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(No se muestran 96 ediciones intermedias de 8 usuarios) |
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| == Hyperspectral Imagery Synthesis tools for MATLAB ==
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| * [[Media:synthesis.zip | Download]] (21.6 Mb)
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| === IC Synthetic Hyperspectral Collection ===
| |
| ==== Legendre Collection ====
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| {| border="1" cellpadding="10"
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| ! colspan="5"|Parameters
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| ! colspan="5"|Signal-to-Noise Ratio (SNR)
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| |-
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| ! ne
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| ! size
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| ! min
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| ! max
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| ! Coef
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| ! Without noise
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| ! 20db
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| ! 40db
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| ! 60db
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| ! 80db
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| |- align="center"
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| | 2
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| | 64
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| | 1
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| | 10
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| | 100
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| | [[Media:2e64x64LegendreDefault.zip | download]]
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| | [[Media:2e64x64LegendreDefaultSNR20.zip | download]]
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| | [[Media:2e64x64LegendreDefaultSNR40.zip | download]]
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| | [[Media:2e64x64LegendreDefaultSNR60.zip | download]]
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| | [[Media:2e64x64LegendreDefaultSNR80.zip | download]]
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| |- align="center"
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| | 2
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| | 64
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| | 1
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| | 5
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| | 100
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| | [[Media:2e64x64LegendreMaxOrder5.zip | download]]
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| | [[Media:2e64x64LegendreMaxOrder5SNR20.zip | download]]
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| | [[Media:2e64x64LegendreMaxOrder5SNR40.zip | download]]
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| | [[Media:2e64x64LegendreMaxOrder5SNR60.zip | download]]
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| | [[Media:2e64x64LegendreMaxOrder5SNR80.zip | download]]
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| |- align="center"
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| | 2
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| | 64
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| | 1
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| | 10
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| | 10
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| | [[Media:2e64x64LegendreMaxCoef10.zip | download]]
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| | [[Media:2e64x64LegendreMaxCoef10SNR20.zip | download]]
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| | [[Media:2e64x64LegendreMaxCoef10SNR40.zip | download]]
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| | [[Media:2e64x64LegendreMaxCoef10SNR60.zip | download]]
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| | [[Media:2e64x64LegendreMaxCoef10SNR80.zip | download]]
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| |- align="center"
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| | 2
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| | 64
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| | 1
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| | 5
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| | 10
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| | [[Media:2e64x64LegendreMaxOrder5MaxCoef10.zip | download]]
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| | [[Media:2e64x64LegendreMaxOrder5MaxCoef10SNR20.zip | download]]
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| | [[Media:2e64x64LegendreMaxOrder5MaxCoef10SNR40.zip | download]]
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| | [[Media:2e64x64LegendreMaxOrder5MaxCoef10SNR60.zip | download]]
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| | [[Media:2e64x64LegendreMaxOrder5MaxCoef10SNR80.zip | download]]
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| |- align="center"
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| | 2
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| | 128
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| | 1
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| | 10
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| | 100
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| |- align="center"
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| | 2
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| | 128
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| | 1
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| | 5
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| | 100
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| |- align="center"
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| | 2
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| | 128
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| | 1
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| | 10
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| | 10
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| |- align="center"
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| | 2
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| | 128
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| | 1
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| | 5
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| | 10
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| |- align="center"
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| | 2
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| | 256
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| | 1
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| | 10
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| | 100
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| |- align="center"
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| | 2
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| | 256
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| | 1
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| | 5
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| | 100
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| |- align="center"
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| | 2
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| | 256
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| | 1
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| | 10
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| | 10
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| |- align="center"
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| | 2
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| | 256
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| | 1
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| | 5
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| | 10
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| |- align="center"
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| | 3
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| | 64
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| | 1
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| | 10
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| | 100
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| |- align="center"
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| | 3
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| | 64
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| | 1
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| | 5
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| | 100
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| |- align="center"
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| | 3
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| | 64
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| | 1
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| | 10
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| | 10
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| |- align="center"
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| | 3
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| | 64
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| | 1
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| | 5
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| | 10
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| |- align="center"
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| | 3
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| | 128
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| | 1
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| | 10
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| | 100
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| |- align="center"
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| | 3
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| | 128
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| | 1
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| | 5
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| | 100
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| |- align="center"
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| | 3
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| | 128
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| | 1
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| | 10
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| | 10
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| |- align="center"
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| | 3
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| | 128
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| | 1
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| | 5
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| | 10
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| |- align="center"
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| | 3
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| | 256
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| | 1
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| | 10
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| | 100
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| |- align="center"
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| | 3
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| | 256
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| | 1
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| | 5
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| | 100
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| |- align="center"
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| | 3
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| | 256
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| | 1
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| | 10
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| | 10
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| |- align="center"
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| | 3
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| | 256
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| | 1
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| | 5
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| | 10
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| |- align="center"
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| | 4
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| | 64
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| | 1
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| | 10
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| | 100
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| |- align="center"
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| | 4
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| | 64
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| | 1
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| | 5
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| | 100
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| |- align="center"
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| | 4
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| | 64
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| | 1
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| | 10
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| | 10
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| |- align="center"
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| | 4
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| | 64
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| | 1
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| | 5
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| | 10
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| |- align="center"
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| | 4
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| | 128
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| | 1
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| | 10
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| | 100
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| |- align="center"
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| | 4
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| | 128
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| | 1
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| | 5
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| | 100
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| |- align="center"
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| | 4
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| | 128
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| | 1
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| | 10
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| | 10
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| |- align="center"
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| | 4
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| | 128
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| | 1
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| | 5
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| | 10
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| |- align="center"
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| | 4
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| | 256
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| | 1
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| | 10
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| | 100
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| |- align="center"
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| | 4
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| | 256
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| | 1
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| | 5
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| | 100
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| |- align="center"
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| | 4
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| | 256
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| | 1
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| | 10
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| | 10
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| |- align="center"
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| | 4
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| | 256
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| | 1
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| | 5
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| | 10
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| |- align="center"
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| | 5
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| | 64
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| | 1
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| | 10
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| | 100
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| |- align="center"
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| | 5
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| | 64
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| | 1
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| | 5
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| | 100
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| |- align="center"
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| | 5
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| | 64
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| | 1
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| | 10
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| | 10
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| |- align="center"
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| | 5
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| | 64
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| | 1
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| | 5
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| | 10
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| |
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| |
| |
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| |- align="center"
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| | 5
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| | 128
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| | 1
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| | 10
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| | 100
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| |
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| |- align="center"
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| | 5
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| | 128
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| | 1
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| | 5
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| | 100
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| |
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| |
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| |- align="center"
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| | 5
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| | 128
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| | 1
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| | 10
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| | 10
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| |- align="center"
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| | 5
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| | 128
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| | 1
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| | 5
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| | 10
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| |- align="center"
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| | 5
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| | 256
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| | 1
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| | 10
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| | 100
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| |- align="center"
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| | 5
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| | 256
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| | 1
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| | 5
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| | 100
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| |- align="center"
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| | 5
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| | 256
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| | 1
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| | 10
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| | 10
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| |- align="center"
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| | 5
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| | 256
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| | 1
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| | 5
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| | 10
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| |}
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|
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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] == |
| {| border="1" cellpadding="10"
| |
| ! colspan="3"|Parameters
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| ! colspan="5"|Signal-to-Noise Ratio (SNR)
| |
| |-
| |
| ! ne
| |
| ! size
| |
| ! theta1
| |
| ! Without noise
| |
| ! 20db
| |
| ! 40db
| |
| ! 60db
| |
| ! 80db
| |
| |- align="center"
| |
| | 2
| |
| | 64
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| | 100
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| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
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| | 2
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| | 64
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| | 50
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| |
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| |
| |
| |
| |
| |
| |
| |
| |- align="center"
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| | 2
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| | 64
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| | 10
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| |
| |
| |
| |
| |
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| |- align="center"
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| | 2
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| | 128
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| | 100
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| |
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| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
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| | 2
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| | 128
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| | 50
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| |
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| |
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| |- align="center"
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| | 2
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| | 128
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| | 10
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| |
| |
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| |- align="center"
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| | 2
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| | 256
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| | 100
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| |
| |
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| |- align="center"
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| | 2
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| | 256
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| | 50
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| |- align="center"
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| | 2
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| | 256
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| | 10
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| |- align="center"
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| | 3
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| | 64
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| | 100
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| |- align="center"
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| | 3
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| | 64
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| | 50
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| |- align="center"
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| | 3
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| | 64
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| | 10
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| |- align="center"
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| | 3
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| | 128
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| | 100
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| |- align="center"
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| | 3
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| | 128
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| | 50
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| |- align="center"
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| | 3
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| | 128
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| | 10
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| |- align="center"
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| | 3
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| | 256
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| | 100
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| |- align="center"
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| | 3
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| | 256
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| | 50
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| |- align="center"
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| | 3
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| | 256
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| | 10
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| |- align="center"
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| | 4
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| | 64
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| | 100
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| |- align="center"
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| | 4
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| | 64
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| | 50
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| |- align="center"
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| | 4
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| | 64
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| | 10
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| |- align="center"
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| | 4
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| | 128
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| | 100
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| |- align="center"
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| | 4
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| | 128
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| | 50
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| |- align="center"
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| | 4
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| | 128
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| | 10
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| |- align="center"
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| | 4
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| | 256
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| | 100
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| |- align="center"
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| | 4
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| | 256
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| | 50
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| |- align="center"
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| | 4
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| | 256
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| | 10
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| |- align="center"
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| | 5
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| | 64
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| | 100
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| |- align="center"
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| | 5
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| | 64
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| | 50
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| |- align="center"
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| | 5
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| | 64
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| | 10
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| |- align="center"
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| | 5
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| | 128
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| | 100
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| |
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| |- align="center"
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| | 5
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| | 128
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| | 50
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| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 128
| |
| | 10
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 256
| |
| | 100
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 256
| |
| | 50
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 256
| |
| | 10
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |}
| |
|
| |
|
| ==== Exponential Gausian Fields Collection ==== | | == agentes inteligentes en videojuegos producido por Tai Nuñez [https://doi.org/10.5281/zenodo.3250975] == |
| {| 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
| |
| | [[Media:2e64x64ExponentialGFDefault.zip | download]]
| |
| | [[Media:2e64x64ExponentialGFDefaultSNR20.zip | download]]
| |
| | [[Media:2e64x64ExponentialGFDefaultSNR40.zip | download]]
| |
| | [[Media:2e64x64ExponentialGFDefaultSNR60.zip | download]]
| |
| | [[Media:2e64x64ExponentialGFDefaultSNR80.zip | download]]
| |
| |- align="center"
| |
| | 2
| |
| | 64
| |
| | 0.5
| |
| | 0.8
| |
| | [[Media:2e64x64ExponentialGFTheta1_0_5Theta2_0_8.zip | download]]
| |
| | [[Media:2e64x64ExponentialGFTheta1_0_5Theta2_0_8SNR20.zip | download]]
| |
| | [[Media:2e64x64ExponentialGFTheta1_0_5Theta2_0_8SNR40.zip | download]]
| |
| | [[Media:2e64x64ExponentialGFTheta1_0_5Theta2_0_8SNR60.zip | download]]
| |
| | [[Media:2e64x64ExponentialGFTheta1_0_5Theta2_0_8SNR80.zip | download]]
| |
| |- align="center"
| |
| | 2
| |
| | 64
| |
| | 0.9
| |
| | 1.9
| |
| | [[Media:2e64x64ExponentialGFTheta1_0_9Theta2_1_9.zip | download]]
| |
| | [[Media:2e64x64ExponentialGFTheta1_0_9Theta2_1_9SNR20.zip | download]]
| |
| | [[Media:2e64x64ExponentialGFTheta1_0_9Theta2_1_9SNR40.zip | download]]
| |
| | [[Media:2e64x64ExponentialGFTheta1_0_9Theta2_1_9SNR60.zip | download]]
| |
| | [[Media:2e64x64ExponentialGFTheta1_0_9Theta2_1_9SNR80.zip | download]]
| |
| |- align="center"
| |
| | 2
| |
| | 128
| |
| | 0.7
| |
| | 1.3
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 2
| |
| | 128
| |
| | 0.5
| |
| | 0.8
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 2
| |
| | 128
| |
| | 0.9
| |
| | 1.9
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 2
| |
| | 256
| |
| | 0.7
| |
| | 1.3
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 2
| |
| | 256
| |
| | 0.5
| |
| | 0.8
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 2
| |
| | 256
| |
| | 0.9
| |
| | 1.9
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 64
| |
| | 0.7
| |
| | 1.3
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 64
| |
| | 0.5
| |
| | 0.8
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 64
| |
| | 0.9
| |
| | 1.9
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 128
| |
| | 0.7
| |
| | 1.3
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 128
| |
| | 0.5
| |
| | 0.8
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 128
| |
| | 0.9
| |
| | 1.9
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 256
| |
| | 0.7
| |
| | 1.3
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 256
| |
| | 0.5
| |
| | 0.8
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 256
| |
| | 0.9
| |
| | 1.9
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 64
| |
| | 0.7
| |
| | 1.3
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 64
| |
| | 0.5
| |
| | 0.8
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 64
| |
| | 0.9
| |
| | 1.9
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 128
| |
| | 0.7
| |
| | 1.3
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 128
| |
| | 0.5
| |
| | 0.8
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 128
| |
| | 0.9
| |
| | 1.9
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 256
| |
| | 0.7
| |
| | 1.3
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 256
| |
| | 0.5
| |
| | 0.8
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 256
| |
| | 0.9
| |
| | 1.9
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 64
| |
| | 0.7
| |
| | 1.3
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 64
| |
| | 0.5
| |
| | 0.8
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 64
| |
| | 0.9
| |
| | 1.9
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 128
| |
| | 0.7
| |
| | 1.3
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 128
| |
| | 0.5
| |
| | 0.8
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 128
| |
| | 0.9
| |
| | 1.9
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 256
| |
| | 0.7
| |
| | 1.3
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 256
| |
| | 0.5
| |
| | 0.8
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 256
| |
| | 0.9
| |
| | 1.9
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |}
| |
|
| |
|
| ==== Rational Gausian Fields Collection ==== | | ==[https://github.com/borjafdezgauna/SimionZoo/wiki A framework for Reinforcement Learning algorithms development in C and Windows] == |
| {| 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
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 2
| |
| | 64
| |
| | 1.85
| |
| | 2
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 2
| |
| | 128
| |
| | 1.6
| |
| | 1.5
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 2
| |
| | 128
| |
| | 1.2
| |
| | 0.85
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 2
| |
| | 128
| |
| | 1.85
| |
| | 2
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 2
| |
| | 256
| |
| | 1.6
| |
| | 1.5
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 2
| |
| | 256
| |
| | 1.2
| |
| | 0.85
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 2
| |
| | 256
| |
| | 1.85
| |
| | 2
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 64
| |
| | 1.6
| |
| | 1.5
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 64
| |
| | 1.2
| |
| | 0.85
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 64
| |
| | 1.85
| |
| | 2
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 128
| |
| | 1.6
| |
| | 1.5
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 128
| |
| | 1.2
| |
| | 0.85
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 128
| |
| | 1.85
| |
| | 2
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 256
| |
| | 1.6
| |
| | 1.5
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 256
| |
| | 1.2
| |
| | 0.85
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 3
| |
| | 256
| |
| | 1.85
| |
| | 2
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 64
| |
| | 1.6
| |
| | 1.5
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 64
| |
| | 1.2
| |
| | 0.85
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 64
| |
| | 1.85
| |
| | 2
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 128
| |
| | 1.6
| |
| | 1.5
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 128
| |
| | 1.2
| |
| | 0.85
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 128
| |
| | 1.85
| |
| | 2
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 256
| |
| | 1.6
| |
| | 1.5
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 256
| |
| | 1.2
| |
| | 0.85
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 4
| |
| | 256
| |
| | 1.85
| |
| | 2
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 64
| |
| | 1.6
| |
| | 1.5
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 64
| |
| | 1.2
| |
| | 0.85
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 64
| |
| | 1.85
| |
| | 2
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 128
| |
| | 1.6
| |
| | 1.5
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 128
| |
| | 1.2
| |
| | 0.85
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 128
| |
| | 1.85
| |
| | 2
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 256
| |
| | 1.6
| |
| | 1.5
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 256
| |
| | 1.2
| |
| | 0.85
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |- align="center"
| |
| | 5
| |
| | 256
| |
| | 1.85
| |
| | 2
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |}
| |
|
| |
|
| ==== Matern Gausian Fields Collection ==== | | ==[[Software for vascular CTA segmentation]]== |
| {| 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
| | |
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| | == [[Coloreado de grafos con enjambres -- Swarm based graph coloring]] == |
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| | == [[Hyperspectral Imagery Synthesis tools for MATLAB | Hyperspectral Image Synthesis toolbox for MATLAB]] == |
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| | == [[Endmember Induction Algorithms (EIAs) for MATLAB and SCILAB | Endmember Induction Algorithms]] == |
| | [[Media:2e64x64MaternGFDefault.zip | download]]
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| | [[Media:2e64x64MaternGFDefaultSNR20.zip | download]]
| | == [[Euclidean skeletons of digital image and volume data in linear time by integer medial axis transform]] == |
| | [[Media:2e64x64MaternGFDefaultSNR40.zip | download]]
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| | [[Media:2e64x64MaternGFDefaultSNR60.zip | download]]
| | == [[Skeletonization, skeleton pruning and simple skeleton graph construction example in Matlab]] == |
| | [[Media:2e64x64MaternGFDefaultSNR80.zip | download]]
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| | == [[NPP estimation software for regional/global applications using Forest-BGC]] == |
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| | ==[http://www.sc.ehu.es/ccwgrrom/webcaras/ Sistema de reconocimiento de caras desarrollado por Iñigo Barandiarán]== |
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| | == [[Graph Coloring Suite, Graph Coloring Solver and Graph generator]] == |
| | [[Media:2e64x64MaternGFTheta1_5Theta2_0_5.zip | download]]
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| | [[Media:2e64x64MaternGFTheta1_5Theta2_0_5SNR20.zip | download]]
| | ==[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]]== |
| | [[Media:2e64x64MaternGFTheta1_5Theta2_0_5SNR40.zip | download]]
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| | [[Media:2e64x64MaternGFTheta1_5Theta2_0_5SNR60.zip | download]]
| | == [http://www.ehu.es/ccwintco/index.php/Usuario:Alexsavio#Software Other tools from Alexandre Savio] == |
| | [[Media:2e64x64MaternGFTheta1_5Theta2_0_5SNR80.zip | download]]
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| | ==[[Hybrid Extreme Rotation Forest (HERF)]]== |
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| | == [http://www.ehu.es/ccwintco/uploads/6/69/HERF.tar.gz Hybrid Extreme Rotation Forest (HERF) basic code] == |
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| | == [http://www.ehu.es/ccwintco/uploads/2/26/AdaHERF.zip Adaptative Hybrid Extreme Rotation Forest (AdaHERF) python code] == |
| | [[Media:2e64x64MaternGFTheta1_20Theta2_2.zip | download]]
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| | [[Media:2e64x64MaternGFTheta1_20Theta2_2SNR20.zip | download]]
| | == [[Hyperspectral Image Nonlinear Unmixing and Reconstruction by ELM Regression Ensemble (MATLAB) | Nonlinear Unmixing and Reconstruction]] == |
| | [[Media:2e64x64MaternGFTheta1_20Theta2_2SNR40.zip | download]]
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| | [[Media:2e64x64MaternGFTheta1_20Theta2_2SNR60.zip | download]]
| | == [http://www.ehu.es/ccwintco/index.php?title=D-RR-QL Distributed Round-Robin Q-Learning (D-RR-QL) for Linked Multicomponent Robotic Systems] == |
| | [[Media:2e64x64MaternGFTheta1_20Theta2_2SNR80.zip | download]]
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| | == [http://www.ehu.eus/ccwintco/index.php?title=Neurocomputing-RL-experiments Experiments of Conditioned Reinforcement Learning in Continuous Space Control Tasks] == |
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| | == [http://www.ehu.eus/ccwintco/index.php?title=SimionZoo SimionZoo] == |
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| | == [http://www.ehu.es/ccwintco/uploads/c/c8/DRIVE_Active_Learning_RF.zip Drive dataset with Active Learning and Random Forests] == |
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| | == [[Influence Maximization]] == |
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| |}
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