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9,302 results for “DETECT”
Fig. 17 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 17. The number of times each kernel returned the best result in the experiment performed for the BWC, RFC, OC and VDS criteria.
Fig. 10 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 10. Representation in PP for all analyzed series of the sets of vectors formed by the SD, SSC, TTC and EDT criteria.
Fig. 13 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 13. Box plots representing the accuracy of the vectors given by the SD, SSC, TTC and EDT criteria. The cross symbol represents the arithmetic mean.
Fig. 18 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 18. CM for the vectors given by the SD, SSC, TTC and EDT criteria. The parameters of the SVM were considered according to Table 5.
Fig. 7 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 7. Example of IMFs and the residue obtained by EMD with SD criterion applied to an audio signal of one second.
Fig. 6 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 6. SVM structure used in the proposed approach. Layer A is the input layer, with m passive elements; layer B is the hidden layer, with n(Xm) active sc elements; and layer C is the output layer, with one active linear element. In layer B, φ 2 = φn(Xm) 1, φ 1 = φn(Xm), w 2 = wn(Xm) 1 and w 1 = wn(Xm).
Fig. 15 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 15. Accuracy as a function of the number of training samples for the best series using minimum features and best overall series, respectively, both provided by the vectors generated by EMD with the EDT criterion.
Fig. 11 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 11. Representation in PP for all analyzed series of the sets of vectors formed by the BWC, RFC, OC and VDS criteria.
Fig. 5 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 5. Representation of the sets P and Q in the Cartesian plane and in PP, both with two classes. (a) The set P of vectors is linearly separable; (b) The set Q is not linearly separable. (c) PP representing the sets P and Q through PFE, suggesting a simple and a robust classifier for P and Q, respectively. The example is only illustrative and not related to the sets analyzed in this paper.
Fig. 14 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 14. Box plots representing the accuracy of the vectors given by the BWC, RFC, VDS and OC criteria. The cross symbol represents the arithmetic mean.
Fig. 19 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 19. CM for the vectors given by the BWC, RFC, OC and VDS criteria. The parameters of the SVM were considered according to Table 5.
Juno Stellar Reference Unit (SRU) image data for high energy ion detections during perijoves 22-35
<p>This is the Juno Stellar Reference Unit (SRU) image data for high energy ion detections during perijoves 22 to 35.</p>
BITS-2 Dataset for Fall Detection
<p>Data was collected using a custom-built wrist-worn end worn on the left wrist. Qualcomm Snapdragon 820c. We used MAX30102 Heart rate and SP02 sensor, MPU6500, which gives 3-axis acceleration, 3-axis linear acceleration and 3-axis gyroscope data and GY273 Magnetometer chip for data collection. All the sensors are interfaced to the SoC via the I2C interface using a Mezzanine board.</p><p>Table, each table having six columns, time-stamp, x-axis data, y-axis data and z-axis data, number of axes and type of sensor(label) except in case of heart-rate where there will be only three columns, time-stamp, beats per minute and label (hrt for heart-rate)</p><p>In this data set, the data was collected from a total of 41 volunteers performing 16 ADLs and 8 Falls. Every activity was repeated for five trials.</p>
Raw NMR Data for Accelerated Acquisition of Wideline Solid-State NMR Spectra of Spin 3/2 Nuclei by Frequency-Stepped Indirect Detection Experiments
<p>Raw NMR data in Bruker Topspin format and SIMPSON simulation files are provided for all main text Figures. </p>
ALMA detection of CO rotational line emission in red supergiant stars of the massive young star cluster RSGC1
<p>Input files and data for the MESA simulations in the paper "ALMA detection of CO rotational line emission in red supergiant stars of the massive young star cluster RSGC1". Runs where performed with version r23.05.1 of the MESA code using the MESA SDK version x86_64-linux-22.6.1. Folder named "Decin" contains the simulation with the new mass loss rate described in the paper, while the folder named NdJ contains the input files for the simulation performed with the Nieuwenhuijzen & de Jager rates.</p>
Infrared Salient Object Detection (ISOD)
<p>It is a Thermal Infrared Image dataset that focuses on salient objects.</p>
Detection of colinear blocks, and synteny and evolutionary analyses based on utilization of MCScanX
Open the record for dataset details and reuse information.
A Open One-Step RT-qPCR for SARS-CoV-2 detection
<p>Sequences of the plasmids needed to purify the required enzymes for a standardized One-Step open RT-qPCR protocol to detect SARS-CoV-2 RNA in clinical samples. Supplementary Information</p>
Detecting Transient Deformation at the Active Volcano Ol Doinyo Lengai in Tanzania with the TZVOLCANO Network: Supplementary software, data, model files
<p><span>These are supplementary data, code, and model files associated with the manuscript "</span><span>Detecting Transient Deformation at the Active Volcano Ol Doinyo Lengai in Tanzania with the TZVOLCANO Network<span>" in consideration for publication in the Geophysical Research Letters. tzvolcano_code_and_models.zip contains all necessary Targeted Projection Operator (TPO) software, input, and output files for the GNSS inversions presented in our manuscript necessary to reproduce the results. The TPO program is a Unix/Linux code developed by <span>Kang-Hyeun Ji working at the Korea Institute for Geoscience and Mineral Resources, Daejeon, South Korea. The source code is available in the supplementary Zenodo repository. We also include input and output model files for the USGS code dMODELS for reproducibility. Please see the README.txt file for more details.</span></span></span></p> <p><span>This study was funded by the US National Science Foundation grant number EAR-1943681 to Virginia Tech, internal university funds via Ardhi University, and Ministry of Science and ICT of Korea Basic Research Project GP2021-006 to the Korea Institute of Geosciences and Mineral Resources. We acknowledge and thank the EarthScope Consortium for archiving and making TZVOLCANO GNSS datasets freely available, supported by the National Science Foundation’s Seismological Facility for the Advancement of Geoscience (SAGE) Award under Cooperative Support Agreement EAR-1851048 and Geodetic Facility for the Advancement of Geoscience (GAGE) Award under NSF Cooperative Agreement EAR-1724794.</span></p>
Amplitude Based Change Detection
<h1><strong>Abstract</strong></h1> <p>Dataset containing a Normalized Decorrelation Change Index used for deriving disturbances based on coherence information inside mine defined areas. Based on Sentinel 1 IW SLC data, the Index is computed using a temporal filter to reduce noise from the coherence information across the whole period, from 01/01/2015 till 01/10/2016. The temporal filtering is applied to ignore noisy data from Semptember till May. The values of the index are higher where decorrelations occured along the study period and lower where no relevant disturbances were identified.</p> <p><em>This depository contains data generated within the European S34 project. The data are in raw form and have not been further processed. Processed data are published in other depositories.</em></p> <h1>Metadata</h1> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p> Amplitude Based Change Detection</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Normalized Decorrelation Change Index used to identify disturbances inside mine areas computed from 2015 till late 2016 with a temporal filter to encompass seasonal decorrelation in data.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p> SAR, mine activity disturbances, change detection, mine monitoring </p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p> Aijala mine area</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p> Aijala mines areas ( composed of 2 mines inside the study area as a .geosjon files)</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p> English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p> /</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p> 05/06/2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p> 15/06/2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p> Raster</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p> .GeoTiff</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p> 15 meters</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>7.5 meters</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p> UTM 30N 32630</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>Free</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>None</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p> george.boldeanu@gmv.com</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p> GMV Innovating Solutions Romania</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p> george.boldeanu@gmv.com</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p> English</p> </td> </tr> </tbody> </table> <p> </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.