Tags: Matlab

All Categories (1-20 of 74)

  1. Data of global wetland methane emissions from artificial neural network modeling v1.0

    2020-01-16 21:41:59 | Datasets | Contributor(s): Licheng Liu, Qianlai Zhuang | doi:10.4231/3YX4-EY30

    Source code of an ANN model, site level data, input data, output data and visualization results, which are presented in the manuscript "Inventorying Global Wetland Methane Emissions Based on In Situ Data and an Artificial Neural Network...

    https://purr.purdue.edu/publications/3372

  2. In vitro CFD, MRI, STB Series

    2019-11-01 21:31:58 | Datasets | Contributor(s): Melissa Brindise, Sean Rothenberger, Benjamin Dickerhoff, Susanne Schnell, Michael Markl, David Saloner, Vitaliy Rayz, Pavlos Vlachos | doi:10.4231/ZP8A-2G12

    Data from a pulsatile volumetric particle velocimetry study using two patient-specific cerebral aneurysm models, processed using Shake the Box (STB). Associated in vivo MRI and CFD datasets are also provided.

    https://purr.purdue.edu/publications/3322

  3. In vitro Volumetric Particle Velocimetry, Computational Fluid Dynamics (CFD), and in vivo 4D Flow MRI Hemodynamic Data in Two Patient-Specific Cerebral Aneurysms - STB

    2019-11-01 21:06:23 | Datasets | Contributor(s): Melissa Brindise, Sean Rothenberger, Benjamin Dickerhoff, Susanne Schnell, Michael Markl, David Saloner, Vitaliy Rayz, Pavlos Vlachos | doi:10.4231/FNF9-E631

    In vitro STB dataset from a pulsatile volumetric particle velocimetry study using two patient-specific cerebral aneurysm models.

    https://purr.purdue.edu/publications/3312

  4. In vitro Volumetric Particle Velocimetry, Computational Fluid Dynamics (CFD), and in vivo 4D Flow MRI Hemodynamic Data in Two Patient-Specific Cerebral Aneurysms - MRI

    2019-11-01 21:06:01 | Datasets | Contributor(s): Melissa Brindise, Sean Rothenberger, Benjamin Dickerhoff, Susanne Schnell, Michael Markl, David Saloner, Vitaliy Rayz, Pavlos Vlachos | doi:10.4231/C6DE-N845

    In vivo MRI dataset from a pulsatile volumetric particle velocimetry study using two patient-specific cerebral aneurysm models.

    https://purr.purdue.edu/publications/3310

  5. In vitro Volumetric Particle Velocimetry, Computational Fluid Dynamics (CFD), and in vivo 4D Flow MRI Hemodynamic Data in Two Patient-Specific Cerebral Aneurysms - CFD

    2019-11-01 21:00:43 | Datasets | Contributor(s): Melissa Brindise, Sean Rothenberger, Benjamin Dickerhoff, Susanne Schnell, Michael Markl, David Saloner, Vitaliy Rayz, Pavlos Vlachos | doi:10.4231/5RW6-4Z50

    In silico CFD dataset from a pulsatile volumetric particle velocimetry study using two patient-specific cerebral aneurysm models.

    https://purr.purdue.edu/publications/3311

  6. Method for Extracting True Stress and Strain Hardening Coefficient from TEM in situ Compression Testing

    2019-08-22 21:05:17 | Datasets | Contributor(s): Haozheng Qu, Kayla Yano, Priyam Patki, Matthew Swenson, Janelle Wharry | doi:10.4231/RDN7-PS52

    This algorithm package can automatically process transmission electron microscope (TEM) in situ micropillar compression test videos to extract the instantaneous pillar dimensions, then determine the true stress-strain curves and strain hardening...

    https://purr.purdue.edu/publications/3252

  7. In vitro Volumetric Particle Velocimetry, Computational Fluid Dynamics (CFD), and in vivo 4D Flow MRI Hemodynamic Data in Two Patient-Specific Cerebral Aneurysms

    2019-08-19 15:35:56 | Datasets | Contributor(s): Melissa Brindise, Sean Rothenberger, Benjamin Dickerhoff, Susanne Schnell, Michael Markl, David Saloner, Vitaliy Rayz, Pavlos Vlachos | doi:10.4231/M5F1-QC84

    Data from a pulsatile volumetric particle velocimetry study using two patient-specific cerebral aneurysm models, processed using Shake the Box (STB). Associated in vivo MRI and CFD datasets are also provided.

    https://purr.purdue.edu/publications/3136

  8. Raw PPG Signal Measured Using Wearable Sensor-kit in Varying Levels of Activity

    2019-05-01 21:23:02 | Datasets | Contributor(s): Tiberius Wehrly, Deena Alabed, Mireille Boutin | doi:10.4231/8VF2-1729

    Photoplethysmogram (PPG) signals collected from five subjects in three scenarios that vary in the level of activity, measured using Asiawill Pulse Heart Rate sensor implemented in an Arduino-based wearable sensor-kit.

    https://purr.purdue.edu/publications/3180

  9. Labeled Raw PPG Signals Measured Using Wearable Sensor-kit

    2019-05-01 21:16:59 | Datasets | Contributor(s): Tiberius Wehrly, Deena Alabed, Mireille Boutin | doi:10.4231/1BE9-YY17

    Photoplethysmogram (PPG) signals collected from nine subjects in a still seated position, measured using Asiawill Pulse Heart Rate sensor implemented in an Arduino-based wearable sensor-kit.

    https://purr.purdue.edu/publications/3179

  10. Combine Kart Truck GPS Data Archive

    2019-02-04 17:15:28 | Datasets | Contributor(s): Yaguang Zhang, James Krogmeier | doi:10.4231/4Z4S-M018

    GPS data for agricultural vehicles collected during wheat harvesting.

    https://purr.purdue.edu/publications/3083

  11. n-TARP Binary Clustering Code

    2018-05-03 14:17:23 | Datasets | Contributor(s): Yellamraju Tarun, Mireille Boutin | doi:10.4231/R74B2ZJV

    Binary clustering algorithm based on random projections

    https://purr.purdue.edu/publications/2973

  12. 32-digit values of the first 100 recurrence coefficients for the lower symmetric subrange Binet weight function on [-c,c], c=1

    2018-01-09 14:00:31 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7862DNF

    32-digit values of the first 100 recurrence coefficients for the weight function w(x)=-log(1-exp(-|x|)) on [-c,c], c=1

    https://purr.purdue.edu/publications/2847

  13. 32-digit values of the first 100 recurrence coefficients for a lower subrange Binet weight function

    2018-01-09 13:48:53 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7CN71XS

    32-digit values of the first 100 recurrence coefficients for the weight function w(x)=-log(1-exp(-x)) on [0,1]

    https://purr.purdue.edu/publications/2537

  14. Source code for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision

    2017-11-17 19:16:53 | Datasets | Contributor(s): Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, Zhongming Liu | doi:10.4231/R7V98675

    This document includes the main source code (Matlab or Python) related to our study.

    https://purr.purdue.edu/publications/2816

  15. 32-digit values of the first 100 recurrence coefficients for a lower subrange Binet weight function

    2017-10-25 16:20:45 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7CN71XS

    32-digit values of the first 100 recurrence coefficients for the weight function w(x)=-log(1-exp(-x)) on [0,1]

    https://purr.purdue.edu/publications/2537

  16. Loading variable-precision recurrence coefficients

    2017-10-25 12:59:44 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7P26W3X

    Loading a text file of variable-precision recurrence coefficients into Matlab symbolic or double-precision arrays

    https://purr.purdue.edu/publications/2271

  17. 32-digit values of the first 100 recurrence coefficients for the lower symmetric subrange Binet weight function on [-c,c], c=1

    2017-10-18 20:08:21 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7862DNF

    32-digit values of the first 100 recurrence coefficients for the weight function w(x)=-log(1-exp(-|x|)) on [-c,c], c=1

    https://purr.purdue.edu/publications/2847

  18. 32-digit values of the first 100 recurrence coefficients for an upper subrange Binet weight function

    2017-10-18 13:12:50 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7JD4TTZ

    32-digit values of the first 100 recurrence coefficients for the weight function w(x)=-log(1-exp(-x)) on [1,Inf]

    https://purr.purdue.edu/publications/2531

  19. 32-digit values of the first 100 recurrence coefficients for the Binet weight function

    2017-10-17 14:52:00 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7P55KH7

    32-digit values of the first 100 recurrence coefficients for the weight function w(x)=-log(1-exp(-|x|)) on [-Inf,Inf]

    https://purr.purdue.edu/publications/2533

  20. 32-digit values of the first 100 recurrence coefficients for the half-range Freud weight function with exponent 10

    2017-10-13 14:56:09 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R72N50FJ

    32-digit values of the first 100 recurrence coefficients for the weight function w(x)=x^mu*exp(-x^nu) on [0,Inf], mu=0, nu=10

    https://purr.purdue.edu/publications/2846

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