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81 results for “sonar”
Erector Spinae Muscle Block Versus Caudal Block by Sonar in Pediatrics Undergoing Lower Abdominal Surgery
ClinicalTrials.gov study NCT04690894. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Captain Sonar Impact on Trauma Patient Management
ClinicalTrials.gov study NCT05628519. IPD Sharing: NO. Countries: 1. Publications: 3.
ShOckwave ballooN or Atherectomy With Rotablation in Calcified Coronary Artery Lesions, the SONAR Trial
ClinicalTrials.gov study NCT05208749. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Diving behaviour of Cuvier's beaked whales exposed to two types of military sonar
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Data from: Sperm whales reduce foraging effort during exposure to 1-2 kHz sonar and killer whale sounds
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Co-occurrence of beaked whale strandings and naval sonar in the Mariana Islands, Western Pacific
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Data from: Mapping coral and sponge habitats on a shelf-depth environment using multibeam sonar and ROV video observations: Learmonth Bank, northern British Columbia, Canada
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Data from: Behavioral responses of individual blue whales (Balaenoptera musculus) to mid-frequency military sonar
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Discrete-space continuous-time models of marine mammal exposure to Navy sonar
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Data from: Northern bottlenose whales in a pristine environment respond strongly to close and distant navy sonar signals
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Data from: First direct measurements of behavioural responses by Cuvier’s beaked whales to mid-frequency active sonar
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Michigan State University, Sonar project, 1998-2000
This database is from a short-term research study designed to assess the effect of application of low doses of the herbicide fluridone (Sonar®) on Eurasian watermilfoil (EWM), other plant species, and water quality in lakes in 7 lakes in southern Michigan. Low doses of fluridone are expected to only impact EWM and not the rest of the plant community. Three of the lakes had no herbicide treatment done on them (Big Seven, Clear and Heron), three of the lakes had treatment done on them such that EWM decreased in the lake during treatment years (Big Crooked, Camp, and Lobdell); and one lake had treatment that was unsuccessful in changing the plant community (Wolverine). Treatment was done in 1998 during the first year of water chemistry sampling. The plant community did not change drastically, and so treatment did not affect water quality (Bremigan et al. 2005). The responses of aquatic invertebrates (Cheruvelil et al 2000, Cheruvelil et al. 2002) and fish (Valley and Bremigan 2002a,b) were also assessed.
Data from: Reliability of fish size estimates obtained from a multibeam imaging sonar
Multibeam imaging sonars have considerable potential for use in fisheries surveys because the video-like images are easy to interpret and contain information about fish size, shape, and swimming behavior, as well as characteristics of occupied habitats. We examined images obtained using a DIDSON multibeam sonar for Atlantic sturgeon Acipenser oxyrinchus, striped bass Morone saxatilis, white perch M. americana, and channel catfish Ictalurus punctatus of known size (20 - 141 cm) to determine the reliability of length estimates. For ranges up to 11 m, percent measurement error (sonar estimate - total length)/total length X 100 varied by species but was not related to the fish's range or aspect angle (orientation relative to the sonar beam). Least-square mean percent error was significantly different from 0.0 for Atlantic sturgeon (x ̅=-8.34, SE=2.39) and white perch (x ̅=14.48, SE=3.99) but not striped bass (x ̅= 3.71, SE=2.58) or channel catfish (x ̅=3.97, SE=5.16). Underestimating lengths of Atlantic sturgeon may be due to difficulty in detecting the snout or the longer dorsal lobe of the heterocercal tail. White perch was the smallest species tested and it had the largest percent measurement errors (both positive and negative) and the lowest percentage of images classified as good or acceptable. Automated length estimates using Echoview software varied with position in the view-field. Estimates tended to be low at more extreme azimuthal angles (fish's angle off-axis within the view-field) but mean and maximum estimates were highly correlated with total length. Software estimates were also biased by fish images partially outside the view-field and when acoustic crosstalk occurred (when a fish perpendicular to the sonar and at relatively close range is detected in the side lobes of adjacent beams). These sources of bias are apparent when files are processed manually and can be filtered out when producing automated software estimates. Multibeam sonar estimates of fish size should be useful for research and management if these potential sources of bias and imprecision are addressed.
Darwin's Hawkmoth (Xanthopan praedicta) responds to bat ultrasound at sonar-jamming rates: Acoustic data set
<p>This paper reveals that <em>Xanthopan praedicta</em> (Lepidoptera: Sphingidae) is part of the bat-moth coevolutionary dynamic. I played recordings of bat echolocation to live <em>X. praedicta</em> and found that males respond with such dense ultrasound they can likely jam bat sonar. Female <em>X. praedicta</em> and two other sphingid species do not produce ultrasonic responses.</p>
ROV-Based Multi-Sensor Dataset: Synchronized Camera and Sonar images taken in the Tropical Waters of the Red Sea, Eilat
<p><strong>Description:</strong></p> <p>This dataset consists of approximately 46,928 synchronized image pairs collected by the Blue-ROV2. The images were captured using a machine-vision camera (IDS UI-3260CP-C-HQ) and a BluePrint Oculus M1200d Forward-Looking Sonar (FLS). Both sensors were installed with the FLS tilted 15 degrees downward to achieve optimal coverage of the terrain and optimal FOV overlap.</p> <p>The data was collected to train and evaluate a comprehensive perception and obstacle avoidance framework.</p> <p> </p> <p><strong>Context:</strong></p> <p>This dataset is the second installment in our collection of synchronized multi-sensor underwater datasets, aimed at enabling advanced research in multi-modal sensor fusion, obstacle detection, and navigation for autonomous underwater vehicles (AUVs). The data was collected using the Blue-ROV2 Remotely Operated Vehicle (ROV) in the tropical waters of the Red Sea, off the coast of Eilat, Israel. This data captures diverse underwater environments and is part of a research project focused on developing fusion models for improved obstacle detection and navigation in AUVs.</p> <p> </p> <p><strong>Content:</strong></p> <p>The data encompasses several sites within the tropical waters of the Red Sea, Eilat, including corals, rocks, shipwrecks, man-made structures, piers, and caves. The ROV platform was operated by divers, ensuring accurate positioning and coverage. Data was acquired at depths ranging from 3 to 12 meters at different times from dawn to dusk.</p> <p><strong> </strong></p> <p><strong>Dataset Composition:</strong><strong><br></strong></p> <div> <table> <tbody> <tr> <td> <p>Site</p> </td> <td> <p>Recording Session</p> </td> <td> <p>Image Pairs</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p>Tropical Site 1</p> </td> <td> <p>20221211_092506</p> <p>20221211_133252</p> </td> <td> <p>10,915</p> <p>7,978</p> </td> <td> <p>Pier, rocks, corals</p> </td> </tr> <tr> <td> <p>Tropical Site 2</p> </td> <td> <p>20221212_095821</p> <p>20221212_141308</p> </td> <td> <p>9,900</p> <p>8,475</p> </td> <td> <p>Man-made structure, </p> <p>rocks, corals</p> </td> </tr> <tr> <td> <p>Tropical Site 3</p> </td> <td> <p>20221213_102542</p> </td> <td> <p>9,390</p> </td> <td> <p>Rocks, corals</p> </td> </tr> <tr> <td> <p>Total</p> </td> <td> </td> <td> <p>46,928 </p> </td> <td> </td> </tr> </tbody> </table> </div> <p><strong> </strong></p> <p>The dataset is organized into separate sessions, each representing a specific dive or experiment. Within each session, data is further categorized into modalities: camera (FLC images), sonar (FLS images), and depth. Each modality directory contains the corresponding data files in PNG format for images and CSV format for depth data.</p> <p><strong> </strong></p> <p>Each modality directory includes:</p> <ul> <li> <p>A `camera.csv` file for the camera modality that maps each image file to its respective timestamp.</p> </li> <li> <p>A `sonar.csv` file for the sonar modality that maps each image file to its respective timestamp.</p> </li> <li> <p>The depth data in `depth.csv` formatted with `timestamp` and `value`.</p> </li> </ul> <p>Additionally, a `samples.json` file documents the relationship between uni-modal and multi-modal samples, enabling easy association of data from different modalities.</p> <p><strong> </strong></p> <p><strong>Technical Details:</strong></p> <ul> <li> <p>Camera: IDS UI-3260CP-C-HQ</p> </li> <ul> <li> <p>Image dimensions: 1936x1216 pixels (downscaled to 968 × 608 for this dataset)</p> </li> <li> <p>Sensor type: Sony IMX249 1/1.2" CMOS</p> </li> <li> <p>Lens: Tamron M112FM06</p> </li> <li> <p>Captured bit depth: 8-bit</p> </li> <li> <p>Frame rate: 5 Hz</p> </li> </ul> <li> <p>Sonar: BluePrint Oculus M1200d</p> </li> <ul> <li> <p>Operating frequency: 1.2 MHz (low frequency mode)</p> </li> <li> <p>Maximum range: 40 m (set to 15 m for this dataset)</p> </li> <li> <p>Horizontal aperture: 130°</p> </li> <li> <p>Vertical aperture: 20°</p> </li> <li> <p>Number of beams: 512</p> </li> <li> <p>Angular resolution: 0.6°</p> </li> <li> <p>Beam separation: 0.25°</p> </li> <li> <p>Image resolution: 544x300 pixels</p> </li> <li> <p>Coordinate system: Polar</p> </li> <li> <p>Frame rate: 5 Hz</p> </li> </ul> <li> <p>Depth: Blue-Robotics Ping2 Sonar Altimeter and Echosounder</p> </li> <ul> <li> <p>Frequency: 115 kHz</p> </li> <li> <p>Source Level: 198 dB re 1µPa @ 1m</p> </li> <li> <p>Beamwidth: 25 degrees</p> </li> <li> <p>Typical Minimum Range: 0.3 m (1 ft)</p> </li> <li> <p>Typical Usable Range: 100 m (328 ft)</p> </li> <li> <p>Range Resolution: 0.5% of range</p> </li> <li> <p>Depth Rating: 300 m (984 ft)</p> </li> <li> <p>Data format: CSV</p> </li> <li> <p>Columns:</p> </li> <ul> <li> <p>timestamp: Unix timestamp (seconds)</p> </li> <li> <p>value: Depth value (meters)</p> </li> </ul> <li> <p>Sample rate: 5 Hz</p> </li> </ul> </ul> <p><strong> </strong></p> <p><strong>Example File Tree Layout:</strong></p> <p>```<br>${session}/<br>${dataset}/<br>camera/<br>camera.csv<br>00000001.png<br>00000002.png<br>…<br>sonar/<br>sonar.csv<br>00000001.png<br>00000002.png<br>…<br>depth/<br>depth.csv<br>samples.json<br>```<strong> <br><br>Example File Content:</strong></p> <p><strong> </strong>camera.csv<br>```<br>timestamp,filename<br>1644234340.181234,00000001.png<br>1644234343.375667,00000002.png<br>```</p> <p><strong> </strong>sonar.csv<br>```<br>timestamp,filename<br>1644234340.181234,00000001.png<br>1644234343.375667,00000002.png<br>```</p> <p><strong> </strong>depth.csv<br>```<br>timestamp,value<br>1644234340.181234,5.4<br>1644234343.375667,6.1<br>```</p> <p><strong> </strong>samples.json</p> <p>```<br>{<br> "samples": [<br> {<br> "camera": [<br> 0<br> ],<br> "depth": [<br> 0<br> ],<br> "sonar": [<br> 0<br> ]<br> },<br> {<br> "camera": [<br> 1<br> ],<br> "depth": [<br> 1<br> ],<br> "sonar": [<br> 1<br> ]<br> }<br>]</p> <p>```</p> <p>By providing synchronized and aligned camera, sonar imagery, and depth data, this dataset enables researchers to explore novel algorithms and techniques for multi-modal sensor fusion in the context of autonomous underwater vehicles operating in the tropical waters of the Red Sea.</p> <p><strong>Acknowledgements</strong></p> <p>The data in this repository is part of the DeeperSense project that received funding from the European Commission, Program H2020-ICT-2020-2 ICT-47-2020, Project Number: 101016958.</p>
Sonar Guided vs Surgeon Delivered Parasternal Block
ClinicalTrials.gov study NCT05518266. IPD Sharing: UNDECIDED. Countries: 0. Publications: 3.
Data from: Tempo and mode of antibat ultrasound production and sonar jamming in the diverse hawkmoth radiation
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Darwin’s Hawkmoth (Xanthopan praedicta) responds to bat ultrasound at sonar-jamming rates: Acoustic data set
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Climate data collected during sonar estimation of summer chum and pink salmon in the Anvik River, Alaska, 2023
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Data from: Reliability of fish size estimates obtained from a multibeam imaging sonar
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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)
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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.