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20 results for “multi-spectral”

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zenodo44/100

MSSWD - Multi-Spectral Ship Wake Dataset

The Multi-Spectral Ship Wake Dataset (MSSWD) is a dataset designed for ship wake detection in multi-spectral satellite imagery. It is structured as follows: - Source: 661 image chips derived from 50 Sentinel-2 images, captured by the Multi-Spectral Instrument (MSI) at 10-meter resolution across the visible, near-infrared (VNIR), and short-wave infrared (SWIR) spectral bands. The chips come already pre-processed to highlight sea surface features by using a Contrast Limited Adaptive Histogram Equalization (CLAHE) technique.     - Content: The dataset includes 1059 ship wakes, with various configurations such as:   - Single ship wakes   - Multiple ship wakes   - False wakes (e.g., airplane wakes, sea crests)   - Sea clutter with no visible wakes - Wake Characteristics: Diverse patterns of ship wakes are captured, including:   - Vertical, horizontal, and tilted wakes   - Cluttered sea scenes   - Partial occlusions due to cloud cover - Data Quality: Focused on quality over quantity, MSSWD reflects real-world complexity by collecting data in congested, crowded maritime environments. - Data Labelling: Manually annotated using polygonal annotations to delineate wake contours, which allows:   - Instance segmentation   - Enhanced refinement during data augmentation

opencc-by-4.0Sep 2024View details →

zenodo40/100

TimeSpec4LULC: A Smart-Global Dataset of Multi-Spectral Time Series of MODIS Terra-Aqua from 2000 to 2021 for Training Machine Learning models to perform LULC Mapping

TimeSpec4LULC is a smart open-source global dataset of multi-spectral time series for 29 Land Use and Land Cover (LULC) classes ready to train machine learning models. It was built based on the seven spectral bands of the MODIS sensors at 500 m resolution from 2000 to 2021 (262 observations in each time series). Then, was annotated using spatial-temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE). TimeSpec4LULC contains two datasets: the original dataset distributed over 6,076,531 pixels, and the balanced subset of the original dataset distributed over 29000 pixels. The original dataset contains 30 folders, namely "Metadata", and 29 folders corresponding to the 29 LULC classes. The folder "Metadata" holds 29 different CSV files describing the metadata of the 29 LULC classes. The remaining 29 folders contain the time series data for the 29 LULC classes. Each folder holds 262 CSV files corresponding to the 262 months. Inside each CSV file, we provide the seven values of the spectral bands as well as the coordinates for all the LULC class-related pixels. The balanced subset of the original dataset contains the metadata and the time series data for 1000 pixels per class representative of the globe. It holds 29 different JSON files following the names of the 29 LULC classes. The features of the dataset are: - ".geo": the geometry and coordinates (longitude and latitude) of the pixel center. - "ADM0_Code": the GAUL country code. - "ADM1_Code": the GAUL first-level administrative unit code. - GHM_Index": the average of the global human modification index. - "Products_Agreement_Percentage": the agreement percentage over the 15 global LULC products available in GEE. - "Temporal_Availability_Percentage": the percentage of non-missing values in each band. - "Pixel_TS": the time series values of the seven spectral bands.

opencc-by-4.0Feb 2022View details →

zenodo40/100

The CAPA Apple Quality Grading Multi-Spectral Image Database

The CAPA Apple Quality Grading Multi-Spectral Image Database consists of multispectral (450nm, 500nm, 750nm, and 800nm) images of health and defected apples of bi-color, manual segmentations of defected regions, and expert evaluations of the apples into 4 quality categories. The defect types consist of bruise, rot, flesh damage, frost damage, russet, etc.  The database can be used for academic or research purposes with the aim of computer vision based apple quality inspection. The CAPA Apple Quality Grading Multi-Spectral Image Database is a propriety of ULG (Gembloux Agro-Bio Tech) - Belgium, and cannot be used without the consent of the ULG (Gembloux Agro-Bio Tech), Belgium.  For consent, contact Devrim Unay, İzmir University of Economics, Turkey: unaydevrim@gmail.com OR Marie-France Destain, Gembloux Agro-Bio Tech, Belgium: mfdestain@ulg.ac.be In disseminating results using this database,  1. the author should indicate in the manuscript that it was acquired by ULG (Gembloux Agro-Bio Tech), Belgium. 2. cite the following article Kleynen, O., Leemans, V., & Destain, M.-F. (2005). Development of a multi-spectral vision system for the detection of defects on apples. Journal of Food Engineering, 69(1), 41-49. Relevant publications: Kleynen et al., 2003 O. Kleynen, V. Leemans and M.F. Destain, Selection of the most efficient wavelength bands for ‘Jonagold’ apple sorting. Postharv. Biol. Technol.,  30  (2003), pp. 221–232. Leemans and Destain, 2004 V. Leemans and M.F. Destain, A real-time grading method of apples based on features extracted from defects. J. Food Eng.,  61  (2004), pp. 83–89. Leemans et al., 2002 V. Leemans, H. Magein and M.F. Destain, On-line fruit grading according to their external quality using machine vision. Biosyst. Eng.,  83  (2002), pp. 397–404. Unay and Gosselin, 2006 D. Unay and B. Gosselin, Automatic defect detection of ‘Jonagold’ apples on multi-spectral images: A comparative study. Postharv. Biol. Technol.,  42  (2006), pp. 271–279. Unay and Gosselin, 2007 D. Unay and B. Gosselin, Stem and calyx recognition on ‘Jonagold’ apples by pattern recognition. J. Food Eng.,  78  (2007), pp. 597–605. Unay et al., 2011 Unay, D., Gosselin, B., Kleynen, O, Leemans, V., Destain, M.-F., Debeir, O, “Automatic Grading of Bi-Colored Apples by Multispectral Machine Vision”, Computers and Electronics in Agriculture, 75(1), 204-212, 2011.

opencc-by-4.0Jul 2018View details →

zenodo32/100

Code produced by the study "Advantages of assimilating multi-spectral satellite retrievals of atmospheric composition: A demonstration using MOPITT carbon monoxide products".

This is the code used in the manuscript entitled "Advantages of assimilating multi-spectral satellite retrievals of atmospheric composition: A demonstration using MOPITT carbon monoxide products".

opencc-by-4.0Mar 2024View details →

zenodo32/100

Datasets supporting the publication: Evaluating night-time light sources and correlation with socio-economic development using high-resolution multi-spectral Jilin-1 satellite imagery of Quito, Ecuador

################################## Datasets supporting the publication: Evaluating night-time light sources and correlation with socio-economic development using high-resolution multi-spectral Jilin-1 satellite imagery of Quito, Ecuador. International Journal of Remote Sensing. <a href="https://doi.org/10.1080/01431161.2023.2205983">https://doi.org/10.1080/01431161.2023.2205983</a> C. Scott Watsona*, John R. Elliotta, Marco Córdovab, Jonathan Menoscalb, Santiago Bonilla-Bedoyac aCOMET, School of Earth and Environment, University of Leeds, LS2 9JT, UK bFacultad Latinoamericana de Ciencias Sociales, FLACSO, Quito, Ecuador cResearch Center for the Territory and Sustainable Habitat, Universidad Tecnológica Indoamérica,Machala y Sabanilla, 170301, Quito, Ecuador -Please refer to the publication for details on the production of each dataset. -Please cite the publication and this dataset repository when using the data. ################################## Data: <table> <tbody> <tr> <td>File</td> <td>Description</td> </tr> <tr> <td>J1_mosaic_max.tif</td> <td>Mosaicked Jilin-1 multi-spectral night-time image of Quito, Ecuador. Acquisition: 8th July 2021 at ~10:30 UTC (05:30 local time)</td> </tr> <tr> <td>corine_landcover_S2_20210705T153621_20210705T154215_T17MQV.tif</td> <td>Land cover classification applied to a Sentinel-2 image (5th July 2021)</td> <td> </td> </tr> <tr> <td>corine_landcover_symbology_qgis.txt</td> <td>Land cover classification symbology for QGIS</td> </tr> <tr> <td>light_type_classification.tif</td> <td>Light type classification: class 1 = LED, class 10 = HPS.</td> </tr> <tr> <td>classified_light_locations.shp</td> <td>Classified light source (point) locations</td> </tr> </tbody> </table>

opencc-by-nc-4.0Mar 2023View details →

ClinicalTrials.gov32/100

Detection of SARS-CoV-2 in Nasopharyngeal Swabs by Using Multi-Spectral Screening System

ClinicalTrials.gov study NCT04860895. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov32/100

Feasibility of Multi-Spectral Endoscopic Imaging for Detection of Early Neoplasia in Barrett's Oesophagus

ClinicalTrials.gov study NCT03388047. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

zenodo28/100

Intertidal topography at 10m Resolution Derived from Sentinel-2 Multi-Spectral Imagery for the Bengal Delta Coastline

This repository contains the intertidal topography (e.g., digital elevation model) at 10m spatial resolution and the source-code of the toolbox used in deriving this dataset from Sentinel-2 Level-2A spectral imagery and a regional tidal model. The dataset covers the coastal Bangladesh and West Bengal located in the northern Bay of Bengal. The method and its performance is described in detail in the following paper -   Khan, M.J.U.; Ansary, M.N.; Durand, F.; Testut, L.; Ishaque, M.; Calmant, S.; Krien, Y.; Islam, A.K.M.S.; Papa, F. High-Resolution Intertidal Topography from Sentinel-2 Multi-Spectral Imagery: Synergy between Remote Sensing and Numerical Modeling. Remote Sens. 2019, 11, 2888. https://doi.org/10.3390/rs11242888 The repository has the following files and directory -   1. `DEM_intertidal.dat`: This text file contains the extracted intertidal DEM as space separated x (column 1), y (column 2), z (column 3) points. The spatial location are given in longitude (x), latitude (y), and the vertical height (z) is given in meters (upward positive).2. `pyIntertidalDEM.zip`: This compressed zip folder contains a snapshot of the toolbox (https://github.com/jamal919/pyIntertidalDEM/tree/f05de55d726fd4356f9fb8979a2cf0f42f0ec9b1) used to extract the instantaneous shorelines for the current version of the intertidal DEM. The code is also accessible in its most updated version from the github repository - https://github.com/jamal919/pyIntertidalDEM.3. `README.txt`: This text file. The dataset is published under a Creative Commons Attribution 4.0 International license. The pyIntertidalDEM toolbox is published under a Apache License 2.0.

opencc-by-4.0Dec 2023View details →

zenodo28/100

Multi-Spectral Reflection Matrix for Ultra-Fast 3D Label-Free Microscopy

Optical data associated with the paper "Multi-Spectral Reflection Matrix for Ultra-Fast 3D Label-Free Microscopy".

opencc-by-4.0Oct 2023View details →

ClinicalTrials.gov28/100

Multi-spectral Imaging to Assess Wounds in Peripheral Vascular Disease Patients

ClinicalTrials.gov study NCT02624674. IPD Sharing: Not stated. Countries: 0. Publications: 18.

restrictedIPD-UNDECIDEDFeb 2026View details →

nasa28/100

HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v1.5

The HLSS30 V1.5 data product was decommissioned on January 4, 2022. Users are encouraged to use the improved [HLSS30 V2](https://doi.org/10.5067/HLS/HLSS30.002) data product.The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard the European Union’s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2-3 days at 30 meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment. The HLSS30 product provides 30 m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Sentinel-2A and Sentinel-2B MSI data products. The HLSS30 and [HLSL30](https://doi.org/10.5067/HLS/HLSL30.015) products are gridded to the same resolution and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) tiling system and thus are “stackable” for time series analysis.The HLSS30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. There are 13 bands included in the HLSS30 product along with four angle bands and a quality assessment (QA) band. For a more detailed description of the individual bands provided in the HLSS30 product, please see the User Guide.Provisional HLS V1.5 data have not been validated for their science quality and should not be used in science research or applications.Known Issues* Interruptions in data service occurred during a restaging of backlogged data between June 1 and June 15, 2021 for both HLSS30 and HLSL30 version 1.5 data products. During this time period increased errors in the processing workflow resulted in a significant number of data ingestion failures and thus, significant gaps in data availability. Given the pending release of the version 2.0, science quality HLS products, these missing data will not be filled for version 1.5. Users of the provisional version 1.5 products should be aware of the significant data gap in this two week window. The version 2.0 products will incorporate these data back into the archive. If you have any feedback or questions on the data please contact [Customer Services](https://www.earthdata.nasa.gov/centers/lp-daac/contact) or join our HLS conversion on the [Earthdata Forum](https://forum.earthdata.nasa.gov/viewtopic.php?f=7&t=618&hilit=hls&sid=95750d868b6448e0f4360a1473def234).

restrictednotspecifiedJun 2025View details →

nasa28/100

HLS Sentinel-2 Multi-spectral Instrument Vegetation Indices Daily Global 30 m V2.0

The Harmonized Landsat and Sentinel-2 (HLS) project provides consistent data products from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30 meter (m) spatial resolution. The HLSS30 Vegetation Indices (HLSS30_VI) product is derived from Sentinel-2A, Sentinel-2B, and Sentinel-2C MSI data products. Vegetation indices combine specific bands of satellite data to quantify various aspects of vegetation. Analysis of vegetation indices allows for tracking changes in vegetation over time, identifying areas of stress or deforestation, and assessing crop health. Vegetation indices provide a reliable and efficient means of understanding the complex dynamics of vegetation health. The HLSS30_VI and HLSL30_VI products are gridded to the same resolution and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) tiling system and thus are “stackable” for time series analysis.The HLSS30_VI product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate file. Nine indicators of vegetation health are included in the HLSS30_VI product: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Triangular Vegetation Index (TVI). See the User Guide for a more detailed description of the individual vegetation health variables provided in the HLSS30_VI product.

restrictednotspecifiedApr 2025View details →

nasa28/100

HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0

The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment. The HLSS30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Sentinel-2A, Sentinel-2B, and Sentinel-2C MSI data products. The HLSS30 and [HLSL30](https://doi.org/10.5067/HLS/HLSL30.002) products are gridded to the same resolution and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) tiling system and thus are “stackable” for time series analysis.The HLSS30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. There are 13 bands included in the HLSS30 product along with four angle bands and a quality assessment (QA) band. See the User Guide for a more detailed description of the individual bands provided in the HLSS30 product.Known Issues* Unrealistically high aerosol and low surface reflectance over bright areas: The atmospheric correction over bright targets occasionally retrieves unrealistically high aerosol and thus makes the surface reflectance too low. High aerosol retrievals, both false high aerosol and realistically high aerosol, are masked when quality bits 6 and 7 are both set to 1 (see Table 9 in the [User Guide](https://lpdaac.usgs.gov/documents/1698/HLS_User_Guide_V2.pdf)); the corresponding spectral data should be discarded from analysis.* Issues over high latitudes: For scenes greater than or equal to 80 degrees north, multiple overpasses can be gridded into a single MGRS tile resulting in an L30 granule with data sensed at two different times. In this same area, it is also possible that Landsat overpasses that should be gridded into a single MGRS tile are actually written as separate data files. Finally, for scenes with a latitude greater than or equal to 65 degrees north, ascending Landsat scenes may have a slightly higher error in the BRDF correction because the algorithm is calibrated using descending scenes.* Fmask omission errors: There are known issues regarding the Fmask band of this data product that impacts HLSL30 data prior to April of 2022. The HLS Fmask data band may have omission errors in water detection for cases where water detection using spectral data alone is difficult, and omission and commission errors in cloud shadow detection for areas with great topographic relief. This issue does not impact other bands in the dataset.* Inconsistent snow surface reflectance between Landsat and Sentinel-2: The HLS snow surface reflectance can be highly inconsistent between Landsat and Sentinel-2. When assessed on same-day acquisitions from Landsat and Sentinel-2, Landsat reflectance is generally higher than Sentinel-2 reflectance in the visible bands.* Unrealistically high snow surface reflectance in the visible bands: By design, the Land Surface Reflectance Code (LaSRC) atmospheric correction does not attempt aerosol retrieval over snow; instead, a default aerosol optical thickness (AOT) is used to drive the snow surface reflectance. If the snow detection fails, the full LaSRC is used in both AOT retrieval and surface reflectance derivation over snow, which produces surface reflectance values as high as 1.6 in the visible bands. This is a common problem for spring images at high latitudes.* Unrealistically low surface reflectance surrounding snow/ice: Related to the above, the AOT retrieval over snow/ice is generally too high. When this artificially high AOT is used to derive the surface reflectance of the neighboring non-snow pixels, very low surface reflectance will result. These pixels will appear very dark in the visible bands. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. In Figure 1, the pixels in front of the glaciers have surface reflectance values that are too low. * Unrealistically low reflectance surrounding clouds: Like for snow, the HLS atmospheric correction does not attempt aerosol retrieval over clouds and a default AOT is used instead. But if the cloud detection fails, an artificially high AOT will be retrieved over clouds. If the high AOT is used to derive the surface reflectance of the neighboring cloud-free pixels, very low surface reflectance values will result. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used.

restrictednotspecifiedApr 2025View details →

zenodo24/100

Multi-spectral Analysis of the Ibex Hills

A collection of header, text, and image files for use in ENVI software. Used in the analysis of ASTER data in the location of the Ibex Hill, CA.

opencc-by-4.0Dec 2020View details →

ClinicalTrials.gov24/100

A Pilot Study to Evaluate Multi-Spectral and Laser Speckle Imaging and Multiphoton Microscopy During Vascular Occlusion

ClinicalTrials.gov study NCT01484730. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov24/100

Image Data Collection Using a Multi-spectral Camera for Computer Vision Algorithms Research and Development

ClinicalTrials.gov study NCT05674149. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov24/100

Assessment of Plaque Vulnerability Using a Novel Technique: Multi-spectral PhotoAcoustic Imaging.

ClinicalTrials.gov study NCT04763603. IPD Sharing: Not stated. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

geo24/100

Color-flu: multi-spectral fluorescent reporter influenza viruses as powerful tools for in vivo studies

GEO Series GSE64473. Mus musculus. 15 samples. Type: Expression profiling by array.

openGEO-OpenDec 2014View details →

ClinicalTrials.gov20/100

Image Data Collection of Tanned Skin During Phototherapy Treatment Using a Multi-spectral Camera

ClinicalTrials.gov study NCT05692544. IPD Sharing: Not stated. Countries: 0. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

zenodo16/100

A Dataset for Evaluating and Validating Blood Perfusion Monitoring During an Occlusion Protocol: Multi-spectral and Plethysmography data

The dataset contains multispectral images and PPG data from 45 subjects who underwent an occlusion protocol  to induce changes in blood perfusion in the dominant hand. All the partipiants signed an informed consent. Blood perfusion Blood perfusion refers to the passage of oxygen and other nutrients through the circulatory system. We look to provide data for the evaluation of non-invasive approaches based on multi-spectral images. PPG reference data from the thumb is provided for validation purposes. Occlusion Protocol The occlusion protocol for each participant lasts around 10 minutes. The participants were seated, and their superior limbs were extended on a table.  An automatic blood pressure monitor measured their systolic and diastolic pressures. A blood cuff was placed in their dominant arm, and a PPG sensor MAX30102 (Maxim Integrated, CA, United States) was placed in their thumb finger.  A multispectral camera was positioned above to record the hand palm throughout the protocol. The camera employed is a CMS-V1-C-EVR1M-USB3 (SILIOS Technologies SA., France), which records 9 multispectral channels in the VNIR region of the spectrum. . The protocol is divided into 5 stages, each one lasts 2 minutes. <ol> <li>Start: No pressure is applied</li> <li>Vascular Occlusion: a fixed pressure of 60 mmHg is applied through the blood cuff</li> <li>Rest: The pressure is liberated</li> <li>Total Occlusion:  A pressure equal to the initial systolic pressure plus 20 mmHg is constantly applied.</li> <li>Hyperemia: The pressure is released.</li> </ol> Data available: The multispectral datasets for each subject are contained in a P#.7z file. Single-channel images are included. The name of each file specifies the participant ID, the time (minutes_seconds_milliseconds) of the acquisition with respect to the start of the occlusion protocol, the multispectral dataset, and the corresponding multispectral channel. Example: the following file name specifies a file for participant number 21. The data was captured at 9 minutes and 59.5290 seconds after the start of the occlusion protocol. The image corresponds to the first channel (the count starts at 0). P21_09_59_5290_d02459_channel0.png Files containing masks for each dataset are available for the whole hand region (hand_masks.7z) and the middle finger (finger_masks.7z). Finger masks for participants with a lot of movement are not available. The PPG data is stored in a simple csv file with txt extension for each participant. It contains the Red and Infrared channel data as well as the acquisition time of each sample. The PPG data for all the participants are contained in the compressed file PPG.7z. Reference multispectral images for calibration are also provided in the file Calibration.7z. This file contains multispectral images with the cap on (Dark_Reference) plus three folders with reference images taken during the same session as the participants.  reference_set_A:  = P1 to P10 reference_set_B:  = P11 to P30 reference_set_C:  = P31 to P45 each folder contains multispectral images of different white reference materials which can be employed for different calibration methods:  WhiteA = white tile  WhiteB = white reference bar  WhiteP = sheet of white paper WhiteT = Teflon (PTFE) sheets The participant’s data is summarized in the next table. <table> <tbody> <tr> <td> ID </td> <td> Age </td> <td> Gender </td> <td> Skin type </td> <td> Systolic Pressure </td> <td> # Multi-spectral Datasets </td> </tr> <tr> <td> P1 </td> <td> 20 </td> <td> M </td> <td> 4 </td> <td> 109 </td> <td> 2,456 </td> </tr> <tr> <td> P2 </td> <td> 20 </td> <td> M </td> <td> 3 </td> <td> 129 </td> <td> 2,458 </td> </tr> <tr> <td> P3 </td> <td> 22 </td> <td> M </td> <td> 4 </td> <td> 117 </td> <td> 2,460 </td> </tr> <tr> <td> P4 </td> <td> 19 </td> <td> F </td> <td> 3 </td> <td> 110 </td> <td> 2,454 </td> </tr> <tr> <td> P5 </td> <td> 18 </td> <td> F </td> <td> 4 </td> <td> 106 </td> <td> 2,458 </td> </tr> <tr> <td> P6 </td> <td> 18 </td> <td> F </td> <td> 4 </td> <td> 111 </td> <td> 2,471 </td> </tr> <tr> <td> P7 </td> <td> 18 </td> <td> F </td> <td> 4 </td> <td> 91 </td> <td> 2,462 </td> </tr> <tr> <td> P8 </td> <td> 18 </td> <td> F </td> <td> 4 </td> <td> 102 </td> <td> 2,474 </td> </tr> <tr> <td> P9 </td> <td> 21 </td> <td> M </td> <td> 3 </td> <td> 113 </td> <td> 2,468 </td> </tr> <tr> <td> P10 </td> <td> 19 </td> <td> F </td> <td> 3 </td> <td> 108 </td> <td> 2,461 </td> </tr> <tr> <td> P11 </td> <td> 18 </td> <td> M </td> <td> 4 </td> <td> 137 </td> <td> 2,461 </td> </tr> <tr> <td> P12 </td> <td> 18 </td> <td> F </td> <td> 3 </td> <td> 132 </td> <td> 2,457 </td> </tr> <tr> <td> P13 </td> <td> 18 </td> <td> M </td> <td> 4 </td> <td> 119 </td> <td> 2,461 </td> </tr> <tr> <td> P14 </td> <td> 18 </td> <td> F </td> <td> 4 </td> <td> 100 </td> <td> 2,460 </td> </tr> <tr> <td> P15 </td> <td> 18 </td> <td> F </td> <td> 3 </td> <td> 104 </td> <td> 2,460 </td> </tr> <tr> <td> P16 </td> <td> 18 </td> <td> M </td> <td> 3 </td> <td> 126 </td> <td> 2,462 </td> </tr> <tr> <td> P17* </td> <td> 18 </td> <td> F </td> <td> 3 </td> <td> 105 </td> <td> 2,469 </td> </tr> <tr> <td> P18 </td> <td> 21 </td> <td> F </td> <td> 4 </td> <td> 122 </td> <td> 2,461 </td> </tr> <tr> <td> P19 </td> <td> 21 </td> <td> F </td> <td> 3 </td> <td> 130 </td> <td> 2,462 </td> </tr> <tr> <td> P20 </td> <td> 21 </td> <td> F </td> <td> 3 </td> <td> 106 </td> <td> 2,457 </td> </tr> <tr> <td> P21 </td> <td> 23 </td> <td> M </td> <td> 3 </td> <td> 133 </td> <td> 2,459 </td> </tr> <tr> <td> P22 </td> <td> 23 </td> <td> M </td> <td> 3 </td> <td> 134 </td> <td> 2,471 </td> </tr> <tr> <td> P23 </td> <td> 21 </td> <td> F </td> <td> 3 </td> <td> 115 </td> <td> 2,472 </td> </tr> <tr> <td> P24 </td> <td> 21 </td> <td> M </td> <td> 3 </td> <td> 111 </td> <td> 2,445 </td> </tr> <tr> <td> P25 </td> <td> 21 </td> <td> M </td> <td> 3 </td> <td> 116 </td> <td> 2,460 </td> </tr> <tr> <td> P26 </td> <td> 21 </td> <td> M </td> <td> 4 </td> <td> 107 </td> <td> 2,462 </td> </tr> <tr> <td> P27 </td> <td> 21 </td> <td> F </td> <td> 4 </td> <td> 106 </td> <td> 2,460 </td> </tr> <tr> <td> P28 </td> <td> 24 </td> <td> F </td> <td> 4 </td> <td> 106 </td> <td> 2,460 </td> </tr> <tr> <td> P29 </td> <td> 22 </td> <td> M </td> <td> 4 </td> <td> 125 </td> <td> 2,460 </td> </tr> <tr> <td> P30 </td> <td> 21 </td> <td> M </td> <td> 4 </td> <td> 125 </td> <td> 2,461 </td> </tr> <tr> <td> P31 </td> <td> 21 </td> <td> F </td> <td> 3 </td> <td> 100 </td> <td> 2,472 </td> </tr> <tr> <td> P32 </td> <td> 22 </td> <td> M </td> <td> 3 </td> <td> 120 </td> <td> 2,460 </td> </tr> <tr> <td> P33 </td> <td> 21 </td> <td> M </td> <td> 4 </td> <td> 127 </td> <td> 2,461 </td> </tr> <tr> <td> P34 </td> <td> 19 </td> <td> F </td> <td> 3 </td> <td> 100 </td> <td> 2,457 </td> </tr> <tr> <td> P35 </td> <td> 19 </td> <td> F </td> <td> 3 </td> <td> 108 </td> <td> 2,460 </td> </tr> <tr> <td> P36 </td> <td> 20 </td> <td> F </td> <td> 3 </td> <td> 136 </td> <td> 2,471 </td> </tr> <tr> <td> P37 </td> <td> 19 </td> <td> M </td> <td> 4 </td> <td> 129 </td> <td> 2,457 </td> </tr> <tr> <td> P38* </td> <td> 19 </td> <td> M </td> <td> 3 </td> <td> 128 </td> <td> 2,463 </td> </tr> <tr> <td> P39* </td> <td> 18 </td> <td> M </td> <td> 3 </td> <td> 111 </td> <td> 2,479 </td> </tr> <tr> <td> P40 </td> <td> 23 </td> <td> M </td> <td> 4 </td> <td> 110 </td> <td> 2,462 </td> </tr> <tr> <td> P41 </td> <td> 21 </td> <td> M </td> <td> 3 </td> <td> 130 </td> <td> 2,465 </td> </tr> <tr> <td> P42 </td> <td> 20 </td> <td> F </td> <td> 3 </td> <td> 126 </td> <td> 2,461 </td> </tr> <tr> <td> P43 </td> <td> 22 </td> <td> F </td> <td> 3 </td> <td> 125 </td> <td> 2,459 </td> </tr> <tr> <td> P44 </td> <td> 22 </td> <td> M </td> <td> 3 </td> <td> 138 </td> <td> 2,467 </td> </tr> <tr> <td> P45 </td> <td> 22 </td> <td> M </td> <td> 4 </td> <td> 104 </td> <td> 2,461 </td> </tr> </tbody> </table> Table 1.- Participant's ID and relevant information.Participants with an * presented a lot of movement during the recording.

restrictedApr 2023View details →

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