Tags: Matlab

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  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 32-digit values of the first 100 recurrence coefficients for the half-range Freud weight function with exponent 6

    2017-10-13 14:42:56 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7B56GW6

    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=6

    https://purr.purdue.edu/publications/2844

  16. 32-digit values of the first 100 recurrence coefficients for the half-range squared generalized Binet weight function with parameter 1/2

    2017-10-12 13:00:53 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7FT8J7R

    32-digit values of the first 100 recurrence coefficients for the weight function w(x)=log^2(1-a*exp(-x)) on [0,Inf], a = 1/2

    https://purr.purdue.edu/publications/2842

  17. 32-digit values of the first 100 recurrence coefficients for the half-range squared Binet weight function

    2017-10-12 12:59:18 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7KK98Z2

    32-digit values of the first 100 recurrence coefficients for the weight function w(x)=log^2(1-exp(-x)) on [0,Inf]

    https://purr.purdue.edu/publications/2841

  18. 32-digit values of the first 100 recurrence coefficients for the half-range generalized Binet weight function with parameter 1/2

    2017-10-12 12:56:47 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7QC01NW

    32-digit values of the first 100 recurrence coefficients for the half-range generalized Binet weight function w(x)=-log(1-a*exp(-x)) on [0,Inf], a = 1/2

    https://purr.purdue.edu/publications/2840

  19. 32-digit values of the first 100 recurrence coefficients for the squared generalized Binet weight function with parameter 1/2

    2017-10-12 12:47:21 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7V40SC7

    32-digit values of the first 100 recurrence coefficients for the weight function w(x)=log^2(1-a*exp(-|x|)) on [-Inf,Inf], a = 1/2

    https://purr.purdue.edu/publications/2839

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

    2017-09-24 21:22:47 | 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

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