)</a> we propose a practical method for radiometrically calibrating cameras using widely available gaseous tritium light sources (<em>betalights</em>). This dataset includes all the recorded data along with the scripts necessary to reproduce the results and figures.</p>
Dataset of the determination of the topographic spatial resolution of a confocal point sensor with a type ASG material measure
<p>These original measurement data relate to the publication: J. Schaude, A. C. Gröschl, T. Hausotte: Effect of a Misidentified Centre of a Type ASG Material Measure on the Determined Topographic Spatial Resolution of an Optical Point Sensor, Metrology 2(1), p. 19-32, 2022, <a href="https://doi.org/10.3390/metrology2010002">https://doi.org/10.3390/metrology2010002</a>. Please refer to this open access publication for a detailed description of the measurement setup and procedure.</p> <p>All data are in ASCII-format. Each file contains four columns, where column one to three are the <em>x</em>, <em>y</em>, and <em>z</em>-coordinates of the positioning system and column four is the signal of the photodetector.</p> <p><strong>Content of the folders</strong></p> <p>10_Plane: Axial probings on 18 points just outside the grooves.</p> <p>20_Edges: Lateral probings from each point just outside the grooves in the direction of the roughly determined centre of the material measure.</p> <p>30_PlaneArea: Repeated axial probing on a plane area near the material measure.</p> <p>40_RadialMeasurement: Radial measurement of the material measure by lateral (radial) probings conducted on different heights (referring to the distance to the plane fitted to the measuring points of 10_Plane), and radii (referring to the centre of the circle fitted to the edges determined in 20_Edges). Each radial probing has its own data file, with the name of the data file being “radial_probing {radius in m} {height in m} {data and time of probing}.txt”.</p> <p>50_LineMeasurement: Lateral probings conducted on different heights (referring to the distance to the plane fitted to the measuring points of 10_Plane), different lateral offsets (referring to the centre of the circle fitted to the edges determined in 20_Edges) and on two angles (on the groove (0°) and the adjacent top level (10°)). Please refer to sec. 5.2 of the aforementioned publication for a detailed description. Each lateral probing has its own data file, with the name of the file being “lateral probing {angle in °} {offset in m} {height in m} {data and time of probing}.txt”.</p> <p><strong>Acknowledgement</strong></p> <p>This project 20IND07 TracOptic has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme. Funder name: European Metrology Programme for Innovation and Research (EMPIR); Funder ID: 10.13039/10001413</p>
Properties of leak detection sensor, Arduino and application code
<p>The dataset contains properties of a leak detection sensor; absorbance spectra and reflectance in different pH environments, ionic strength, reversibility, FT-IR (Fourier-transform infrared spectroscopy), stability, TGA (Thermogravimetric analysis), and photoisomerization data. The microprocessor and application codes are also included. The information included herein will be helpful for users of the sensor.</p>
Home-based measurements of dystonia and choreoathetosis in cerebral palsy using smartphone-coupled inertial sensor technology and machine learning: A proof-of-concept study - dataset
<p>Home-based measurements of dystonia in cerebral palsy using smartphone-coupled inertial sensor technology and machine learning: A proof-of-concept study</p> <p> </p> <p>This project contains:</p> <p>- 1 main MATLAB script: MODYSathome_main.m<br> - 12 MATLAB functions:<br> - function_calc_mean_recall_precision.m<br> - function_create_dataframes.m<br> - function_deep_learning.m<br> - function_determine_best_ML_model.m<br> - function_display_DL_results.m<br> - function_display_ML_results.m<br> - function_index_extremities.m<br> - function_machine_learning.m<br> - function_oversample.m<br> - function_partition_data.m<br> - function_pick_best_models.m<br> - function_prepare_DL_data.m</p> <p>Downloading the Matlab scripts</p> <p> - Create a folder named 'MODYS' and create a subfolder named 'results'<br> - Download the zip file via <a href="https://zenodo.org/record/6379348">RehabAUmc/modys-at-home: v1.0 | Zenodo</a><br> - Unzip the zip file in the path MODYS\</p> <p>STEPS<br> 1. Open MATLAB<br> 2. In MATLAB, go to the 'HOME' tab and click on 'Set Path'<br> 3. Click on 'Add Folder' and browse to MODYS/RehabAUmc-modys-at-home-86b14c3/functions<br> 4. Click on 'Select Folder' and click on 'Save'<br> 5. Click on 'Browse to folder' and browse to a patients' data in MODYS/data/PatientXXX, then click on 'Select Folder'<br> 6. In the 'HOME' tab click on 'Open' and open MODYSathome.m in MODYS/RehabAUmc-modys-at-home-86b14c3<br> 7. In the 'EDITOR' tab click on 'Run Section' to run the script<br> 8. When the code has been run, the results are displayed in the Command Window and saved in MODYS/results/PatientXXX</p>
Data for A Locally Activatable Sensor for Robust Quantification of Organellar Glutathione
<p>Supporting data to paper A Locally Activatable Sensor for Robust Quantification of Organellar Glutathione,</p> <p>including NMR, MS, microscopy, etc</p>
Laboratory validation of a smartphone-based sensor for diffuse optical volume properties
<p>This data set contains raw image data for laboratory validation of a diffuse-optical, smartphone-based sensor. The measurements were taken using scattering phantoms with known scattering and absorption coefficient. The raw image files have been converted to an uncompressed Adobe-.dng file format, file names indicate whether the file contains data for the three scattering phantoms (One, Two, Three) or spatial calibration data using a 9mm x 9mm calibration pattern (calib). The raw images are located in the folder ./calib. Matlab code is contained in the filder ./matlab. It can be run on Matlab R2021b.</p> <p>For analyzing the raw data set, use "CameraBatchCalib.m". It wraps around the function "CameraAnalysisCalib.m", which performs the image analysis and least-square fit to resorted and rescaled data, employing in turn the model function "theosurfG.m". The resulting data is plotted for comparison with the nominal attenuation length of the scattering phantoms.</p> <p>If you wish to use this data set please contact Markus Allgaier at markusa@uoregon.edu with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset and code. When using the data set within a publication, please cite:</p> <p>Markus Allgaier & Brian Smith, "A Smartphone-Based Sensor for Measuring the Optical Properties of Snow", in preparation, (2022).</p> <p>The underlying fit function is based on the calculations from:</p> <p>Markus Allgaier and Smith, Diffuse optics for glaciology, Opt. Express 29, 18845–18864 (2021)</p> <p> </p>
GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion (presentation recording)
<p>Video recording of the presentation for the publication N. Souli et al., "GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion," 2020 22nd International Conference on Transparent Optical Networks (ICTON), Bari, Italy, 2020, pp. 1-4, doi: 10.1109/ICTON51198.2020.9203087.</p>
Technical Reports: Methods - The application of temperature and light intensity as intermittency sensors in a temporary pond
<p>Dataset for Technical Reports: Methods - The application of temperature and light intensity as intermittent sensors in a temporary pond.</p>
Broadband microwave detection using electron spins in a hybrid diamond-magnet sensor chip
<p>Dataset accompanying "Broadband microwave detection using electron spins in a hybrid diamond-magnet sensor chip". </p>
BIM4EEB ITALIAN BUILDING SENSORS MEASUREMENTS DATASET
<p><strong>BIM4EEB ITALIAN BUILDING SENSORS MEASUREMENTS DATASET (MONZA)<br> 10.5281/zenodo.6783695</strong></p> <p><strong>Released under CC BY-NC-ND 4.0 - https://creativecommons.org/licenses/by-nc-nd/4.0/</strong></p> <p><strong>H2020 BIM4EEB Project </strong><br> <a href="https://www.bim4eeb-project.eu/">https://www.bim4eeb-project.eu/</a><br> https://zenodo.org/communities/bim4eeb_eu_project<br> 10.5281/zenodo.6783695</p> <p>BIM4EEB - BIM based fast toolkit for Efficient rEnovation in Buildings<br> This project has received funding from European Union's H2020 research and innovation programme under grant agreement N. 820660. The content of this document reflects only the author's view only and the Commission is not responsible for any use that may be made of the information it contains.</p>
DS.RFSAT.ENV-SENSORS
<p>This dataset contains environmental data collected by <a href="https://www.rfsat.com"><em>RFSAT Limited</em></a> and uploaded to the <strong><a href="https://www.cs.ingv.it/ARCHPortal/">Threats and Hazard Information System (THIS)</a> </strong>server hosted by the <a href="https://www.ingv.it/"><em>Istituto Nazionale di Geofisica e Vulcanologia (INGV)</em></a> in the frame of the ARCH project. The sources of such data included:</p> <ul> <li>Over 50 air quality parameters (including forecasts) from <a href="https://insitu.copernicus.eu/FactSheets/CAMS/">Copernicus Atmosphere Monitoring Service (CAMS)</a>, obtained via an FTP service of the <a href="https://www.ecmwf.int ">European Centre for Medium-Range Weather Forecasts (ECMWF)</a></li> <li>Air quality parameters from the <em>World Air Quality Index</em> project (<a href="https://aqicn.org">https://aqicn.org</a> and <a href="https://waqi.info/">https://waqi.info/</a>)</li> <li> Сurrent weather and forecasts from <a href="https://openweathermap.org/">OpenWeatherMap</a></li> <li>Climate and air quality (incl. PM1/2.5/10, eCO2, TVOC, light, noise etc.) from <a href="https://smartcitizen.me/">Smart Citizen</a> project</li> <li>Climate data (incl. temperature, humidity, pressure, wind, rainfall etc.) from <a href="https://weathermap.netatmo.com/">Netatmo Weathermap</a></li> <li>Historical weather and air quality data from <a href="https://www.weatherbit.io/features">(weatherbit.io)</a></li> <li>Custom RFSAT MVP sensors built in collaboration with Analog Devices</li> <li>Custom RFSAT sensors built in collaboration with <a href="https://turta.io/">Turta.io</a></li> </ul> <p> The dataset contains data stored for backup purposes on RFSAT server. Those files are in CSV format, where the header line provides an explanation of the fields. Since amount of available data worldwide might have overloaded the capabilities of the THIS server repository, acquisition has been limited to areas of interest for each of the pilot cities, specifically:</p> <ul> <li>Rome [41.975615, 12.377350, 41.795675, 12.632782]</li> <li>Valencia [39.7187, 0.1573, 39.0918, -0.8075]</li> <li>Camerino [43.062168, 12.928046, 43.195119, 13.181235]</li> <li>Bratislava [48.181925, 17.120197, 48.139325, 16.970385]</li> <li>Dublin [53.424156, -6.464456, 53.253756, -6.105984]</li> <li>Athens [38.050994, 23.624697, 37.857165, 23.826571]</li> <li>Maribor [46.574865, 15.586470, 46.511919, 15.709023]</li> </ul> <p><strong>NOTE that:</strong></p> <ul> <li>ONLY files for data sources that contain info within pre-defined geographical areas requested by end users are provided (refer to definition of those areas above)</li> <li>Data from core ARCH cities has started nearly two years ago , while Maribor has been added to the list only in June 2022 after the Dialog Conference in Thessaloniki.</li> <li>Data from Netatmo Weathermap covers more than six past years, having included also historical data</li> </ul> <p><strong>The data acquisition still continues and hence subsequent updates can be expected in the future.</strong></p>
Data/Code: Objective monitoring of functional recovery after total knee and hip arthroplasty using sensor-derived gait measures
<p>Abstract</p> <p>Background: Inertial sensors hold the promise to objectively measure functional recovery after total knee (TKA) and hip arthroplasty (THA), but their value in addition to patient-reported outcome measures (PROMs) has yet to be demonstrated. This study investigated recovery of gait after TKA and THA using inertial sensors, and compared results to recovery of self-reported scores of pain and function.</p> <p>Methods: PROMs and gait parameters were assessed before and at two and fifteen months after TKA (n=24) and THA (n=24). Gait parameters were compared with healthy individuals (n=27) of similar age. Gait data were collected using inertial sensors on the feet, lower back, and trunk. Participants walked for two minutes back and forth over a 6m walkway with 180° turns. PROMs were obtained using the Knee Injury and Osteoarthritis Outcome Scores and Hip Disability and Osteoarthritis Outcome Score.</p> <p>Results: Gait parameters recovered to the level of healthy controls after both TKA and THA. Early improvements were found in gait-related trunk kinematics, while spatiotemporal gait parameters mainly improved between two and fifteen months after TKA and THA. Compared to the large and early improvements found in of PROMs, these gait parameters showed a different trajectory, with a marked discordance between the outcome of both methods at two months post-operatively.</p> <p>Conclusion: Sensor-derived gait parameters were responsive to TKA and THA, showing different recovery trajectories for spatiotemporal gait parameters and gait-related trunk kinematics. Fifteen months after TKA and THA, there were no remaining gait differences with respect to healthy controls. Given the discordance in recovery trajectories between gait parameters and PROMs, sensor-derived gait parameters seem to carry relevant information for evaluation of physical function that is not captured by self-reported scores.</p>
ScienceDex guides
Understand access before you commit
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.