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100 results for “High-speed”
DNA Origami Raw AFM Data - NanoLocz: Image analysis platform for AFM, high-speed AFM and localization AFM
<p>The data file is in the original ARIS data format as captured on a Cypher VRS1250 AFM (Oxford Instruments)<br><br><br></p>
MATLAB codes for : "Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach".
<p>The package contains all the materials needed to reproduce the findings of our paper. The paper is published by MDPI Applied Sciences journal and its details are as follow.</p> <p>Berghout, T.; Benbouzid, M. Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach. <em>Appl. Sci.</em> <strong>2023</strong>, <em>13</em>, 10916. https://doi.org/10.3390/app131910916</p> <p>1) Please you need to download the dataset from original link provided by introductory paper (Please read the above paper to find out about the datset used).<br> 2) Put the data in folders "RawData" for both experments.<br> 3) Please run the files for each experiment as provided, in alphabetical order.</p>
Data files for: Comparison of high-speed optical observations of a lightning flash from space and the ground
<p>This dataset accompanies the paper in the AGU open acces journal <em>Earth and Space Science</em>, special section "A New Era of Lightning Observations From Space"<em> </em>and contains data of the lightning flash in Colombia detected by:</p> <ul> <li>Geostationary Lightning Mapper (GLM) </li> <li>Lightning Imaging Sensor on the International Space Station (ISS-LIS) </li> <li>Atmosphere-Space Interactions Monitor - Modular Multi-spectral Imaging Array (ASIM MMIA)</li> <li>High-speed intensified Phantom V7.3 camera fielded in Cartagena, Colombia.</li> </ul> <p>The high-speed video .cine files can be read by (free) CineViewer and PCC software of Vision Research Inc. which can convert to avi files. For any questions, contact the first author.</p>
High-Speed Video Recordings of Wheel-Rail Traction Enhancement Using a Full-Scale Testing Platform - Granular Material Candidates
<p>A database of 14 high-speed video recordings of rail-sanding process using a full-scale testing platform is provided in this data note. The videos are recorded for various case studies, namely different positioning of the sander nozzle aiming at the rail, nip, and wheel with various angles, and different materials used as rail-sand. The particle velocities can be extracted from these high-speed videos using particle image velocimetry software. The spread angle of the particles as they flow out of the nozzle can also be measured with the use of image processing software. The data extracted from these high-speed recording can be utilised for calibration, validation, and verification of experimental and numerical set-ups, as well as for training artificial intelligence models.</p>
Damped Cantilever Microprobes for High-Speed Contact Metrology with 3D Surface Topography (Data)
<p>Raw data and figures used for the article "Damped Cantilever Microprobes for High-Speed Contact Metrology with 3D Surface Topography", published in <em>Sensors</em> <strong>2023</strong>, <em>23</em>(4), 2003.</p>
Calibrating a high-speed contact-resonance profilometer (Data)
<p>Raw data, scripts and figures used for the article "Calibrating a high-speed contact-resonance profilometer", published in <em>Journal of Sensors and Sensor Systems</em> on 07 Jul 2020.</p> <p>The data/scripts can be opened/executed by the software "Matlab"</p>
Dataset for the manuscript: Pixel-wise programmability enables dynamic high-SNR cameras for high-speed microscopy
<p>These are the data files used to generate the figures in the paper: Pixel-wise programmability enables dynamic high-SNR cameras for high-speed microscopy. DOI: 10.1101/2023.06.27.546748</p>
Ultrabroadband thin-film lithium tantalate modulator for high-speed communications
<p>The data sets contain the data and the scripts for generating all the plots of the manuscript "Ultrabroadband thin-film lithium tantalate modulator for high-speed communications" </p> <p>The data and the scripts are separated into sub-folders corresponding to the figures. These include the main text Fig.1-3. The scripts are in the .m format. The color and size of some curves are edited in Adobe AI.</p>
Machine Learning based scratches on printed paper detection, in high-speed printing systems [Dataset]
<p>Printing industry rapidly is adopting digital technologies and the requirements in terms of speed and print quality are also becoming more demanding. The is a wide range of possible quality defects in printed paper. This makes it impossible to have humans inspect the printed paper for such a big amount of possible quality defects at the high-speeds the printouts are produced.</p> <p>Printing industry is not taking advantage of the Artificial Intelligence to detect defects in printed paper at speed without human intervention. It is possible to generate millions of images (captures) with printed content from a printing system every day. Most of these images will not have any defect but some other will and can be used to generate a data set to be used in a machine learning system.</p> <p>The intention of this research work is to find ways artificial intelligence can help on automatically detecting defects on printed paper in a printing system and classifying them, without human intervention. Focusing on scratches, I’ve explored what are the actual proposals and solutions, and how machine learning can help improving them by using datasets with different techniques, implementing possible solutions and comparing the obtained results.</p>
Lower complexity of motor primitives ensures robust control of high-speed human locomotion
<p>Walking and running are mechanically and energetically different locomotion modes. For selecting one or another, speed is a parameter of paramount importance. Yet, both are likely controlled by similar low-dimensional neuronal networks that reflect in patterned muscle activations called muscle synergies. Here, we investigated how humans synergistically activate muscles during locomotion at different submaximal and maximal speeds. We analysed the duration and complexity (or irregularity) over time of motor primitives, the temporal components of muscle synergies. We found that the challenge imposed by controlling high-speed locomotion forces the central nervous system to produce muscle activation patterns that are wider and less complex relative to the duration of the gait cycle. The motor modules, or time-independent coefficients, were redistributed as locomotion speed changed. These outcomes show that robust locomotion control at challenging speeds is achieved by modulating the relative contribution of muscle activations and producing less complex and wider control signals, whereas slow speeds allow for more irregular control.</p> <p> </p> <p>In this supplementary data set we made available: a) the metadata with anonymized participant information, b) the raw EMG, c) the touchdown and lift-off timings of the recorded limb, d) the filtered and time-normalized EMG, e) the muscle synergies extracted via NMF and f) the code to process the data, including the scripts to calculate the Higuchi's fractal dimension (HFD) of motor primitives. In total, 180 trials from 30 participants are included in the supplementary data set.</p> <p>The file “metadata.dat” is available in ASCII and RData format and contains:</p> <ul> <li>Code: the participant’s code</li> <li>Group: the experimental group in which the participant was involved (G1 = walking and submaximal running; G2 = submaximal and maximal running)</li> <li>Sex: the participant’s sex (M or F)</li> <li>Speeds: the type of locomotion (W for walking or R for running) and speed at which the recordings were conducted in 10*[m/s]</li> <li>Age: the participant’s age in years</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg]</li> <li>PB: 100 m-personal best time (for G2).</li> </ul> <p>The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Please note that the following trials include less than 30 gait cycles (the actual number shown between parentheses): P16_R_83 (20), P16_R_95 (25), P17_R_28 (28), P17_R_83 (24), P17_R_95 (13), P18_R_95 (23), P19_R_95 (18), P20_R_28 (25), P20_R_42 (27), P20_R_95 (25), P22_R_28 (23), P23_R_28(29), P24_R_28 (28), P24_R_42 (29), P25_R_28 (29), P25_R_95 (28), P26_R_28 (29), P26_R_95 (28), P27_R_28 (28), P27_R_42 (29), P27_R_95 (24), P28_R_28 (29), P29_R_95 (17). All the other trials consist of 30 gait cycles. Trials are named like “P20_R_20,” where the characters “P20” indicate the participant number (in this example the 20th), the character “R” indicate the locomotion type (W=walking, R=running), and the numbers “20” indicate the locomotion speed in 10*m/s (in this case the speed is 2.0 m/s). The filtered and time-normalized emg data is named, following the same rules, like “FILT_EMG_P03_R_30”.</p> <p><strong>Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a></strong></p> <p>The files containing the gait cycle breakdown are available in RData format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with as many rows as the available number of gait cycles and two columns. The first column named “touchdown” contains the touchdown incremental times in seconds. The second column named “stance” contains the duration of each stance phase of the right foot in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_P20_R_20,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “P20” indicate the participant number (in this example the 20th), the character “R” indicate the locomotion type (W=walking, R=running), and the numbers “20” indicate the locomotion speed in 10*m/s (in this case the speed is 2.0 m/s). Please note that the following trials include less than 30 gait cycles (the actual number shown between parentheses): P16_R_83 (20), P16_R_95 (25), P17_R_28 (28), P17_R_83 (24), P17_R_95 (13), P18_R_95 (23), P19_R_95 (18), P20_R_28 (25), P20_R_42 (27), P20_R_95 (25), P22_R_28 (23), P23_R_28(29), P24_R_28 (28), P24_R_42 (29), P25_R_28 (29), P25_R_95 (28), P26_R_28 (29), P26_R_95 (28), P27_R_28 (28), P27_R_42 (29), P27_R_95 (24), P28_R_28 (29), P29_R_95 (17).</p> <p>The files containing the raw, filtered and the normalized EMG data are available in RData format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with as many rows as the amount of recorded data points and 13 columns. The first column named “time” contains the incremental time in seconds. The remaining 12 columns contain the raw EMG data, named with muscle abbreviations that follow those reported above. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_P03_R_30”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “P03” indicate the participant number (in this example the 3rd), the character “R” indicate the locomotion type (see above), and the numbers “30” indicate the locomotion speed (see above). The filtered and time-normalized emg data is named, following the same rules, like “FILT_EMG_P03_R_30”.</p> <p>The files containing the muscle synergies extracted from the filtered and normalized EMG data are available in RData format, in the files named “SYNS_H.RData” and “SYNS_W.RData”. The muscle synergies files are divided in motor primitives and motor modules and are presented as direct output of the factorisation and not in any functional order. Motor primitives are data frames with 6000 rows and a number of columns equal to the number of synergies (which might differ from trial to trial) plus one. The rows contain the time-dependent coefficients (motor primitives), one column for each synergy plus the time points (columns are named e.g. “time, Syn1, Syn2, Syn3”, where “Syn” is the abbreviation for “synergy”). Each gait cycle contains 200 data points, 100 for the stance and 100 for the swing phase which, multiplied by the 30 recorded cycles, result in 6000 data points distributed in as many rows. This output is transposed as compared to the one discussed in the methods section to improve user readability. Each set of motor primitives is saved as an element of a single R list. Trials are named like “SYNS_H_P12_W_07”, where the characters “SYNS_H” indicate that the trial contains motor primitive data, the characters “P12” indicate the participant number (in this example the 12th), the character “W” indicate the locomotion type (see above), and the numbers “07” indicate the speed (see above). Motor modules are data frames with 12 rows (number of recorded muscles) and a number of columns equal to the number of synergies (which might differ from trial to trial). The rows, named with muscle abbreviations that follow those reported above, contain the time-independent coefficients (motor modules), one for each synergy and for each muscle. Each set of motor modules relative to one synergy is saved as an element of a single R list. Trials are named like “SYNS_W_P22_R_20”, where the characters “SYNS_W” indicate that the trial contains motor module data, the characters “P22” indicate the participant number (in this example the 22nd), the character “W” indicates the locomotion type (see above), and the numbers “20” indicate the speed (see above). Given the nature of the NMF algorithm for the extraction of muscle synergies, the supplementary data set might show non-significant differences as compared to the one used for obtaining the results of this paper.</p> <p>The files containing the HFD calculated from motor primitives are available in RData format, in the file named “HFD.RData”. HFD results are presented in a list of lists containing, for each trial, 1) the HFD, and 2) the interval time <em>k</em> used for the calculations. HFDs are presented as one number (mean HFD of the primitives for that trial), as are the interval times <em>k</em>. Trials are named like “HFD_P01_R_95”, where the characters “HFD” indicate that the trial contains HFD data, the characters “P01” indicate the participant number (in this example the 1st), the character “R” indicates the locomotion type (see above), and the numbers “95” indicate the speed (see above).</p> <p>All the code used for the pre-processing of EMG data, the extraction of muscle synergies and the calculation of HFD is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”.</p>
Raw data accompanying the manuscript "Cost-effective high-speed, three-dimensional live-cell imaging of HIV-1 transfer at the T cell virological synapse"
<p>These are the raw datasets used to generate the figures for the manuscript entitled "Cost-effective high-speed, three-dimensional live-cell imaging of HIV-1 transfer at the T cell virological synapse". The data files are 3D image stacks of a custom-built wide field deconvolution fluorescence microscope (.tif) and super-resolution structured illumination microscopy data (.dv) of Jurkat T cells transferring HIV-1 virus particles to previously uninfected primary T cells.</p>
Vallée de la Sionne Snow Avalanche n. 20213009: High-speed camera recording and derived variables
<p>This repository hosts data obtained from high-speed camera measurements conducted within a large powder snow avalanche (No. 20213009) that occurred naturally at the Vallée de la Sionne test site in Switzerland. Positioned 14 meters above the ground on a vertical pylon, the high-speed camera captures visualizations of snow particles within the aerial layers. These images reveal diverse particle clusters, identifiable as bright spots due to their higher light reflectance compared to the surrounding air-snow crystal mixture.</p> <p>Contained within this repository is an overview video recording along with corresponding data on the average brightness of each image captured by the high-speed camera. This dataset facilitates the reconstruction of the temporal evolution and frequency of particle clustering, with brightness intensity acting as a proxy for mass transport. The average brightness for each image is computed from the averaging of values from 2048 x 2048 pixels (greyscale 0 to 255). These datasets complement the findings presented in the following publication:</p> <p>B. Sovilla, E. Marchetti, M. Kyburz, A. Koehler, P. Huguenin, I. Calic, M.J. Kohler, E. Surinach, and C. Pérez-Guillén, under review. "The dominant source mechanism of infrasound generation in powder snow avalanches," submitted to Geophysical Research Letters.</p>
High-Speed Video Recordings of Wheel-Rail Traction Enhancement Using a Full-Scale Testing Platform - Sander Position & Angle Aimed at Nip & Wheel
<p>A database of 5 high-speed video recordings of rail-sanding process using a full-scale testing platform is provided in this data note. The videos are recorded for various case studies, namely different positioning of the sander nozzle aiming at the rail, nip, and wheel with various angles, and different materials used as rail-sand. The particle velocities can be extracted from these high-speed videos using particle image velocimetry software. The spread angle of the particles as they flow out of the nozzle can also be measured with the use of image processing software. The data extracted from these high-speed recording can be utilised for calibration, validation, and verification of experimental and numerical set-ups, as well as for training artificial intelligence models.</p>
High-Speed Video Recordings of Wheel-Rail Traction Enhancement Using a Full-Scale Testing Platform - Sander Position & Angle Aimed at Rail
<p>A database of 4 high-speed video recordings of rail-sanding process using a full-scale testing platform is provided in this data note. The videos are recorded for various case studies, namely different positioning of the sander nozzle aiming at the rail, nip, and wheel with various angles, and different materials used as rail-sand. The particle velocities can be extracted from these high-speed videos using particle image velocimetry software. The spread angle of the particles as they flow out of the nozzle can also be measured with the use of image processing software. The data extracted from these high-speed recording can be utilised for calibration, validation, and verification of experimental and numerical set-ups, as well as for training artificial intelligence models.</p>
Movies (II) for "3D Space-Time Adaptive Hybrid Simulations of Magnetosheath High-Speed Jets"
<p>Movies for the aforementioned paper showing magnetic field lines, dynamic pressure and plasma density in global magnetospheric simulations. NW and SW refer to quasi-radial northward and southward IMF. </p> <p>https://doi.org/10.1029/2020JA029035</p>
Supplemental Raw Data for Imboden et al., High-speed mechano-active multielectrode array for investigating rapid stretch effects on cardiac tissue
<p>The file contains the central raw data underlying Figure 5 and, in the supplementary material, Figure 13 of the article 'High-speed mechano-active multielectrode array for investigating rapid stretch effects on cardiac tissue'.</p>
Raw data for High-speed shear mixing: a versatile energy-efficient ultra-fast strategy for solvent-free amine-functionalised solid CO2 adsorbents for direct air capture
<p><strong>Specification of affiliations:</strong></p> <ul> <li>Pavol Suly - Centre of Polymer Systems</li> <li>Barbora Hanulikova - Centre of Polymer Systems</li> <li>Abdulkadir Bozarslan - Centre of Polymer Systems</li> <li>Milan Masar - Centre of Polymer Systems</li> <li>Michal Urbanek - Centre of Polymer Systems</li> <li>Eva Domincova Bergerova - Centre of Polymer Systems</li> <li>Michal Machovsky - Centre of Polymer Systems</li> <li>Ivo Kuritka - Centre of Polymer Systems</li> </ul> <p> </p> <p>Raw data for the research paper. Information on the data collection are described in the manuscript. </p>
Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction - sample treated_213nm
<p>RHEED intensity image dataset of sample "<strong>treated_213nm"</strong> in work "Predicting Pulsed Laser Deposition SrTiO<sub>3 </sub>Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."</p>
Datasets for Work "Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction"
<p>RHEED raw dataset and Gaussia fitting parameter dataset for samples "treated_213nm", "treated_81nm" and "untreated_162nm" in the work "Predicting Pulsed Laser Deposition SrTiO<sub>3 </sub>Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."</p>
Data for: High-Speed 3D Imaging of Multiphase Systems: Applying SCAPE Microscopy to Analogue Experiments in Volcanology and Earth Sciences
<p>Microscale processes in three-phase suspensions (mixtures of gas, liquids, and solids) can affect the macroscale behavior of the whole suspension. To visualize these small-scale processes at high speed and in 3D, we use a recently developed imaging system: Swept Confocally-Aligned Planar Excitation (SCAPE) microscopy. This dataset contains 3D videos taken with SCAPE microscopy of experiments where different phases interact with each other. Each zipped folder contains raw data and processed data for a single experiment. "Case 1" experiments show CO2 bubbles growing on PMMA (acrylic) particles in sparkling water. The "Case 2" experiment shows water droplets suspended in canola oil and flowing through a porous medium made of packed PMMA particles. "Case 3" experiments show growth of injected air bubbles in particle suspensions (either glass beads in immersion oil, or PMMA particles in a refractive index matched liquid).</p> <p>All scaling parameters are provided in Table 1. "info.txt" files contain metadata for the processed hyperstacks.</p> <p>The experiments provided here are discussed in the following publication:<br> Oppenheimer, J.*, Patel, K.*, Lindoo, A., Hillman, E. M. C., and Lev, E.: High-Speed 3D Imaging of Multiphase Systems: Applying SCAPE Microscopy to Analogue Experiments in Volcanology and Earth Sciences. <em>Geochemistry, Geophysics, Geosystems.</em> (In press, 12/2020)</p> <p><br> </p>
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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.