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69 results for “optical systems”
Optical tweezer platform for the characterization of pH-triggered colloidal transformations in the oleic acid/water system
<p>Hypothesis: Soft colloidal particles that respond to their environment have innovative potential for many fields ranging from food and health to biotechnology and oil recovery. The in situ characterisation of colloidal transformations that triggers the functional response remain a challenge.</p> <p>Experiments: This study demonstrates the combination of an optical <a href="https://www.sciencedirect.com/topics/physics-and-astronomy/micromanipulation">micromanipulation</a> platform, polarized optical video microscopy and <a href="https://www.sciencedirect.com/topics/physics-and-astronomy/microfluidics">microfluidics</a> in a comprehensive approach for the analysis of pH-driven structural transformations in emulsions. The new platform, together with <a href="https://www.sciencedirect.com/topics/physics-and-astronomy/synchrotron">synchrotron</a> small angle X-ray scattering, was then applied to research the food-relevant, pH-responsive, <a href="https://www.sciencedirect.com/topics/chemistry/oleic-acid">oleic acid</a> in water system.</p> <p>Findings: The experiments demonstrate structural transformations in individual oleic acid particles from micron-sized onion-type multilamellar oleic acid vesicles at pH 8.6, to nanostructured emulsions at pH < 8.0, and eventually oil droplets at pH < 6.5. The smooth particle-water interface of the onion-type vesicles at pH 8.6 was transformed into a rough particle surface at pH below 7.5. The pH-triggered changes of the interfacial tension at the droplet-water interface together with mass transport owing to structural transformations induced a self-propelled motion of the particle. The results of this study contribute to the fundamental understanding of the structure–property relationship in pH-responsive emulsions for nutrient and drug delivery applications.</p>
Investigations on single and multi-grain optically stimulated luminescence (OSL) sensitivity and electron spin resonance (ESR) signals in quartz derived from sandstones: Insights on provenance of quartz in ancient depositional systems
<p><span>Trapped charge techniques of luminescence and electron spin resonance (ESR) are classic tools for dating Quaternary deposits. Over the past decade, these techniques have been routinely applied to investigate provenance and /or the sedimentary history of grains based on the different luminescence and ESR characteristics of quartz. Of these, optically stimulated luminescence (OSL) sensitivity is one of the most widely investigated parameter for luminescence-based provenance approach. A majority of studies on this parameter are based on evaluation of multi-grain OSL sensitivity of the samples. This is particularly concerning because single-grain quartz luminescence studies have shown that the luminescence signal of a multi-grain aliquot is contributed by less than ~1-10% of the total grains. Since the sole criteria for discrimination of sources based on luminescence sensitivity relies on its intensity, therefore the results based on multi-grain analysis will most likely be skewed depending on the proportion and ‘brightness’ of a few grains. This demands a need to evaluate the potential of single-grain quartz OSL sensitivity in provenance studies. In this study, we investigate single and multi-grain quartz OSL sensitivities from compositionally different sandstones with well-characterised sources based on U-Pb zircon ages. We further complement this analysis with characterisation of ESR centres commonly used in quartz provenance, namely E’<sub>1</sub> and [AlO<sub>4</sub>]<sup>0</sup> centres. Our study shows that single-grain quartz OSL sensitivity can help distinguish between sediments that have a predominant input from a single source as compared to those with contribution from multiple sources, which otherwise cannot be inferred from multi-grain studies. Moreover, our results on characterisation of quartz-based ESR intensity of E’<sub>1</sub> and saturated [AlO<sub>4</sub>]<sup>0</sup> centres successfully differentiates between sandstones and further complements the luminescence-based characterisation. </span></p>
Data for: "High-resolution Soil Moisture Evolution in Hyper-arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the southern Arabian Peninsula"
<p>Data accompanying the publication: High-resolution Soil Moisture Evolution in Hyper-arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the southern Arabian Peninsula. For filenames starting with T: Exponential fit parameters time0 and mag0 for InSAR coherence data. they are binary files, where fit = a*exp(-b*x); a = -log(mag0); b = 1/time0. timeerr contains the uncertainty of the time0 parameter, and maghigh/maglow contain the high and low uncertainty for the mag0 parameter, respectively. For for each frame or overlap region (T101, T28, T130, T28_T101, T130_T28), there is a vrt file (T..._20180524.time0.vrt), which is the metadata file applicable to all files of the same frame. Files starting with mags_times: Exponential fit parameters for ASCAT/SMAP/GLDAS data. the same parameters (time0, timeerr, mag0, maghigh, maglow) can be found in these matlab structure files. In addition, the .mat files contain the offset parameter and related uncertainty, as well as lat/lon information. </p>
Global Positioning System Based on Optical Flow and Convolutional Neural Network
<p>Two datasets used in the paper Global Positioning System Based on Optical Flow and Convolutional Neural Network, to evaluate the proposed CNN model to the task of position estimation. The datasets cover 2 different modes of motion of a drone, and were captured using Google API.</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 for: A coupled optical waveguides system in a fluidic medium that elucidates different parity-time-symmetric phases
<p>This research introduces a novel methodology of harnessing liquids to facilitate the realization of parity-time (<em>PT</em>)- symmetric optical waveguides on highly integrated microscale platforms. Additionally, we propose a realistic and detailed fabrication process flow, demonstrating the practical feasibility of fabricating our optofluidic system, thereby bridging the gap between theoretical design and actual implementation. Extensive research has been conducted over the past two decades on <em>PT</em>-symmetric systems across various fields, given their potential to foster a new generation of compact, power-efficient sensors and signal processors with enhanced performance. Passive <em>PT</em>-symmetry in optics can be achieved by evanescently coupling two optical waveguides and incorporating an optically lossy material into one of the waveguides. The essential coupling distance between two optical waveguides in air is usually less than 500 nm for nearinfrared wavelengths and under 100 nm for ultraviolet wavelengths. This necessitates the construction of the coupling region via expensive and time-consuming electron beam lithography, posing a significant manufacturing challenge for the mass production of <em>PT</em>-symmetric optical systems. We propose a solution to this fabrication challenge by introducing liquids capable of dynamic flow between optical waveguides. This technique allows the attainment of evanescent wave coupling with coupling gap dimensions compatible with standard photolithography processes. Consequently, this paves the way for the cost-effective, rapid and large-scale production of <em>PT</em>-symmetric optofluidic systems, applicable across a wide range of fields.</p>
Optics Continuum: F-Number and Focal Length of Light Field Systems
<p>Reference:</p> <p>Ivo Ihrke, <em>„F-Number and Focal Length of Light Field Systems: A Comparative Study of Field of View, Light Efficiency, Signal to Noise Ratio, and Depth of Field“</em>, OSA Continuum, 2022</p> <p>Contact:</p> <p>Ivo Ihrke [at] uni [minus] siegen [dot] de</p> <p>Content:</p> <p>The data set contains the raw images that have been used in generating the experimental Figures (4 and 6) in the article as well as a code snippet (see README.md) to extract the Lytro data.</p>
Dataset: Two-dimensional wavefront characterization of adaptable corrective optics and Kirkpatrick–Baez mirror system using ptychography
<p>The ptychography datasets and processed wavefront data in support of the publication "Two-dimensional wavefront characterization of adaptable corrective optics and Kirkpatrick–Baez mirror system using ptychography". File are in the HDF format and contain a number of datasets detailed below. If you require more information, please contact the corresponding author of the publication or thomas.moxham@eng.ox.ac.uk</p>
Artificial Neural Network Symbol Demapper for Coherent Optical Fiber Systems
<p>M-files and datasets that implement an artificial neural network (ANN) demapper targeted to the compensation of fiber nonlinearities in coherent optical transmission systems. </p> <p>The dataset contains simulation data of a 11-channel WDM fiber link with numerical propagation implemented by the split-step Fourier method over standard single-mode fiber with 100 km per span and inline optical amplification with 5 dB noise figure. The launched optical power is varied in the range of 0 to 5 dBm and the distance is swept up to 30 fiber spans. The transmitted signal is a root-raised cosine single-carrier 16QAM at 64 Gbaud. </p>
MAVIS adaptive optics system matrices dataset
<p>MAVIS adaptive optics system matrices dataset.</p>
Code and extensive data for training neural networks for radiation, used in "Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0""
<p>Data and code used in a paper submitted to JAMES titled :<em> Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System</em></p> <p>1) The files <strong>ml_training_*.7z</strong> contain extensive datasets (in NetCDF format) for training neural network versions of the RRTMGP gas optics scheme as described in the paper. The datasets are read by <a href="https://github.com/peterukk/rte-rrtmgp-nn/blob/main/examples/rrtmgp-nn-training/ml_train.py">ml_train.py.</a></p> <p>2) The ML datasets were in turn generated using the input profiles (in NetCDF format) inside <strong>inputs_to_RRTMGP.zip </strong>by running the Fortran programs <code>rrtmgp_sw_gendata_rfmipstyle.F90 and rrtmgp_lw_gendata_rfmipstyle.F90 </code>in <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em>, which call the RRTMGP gas optics scheme, The input profiles contain <strong>millions of columns, hundreds of perturbation experiments (including hypercube-sampled gas concentrations), are derived from several different data sources (including CAMS reanalysis, GCM, and CKDMIP-MMM), and span present-day, preindustrial, and future atmospheric conditions.</strong> They could be used to generate training data for developing emulators of the full RTE+RRTMGP radiation scheme, not just gas optics (see nn_dev on the <a href="https://github.com/peterukk/rte-rrtmgp-nn">RTE+RRTMGP-NN repository on Github</a>, used in a previous paper where different emulation methods were compared)</p> <p>3) The Fortran and Python code used for data generation and NN training are found in<a href="https://github.com/peterukk/rte-rrtmgp-nn/tree/main/examples/rrtmgp-nn-training"> <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em> </a>on the main branch on Github; <strong>an archived version is also included here </strong>(<strong>rte-rrtmgp-nn-2.0.zip</strong>). See the readme in the above sub-directory for further information.</p> <p> </p>
Novel Optical Surface Image Guidance System for Beam-Gated Online Adaptive SBRT Delivery in Mobile Lower Lung and Upper Abdominal Malignancies
ClinicalTrials.gov study NCT05030454. IPD Sharing: YES. Countries: 1. Publications: 1.
Data for: A coupled optical waveguides system in a fluidic medium that elucidates different parity-time-symmetric phases
Open the record for dataset details and reuse information.
Generation of reconfigurable hypercubic graph states in 1-4 dimensions in a simple optical system
Open the record for dataset details and reuse information.
Open-Source Low-Cost Cardiac Optical Mapping System
<p>Optical fluorescence imaging data for <strong>Open-Source Low-Cost Cardiac Optical Mapping System </strong>PLOS ONE paper submission</p>
Nanosecond Optical Switching and Control System for Data Center Networks(data)
<p>This file contains the data and codes for the paper "Nanosecond Optical Switching and Control System for Data Center Networks" </p>
Subsurface Multi-Physical Monitoring of a Reservoir Landslide with the Fiber-Optic Nerve System
<p>All data used in the study to support this research is available on repository via 10.5281/zenodo.6541529</p>
Theoretical Framework for Measuring Cloud Effective Supersaturation Fluctuations using an Advanced Optical System
Open the record for dataset details and reuse information.
Datasets for "Irradiance and cloud optical properties from solar photovoltaic systems" (final version)
<p>This dataset contains all the relevant data for the algorithms described in the paper "<a href="https://amt.copernicus.org/articles/16/4975/2023/">Irradiance and cloud optical properties from solar photovoltaic systems</a>", which were developed within the framework of the <a href="https://www.h-brs.de/de/satelliten-und-meteorologie-unterstuetzte-vorhersage-der-energieerzeugung-von-pv-anlagen-auf">MetPVNet</a> project.</p> <p><strong>Input data:</strong></p> <ol> <li><a href="http://www.cosmo-model.org/">COSMO</a> weather model data (DWD) as NetCDF files (cosmo_d2_2018(9).tar.gz) <ol> <li>COSMO atmospheres for <a href="http://www.libradtran.org/">libRadtran</a> (cosmo_atmosphere_libradtran_input.tar.gz)</li> <li>COSMO surface data for calibration (cosmo_pvcal_output.tar.gz)</li> </ol> </li> <li><a href="https://aeronet.gsfc.nasa.gov/">Aeronet</a> data as text files (MetPVNet_Aeronet_Input_Data.zip)</li> <li>Measured data from the <a href="https://www.h-brs.de/de/satelliten-und-meteorologie-unterstuetzte-vorhersage-der-energieerzeugung-von-pv-anlagen-auf">MetPVNet</a> measurement campaigns as text files (MetPVNet_Messkampagne_2018(9).tar.gz) <ol> <li>PV power data</li> <li>Horizontal and tilted irradiance from pyranometers</li> <li>Longwave irradiance from pyrgeometer</li> </ol> </li> <li>MYSTIC-based lookup table for translated tilted to horizontal irradiance (gti2ghi_lut_v1.nc)</li> </ol> <p><strong>Output data:</strong></p> <ol> <li>Global tilted irradiance (GTI) inferred from PV power plants (with calibration parameters in comments) <ol> <li>Linear temperature model: MetPVNet_gti_cf_inversion_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_gti_cf_inversion_results_faiman.tar.gz</li> </ol> </li> <li>Global horizontal irradiance (GHI) inferred from PV power plants <ol> <li>Linear temperature model: MetPVNet_ghi_inversion_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_ghi_inversion_results_faiman.tar.gz</li> </ol> </li> <li>Combined GHI averaged to 60 minutes and compared with COSMO data <ol> <li>Linear temperature model: MetPVNet_ghi_inversion_combo_60min_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_ghi_inversion_combo_60min_results_faiman.tar.gz</li> </ol> </li> <li>Cloud optical depth inferred from PV power plants <ol> <li>Linear temperature model: MetPVNet_cod_cf_inversion_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_cod_cf_inversion_results_faiman.tar.gz</li> </ol> </li> <li>Combined COD averaged to 60 minutes and compared with COSMO and APOLLO_NG data <ol> <li>Linear temperature model: MetPVNet_cod_inversion_combo_60min_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_cod_inversion_combo_60min_results_faiman.tar.gz</li> </ol> </li> </ol> <p><strong>Validation data:</strong></p> <ol> <li>COSMO cloud optical depth (cosmo_cod_output.tar.gz)</li> <li>APOLLO_NG cloud optical depth (MetPVNet_apng_extract_all_stations_2018(9).tar.gz)</li> <li>COSMO irradiance data for validation (cosmo_irradiance_output.tar.gz)</li> <li><a href="https://www.soda-pro.com/web-services/radiation/cams-radiation-service">CAMS</a> irradiance data for validation (CAMS_irradiation_detailed_MetPVNet_MK_2018(9).zip)</li> </ol> <p><strong>How to import results:</strong></p> <p>The results files are stored as text files ".dat", using Python multi-index columns. In order to import the data into a Pandas dataframe, use the following lines of code (replace [filename] with the relevant file name):</p> <p>import pandas as pd<br>data = pd.read_csv("[filename].dat",comment='#',header=[0,1],delimiter=';',index_col=0,parse_dates=True)</p> <p>This gives a multi-index Dataframe with the index column the timestamp, the first column label corresponds to the measured variable and the second column to the relevant sensor</p> <p><strong>Note:</strong></p> <p>The output data has been updated to match the latest version of the paper, whereas the input and validation data remains the same as in Version 1.0.0</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.