Tags: Matlab

All Categories (1-20 of 73)

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 32-digit values of the first 100 recurrence coefficients for the half-range Freud weight function with exponent 8

    2017-10-13 14:54:27 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R76D5R5W

    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=8

    https://purr.purdue.edu/publications/2845

The Purdue University Research Repository (PURR) is a university core research facility provided by the Purdue University Libraries, the Office of the Executive Vice President for Research and Partnerships, and Information Technology at Purdue (ITaP).