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2,649 results for “optics”
Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images - dataset
<p>This dataset includes 4 files with segmentation results for 4 different ODT reconstructions of SH-SY5Y neuroblastoma cell. The segmentation results contain:</p> <ol> <li>3D binary masks of biological cells obtained through Cellpose [1] and <a href="https://github.com/biopto/ODT-SAS.git">ODT-SAS</a>;</li> <li>3D binary masks of organelles: nucleoli and lipid structures (LS) obtained through slice-by-slice manual segmentation and ODT-SAS.</li> </ol> <p>All files are .*mat files.</p> <p>The files <em>REC_SH-SY5Y_1.mat, REC_SH-SY5Y_2.mat</em> and<em> REC_SH-SY5Y_3.mat</em> consist of 7 variables:</p> <p>RECON – tomographic reconstruction of SH-SY5Y neuroblastoma cell;<br> n_imm – refractive index of object immersion medium;<br> dx – object space sample size in XY [<span class="math-tex">\(\mu m\)</span>];<br> rayXY – xy-coordinates of illumination vectors;</p> <p>maskManual – table with manually determined 3D binary masks of organelles;<br> maskCellpose – 3D binary mask of biological cell obtained through Cellpose;<br> maskODTSAS – table with 3D binary masks of biological cell and their organelles obtained through ODT-SAS.</p> <p>File <em>REC_SH-SY5Y_4.mat</em> includes masks for the ODT-SAS and Cellpose segmentation of three closely packed cells and consists of 5 variables: RECON, n_imm, dx, maskCellpose and maskODTSAS.<br> <br> Access a particular 3D binary mask from 'maskManual' and 'maskODTSAS' tables, using the following names: 'Cell', 'Nucleoli', 'LS'.<br> For example:</p> <pre><code>cellMask = maskODTSAS.Cell{1};</code></pre> <p><br> [1] Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). Cellpose: a generalist algorithm for cellular segmentation. Nature methods, 18(1), 100-106.</p> <p> </p>
Temporal Talbot effect of optical dark pulse trains
<p>The data and code used to produce the results in the paper "<em>Temporal Talbot effect of optical dark pulse trains</em>".</p>
Optical constants of CO and CO2 in the IR ans THz ranges and code to calculate the opacity of icy interstellar grains
<p>Data set of optical constants of CO and CO2 in the IR and THz ranges (0.3–12.0 THz). These data are used to compute the opacity of coated materials in an astrophysical context in our accepted article for publication in A&A, 2022.</p> <p>The following link provides access to the "qabs" open access code and its source to calculate the opacity using the optical constants. The code computes Qabs, Qsca, and Qext from the refractive index or the dielectric constant using Mie's theory:</p> <p>https://bitbucket.org/tgrassi/compute_qabs/src/master/</p> <p>The link redirects you to an open-access environment where to execute the code properly.</p>
Classification and quantification of sucrose from sugar beetand sugarcane using optical spectroscopy and chemometrics
<p>Sucrose, obtained from either sugar beet or sugarcane, is one of the main ingredients used in the food industry. Due to the same molecular structure, chemical methods cannot distinguish sucrose from both sources. More practical and affordable methods would be valuable. Sucrose samples (cane and beet) were collected from nine countries, 25% (w/w) aqueous solutions were prepared and their absorbances recorded from 200 to 1380 nm. Spectral differences were observable in the ultraviolet–visible (UV–Vis) region from 200 to 600 nm due to impurities in sugar. Linear discriminant analysis (LDA), classification and regression trees, and soft independent modeling of class analogy were tested for the UV–Vis region. All methods showed high performance accuracies. LDA, after selection of five wavelengths, gave 100% correct classification with a simple interpretation. In addition, binary mixtures of the sugar samples were prepared for quantitative analysis by means of partial least squares regression and multiple linear regression (MLR). MLR with first derivative Savitzky–Golay were most accept- able with root mean square error of cross-validation, prediction, and the ratio of (standard error of) prediction to (standard) deviation values of 3.92%, 3.28%, and 9.46, respectively. Using UV–Vis spectra and chemometrics, the results show promise to distinguish between the two different sources of sucrose. An affordable and quick analysis method to differentiate between sugars, produced from either sugar beet or sugarcane, is suggested. This method does not involve complex chemical analysis or high-level experts and can be used in research or by industry to detect the source of the sugar which is important for some countries’ agricultural policies.</p>
Coseismic Kinematics of the 2023 Kahramanmaras, Turkey Earthquake Sequence from InSAR and Optical Data
<p>We upload the supporting information and four high-resolution images Figures 1-4 in the manuscript accepted by the GRL journal (2023.07.31).</p>
DS4. Experimental evaluation of the TERAWAY physical layer system, implementing optical injection locking and optical beamforming, using bulk components
<p>This set comprises experimental results from the performance evaluation of the TERAWAY system in the physical layer, implementing bulk components. This performance evaluation focused on the optical generation of millimeter-wave signals implementing injection locking technique by means of inserting an optical frequency comb into the reference optical laser source, the fiber transmission of the optical signals, as well as the processing of this signal via an optical beamforming network.</p> <p>The data set consists of multiple waveforms that correspond to 16-QAM electrical signals of 500 MBaud symbol rate, at a central frequency of 60 GHz. These waveforms were obtained directly after the photodetection stage that consists of two high-speed photodetectors in order to emulate an optical beamforming scenario. The set is separated into different folders that correspond to the injection-locked and unlocked scenarios, both in back-to-back configuration and after a 25 km transmission of the signal through an optical fiber. Each folder contains the waveforms of the electrical signals, as they were generated at the photodetection stage, under various optical power values.</p>
Optical Navigation Dataset for Solar System Small Bodies
<p>This dataset has been curated for the purpose of training and evaluating a variety of local feature extractors intended for optical navigation in the proximity of Solar System small bodies (SSSBs). It aims to serve as a resource for researchers in the field and it is referenced in the related article titled "CNN-based local features for navigation near an asteroid" [1]. Additionally, the associated Python code for this dataset can be found in [2].</p> <p>The dataset is a compilation of images obtained from four distinct space missions focused on SSSBs, specifically NEAR Shoemaker (Eros) [3], Hayabusa (Itokawa) [4], Rosetta (67P/Churyumov-Gerasimenko) [5, 6], and OSIRIS-REx (Bennu) [7]. It also incorporates synthetic data generated through the utilization of a Bennu shape model [8] and OpenGL-based rendering software [9, 10]. Access to mission-specific images is available through the NASA Planetary Data System (PDS), and for the Rosetta mission, via the ESA Planetary Science Archive [11].</p> <p>The prefix <code>rot-</code> has been applied to subsets in which images have been pre-rotated to orient the SSSB's rotation axis upwards within the image frame. These subsets are primarily intended for training purposes and encompass image pairs with pixel correspondences that can be found in the <code>aflow</code> directory. Pixel correspondences are stored as 16-bit PNG images, where the G- and B-channels respectively represent the x and y image coordinates. To facilitate data compression and storage, a fixed scaling coefficient of 8 has been employed to convert the pixel correspondence float array into a 16-bit integer array to be used by the PNG compression. These pixel correspondence files can be loaded using the <code>navex.datasets.tools.load_aflow</code> function from [2].</p> <p>On the other hand, subsets designated with a <code>-d</code> postfix include depth information (<code>*.d</code> files) and are exclusively employed during the evaluation of the proposed feature extractors. The depth data is stored as scaled grayscale 16-bit integer arrays using PNG compression. A custom additional header accompanies these images, providing two 32-bit float values, namely the subtracted offset <em>v<sub>0</sub> </em>and the scale multiplier <em>s</em> utilized in the calculation of image pixel values as <em>v</em>' = (<em>v</em> - <em>v<sub>0</sub></em>)·<em>s</em>. To access the depth data as a 32-bit float array, researchers can utilize the <code>navex.datasets.tools.load_mono</code> function from [2].</p> <p>Please note that the file paths in e.g. <code>rot-cg67p-osinac.tar</code> and <code>cg67p-osinac-d.tar</code> archives are the same, so you need to either rename the extracted folder, extract them to different folders, or only extract the archive that you need.</p> <p>For clarity, it should be noted that subsets lacking the aforementioned pre- or postfixes do not contain paired images and consequently lack pixel correspondences. These subsets were exclusively used for feature extractor training in [1].</p> <p>The dataset also includes <code>*.ckpt</code> files, which are the trained feature extractor models referred to in [1]. More details about how to use them can be found in [2].</p>
Data - Zero-Offset Frequency Locking of Lasers at Low Optical Powers with an Optical Phase Locked Loop
<p>This dataset contains measurement data and processing code for the results published in "Zero-Offset Frequency Locking of Lasers at Low Optical Powers with an Optical Phase Locked Loop". </p>
dataset of 'Electric control of optically-induced magnetization dynamics in a van der Waals ferromagnetic semiconductor'
<p>This .zip archive contains all raw data used for the paper 'Electric control of optically-induced magnetization dynamics in a van der Waals ferromagnetic semiconductor'. In addition, it contains (partially) processed data, and the data that is plotted in the figures of this paper.</p>
Voltage-tunable optical parametric oscillator with an alternating dispersion dimer integrated on chip
<p>With this commit we present or theoretical and experimental data together with plotting codes for our manuscript entitled "Voltage-tunable optical parametric oscillator with an alternating dispersion dimer integrated on chip".</p> <p>Optical parametric oscillators enable the conversion of pump light to new frequency bands using nonlinear optical processes. Recent advances in integrated nonlinear photonics have led to the creation of compact, chip-scale sources via Kerr nonlinearity-induced parametric oscillations. While these sources have provided broadband wavelength tuning, the ability to tune the emission wavelength via dynamically altering the dispersion, has not been attained so far. Here we present a voltage-tunable, on-chip integrated optical parametric oscillator based on an alternating-dispersion dimer, allowing us to tune the emission over nearly 20 THz near 1550 nm. Unlike previous approaches, our device eliminates the need for a widely tunable pump laser source and provides efficient pump filtering at the drop port of the auxiliary ring. Integration of this scheme on a chip opens up the possibility of compact and low-cost voltage-tunable parametric oscillators with diverse application possibilities.</p>
Genesis of column sprites: Formation mechanisms and optical structures
<p>The dataset contains compilable files and input files for the paper "Genesis of column sprites: Formation mechanisms and optical structures".</p>
A self-referenced optical phase noise analyzer for quantum technologies
<p>Raw data used to create plots accompanying the publication. Includes time traces from mixed-domain oscilloscope for COSH analysis as well as pre-processed data directly from commercial phase noise analyzer. Includes README.txt for notes on format and processing.</p>
Data of findings in the article "Optomechanical ring resonator for efficient microwave-optical frequency conversion" by I.T. Chen et al.
<p>Data of findings in the article "Optomechanical ring resonator for efficient microwave-optical frequency conversion" by I.T. Chen et al.</p>
Research data of "Quantum resonant optical bistability with a narrow atomic transition: bistability phase diagram in the bad cavity regime"
<p>The data set includes the matlab programs, measured data and drawings used for the figures in D Rivero et al 2023 New J. Phys. 25 093053</p>
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
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Data and original code for: A generalized approach to characterise optical properties of natural objects
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Data from: Optical projection tomography implemented for accessibility and low cost (OPTImAL)
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Using optical flow temporal interpolation of satellite imagery to assist multi-sensor global cloud product composites
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On-demand entanglement of molecules in a reconfigurable optical tweezer array
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Supplementary data for: Nowcasting 3D cloud fields using forward warping optical flow
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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.