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9 results for “Object reconstruction”
Phase Object Reconstruction for 4D-STEM using Deep Learning, (4D-STEM Example Data)
<p><strong>Overview </strong></p> <p>This repository contains 2 example 4D-STEM datasets format from the paper <a href="https://arxiv.org/abs/2202.12611">"Phase Object Reconstruction for 4D-STEM using Deep Learning"</a>. The data was written to hdf5 for compatibility with the python programming language. When reading from these files consider possibly different storage conventions (Row major vs. column major format). Data may need to be transposed accordingly.</p> <p> </p> <p><strong>Parameters</strong></p> <p>The twisted bilayer graphene dataset is simulated. The smaller file is an experimental SrTiO<sub>3</sub> dataset.</p> <table> <thead> <tr> <th scope="row"> </th> <th scope="col">Graphene</th> <th scope="col">STO</th> </tr> </thead> <tbody> <tr> <th scope="row">E0</th> <td>200kV</td> <td>300kV</td> </tr> <tr> <th scope="row">Apeture</th> <td>25 mrad</td> <td>20 mrad</td> </tr> <tr> <th scope="row">Detector Size</th> <td>2.5 Å<sup>-1</sup></td> <td>1.6671 Å<sup>-1</sup></td> </tr> <tr> <th scope="row">Dimensions</th> <td>101x101x128x128</td> <td>60x60x64x64</td> </tr> <tr> <th scope="row">Step Size</th> <td>0.2</td> <td>0.1818</td> </tr> </tbody> </table> <p><br> </p>
Phase Object Reconstruction for 4D-STEM using Deep Learning, (4D-STEM Training Data)
<p><strong>Overview </strong></p> <p>This repository contains 742,688 samples of simulated Convergent Beam Electron Diffraction patterns (CBEDs); the training data for the paper <a href="https://arxiv.org/abs/2202.12611">"Phase Object Reconstruction for 4D-STEM using Deep Learning"</a>. The folder contains multiple hdf5 datasets. Each dataset has a corresponding Excel-sheet containing detailed information and simulation parameters for every datapoint, as well as a summary-report containing the parameter distributions, hdf5-infos and random number generator settings. This makes every dataset reproducible, using the simulation codes provided in <a href="https://github.com/ThFriedrich/ap_data_generation">https://github.com/ThFriedrich/ap_data_generation</a>.</p> <p><strong>Technical details</strong></p> <p>Every Datapoint consists of a 3x3 set of adjacent Convergent Beam Electron Diffraction pattern (CBEDs), the coherent exit wave phase and amplitude in real and reciprocal space, and the probe functions phase and amplitude in real space. All patterns are 64x64 pixel in 16 bit unsigned integer data format.</p> <p>Every hdf5 file has the following structure:</p> <table> <tbody> <tr> <td>Attributes</td> <td>'Seed': 6108236<br> 'State': 251786606 ...<br> 'Type': 'twister'<br> 'arch': 'glnxa64'<br> 'gpu': 'NVIDIA GeForce RTX 3080'<br> 'matlab_ver': '2021a'</td> </tr> <tr> <td>Dataset 'features'</td> <td> <p>Size: 64x64x9x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'labels_k'</td> <td> <p>Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'labels_r'</td> <td> <p>Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'probe_r'</td> <td> <p>Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'meta'</td> <td> <p>Size: 19x5000<br> Datatype: H5T_IEEE_F32LE (single)</p> </td> </tr> </tbody> </table> <p>The data was written to hdf5 in matlab. When reading from these files consider possibly different storage conventions (Row major vs. column major format). Data may need to be transposed accordingly. The integer arrays were scaled to use the full range of the uint16 datatype. The scaling values are stored under "meta". To restore the original values in floating point numbers, convert the arrays like this:</p> <p>Matlab:</p> <pre><code>hdf_file = ['db_h5_b_5_Training.h5']; n = 128; % load `n` k-space exit waves x = single(h5read(hdf_file, '/labels_k', [1,1,1,1], [64,64,2,n])); % `meta` contains parameters and scaling factors for a given datapoint in following order: [E_0(keV), cond_lens_outer_aper_ang(mrad), collection angle(rA), step_size(A), scale_cbed_1 ... scale_cbed_9, scale_phase_k, scale_amp_k, scale_phase_r, scale_amp_r, scale_probe_phase_r, scale_probe_amp_r] s = h5read(hdf_file, '/meta', [14,1], [2,n]); amplitude = zeros(64,64,n); phase = zeros(64,64,n); for ix = 1:n phase(:,:,n) = (x(:,:,1,n)*s(1,ix) / 65536) - pi; amplitude(:,:,n) = (x(:,:,2,n)*s(2,ix)) / 65536; end % The 9 CBEDs correspond to a 3x3 kernel of patterns. The order in [x,y] is: %[[3, 6, 9]; % [2, 5, 8]; % [1, 4, 7]] </code></pre>
Neural Network and objective analysis reconstruction of 3D Mediterranean physical fields from surface satellite and in situ observations at 1/24 deg
<p>Daily Mediterranean 3D fields of temperature, salinity and geostrophic current at 1/24° of resolution, up to 150m-depth and from 2016 to mid 2022, obtained through a 3 steps approach: (1) Temperature and salinity 3D fields have been first estimated by a machine learning approach by using mediterranean reanalysis outputs (https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R) together with satellite observations, (2) a combination of this first step with in situ observations through an Optimal interpolation to remove part of large scale biases, (3) the computation of geostrophic currents using the thermal wind equation. This work has been funded by the European Space Agency through the 4DMED-SEA project [ESA contract No. 4000141547/23/I-DT].</p>
3D reconstructions of semi-transparent submerged objects: Nanomia, Cystisoma, and validation object
<p>These data were used to support the conclusions published in the article titled, "New method for rapid 3D reconstruction of semi-transparent underwater animals and structures", accepted for publication in Integrative Organismal Biology on May 9th, 2023.</p> <p>It focuses on three physical objects, two of which were live animals, collected under permit in the Monterey Bay NAtional MArine Sanctuary:</p> <ul> <li>A siphonophore of the species <em>Nanomia bijuga</em></li> <li>An amphipod of the family <em>Cystisoma</em></li> <li>A thin-walled plastic cylinder, for validation purposes.</li> </ul> <p>For each of these objects, we provide the original .cine file, as recorded by the Phantom 640S highspeed camera, and the derived high-quality .mov video file. We exported video frames as image stacks, included as ZIP files in this repository. We chose to either extract the red or green channel, or a balanced luminance value of each frame. Background subtraction was performed in some cases, by applying a minimum filter or median filter with a certain Z (time axis) extent, and subtracting this from the original frames. Furthermore, additional smoothing was performed in some cases as indicated by the filenames, in the form of a median filter with an X-by-Y-by-Z extent.</p> <p>3D Slicer software was used for segmentation, and bundled .mrb files are included, which have been tested with 3D Slicer version 5.0.3. Derived .STL or .PLY model files are included as well.</p>
Dataset: Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data
<p>This dataset is the dataset used to train and test the object condensation particle flow approach described in <a href="https://arxiv.org/abs/2002.03605">arxiv:2002.03605</a>.</p> <p>The data can be read with DeepJetCore 3.1 (https://github.com/DL4Jets/DeepJetCore)<br> The entries in the truth array are of dimension (batch, 200, N_truth). The truth inputs are:</p> <p>isElectron,<br> isGamma,<br> isPositron,<br> true_energy,<br> true_x,<br> true_y</p> <p>The entries in the feature array are of dimension (batch, 200, N_features), with the features being:</p> <p>rechit_energy,<br> rechit_x,<br> rechit_y,<br> rechit_z,<br> rechit_layer,<br> rechit_detid</p> <p>The "train.zip" file contains the training sample<br> The "test.zip" file the test sample</p> <p>The main test sample is identical to the training sample in composition, but statistically independent.<br> Other samples can be found in subfolders:</p> <p>test/flatNpart: sample with flat distribution of additional particles in the event w.r.t. each individual particle<br> Test/hiNPart: sample with up to 15 particles per event</p>
3D models of Catalhoyuk reconstructed houses and objects
<p>3D models of the Catalhoyuk reconstructed houses and objects included in the EMOTIVE Catalhoyuk virtual reality experience. More information can be found here: https://emotiveproject.eu/index.php/what-we-do/experiences/</p>
Objective Results of Anterior Cruciate Ligament Reconstruction With and Without Internal Suture Augmentation Technique
ClinicalTrials.gov study NCT04906538. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Objective Flap Assessment During Reconstructive Surgery
ClinicalTrials.gov study NCT00952107. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Objective Assessment of Outcomes Following Immediate Lymphatic Reconstruction / LYMPHA
ClinicalTrials.gov study NCT06650592. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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