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81 results for “sonar”
Sonar estimation of summer chum and pink salmon in the Anvik river, Alaska, 2022
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Climate data collected during sonar estimation of summer chum and pink salmon in the Anvik River, Alaska, 2024
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Delta-X: Sonar Bathymetry Survey of Channels, MRD, Louisiana, 2021
This dataset includes bathymetry data for water channels in a portion of the Mississippi River Delta (MRD) of coastal Louisiana. The data were collected using sonar during field efforts of the Delta-X Campaign taking place during 2021. In situ continuous surveys of channel bathymetry were conducted in the Atchafalaya and Terrebonne basins using either a Lowrance HDS-Live Fish Finder with Active Imaging 3-in-1 Transducer or a SonarMite Echo Sounder mounted on the side or back of the research boat. The sounder depth observations were delineated by the date, time, and location. These bathymetry measurements were used to generate a digital elevation model (DEM) through interpolation with ancillary DEMs. The data are provided in comma-separated values (CSV) format. A map of data collection routes is provided in compressed keyhole markup language (KMZ).
Gene expression profile of SoNar-high and SoNar-low B-cell acute lymphoblastic leukemia (B-ALL) cells
GEO Series GSE151106. Mus musculus. 2 samples. Type: Expression profiling by array.
Gene expression profile of SoNar-high and SoNar-low fetal liver blood cells and B-cell acute lymphoblastic leukemia (B-ALL) cells
GEO Series GSE110112. Mus musculus. 4 samples. Type: Expression profiling by array.
SONAR discovers RNA binding proteins from analysis of large-scale protein-protein interactomes.
GEO Series GSE86035. Homo sapiens. 17 samples. Type: Other.
Urinary Clusterin as a Biomarker of Human Kidney Disease Progression and Response to the Endothelin Receptor Antagonist Atrasentan: An Exploratory Analysis from the SONAR Trail
GEO Series GSE307122. Mus musculus. 17 samples. Type: Expression profiling by high throughput sequencing.
Sonar Estimation of Summer Chum and Pink Salmon in the Anvik River, Alaska, 2021
<p>Dual-frequency identification sonar (DIDSON) was used to estimate adult summer chum salmon (<em>Oncorhynchus keta</em>) and pink salmon (<em>O. gorbuscha</em>) passage in the Anvik River from June 15 to July 26, 2021. Apportionment to species was determined from data collected from tower counts. A total of 18,819 (SE 273) summer chum and 0 pink salmon were estimated to have passed the sonar site. A beach seine sample fishery was conducted to collect age, sex, and length information; however, high water prevented fishing for part of the season. In addition, because of low salmon passage, few fish were caught, and the sample fishery was discontinued early. Both sonar systems functioned well with minimal interruptions to operation. Range of ensonification was considered adequate for most fish that migrated upstream.</p>
EchoPT: A Pretrained Transformer Architecture for Predicting 2D In-Air Sonar Images in Mobile Robotics
<h1>EchoGPT</h1> <p>This folder contains the supplementary data and code for the submission "EchoPT: A Pretrained Transformer Architecture for Predicting 2D In-Air Sonar Images in Mobile Robotics" to the NeurIPS 2024 conference.</p> <h2>Dependencies</h2> <p>Matlab 2024a or higher is needed with the following toolboxes:</p> <ul> <li>Image Processing Toolbox</li> <li>Parallel Computing Toolbox</li> <li>Deep Learning Toolbox</li> <li>Signal Processing Toolbox</li> <li>System Identification Toolbox</li> </ul> <h2>Data</h2> <h3>Simulation</h3> <p>This folder contains the saved sonar images (energyscapes) as well as the motion data for each frame from a particular simulation run (LongRun1). These are saved into batches and saved as compressed .mat files within the <em>\DataCalculated\SimulationData\LongRun1\Raw</em> folder.</p> <h3>Trained model</h3> <p>The model used in the experiments of this submission is saved in <em>\DataCalculated\Networks</em> as a Matlab dlnetwork object. There is a trained (with weights) and an untrained version available.</p> <h2>Code</h2> <h3>Data pre-processing</h3> <p>To extract the data from the batch .mat files into seperate frames a script <strong>preprocessEchoPT.m</strong> is available in the main folder. This will save these individual frames once again to individual .mat files into a folder <em>\DataCalculated\ESSequences</em> by default.</p> <h3>Training</h3> <p>To train the model a Matlab script <strong>trainEchoPT.m</strong> is provided.</p> <h3>Evaluation</h3> <p>To generate the figures of the submission as well as additional outputs such as GIFs three different evaluation scripts are available:</p> <ul> <li><strong>evaluateEchoPT.m</strong>: Evaluate the trained model on the dataset.</li> <li><strong>evaluateEchoPT_ComparisonAccFlow_ARLoop.m</strong>: Evaluate the trained model on the dataset and compare it to acoustic flow with autoregressive prediction.</li> <li><strong>evaluateEchoPT_ComparisonAccFlow_NoAR.m</strong>: Evaluate the trained model on the dataset and compare it to acoustic flow without autoregressive prediction.</li> </ul> <h3>Source</h3> <p>Within the folder <em>\Source</em> all additional Matlab functions and classes can be found.</p> <h2>Open-Source libraries included in this project</h2> <ul> <li>Progress bar by HyunGwang Cho <a href="https://www.mathworks.com/matlabcentral/fileexchange/121363-progress-bar-cli-gui-parfor?s_tid=srchtitle">(link)</a></li> </ul>
Measuring Blood Pressure in the Lung Circulation With Sonar Technology (Echo-Doppler)
ClinicalTrials.gov study NCT01357746. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Reaching a Caloric Goal Using a Gastric Sonar Versus Aspiration of Gastric Contents in Critically Ill Patients Receiving Nasogastric Enteral Nutrition
ClinicalTrials.gov study NCT06557629. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Sonar estimation of summer chum and pink salmon in the Anvik River, Alaska, 2019
Open the record for dataset details and reuse information.
Sonar Estimation of Summer Chum and Pink Salmon in the Anvik River, Alaska, 2021
Open the record for dataset details and reuse information.
Gene expression profile of SoNar-high and SoNar-low fetal liver blood cells
GEO Series GSE110109. Mus musculus. 2 samples. Type: Expression profiling by array.
Submarine Upward Looking Sonar Ice Draft Profile Data and Statistics, Version 1
This data set consists of upward looking sonar sea ice draft data collected by submarines in the Arctic Ocean. It includes data from both U.S. Navy and Royal Navy submarines. Data are provided as ice draft profiles and as statistics derived from the profile data. Statistics files include information concerning ice draft characteristics, keels, level ice, leads, un-deformed and deformed ice.
AIDJEX Beaufort Sea Upward Looking Sonar April 1976, Version 1
This data contains Upward Looking Sonar (ULS) profiles of the underside of the Arctic pack ice along three transects whose total length is 777 nautical miles. The data were obtained by the USS Gurnard (SSN-662), a U.S. Navy submarine, on a traverse of the AIDJEX Main Experiment area in the Beaufort Sea from 07 April 1976 to 10 April 1976. The sea ice thickness derived from the ULS is given in feet.The data are in a single ASCII text file: Aidjex_04_1976_uls.txt. The data in this text file are not formatted into columns; all data are presented in one long row separated by spaces. Little is known about the format of the file, so caution should be used when working with the data. NSIDC is providing this data as part of our effort to preserve historical data. The data file begins with nine values that appear to be header information. These nine values include latitude and longitude values along with other unknown values. After the header, there are approximately 2100 measurements of what NSIDC believes is sea ice thickness in feet, however it is unclear how often these measurements were taken. After these 2100 values, another header of nine values occurs followed again by 2100 measurements. The file continues in this pattern through the remainder of the file. Users with information about the contents of the file are encouraged to contact <a href="mailto:nsidc.org">NSIDC User Services</a>.Two supporting documents that provide some background have been scanned and included as PDF files. These are AIDJEX_ULS_background.pdf and AIDJEX_ULS_format.pdf. These data are available via FTP. <strong>Note:</strong> These data are in a raw format with unknown fields and are being provided as is for preservation purposes. A processed version of the data are available in the <a href="/data/g01360.html">Submarine Upward Looking Sonar Ice Draft Profile Data and Statistics</a> data set.
Paired Sonar / Optical Video Dataset
<p>This dataset was collected for L. Viney's undergraduate engineering honors thesis at UNSW in collaboration with Reach Robotics. <br>It features paired forward facing multibeam sonar and optical video recordings gathered using an Oculus M series sonar mounted on a FUSION ROV in Sydney Harbour. <br>Reach Robotics retains rights and ownership of the data, which is released under a Creative Commons CC BY-NC-SA license.</p> <p>Dataset Contents<br>Sonar Recordings: Proprietary-format sonar files compatible with Oculus Viewer software.<br>Optical Video Recordings: Synchronized optical video files paired with each sonar recording.<br>Bounding Box Labels: A limited number of sessions include bounding box annotations for sonar data, provided as CSV files.</p> <p>Prerequisites<br>To view and analyze the sonar data, download the Oculus Viewer software from Blueprint Subsea:<br>https://www.blueprintsubsea.com/oculus/support</p> <p>Folder Structure<br>The dataset is organized with each session containing both sonar and optical video files. Sessions with annotations also contain CSV files for bounding box labels.</p> <p>dataset/<br>├── session1/<br>│ ├── sonar.oculus<br>│ ├── optical.mp4<br>│ └── labels.csv # Bounding box annotations (if available)<br>├── session2/<br>│ ├── sonar.oculus<br>│ ├── optical.mkv<br>│ └── labels.csv # Only in labeled sessions<br>└── README.md</p> <p><br>Data Annotations<br>Annotations are provided as CSV files in specific sessions. Each CSV file includes bounding box coordinates for objects in the sonar frames, with columns typically structured as:</p> <p>frame_id, object_1_x_min, object_1_y_min, object_1_x_max, object_1_y_max, object_2 ...</p> <p>Usage<br>Sonar Data: Use the Oculus Viewer software to load and view .oculus files.<br>Annotations: Use the CSV files for supervised learning or object detection by overlaying bounding boxes on sonar data.<br>Optical Video: Optical video files are compatible with standard video analysis tools.</p> <p>License<br>This dataset is licensed under the Creative Commons CC BY-NC-SA license. Reach Robotics retains ownership and rights to the data.</p>
Lung Sonar in Neonatal Respiratory Disorders
ClinicalTrials.gov study NCT06292338. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Learning the sequence code for mRNA and protein abundance in human immune cells [SONAR_MPRA]
GEO Series GSE291719. Homo sapiens. 27 samples. Type: Other.
Gene expression profile of SoNar-high and SoNar-low B-cell acute lymphoblastic leukemia (B-ALL) cells
GEO Series GSE110111. Mus musculus. 2 samples. Type: Expression profiling by array.
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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.