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134 results for “MATLAB”
Matlab data code
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MATLAB Codes for: A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults
<p><strong>Description:</strong></p> <p>This repository contains the MATLAB codes used in our paper [1] on fault diagnosis of inter-shaft aircraft bearings, published by MDPI Machines. The codes encompass all the necessary materials to reproduce the findings outlined in the paper. </p> <p><strong>Dataset Access:</strong></p> <p>The dataset utilized in this study is available under request from the authors of reference [8] in our paper. To obtain the dataset, please follow the instructions provided by the respective authors.</p> <p><strong>Data Format:</strong></p> <p>The dataset is saved in '*.npy' 3D variable format. To reproduce this study, it is necessary to transform these variables to '.mat' format since the codes are implemented in MATLAB. You can find the codes for transferring the 3D '*.npy' files to '*.mat' files here [<a href="../records/10184606">here</a>]</p> <p>We appreciate your interest in our work.</p> <p>[1] Berghout, Tarek, Toufik Bentrcia, Wei Hong Lim, and Mohamed Benbouzid. 2023. "A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults" <em>Machines</em> 11, no. 12: 1089. https://doi.org/10.3390/machines11121089</p>
MATLAB results files of MS-based analysis and raw photometer data - Systematic identification of allosteric effectors in Escherichia coli metabolism
<p>MATLAB result tables from progress curve analysis for each of the 19 enzymes tested with 79 potential effectors metabolites in MS-based approach. Excel tables with labelled photometer data.</p>
datos CIFAR-10 agregados para experimentación en Matlab con CNNs
<p>El fichero .mat contiene los datos agregados en matrices (data, labels) para training y (data_test, labels_test) para test. Se proporciona un pequeño script para entrenamiento de CNNs con estos datos. La referencia a la pagina web original está incluida: https://www.cs.toronto.edu/~kriz/cifar.html</p>
Walking stride modulations in Drosophila HS cells, data and MATLAB analysis codes
<p>Data and MATLAB analysis codes for an article titled: Walking strides direct rapid and flexible recruitment of visual circuits for course control in <em>Drosophila.</em></p>
Versatile strategy for homogeneous drying patterns of dispersed particles: Matlab code
<p>Matlab code of "Versatile strategy for homogeneous drying patterns of dispersed particles"</p>
Matlab Dataset and Codes For the Article (Unsteady State Capillary Retention in Porous Media)
<p>The uploaded dataset and software codes contain the Matlab scripts used to solve the capillary retention equation numerically and output results (in the zip file). The file 'plot_article.m' contains the scripts used to display the results as figures attached in the article. </p>
Dataset (MATLAB format) from Yang et al (2022) Thalamus-driven functional populations in frontal cortex support decision-making. Nat. Neurosci.
<p><strong>Summary</strong></p> <p>These experiments measure neuronal responses from the left hemisphere of premotor cortex (anterior lateral motor cortex, ALM) of adult mice performing pole location discrimination with a short-term memory. In a subset of the recordings, we inactivate activity of one of the brain regions providing inputs to ALM (ipsilateral S1/S2, contralateral ALM, and ipsilateral Thal<sub>ALM</sub>) in some trials.</p> <p>This dataset contains data from 9626 single units, 73 mice, 347 sessions. The dataset is described as “the primary dataset” in the paper below. The experiments (including experiment methods) are described in the paper.</p> <p><em>Yang W, Tipparaju SL, Chen G, Li N, (2022). Thalamus-driven functional populations in frontal cortex activity supports decision-making. Nat Neurosci, in press.</em></p> <p> </p> <p>The second dataset used in the paper can be downloaded also from Zenodo at</p> <pre><a href="https://doi.org/10.5281/zenodo.6713616">https://doi.org/10.5281/zenodo.6713616</a></pre> <p> </p> <p><strong>How to cite the data</strong></p> <p>If you publish any work using the data, please cite the Chen et. al., (2021) publication above and also cite the dataset in the following recommended format:</p> <p>Li N (2022); Data and simulations related to: Thalamus-driven functional populations in frontal cortex activity supports decision-making. Yang et al (2022) Nat Neurosci.</p> <p><a href="http://dx.doi.org/10.5281/zenodo.6846161">http://dx.doi.org/10.5281/zenodo.6846161</a></p> <p> </p> <p><strong>How to get started</strong></p> <p>Once downloaded</p> <p>1) unzip “<strong>func</strong>”</p> <p>2) unzip "<strong>scripts</strong>"</p> <p>3) Run any scripts "<strong>demo_*.m</strong>" within "<strong>scripts</strong>"</p> <p> </p> <p>A collection of analyses scripts that reproduce figures in " Yang et al (2022)" is included in the folder "<strong>\scripts\</strong>"</p> <p><strong>demo_compute_activity_modes_independentTrials.m</strong> – plot t-SNE embedding and all the response profile clusters; plots PSTHs from an example cluster; plots neurons connected to S1/S2, cALM, ThalALM on the t-SNE.</p> <p><strong>demo_compute_tSNE_embedding.m</strong> – t-SNE embedding of neuronal response profiles (based on PSTH shape)</p> <p><strong>demo_compute_activity_modes.m</strong> – compute activity modes from neuronal population responses.</p> <p><strong>demo_compute_selectivity_vector_stability.m</strong> – analysis of selectivity vectors</p> <p> </p> <p> </p>
Dataset (MATLAB format) from Chen Kang et al (2021) Modularity and robustness of frontal cortex networks. Cell, 184(14):3717-3730.
<p><strong>Summary</strong></p> <p>These experiments simultaneously measure neuronal responses from the two hemispheres of premotor cortex (anterior lateral motor cortex, ALM) of adult mice performing pole location discrimination with a short-term memory. We inactivate activity of one hemisphere (left or right) in some trials and both hemispheres in others. Data from 135 recording sessions are included in this release.</p> <p>This dataset contains data from 39 mice (age > P60). 9 VGAT-ChR2-EYFP mice (Jackson laboratory, JAX Stock#014548) and 30 PV-IRES-Cre (JAX Stock#008069) crossed to Rosa26-LSL-ReaChR, red-shifted channelrhodopsin reporter mice (JAX 24846), were used for simultaneous electrophysiology and photoinhibition. The experiments (including experiment methods) are described in the following publication:</p> <p><em>Chen, G., Kang, B., Lindsey, J., Druckmann, S., Li, N. (2021). Modularity and robustness of frontal cortical networks. Cell 184, 1-14. doi: 10.1016/j.cell.2021.05.026</em></p> <p> </p> <p><strong>How to cite the data</strong></p> <p>If you publish any work using the data, please cite the Chen et. al., (2021) publication above and also cite the dataset in the following recommended format:</p> <p>Chen G, Li N (2021); Data and simulations related to: Modularity and robustness of frontal cortical networks. Chen et al (2021) Cell, 184(14):3717-3730.</p> <p><a href="http://dx.doi.org/10.5281/zenodo.6713616">http://dx.doi.org/10.5281/zenodo.6713616</a></p> <p> </p> <p><strong>How to get started</strong></p> <p>Once downloaded</p> <p>1) unzip “<strong>analysis_scripts</strong>”</p> <p>2) unzip and combine all "<strong>data_structure_*.mat</strong>" and "<strong>meta_data_*.mat</strong>" files into a single folder “<strong>datafiles</strong>”.</p> <p> • <strong>data_structure_*.mat </strong>- contains raw spike data for one session.</p> <p> • <strong>meta_data_*.mat</strong> - contains the meta data information for one session.</p> <p> There is one "<strong>meta_data_*.mat</strong>" file for each "<strong>data_structure_*.mat</strong>"</p> <p>3) Run scripts within "<strong>analysis_scripts</strong>". Follow the instruction in the "<strong>code instruction</strong>".</p> <p> </p> <p><strong>Data analysis</strong></p> <p>The extracellular recording traces were band-pass filtered (300-6 kHz). Events that exceeded an amplitude threshold (4 standard deviations of the background) were subjected to spike sorting to extract single units. Spike sorting was either manual (Guo et al., 2014b) or using Kilosort2 (https://www.github.com/MouseLand/Kilosort2) (Pachitariu et al., 2016) followed by manually curated with the Phy 2.0 beta 1 GUI (https://github.com/cortex-lab/phy) (Rossant et al., 2016) and manual inspection (Guo et al., 2014a). Spike widths were computed as the trough-to-peak interval in the mean spike waveform. Units with spike width < 0.35 ms were defined as fast-spiking (FS) neurons and units with spike widths > 0.45 ms as putative pyramidal neurons. Units with intermediate values (0.35 - 0.45 ms) were excluded. We concentrated our analyses on the putative pyramidal neurons.</p>
Data and MATLAB code: Glacier contributions to river discharge during the current Chilean megadrought
<p>Data and MATLAB code to support the article <em>Glacier contributions to river discharge during the current Chilean megadrought </em>in <em>Earth's Future</em> by McCarthy and others</p>
Dataset,MATLAB Programs, and supported model of "Top-down control of ammonia oxidizers by grazing in the North Pacific"
<p>Dataset,MATLAB Programs, and supported model of "Top-down control of ammonia oxidizers by grazing in the North Pacific". For the detailed information, see in the paper "Top-down control of ammonia oxidizers by grazing in the North Pacific".</p>
MATLAB Implementation for Wind Turbine Prognosis Using Uncertainty Bayesian-Optimized Lightweight Neural Network
<p>These MATLAB codes accompany the paper titled "---," currently submitted to the 11th International Electronic Conference on Sensors and Applications (ECSA-11). The paper presents a novel approach to wind turbine prognosis for maintenance purposes using the Uncertainty Bayesian-Optimized Extreme Learning Machine (UBO-ELM) algorithm.</p> <p>The codes provided here implement the methodology described in the paper, including data preprocessing, model training and evaluation, uncertainty quantification, and visualization of results. These codes are intended for researchers and practitioners in the field of wind energy systems and predictive maintenance.</p> <p>Please note that the paper is currently under review at ECSA-11. Once the paper is approved and the embargo is lifted, these codes will be accessible openly. Users are kindly requested to cite our paper when utilizing these codes for their research.</p>
IMD New High Spatial Resolution (0.25X0.25 degree) Long Period (1901-2022) Daily Gridded Rainfall Data Set Over India: Matlab script for merging into single netcdf/mat file with datetime stamping
Open the record for dataset details and reuse information.
The benchmark test suites of the FJSP-MW (MATLAB version)
<p>Two benchmark test suites are provided in this file.</p> <p>LLG-A1--LLGA20 (the multitasking level is less than 3)<br>LLG-B1--LLGB20 (the multitasking level is less than 5)</p> <p><br>------------------------------------------------------</p> <p>The meaning of the main variables:<br>1. dt: The delivery time of each job<br>2. Jm: The eligible machine set of each operation.<br>3. JmNumber: The total number of the machines<br>4. Jw: The eligible worker set of each operation.<br>5. multitask: The multitasking level of each worker (The multitasking level of the worker is 3 indicates that this worker can process three operations at the same time).<br>6. pjob: The priority of each job.<br>7. rt: The release time of each job.<br>8. T: The processing time of each operation.</p>
The benchmark instances of the FJSP-F (MATLAB version)
<p>The flexible job shop scheduling problem considering the on-site machining fixtures (FJSP-F) benchmark test suite is provided in this file [1].</p> <p>The LLG-F includes 20 instances (LLG-F1 to LLG-F20) with various scales ranging from three jobs, four machines, and two fixtures to 20 jobs, ten machines, and 12 fixtures.</p> <p>------------------------------------------------------</p> <p>The meaning of the main variables:<br>1. dt: The delivery time of each job<br>2. Jm: The eligible machine set of each operation.<br>3. JmNumber: The total number of the machines<br>4. Jf: The eligible fixture set of each operation.<br>5. pjob: The priority of each job.<br>6. rt: The release time of each job.<br>7. T: The processing time of each operation.<br>8. fixnumbegin: The number of Type I fixtures of each fixture (The number of each fixture in inventory)<br>9. fixnumend: The sum of the number of Type I fixtures and Type II fixtures of each fixture (The number of each fixture after the production)<br>10. fixtype1: Categories of Type I fixtures<br>11. fixtype2: Categories of Type II fixtures<br>12. fixtype1seq: Sequences of Type I fixtures<br>13. fixtypewseq: Sequences of Type I fixtures</p> <p> </p> <p><span>[1] J. Li, X. Li, L. Gao, and Q. Liu, “A Flexible Job Shop Scheduling Problem Considering On-Site Machining Fixtures: A Case Study From Customized Manufacturing Enterprise,” </span><span>IEEE Transactions on Automation Science and Engineering</span><span>, pp. 1–12, 2024, doi: 10.1109/tase.2024.3485810.</span></p>
MATLAB codes for paper: UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings
<p>These codes belong to the following paper. Please cite our work.</p> <p>Berghout T, Benbouzid M. UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings. <em>Electronics</em>. 2024; 13(12):2419. https://doi.org/10.3390/electronics13122419</p>
Matlab code for the 'Force–displacement response of in-plane loaded unreinforced brick masonry walls: the Critical Diagonal Crack model'
<p>The repository contains the the Matlab code and the test data (.csv files) used for the analyses and results presented in the research article:</p> <p>Force–displacement response of in-plane loaded unreinforced brick masonry walls: the Critical Diagonal Crack model, <em>Bulletin of Earthquake Engineering</em>,<em> </em>2017, <a href="https://link.springer.com/article/10.1007%2Fs10518-016-0049-7">Link</a></p>
Matlab code for 'Analytical model for the out-of-plane response of vertically spanning unreinforced masonry walls'
<p>This repository contains the Matlab scripts needed to reproduce the results shown in the article:</p> <blockquote> <p>Godio M, Beyer K. Analytical model for the out-of-plane response of vertically-spanning unreinforced masonry walls. Earthquake Engng Struct Dyn. 2017;46:2757-2776. <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/eqe.2929">DOI:10.1002/eqe.2929</a></p> </blockquote> <p>Version history:<br> V1: Matlab code needed to reproduce the plots of Figure 4 of the article is added<br> V2: content PDF-file is added</p>
Matlab files for: Mathematical Modelling of Polymer Trajectory during Electrospinning
<p>Matlab files used within this publication to be able to replicate the results.</p>
Collection of data and Matlab-codes for analyzing water dynamic at the Zugspitze.
<p>This collection includes the raw data and the Matlab codes used for the analyses of the article: "Decadal in-situ hydrological observations and empirical modeling of pressure head in a high-alpine, fractured calcareous rock slope" by Scandroglio et al. 2024 [Link].<br>Explanations are included in the file METADATA.docx. See the paper for further details.</p> <p>Documentation and code by Riccardo Scandroglio (<a href="mailto:johannes.leinauer@tum.de">r.scandroglio@tum.de</a>)<br>Technische Universität München, Arcisstr. 21, 80333 München, Germany</p> <p>For updating the analysis: <br>- Water discharges are collected by the Environmental Research Station Schneefernerhaus (contact: Dr. Till Rehm).<br>- Data from the German Weather Service can be downloaded from the official server: <a href="https://cdc.dwd.de/portal/">https://cdc.dwd.de/portal/</a>.<br>- Snow data for SNOWPACK can be obtained from the Bavarian Avalanche Center (contact: Dr. Thomas Feistl).</p> <p>This study was supported by the AlpSenseRely project, funded by the Bavarian State Ministry of the Environment and Consumer Protection (TUS01UFS-76976), and by the Hydro-PF project, funded by the TUM International Graduate School of Science and Technology IGSSE (Team 12.9). </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)
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.