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134 results for “MATLAB”
SeqSleepNet Matlab data
<p>This is Matlab data of the SeqSleepNet paper:</p> <p><strong>H. Phan</strong>, F. Andreotti, N. Cooray, O. Y. Chén, and M. De Vos. <strong><em>SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging</em></strong>. <em>IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)</em>: 27(3), pp. 400-410, 2019</p>
Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm - deep net Matlab
<p>The file is the trained convolutional neural network (CNN) developed in "De Logu, Francesco, et al. "Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm." <em>Frontiers in Oncology</em> 10 (2020)". The CNN is based on a pretrained Inception-ResNet-v2 to automatically recognizes cutaneous melanoma from histopathological digitalized slides. The file is in a Matlab format (.mat).</p>
Supplementary data and Matlab code for China's HWP carbon balance assessment using Trade-Linked Approach
<p>The supplemtary data describe the fractions of the HWPs disposed of by each option in major countries/regions worldwide.</p> <p>The Matlab code is developed to produce the results of China's HWP carbon balance assessment using Trade-Linked Approach, an innovative generalizable approach for HWP carbon assessment.</p> <p>The data and code are free for academic use only.</p>
Dataset of DICOM and MatLab images for review purposes relative to JAPPL-00449-2018
<p>See rebuttal note.</p>
Improving Maternal Nutrition in Matlab
ClinicalTrials.gov study NCT04868669. IPD Sharing: NO. Countries: 1. Publications: 0.
HIRENASD Experimental Data - matlab format
This resource contains the experimental data that was included in tecplot input files but in matlab files. dba1_cp has all the results is dimensioned (7,2) <LI>first dimension is 1-7 for each span station <LI>2nd dimension is 1 for upper surface, 2 for lower surface. <p> <LI>dba1_cp(ispan,isurf).x are the x/c locations at span station (ispan) and upper(isurf=1) or lower(isurf=2) <LI>dba1_cp(ispan,isurf).y are the eta locations at span station (ispan) and upper(isurf=1) or lower(isurf=2) <LI>dba1_cp(ispan,isurf).cp are the pressures at span station (ispan) and upper(isurf=1) or lower(isurf=2) <p> Unsteady CP is dimensioned with 4 columns 1st column, real 2nd column, imaginary 3rd column, magnitude 4th column, phase, deg M,Re and other pertinent variables are included as variables and also included in casedata.M, etc
Matlab Codes for the data-driven model
<p>Main function: f_data_driven_model</p> <p>For detail of the methodology, please see the paper entitled "A machine-learning-based modeling for water quality in coastal waters- taking dissolved oxygen and hypoxia in Chesapeake Bay as an example" by Yu et al. </p> <p>This is the first version. </p>
Data for Matlab package FISH-sim to simulate realistic 3d smFISH images
<p>Different data-sets needed by the Matlab package locFISH. locFISH allows the simulation and analysis of realistic single molecule FISH (smFISH) images.</p> <p><strong>data_simulation.zip</strong><br> Contains all necessary data to simulated smFISH images. Specifically, the zip archive contains a library of 3D cell shapes, realistic imaging background, and a simulated PSF (Point Spread Function). </p> <p><strong>GAPDH.zip</strong><br> Contains the smFISH data of GAPDH and the corresponding analysis results, which were used to create the library of cell shapes provided in data_simulation.zip </p> <p> </p> <p>For more details on these data and how do to use them, please consult the detailed user-manual provided with <strong>locFISH</strong>, available at</p> <p>https://bitbucket.org/muellerflorian/locfish</p> <p> </p>
Klang Valley MATLAB codes and data sets
<p>These are the extended SEIR model codes to simulate the ten-compartmental behavior, and optimization of the model for the time frame of 1st October to 31st January in Klang Valley, Malaysia.</p>
Efficient Matlab Programs
Matlab has a reputation for running slowly. Here are some pointers on how to speed computations, to an often unexpected degree. Subjects currently covered: Matrix Coding Implicit Multithreading on a Multicore Machine Sparse Matrices Sub-Block Computation to Avoid Memory Overflow -------------------------------------------------------------------------------------------------------- Matrix Coding - 1 Matlab documentation notes that efficient computation depends on using the matrix facilities, and that mathematically identical algorithms can have very different runtimes, but they are a bit coy about just what these differences are. A simple but telling example: The following is the core of the GD-CLS algorithm of Berry et.al., copied from fig. 1 of Shahnaz et.al, 2006, "Document clustering using nonnegative matrix factorization': for jj = 1:maxiter A = W'*W + lambda*eye(k); for ii = 1:n b = W'*V(:,ii); H(:,ii) = A \ b; end H = H .* (H>0); W = W .* (V*H') ./ (W*(H*H') + 1e-9); end Replacing the columwise update of H with a matrix update gives: for jj = 1:maxiter A = W'*W + lambda*eye(k); B = W'*V; H = A \ B; H = H .* (H>0); W = W .* (V*H') ./ (W*(H*H') + 1e-9); end These were tested on an 8049 x 8660 sparse matrix bag of words V (.0083 non-zeros), with W of size 8049 x 50, H 50 x 8660, maxiter = 50, lambda = 0.1, and identical initial W. They were run consecutivly, multithreaded on an 8-processor Sun server, starting at ~7:30PM. Tic-toc timing was recorded. Runtimes were respectivly 6586.2 and 70.5 seconds, a 93:1 difference. The maximum absolute pairwise difference between W matrix values was 6.6e-14. Similar speedups have been consistantly observed in other cases. In one algorithm, combining matrix operations with efficient use of the sparse matrix facilities gave a 3600:1 speedup. For speed alone, C-style iterative programming should be avoided wherever possible. In addition, when a couple lines of matrix code can substitute for an entire C-style function, program clarity is much improved. ---------------------------------------------------------------------------------------------------------------------- Matrix Coding - 2 Applied to integration, the speed gains are not so great, largely due to the time taken to set up the and deal with the boundaries. The anyomous function setup time is neglegable. I demonstrate on a simple uniform step linearly interpolated 1-D integration of cos() from 0 to pi, which should yield zero: tic; step = .00001; fun = @cos; start = 0; endit = pi; enda = floor((endit - start)/step)*step + start; delta = (endit - enda)/step; intF = fun(start)/2; intF = intF + fun(endit)*delta/2; intF = intF + fun(enda)*(delta+1)/2; for ii = start+step:step:enda-step intF = intF + fun(ii); end intF = intF*step toc; intF = -2.910164109692914e-14 Elapsed time is 4.091038 seconds. Replacing the inner summation loop with the matrix equivalent speeds things up a bit: tic; step = .00001; fun = @cos; start = 0; endit = pi; enda = floor((endit - start)/step)*step + start; delta = (endit - enda)/step; intF = fun(start)/2; intF = intF + fun(endit)*delta/2; intF = intF + fun(enda)*(delta+1)/2; intF = intF + sum(fun(start+step:step:enda-step)); intF = intF*step toc; intF = -2.868419946011613e-14 Elapsed time is 0.141564 seconds. The core computation take
Matlab code associated with publication "Mathematical model of the multi-amino acid multi-transporter system predicts uptake flux in CHO cells"
<p>Matlab code associated with publication "Mathematical model of the multi-amino acid multi-transporter system predicts uptake flux in CHO cells" </p> <p>Published version : Ashley Sreejan, Mugdha Gadgil, Chetan J. Gadgil, Mathematical model of the multi-amino acid multi-transporter system predicts uptake flux in CHO cells, Journal of Biotechnology, Volume 344, 2022, Pages 40-49, ISSN 0168-1656</p> <p>Published version available at https://doi.org/10.1016/j.jbiotec.2021.12.003</p> <p>One version of the manuscript is available at doi:10.1101/2021.04.26.441392</p>
Data and MATLAB code: Unsustainability of glacier contributions to river discharge during the current Chilean megadrought
<p>MATLAB code and data to support the manuscript 'Unsustainability of glacier contributions to river discharge during the current Chilean megadrought' during peer review</p>
Input data and MATLAB scripts used in the Master's project
<p>This folder contains the input data and MATLAB scripts used during the Master's project.</p> <p>The master's project investigates the effects of yaw misalignment on power production for a passively yawed floating MR system. High temporal resolution experimental wind data is analyzed and used for various analyses. An in-house Matlab code is developed to simulate the floating MR system's yawing motions based on wind speed and direction input time series. </p> <p>The input data is provided in ten ASCII TXT files containing experimental wind data during the year 2014. The experimental data is sampled at the Skipheia wind measurement station located at Frøya in Trøndelag. The ten ASCII TXT files represent each month during 2014, with the exception of January and February. The ASCII TXT files were provided by the data source as presented in the folder, which explains how the files are named.</p> <p>A short overview of how the scripts were used:</p> <ul> <li>April_30days.m and June_30days.m were used for providing Weibull distributions and a histogram in the Theory chapter</li> <li>Ideal_conditions_9months.m was used for providing a Gaussian distribution in the Theory chapter</li> <li>Yaw_misalignment.m was used for a percentage power-loss overview for yaw misalignments up to 90 degrees</li> <li>Ideal_conditions_9months.m was used for power production analyses in ideal conditions (no yaw misalignment)</li> <li>Low_speed_yawmis.m was used for an aero-hydro-dynamic analysis of a low wind speed situation</li> <li>High_speed_yawmis.m was used for an aero-hydro-dynamic analysis of a high wind speed situation</li> <li>Extreme_speed_yawmis.m was used for an aero-hydro-dynamic analysis of an extreme wind speed situation</li> <li>Low_speed_yawmis.m, High_speed_yawmis.m, and Extreme_speed_yawmis.m were used for an angular rate of yaw correction analysis</li> <li>June_30days.m and March_8days.m were used for illustrating the variance in experimental wind data</li> <li>WindRose.m was used for distributing the wind speeds and wind directions of the experimental data (developed by other researchers)</li> </ul>
SHHS matlab Data Part 1
<p>SHHS data (Part 1) with EEG, EOG, and EMG saved in matlab format.</p>
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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.