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35 results for “acoustic imaging”
TUT Acoustic scenes 2017, Evaluation & Development datasets, processed image
<p>Unseparated Pulse Energy Spectrogram</p> <p>Processed audio data.</p> <p>Sound source separation is a <strong>preliminary</strong> for <strong>acoustic scene classification</strong>. It can be argued that rare sound detection can be performed without separation, but in most cases it also depends on it.</p> <p>I have come up with the theory that the full <strong>time-domain</strong>, or if assumptions are made on the amplitude-waveform or the phase-profile, even the <strong>sequence</strong> of events can be <strong>discarded</strong> for acoustic scene classification.</p> <p>For short time frame bins, a <strong>statistical representation</strong> should be enough to correctly identify the scene. Even more so, if deep learning methods are applied.</p> <p>I have also come up with the theory that <strong>energy</strong> scalograms are applied <strong>pulse-length</strong> or waveform/profile-length wise. This can enhance the input representation for machine learning.</p> <p>Furthermore I have used derivatives of the time signal and applied similar signal processing methods to them. For visualisation I have added them to the original scalogram in different colors. The use of <strong>derivatives</strong> is very much <strong>distorted</strong>, if the sound is not separated.</p>
Multigrid spatially constrained dispersion curve inversion package: towards distributed acoustic sensing surface wave imaging
<p>Surface wave methods, commonly applied in diverse fields, encounter challenges in complex subsurface environments due to limitations inherent in traditional inversion techniques. Conventional one-dimensional inversion (1DI), with its reliance on fixed grids and deterministic linear approaches, often introduces biases, diminishing lateral resolution. Laterally constrained inversion (LCI) improves robustness by addressing lateral coherency but falls short in delineating arbitrary interfaces due to its dependency on fixed grid models. The advent of Distributed Acoustic Sensing (DAS) technology offers extensive seismic data, yet its potential for high-resolution imaging remains underutilized. We introduce a Multigrid Spatially Constrained Dispersion Curve Inversion (MCI) method to overcome these challenges, aiming to harness high-resolution DAS surface wave imaging capabilities. </p> <p>The package includes essential scripts and models required to replicate key figures from the study by Guan et al. (2023, currently under review). These codes are designed to help readers evaluate the effectiveness of the MCI approach using synthetic demonstrations. Additionally, the package includes a refined 2D Vs (shear wave velocity) model derived from a DAS (Distributed Acoustic Sensing) field study conducted in Imperial Valley, California. This model offers new insights into the regional fault system, underscoring the importance of enhanced spatial resolution in large-scale geophysical investigations.</p> <p>It is organized into three directories and contains a total of 14 files. The directory structure is as follows:<br>├── DAS field data<br>│ ├── Pltmodels.m<br>│ ├── README.txt<br>│ ├── field_models.pdf<br>│ ├── model_1DI.mat<br>│ ├── model_LCI.mat<br>│ └── model_MCI.mat<br>├── MCI_Main<br>│ ├── DisForward.p<br>│ ├── InvForward.p<br>│ ├── InvJacobian.p<br>│ ├── MCI.p<br>│ ├── readme.txt<br>│ └── whitejet3.m<br>└── Synthetic demos<br> ├── MCI_Main.m<br> └── syndata.mat</p>
Dataset used in "Ocean floor imaging with Distributed Acoustic Sensing and water phases reverberations" by Spica et al. in Geophysical Research Letters
<p>earthquake #1<br> earthquake #2</p>
Figure 4 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 4. The SVM structure used in application one approach. The weights determined during the superoised part of the training are {w0, w1, ..., wX̅1}. The output element linearly combines the outputs of the hidden layer with the weights.
Figure 2. The paraconsistent plane where the axes G1 and G2 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 2. The paraconsistent plane where the axes G1 and G2 represent the degrees of certainty and contradiction, respectioely. P = (G1, G2) = (α ̅ β, α + β ̅ 1), drawn in blue just to exemplify, is an important element for our analysis: The closer it is to the corner (1,0), the weaker the classifier associated with the features oector can be. The oalues of α and β are derioed from intra-class and inter-class analyses, respectioely, as detailed in [17].
Figure 1 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 1. Quesada gigas. On the left, male emitting acoustic signals. On the right, lateral oiew of male resting.
Data from: Characterizing fish habitat use of fringing oyster reefs using acoustic imaging
<p>Data and scripts associated with the paper "Characterizing fish habitat use of fringing oyster reefs using acoustic imaging"</p>
Scanning Acoustic Microscopy (SAM) images of human tibiae
<p><strong>SAM images of human tibiae</strong></p> <p>preliminary operations:</p> <ul> <li>SAM RF signals are already converted to maps of the surface's acoustic impedance</li> <li>All images are rotated in a common APML ref. system: ANT looks towards the bottom of the image</li> <li>Endosteum masks already drawn</li> </ul> <p>more info can be found here:</p> <ul> <li>Iori G, Schneider J, Reisinger A, Heyer F, Peralta L, Wyers C, et al. Large cortical bone pores in the tibia are associated with proximal femur strength. PLOS ONE. doi:<a href="http://10.1371/journal.pone.0215405">10.1371/journal.pone.0215405</a></li> </ul> <p>software for the analysis of these images:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.2605365">https://doi.org/10.5281/zenodo.2605365</a></li> <li><a href="https://doi.org/10.5281/zenodo.2628321">https://doi.org/10.5281/zenodo.2628321</a></li> </ul> <p> </p>
Figure 8 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 8. Cross-oalidation algorithm.
Figure 6 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 6. The experimental setup for the proposed application two.
Figure 5 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 5. Examples of images used: high, low, and zero density, respectioely.
Figure 3 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 3. The experimental setup for the proposed application one.
Imaging the northeast lobe of the Sudbury Structure through 2D and 2.5D visco-acoustic full-waveform inversion
<p>Seismic reflection profile LN182 - Northeast lobe of the Sudbury Structure. The LN182 transect comprises over 1,300 receivers (single wireless vertical-component 5-Hz geophones) and over 1,500 vibroseis sources. A linear upsweep of 5-120 Hz was generated by four vibroseis trucks forming the vibroseis source system. </p>
Laboratory visualization of fault asymmetry formation via acoustic emission and digital imaging correlation
Open the record for dataset details and reuse information.
Data and Code for [Localization of Mechanical and Electrical Defects in Dry-Type Transformers Using an Optimized Acoustic Imaging Approach]
Open the record for dataset details and reuse information.
Acoustic Radiation Force Impulse Imaging (ARFI) : a New Technique to Assess Liver Elasticity
ClinicalTrials.gov study NCT01082419. IPD Sharing: Not stated. Countries: 1. Publications: 5.
3D Contrast Enhanced Acoustic Perfusion Imaging in Adult After Subarachnoid Hemorrhage
ClinicalTrials.gov study NCT06793839. IPD Sharing: NO. Countries: 1. Publications: 2.
DATASETS - Integration of Non-Destructive Acoustic Imaging investigation with Photogrammetric and Morphological Analysis to Study the "Graecia Vetus" in the Chigi Palace of Ariccia
<p>Datasets of an integrated investigation on the monochrome painting "Graecia Vetus" located in the Ariosto Room in the historical Chigi Palace of Ariccia.</p> <p>Three datasets including:</p> <p>- <strong>Dense Cloud from the surface morphological investigation of the surface by Photogrammetric Survey:</strong></p> <p>1. Zip file containing the "*.obj", "*.mtl", "*.jpg" files to open the Model.</p> <p><strong>- Four matrices from structural damage investigation of the painting by Frequency Resolved Acoustic Imaging:</strong></p> <p>1. Integrated Acoustic Image (IAI) in the wideband (500 - 12,000) Hz;</p> <p>2. Frequency Resolved Acoustic Image (FRAI) in the narrow frequency band 1/3 octave band centred at 1000 Hz;</p> <p>3. Frequency Resolved Acoustic Image (FRAI) in the narrow frequency band 1/3 octave band centred at 6300 Hz;</p> <p>4. Frequency Resolved Acoustic Image (FRAI) in the narrow frequency band 1/3 octave band centred at 10,000 Hz.</p> <p><strong>- One profile and one matrix from structural damage investigation of the wall by Sonic Tests with Impact Hammer:</strong></p> <p>1. Relative variation of pulse velocity along the horizontal axis;</p> <p>2. Tomographic map of the relative variation of pulse velocity inside the wall</p>
Figure 7 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 7. The SVM structure used in application two. Similar to AP1, the weights determined during the superoised part of the training are {w0, w1, …, wX̅1}. The output element linearly combines the outputs of the hidden layer with the weights.
Clinical Evaluation of Opto-Acoustic Image Quality With the Gen 2 Imagio System
ClinicalTrials.gov study NCT05022602. IPD Sharing: NO. Countries: 1. Publications: 0.
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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.
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DANDI Archive for NWB datasets
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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.