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26 results for “particle detection”
Raw Particle Number Size-Distribution Data of twin-DMPS equipped with two CPCs for nanoparticle detection for SMEAR II station, Hyytiälä, Finland, Spring 2017
<p>Raw size-Distribution data from twin-DMPS system (Aalto et al., 2001), where the nano-DMA (measuring up to 40 nm, short Hauke type DMA) is quipped with two detectors:<br> a TSI 3776 and a modified Airmodus A20 (Kangasluoma et al., 2015)</p> <p>Data acquired during in March-May 2017 at the SMEAR II station in Hyytiälä, Finland.<br> Data associated with the publication Stolzenburg, Laurila et al. (2023), Atmos. Meas. Techn., "Improved counting statistics of an ultrafine differential mobility particle size spectrometer system"</p> <p>Files DMYYDDMM_A20.Dat contain the raw DMPS data, with YYMMDD indicating the day of the measurement.<br> Data are provided alternating between data acquired with the nano-DMA and with the long-DMA, on a scan by scan basis.<br> First line of each scan cycle (for both DMAs) always indicates the start and end times of the voltage scan.<br> Second line gives the parameters related to the DMPS as given below:<br> (sheath flow in [l per min], aerosol flow in [l per min], DMA inner electrode diameter in [m], DMA outer electrode diameter in [m], DMA classification length in [m], other parameters)<br> Following lines give<br> (for long-DMA): set voltage at DMA [in V], concentration measured by TSI3772 in [per cm3]<br> (for nano_DMA): et voltage at DMA [in V], concentration measured by TSI 3776 in [per cm3], concentration measured by mod. Airmodus A20 in [per cm3]</p> <p>File dmps_data_format_specifier.text gives a conversion from voltage to diameter and indicates the measurement time at each voltage during the stepping of the DMPS.<br> Needs to be used to convert measured concentrations in counts per set-interval.</p> <p>Files GR_J_overview.xlsx gives size-distribution derived quantities during that campaign.<br> Header defines Date, Growth Rate and Formation Rate measured at different sizes [in nm] and by the two different CPCs connected to the nano-DMA.<br> Growth rates in [nm per h], formation rate in [per cm3 per s].</p> <p>Other data related to the campaign can be obtained from the corresponding author upon reasonable request.<br> juha.kangasluoma@helsinki.fi</p> <p>References:</p> <p>Stolzenburg, Laurila et al. "Improved counting statistics of an ultrafine differential mobility particle size spectrometer system",<br> Atmos. Meas. Techn., in press, 2023</p> <p>Aalto et al., "Physical characterization of aerosol particles during nucleation events",<br> Tellus B, vol. 53, pp. 344-358, 2001</p> <p>Kangasluoma et al., "Sub-3 nm Particle Detection with Commercial TSI 3772 and Airmodus A20 Fine Condensation Particle Counters",<br> Aerosol Sci. Techn., vol. 49, pp. 674-681, 2015</p>
ICELEARNING - Detection of ice core particles via deep neural networks
<p>This dataset refers to the ICELEARNING project - Detection of ice core particles via deep neural networks, by Maffezzoli N. et al., <em>The Cryosphere</em>, 10.5194/tc-17-539-2023, 2023.</p> <p>The main folder contains all TRAINING data. </p> <p>The TEST data are contained in the folder /test. </p> <p>Please refer to the <a href="https://github.com/nmaffe/icelearning">icelearning GitHub</a> repository for instructions. </p>
Figure data for "Detection of tar brown carbon with the single particle soot photometer (SP2)"
<p>Data contained in Figures 2 and 4 of Corbin and Gysel-Beer 2019. https://doi.org/10.5194/acp-2019-568</p>
Figures -using Particle Swarm Optimization (PSO)-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>The performance of WML quantification is evaluated using clustering algorithms. When the<br> image is pre-processed, contrast of the image is enhanced. The resulting enhanced image is<br> clustered using the effective clustering algorithms. Figure 3 represents the input image for WML<br> detection. In order to increase robustness, the noisy medical image is pre-processed. Figure 4<br> depicts the pre-processed image. Bright contrast stretching, which is one of the image enhancement<br> (pre-processing) techniques is applied. After pre-processing the enhanced image is subjected to<br> clustering. Three clustering models are proposed to provide accurate results.</p> <p>All scans obtained from different image clustering models are manually ranked based on<br> values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and<br> GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO)<br> are applied on a dataset of 208 images and ranking is done in terms of under detected, over<br> detected, properly detected as shown in figure 8 and figure 9. The number of images detected<br> properly in GFCM is comparatively high than FCM and GPC.</p>
TUD-IPB Dataset for Detecting the Interaction between Particles and Biomass
<p>This dataset contains the data used for the following publications:</p> <div> <pre>(1) Jia, T., Peng, Z., Yu, J., Piaggio, A. L., Zhang, S., & de Kreuk, M. K. (2024). Detecting the interaction between microparticles and biomass in biological wastewater treatment process with Deep Learning method. Science of The Total Environment, 175813. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.scitotenv.2024.175813" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.scitotenv.2024.175813</span></span></a><br><br>(2) Jia, T., Yu, J., Sun, A., Wu, Y., Zhang, S., & Peng, Z. (2025). Semi-supervised learning-based identification of the attachment between sludge and microparticles in wastewater treatment. Journal of Environmental Management, 375, 124268. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jenvman.2025.124268" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.jenvman.2025.124268</span></span></a></pre> </div> <p>The "TU Delft-Interaction between Particles and Biomass” (TUD-IPB) dataset is for detecting the interaction between microparticles and biomass in biological wastewater treatment process with computer vision. We created this dataset from the experiments conducted in water lab of TU Delft (Stevinweg 1, Delft, Netherlands). The experiment details can be found in the publication. This dataset includes microscope images and mask annotations, that shows the shape and location of free particles and entrapped particles in images.</p> <p>The images are stored in the <em>images.zip</em> file, the annotations are stored in the <em>labels_txt.zip</em> file, and the classes of the annotation (i.e., entrap particle and free particle) is stored in the <em>classes.txt</em> file.</p> <p>The filenames of images and annotations includes three parts: Sampling time (the first part), the diameter of biomass (the middle part), and image number (the last part). Details of the first two parts and their meanings are provided in the following table.</p> <p>Explanation of Filename Components and Their Meanings</p> <table> <tbody> <tr> <td>The first part of filename</td> <td>Sampling time </td> <td>The middle part of filename</td> <td>Diameter of biomass (mm)</td> </tr> <tr> <td>A10</td> <td>10 min</td> <td>bigger3-0</td> <td>>3.1</td> </tr> <tr> <td>A30</td> <td>30 min</td> <td>2-0-3-0</td> <td>2.0-3.1</td> </tr> <tr> <td>A60</td> <td>60 min</td> <td>1-0-2-0</td> <td>1.0-2.0</td> </tr> <tr> <td> </td> <td> </td> <td>0-2-1-0</td> <td>0.2-1.0</td> </tr> <tr> <td> </td> <td> </td> <td>smaller0-2</td> <td>< 0.2</td> </tr> </tbody> </table> <p> </p> <p>If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry:</p> <pre>@article{jia2024detecting, title={Detecting the interaction between microparticles and biomass in biological wastewater treatment process with Deep Learning method}, author={Jia, Tianlong and Peng, Zhaoxu and Yu, Jing and Piaggio, Antonella L and Zhang, Shuo and de Kreuk, Merle K}, journal={Science of The Total Environment}, pages={175813}, year={2024}, publisher={Elsevier} }<br><br>@article{jia2025semi,<br> title={Semi-supervised learning-based identification of the attachment between sludge and microparticles in wastewater treatment},<br> author={Jia, Tianlong and Yu, Jing and Sun, Ao and Wu, Yipeng and Zhang, Shuo and Peng, Zhaoxu},<br> journal={Journal of Environmental Management},<br> volume={375},<br> pages={124268},<br> year={2025},<br> publisher={Elsevier}<br>}</pre>
Detecting Lunar and Martian Water via Backscattered Cosmic Particles using Muon Tomography
<p><strong>Introduction</strong></p> <p>The search for water on the Lunar and Martian surfaces is a cornerstone of space exploration, playing a key role in expanding our understanding of the history and evolution of these celestial bodies. Despite its importance, current knowledge about the distribution, concentration, origin, and migration of water on the Moon and Mars is still limited. This study aims to address these gaps by employing a novel approach that leverages cosmic-ray muon detectors and backscattered radiation. Through the use of advanced muon tracking systems and preliminary simulations conducted with GEANT4, the research suggests that muon tomography holds significant promise for improving our understanding of water-ice content on the Lunar and Martian surfaces.</p> <p><strong>Data Description</strong></p> <p>Data and detector models were generated using GEANT4. The simulations include:</p> <ul> <li>Lunar and Martian dry regolith</li> <li>Lunar and Martian regolith with water-ice beneath the surface</li> </ul> <p><strong>Contents</strong></p> <p>This record includes:</p> <ul> <li><code>*.csv</code>: Output raw files from GEANT4, including 5D information, scattering angle, detector plate position, and particle type.</li> <li><code>backscatter_eventselection.py</code>: Python code to filter events and generate a CSV file of selected backscattered events.</li> <li><code>*.tiff</code>: Visualization files depicting Lunar and Martian scenarios, including detector geometry and particle events.</li> <li><code>ml_classifier.py</code>: Python code for machine learning tasks to classify backscattered events.</li> <li><code>OP_Muographers_2023.pdf</code>: Detailed description of chemical composition and simulated scenarios.</li> <li>Tracking_EKF: Performs track reconstruction and computes track lengths using extended Kalman Filter.</li> </ul> <p><strong>Disclaimer</strong></p> <p>The provided datasets are simulated samples suitable for conceptual R&D and performance studies. They have not been calibrated against real data and should not be used for physics projections about the detectors.</p>
High Sensitivity Detection of a Solubility Limiting Surface Transformation of Drug Particles by DNP SENS
<p>NMR raw data related to publication in J. Pharm. Sci. The raw data content is described in the read me file.</p>
A supervised Graph-based deep learning algorithm to detect and quantify clustered particles
<p>In this data repository, we provide the necessary data for replicating results, including both simulated and biological datasets. Additionally, the repository includes trained models to infer from these datasets.</p>
Detectability of unresolved particles in off-axis digital holographic microscopy
<p>Off-axis digital holographic microscopy (DHM) provides both amplitude and phase images, and so may be used for label-free 3D tracking of micro- and nano-sized particles of different compositions, including biological cells, strongly absorbing particles, and strongly scattering particles. Contrast is provided by differences in either the real or imaginary parts of the refractive index (phase contrast and absorption) and/or by scattering. While numerous studies have focused on phase contrast and improving resolution in DHM, particularly axial resolution, absent have been studies quantifying the limits of detection for unresolved particles. This limit has important implications for microbial detection, including in life-detection missions for space flight. Here we examine the limits of detection of nanosized particles as a function of particle optical properties, microscope optics (including camera well depth and substrate), and data processing techniques and find that DHM provides contrast in both amplitude and phase for unresolved spheres, in rough agreement with Mie theory scattering cross-sections. Amplitude reconstructions are more useful than phase for low-index spheres and should not be neglected in DHM analysis.</p>
COCO dataset and neural network weights for micro-FTIR particle detection on filters.
<h3>The IMPTOX project has received funding from the EU's H2020 framework programme for research and innovation under grant agreement n. 965173. Imptox is part of the European MNP cluster on human health.</h3> <p>More information about the project <a href="https://www.imptox.eu/en/">here</a>.</p> <p><strong>Description:</strong> This repository includes the trained weights and a custom COCO-formatted dataset used for developing and testing a Faster R-CNN R_50_FPN_3x object detector, specifically designed to identify particles in micro-FTIR filter images.</p> <p><strong>Contents:</strong></p> <ol> <li> <p><strong>Weights File (<code>neuralNetWeights_V3.pth</code>):</strong></p> <ul> <li>Format: .pth</li> <li>Description: This file contains the trained weights for a Faster R-CNN model with a ResNet-50 backbone and a Feature Pyramid Network (FPN), trained for 3x schedule. These weights are specifically tuned for detecting particles in micro-FTIR filter images.</li> </ul> </li> <li> <p><strong>Custom COCO Dataset (<code>uFTIR_curated_square.v5-uftir_curated_square_2024-03-14.coco-segmentation.zip</code>):</strong></p> <ul> <li>Format: .zip</li> <li>Description: This zip archive contains a custom COCO-formatted dataset, including JPEG images and their corresponding annotation file. The dataset consists of images of micro-FTIR filters with annotated particles.</li> <li>Contents: <ul> <li><strong>Images:</strong> JPEG format images of micro-FTIR filters.</li> <li><strong>Annotations:</strong> A JSON file in COCO format providing detailed annotations of the particles in the images.</li> </ul> </li> <li>Management: The dataset can be managed and manipulated using the <a href="https://pypi.org/project/pycocotools/">Pycocotools</a> library, facilitating easy integration with existing COCO tools and workflows.</li> </ul> </li> </ol> <p><strong>Applications:</strong> The provided weights and dataset are intended for researchers and practitioners in the field of microscopy and particle detection. The dataset and model can be used for further training, validation, and fine-tuning of object detection models in similar domains.</p> <p><strong>Usage Notes:</strong></p> <ul> <li>The <code>neuralNetWeights_V3.pth</code> file should be loaded into a PyTorch model compatible with the Faster R-CNN architecture, such as Detectron2.</li> <li>The contents of <code>uFTIR_curated_square.v5-uftir_curated_square_2024-03-14.coco-segmentation.zip</code> should be extracted and can be used with any COCO-compatible object detection framework for training and evaluation purposes.</li> <li>Code can be found on the related <a href="https://github.com/ThibaultSchowing/IMPTOX" target="_blank" rel="noopener">Github repository.</a></li> </ul> <p> </p>
Magnetic particles (Fe3O4) magnify ion transfer processes at the electrified liquid-liquid interface. Case study: Levamisole detection.
<p>Data set for the publication Magnetic particles (Fe3O4) magnify ion transfer processes at the electrified liquid-liquid interface. Case study: Levamisole detection. </p>
Honey Particle Detection Dataset
<p>A synthetically generated dataset of honey samples observed by a digital microscope. The goal of this dataset is to train machine learning models to detect the pollen found in the images. This dataset contains 500 images with three different classes of particles commonly found in honey.</p> <p>The annotations are provided in YOLO format in a different directory. Each annotation is associated with the image by an unique ID. The images follow the naming convention "image_{ID}.png" and the annotations follow the convention "image_{ID}.txt".</p> <p>This dataset has been created by Sonicat Systems and is published under Creative Commons Attribution Non-Commercial Share Alike license.</p> <p> </p>
Detectability of unresolved particles in off-axis digital holographic microscopy
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Supplementary material 4 from: Rosenblad MA, Martín MP, Tedersoo L, Ryberg M, Larsson E, Wurzbacher C, Abarenkov K, Nilsson RH (2016) Detection of signal recognition particle (SRP) RNAs in the nuclear ribosomal internal transcribed spacer 1 (ITS1) of three lineages of ectomycorrhizal fungi (Agaricomycetes, Basidiomycota). MycoKeys 13: 21-33. https://doi.org/10.3897/mycokeys.13.8579
SRP RNA multiple sequence alignment : Explanation note: Multiple sequence alignment with the SRP RNA sequences of Dumesic et al. (2015; Stereum hirsutum, Heterobasidion irregulare, and Heterobasidion annosum) aligned to our newly generated ITS sequences of Russula and Lactarius.
Supplementary material 3 from: Rosenblad MA, Martín MP, Tedersoo L, Ryberg M, Larsson E, Wurzbacher C, Abarenkov K, Nilsson RH (2016) Detection of signal recognition particle (SRP) RNAs in the nuclear ribosomal internal transcribed spacer 1 (ITS1) of three lineages of ectomycorrhizal fungi (Agaricomycetes, Basidiomycota). MycoKeys 13: 21-33. https://doi.org/10.3897/mycokeys.13.8579
ITS/SRP RNA multiple sequence alignment : Explanation note: Multiple sequence alignment comprising the 63 public ITS1 sequences with SRP RNA found in them, the three newly generated sequences, and the SRP RNA sequences from Dumesic et al. (2015) (Stereum hirsutum, Heterobasidion irregulare, and Heterobasidion annosum).
Supplementary material 2 from: Rosenblad MA, Martín MP, Tedersoo L, Ryberg M, Larsson E, Wurzbacher C, Abarenkov K, Nilsson RH (2016) Detection of signal recognition particle (SRP) RNAs in the nuclear ribosomal internal transcribed spacer 1 (ITS1) of three lineages of ectomycorrhizal fungi (Agaricomycetes, Basidiomycota). MycoKeys 13: 21-33. https://doi.org/10.3897/mycokeys.13.8579
ITS multiple sequence alignment : Explanation note: A multiple sequence alignment in the NEXUS format (Maddison et al. 1997) comprising all 63 matching ITS sequences, plus the three newly generated ones (KU356730, KU356731, and KU356732). The alignment was produced in MAFFT without manual adjustment (Katoh and Standley 2013). The alignment is composed of partial nSSU (bases 1-34 in the alignment), the full ITS1 (bases 35-678), the full 5.8S (bases 679-838), the full ITS2 (bases 839-1395), and partial nLSU (bases 1396-end). The SRP RNA occupies position 203-474 in the alignment. The alignment is provided for overview purposes only; the two-order nature of the taxa (Boletales and Russulales) coupled with the high variability of the ITS region jointly mean that the alignment will not be suited for phylogenetic inference.
Supplementary material 1 from: Rosenblad MA, Martín MP, Tedersoo L, Ryberg M, Larsson E, Wurzbacher C, Abarenkov K, Nilsson RH (2016) Detection of signal recognition particle (SRP) RNAs in the nuclear ribosomal internal transcribed spacer 1 (ITS1) of three lineages of ectomycorrhizal fungi (Agaricomycetes, Basidiomycota). MycoKeys 13: 21-33. https://doi.org/10.3897/mycokeys.13.8579
Output from cmsearch and primers used : Explanation note: A) The output from cmsearch showing all 63 relevant matches to the three ectomycorrhizal lineages. B) Detail of the primers used to re-amplify the specimens.
Electrical Alignment Signatures of Ice Particles before Intracloud Lightning Activity Detected by Dual-polarized Phased Array Weather Radar
<p>lightning data from LIDEN system and the data of the Dual-polarized Phased Array Weather Radar (DP-PAWR) on August 20, 2019. </p>
Acoustic particle motion detection in the snapping shrimp (Alpheus richardsoni)
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Data from: Automated single particle detection and tracking for large microscopy datasets
Recent advances in optical microscopy have enabled the acquisition of very large datasets from living cells with unprecedented spatial and temporal resolutions. Our ability to process these datasets now plays an essential role in order to understand many biological processes. In this paper, we present an automated particle detection algorithm capable of operating in low signal-to-noise fluorescence microscopy environments and handling large datasets. When combined with our particle linking framework, it can provide hitherto intractable quantitative measurements describing the dynamics of large cohorts of cellular components from organelles to single molecules. We begin with validating the performance of our method on synthetic image data, and then extend the validation to include experiment images with ground truth. Finally, we apply the algorithm to two single-particle-tracking photo-activated localization microscopy biological datasets, acquired from living primary cells with very high temporal rates. Our analysis of the dynamics of very large cohorts of 10 000 s of membrane-associated protein molecules show that they behave as if caged in nanodomains. We show that the robustness and efficiency of our method provides a tool for the examination of single-molecule behaviour with unprecedented spatial detail and high acquisition rates.
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