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Línea 1: Línea 1:
== Hyperspectral Imagery Synthesis tools for MATLAB ==
* [[Media:synthesis.zip | Download]] (21.6 Mb)


=== IC Synthetic Hyperspectral Collection ===
==== Legendre Collection ====
{| align="center" border="1" cellpadding="10"
! colspan="5"|Parameters
! colspan="5"|Signal-to-Noise Ratio (SNR)
|-
! ne
! size
! min
! max
! Coef
! Without noise
! 20db
! 40db
! 60db
! 80db
|- align="center"
| 2
| 64
| 1
| 10
| 100
|
|
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|
|- align="center"
| 2
| 64
| 1
| 5
| 100
|
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|- align="center"
| 2
| 64
| 1
| 10
| 10
|
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|- align="center"
| 2
| 64
| 1
| 5
| 10
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|- align="center"
| 2
| 128
| 1
| 10
| 100
|
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|- align="center"
| 2
| 128
| 1
| 5
| 100
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|- align="center"
| 2
| 128
| 1
| 10
| 10
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|- align="center"
| 2
| 128
| 1
| 5
| 10
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|- align="center"
| 2
| 256
| 1
| 10
| 100
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|- align="center"
| 2
| 256
| 1
| 5
| 100
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|- align="center"
| 2
| 256
| 1
| 10
| 10
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|- align="center"
| 2
| 256
| 1
| 5
| 10
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|- align="center"
| 3
| 64
| 1
| 10
| 100
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|- align="center"
| 3
| 64
| 1
| 5
| 100
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|- align="center"
| 3
| 64
| 1
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| 10
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|- align="center"
| 3
| 64
| 1
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|- align="center"
| 3
| 128
| 1
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| 100
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|- align="center"
| 3
| 128
| 1
| 5
| 100
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|- align="center"
| 3
| 128
| 1
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| 10
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|- align="center"
| 3
| 128
| 1
| 5
| 10
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|- align="center"
| 3
| 256
| 1
| 10
| 100
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|- align="center"
| 3
| 256
| 1
| 5
| 100
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|- align="center"
| 3
| 256
| 1
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| 10
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|- align="center"
| 3
| 256
| 1
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|- align="center"
| 4
| 64
| 1
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| 100
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|- align="center"
| 4
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|- align="center"
| 4
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|- align="center"
| 4
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|- align="center"
| 4
| 128
| 1
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| 100
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|- align="center"
| 4
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| 1
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|- align="center"
| 4
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| 1
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| 10
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|- align="center"
| 4
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| 1
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|- align="center"
| 4
| 256
| 1
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|- align="center"
| 4
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| 1
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|- align="center"
| 4
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| 1
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|- align="center"
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|- align="center"
| 5
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| 1
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|- align="center"
| 5
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| 1
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|- align="center"
| 5
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| 1
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|- align="center"
| 5
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| 1
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|- align="center"
| 5
| 128
| 1
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| 100
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|- align="center"
| 5
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| 1
| 5
| 100
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|- align="center"
| 5
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| 1
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|- align="center"
| 5
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|- align="center"
| 5
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| 1
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|- align="center"
| 5
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| 1
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| 100
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|- align="center"
| 5
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| 1
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|- align="center"
| 5
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| 1
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|}


==== Spheric Gaussian Fields Collection ====
== [http://www.ehu.eus/ccwintco/uploads/3/36/MPC-double-pendulum.zip Model Predictive control for a quad rotor moving a double pendulum] ==
{| align="center" border="1" cellpadding="10"
! colspan="3"|Parameters
! colspan="5"|Signal-to-Noise Ratio (SNR)
|-
! ne
! size
! theta1
! Without noise
! 20db
! 40db
! 60db
! 80db
|- align="center"
| 2
| 64
| 100
|
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|- align="center"
| 2
| 64
| 50
|
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|- align="center"
| 2
| 64
| 10
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|- align="center"
| 2
| 128
| 100
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|- align="center"
| 2
| 128
| 50
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|- align="center"
| 2
| 128
| 10
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|- align="center"
| 2
| 256
| 100
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|- align="center"
| 2
| 256
| 50
|
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|- align="center"
| 2
| 256
| 10
|
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|- align="center"
| 3
| 64
| 100
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|- align="center"
| 3
| 64
| 50
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|- align="center"
| 3
| 64
| 10
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|- align="center"
| 3
| 128
| 100
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|- align="center"
| 3
| 128
| 50
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|- align="center"
| 3
| 128
| 10
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|- align="center"
| 3
| 256
| 100
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|- align="center"
| 3
| 256
| 50
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|- align="center"
| 3
| 256
| 10
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|- align="center"
| 4
| 64
| 100
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|- align="center"
| 4
| 64
| 50
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|- align="center"
| 4
| 64
| 10
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|- align="center"
| 4
| 128
| 100
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|- align="center"
| 4
| 128
| 50
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|- align="center"
| 4
| 128
| 10
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|- align="center"
| 4
| 256
| 100
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|- align="center"
| 4
| 256
| 50
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|- align="center"
| 4
| 256
| 10
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|- align="center"
| 5
| 64
| 100
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|- align="center"
| 5
| 64
| 50
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|- align="center"
| 5
| 64
| 10
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|- align="center"
| 5
| 128
| 100
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|- align="center"
| 5
| 128
| 50
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|- align="center"
| 5
| 128
| 10
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|- align="center"
| 5
| 256
| 100
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|- align="center"
| 5
| 256
| 50
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|- align="center"
| 5
| 256
| 10
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|}


==== Exponential Gausian Fields Collection ====
== agentes inteligentes en videojuegos producido por Tai Nuñez [https://doi.org/10.5281/zenodo.3250975] ==
{| align="center" border="1" cellpadding="10"
! colspan="4"|Parameters
! colspan="5"|Signal-to-Noise Ratio (SNR)
|-
! ne
! size
! theta1
! theta2
! Without noise
! 20db
! 40db
! 60db
! 80db
|- align="center"
| 2
| 64
| 0.7
| 1.3
|
|
|
|
|
|- align="center"
| 2
| 64
| 0.5
| 0.8
|
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|- align="center"
| 2
| 64
| 0.9
| 1.9
|
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|
|
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|- align="center"
| 2
| 128
| 0.7
| 1.3
|
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|- align="center"
| 2
| 128
| 0.5
| 0.8
|
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|- align="center"
| 2
| 128
| 0.9
| 1.9
|
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|- align="center"
| 2
| 256
| 0.7
| 1.3
|
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|- align="center"
| 2
| 256
| 0.5
| 0.8
|
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|- align="center"
| 2
| 256
| 0.9
| 1.9
|
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|- align="center"
| 3
| 64
| 0.7
| 1.3
|
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|- align="center"
| 3
| 64
| 0.5
| 0.8
|
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|- align="center"
| 3
| 64
| 0.9
| 1.9
|
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|- align="center"
| 3
| 128
| 0.7
| 1.3
|
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|- align="center"
| 3
| 128
| 0.5
| 0.8
|
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|- align="center"
| 3
| 128
| 0.9
| 1.9
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|- align="center"
| 3
| 256
| 0.7
| 1.3
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|- align="center"
| 3
| 256
| 0.5
| 0.8
|
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|- align="center"
| 3
| 256
| 0.9
| 1.9
|
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|- align="center"
| 4
| 64
| 0.7
| 1.3
|
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|- align="center"
| 4
| 64
| 0.5
| 0.8
|
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|- align="center"
| 4
| 64
| 0.9
| 1.9
|
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|- align="center"
| 4
| 128
| 0.7
| 1.3
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|- align="center"
| 4
| 128
| 0.5
| 0.8
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|- align="center"
| 4
| 128
| 0.9
| 1.9
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|- align="center"
| 4
| 256
| 0.7
| 1.3
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|- align="center"
| 4
| 256
| 0.5
| 0.8
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|- align="center"
| 4
| 256
| 0.9
| 1.9
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|- align="center"
| 5
| 64
| 0.7
| 1.3
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|- align="center"
| 5
| 64
| 0.5
| 0.8
|
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|- align="center"
| 5
| 64
| 0.9
| 1.9
|
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|- align="center"
| 5
| 128
| 0.7
| 1.3
|
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|- align="center"
| 5
| 128
| 0.5
| 0.8
|
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|- align="center"
| 5
| 128
| 0.9
| 1.9
|
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|- align="center"
| 5
| 256
| 0.7
| 1.3
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|- align="center"
| 5
| 256
| 0.5
| 0.8
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|- align="center"
| 5
| 256
| 0.9
| 1.9
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|}


==== Rational Gausian Fields Collection ====
==[https://github.com/borjafdezgauna/SimionZoo/wiki A framework for Reinforcement Learning algorithms development in C and Windows] ==
{| align="center" border="1" cellpadding="10"
! colspan="4"|Parameters
! colspan="5"|Signal-to-Noise Ratio (SNR)
|-
! ne
! size
! theta1
! theta2
! Without noise
! 20db
! 40db
! 60db
! 80db
|- align="center"
| 2
| 64
| 1.6
| 1.5
|
|
|
|
|
|- align="center"
| 2
| 64
| 1.2
| 0.85
|
|
|
|
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|- align="center"
| 2
| 64
| 1.85
| 2
|
|
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|
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|- align="center"
| 2
| 128
| 1.6
| 1.5
|
|
|
|
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|- align="center"
| 2
| 128
| 1.2
| 0.85
|
|
|
|
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|- align="center"
| 2
| 128
| 1.85
| 2
|
|
|
|
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|- align="center"
| 2
| 256
| 1.6
| 1.5
|
|
|
|
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|- align="center"
| 2
| 256
| 1.2
| 0.85
|
|
|
|
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|- align="center"
| 2
| 256
| 1.85
| 2
|
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|
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|- align="center"
| 3
| 64
| 1.6
| 1.5
|
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|- align="center"
| 3
| 64
| 1.2
| 0.85
|
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|- align="center"
| 3
| 64
| 1.85
| 2
|
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|
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|- align="center"
| 3
| 128
| 1.6
| 1.5
|
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|
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|- align="center"
| 3
| 128
| 1.2
| 0.85
|
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|- align="center"
| 3
| 128
| 1.85
| 2
|
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|
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|- align="center"
| 3
| 256
| 1.6
| 1.5
|
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|- align="center"
| 3
| 256
| 1.2
| 0.85
|
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|- align="center"
| 3
| 256
| 1.85
| 2
|
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|
|
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|- align="center"
| 4
| 64
| 1.6
| 1.5
|
|
|
|
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|- align="center"
| 4
| 64
| 1.2
| 0.85
|
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|- align="center"
| 4
| 64
| 1.85
| 2
|
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|- align="center"
| 4
| 128
| 1.6
| 1.5
|
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|- align="center"
| 4
| 128
| 1.2
| 0.85
|
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|- align="center"
| 4
| 128
| 1.85
| 2
|
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|- align="center"
| 4
| 256
| 1.6
| 1.5
|
|
|
|
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|- align="center"
| 4
| 256
| 1.2
| 0.85
|
|
|
|
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|- align="center"
| 4
| 256
| 1.85
| 2
|
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|
|
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|- align="center"
| 5
| 64
| 1.6
| 1.5
|
|
|
|
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|- align="center"
| 5
| 64
| 1.2
| 0.85
|
|
|
|
|
|- align="center"
| 5
| 64
| 1.85
| 2
|
|
|
|
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|- align="center"
| 5
| 128
| 1.6
| 1.5
|
|
|
|
|
|- align="center"
| 5
| 128
| 1.2
| 0.85
|
|
|
|
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|- align="center"
| 5
| 128
| 1.85
| 2
|
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|
|
|
|- align="center"
| 5
| 256
| 1.6
| 1.5
|
|
|
|
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|- align="center"
| 5
| 256
| 1.2
| 0.85
|
|
|
|
|
|- align="center"
| 5
| 256
| 1.85
| 2
|
|
|
|
|
|}


==== Matern Gausian Fields Collection ====
==[[Software for vascular CTA segmentation]]==
{| align="center" border="1" cellpadding="10"
 
! colspan="4"|Parameters
==[[Robotic Vision: Technologies for Machine Learning and Vision Applications ]] ==
! colspan="5"|Signal-to-Noise Ratio (SNR)
 
|-
==[[Relevance Dendritic Computing: codes and examples]]==
! ne
 
! size
== [[Hybrid Image Segmentation]] ==
! theta1
 
! theta2
== [[Illumination Source Chromaticity Estimation (ISC)]] ==
! Without noise
 
! 20db
== [[A simulation environment in Matlab for Linked Muticomponent Robotic Systems (LMRS)]] ==
! 40db
 
! 60db
== [[media:MCNN_code.zip|Morphological Cellular Neural Network]] ==
! 80db
 
|- align="center"
== [[Coloreado de grafos con enjambres -- Swarm based graph coloring]] ==
| 2
 
| 64
== [[Hyperspectral Imagery Synthesis tools for MATLAB | Hyperspectral Image Synthesis toolbox for MATLAB]] ==
| 10
 
| 100
== [[Endmember Induction Algorithms (EIAs) for MATLAB and SCILAB | Endmember Induction Algorithms]] ==
|
 
|
== [[Euclidean skeletons of digital image and volume data in linear time by integer medial axis transform]] ==
|
 
|
== [[Skeletonization, skeleton pruning and simple skeleton graph construction example in Matlab]] ==
|
 
|- align="center"
== [[NPP estimation software for regional/global applications using Forest-BGC]] ==
| 2
 
| 64
==[http://www.sc.ehu.es/ccwgrrom/webcaras/ Sistema de reconocimiento de caras desarrollado por Iñigo Barandiarán]==
| 5
 
| 100
== [[Graph Coloring Suite, Graph Coloring Solver and Graph generator]] ==
|
 
|
==[https://github.com/alexsavio/aizkolari#aizkolari Aizkolari: Supervised feature extraction from brain MRI (or any kind Nifti or Analyze files) and supervised classification tool for disease detection] [[Media:Alexsavio-aizkolari.zip | Local copy]]==
|
 
|
== [http://www.ehu.es/ccwintco/index.php/Usuario:Alexsavio#Software Other tools from Alexandre Savio] ==
|
 
|- align="center"
==[[Hybrid Extreme Rotation Forest (HERF)]]==
| 2
 
| 64
== [http://www.ehu.es/ccwintco/uploads/6/69/HERF.tar.gz Hybrid Extreme Rotation Forest (HERF) basic code] ==
| 10
 
| 10
== [http://www.ehu.es/ccwintco/uploads/2/26/AdaHERF.zip Adaptative Hybrid Extreme Rotation Forest (AdaHERF) python code] ==
|
 
|
== [[Hyperspectral Image Nonlinear Unmixing and Reconstruction by ELM Regression Ensemble (MATLAB) | Nonlinear Unmixing and Reconstruction]] ==
|
 
|
== [http://www.ehu.es/ccwintco/index.php?title=D-RR-QL Distributed Round-Robin Q-Learning (D-RR-QL) for Linked Multicomponent Robotic Systems] ==
|
 
|- align="center"
== [http://www.ehu.eus/ccwintco/index.php?title=Neurocomputing-RL-experiments Experiments of Conditioned Reinforcement Learning in Continuous Space Control Tasks] ==
| 2
 
| 128
== [http://www.ehu.eus/ccwintco/index.php?title=SimionZoo SimionZoo] ==
| 1
 
| 10
== [http://www.ehu.es/ccwintco/uploads/c/c8/DRIVE_Active_Learning_RF.zip Drive dataset with Active Learning and Random Forests] ==
| 100
 
|
== [[Influence Maximization]] ==
|
|
|
|
|- align="center"
| 2
| 128
| 5
| 100
|
|
|
|
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|- align="center"
| 2
| 128
| 1
| 10
|
|
|
|
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|- align="center"
| 2
| 128
| 1
| 5
|
|
|
|
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|- align="center"
| 2
| 256
| 5
| 100
|
|
|
|
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|- align="center"
| 2
| 256
| 10
| 10
|
|
|
|
|
|- align="center"
| 2
| 256
| 1
| 10
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Revisión actual - 19:43 1 jun 2021


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Obtenido de «http://ehu.eus/ccwintco/index.php?title=GIC-source-code-free-libre&oldid=19979»