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Libros sobre el tema "Spectral mixtures"

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Autor: Grafiati
Publicado: 6 de septiembre de 2023

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1

Hawaii Institute of Geophysics. Planetary Geosciences Division. y United States. National Aeronautics and Space Administration., eds. Spectral reflectance (0.4 - 5.0 [microns]) of sulfur related compounds and mixtures. Honolulu, Hawaii: Planetary Geosciences Division, Hawaii Institute of Geophysics, University of Hawaii, 1987.

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2

Hawaii Institute of Geophysics. Planetary Geosciences Division. y United States. National Aeronautics and Space Administration., eds. Spectral reflectance (0.4 - 5.0 [microns]) of sulfur related compounds and mixtures. Honolulu, Hawaii: Planetary Geosciences Division, Hawaii Institute of Geophysics, University of Hawaii, 1987.

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3

Shimabukuro, Yosio Edemir y Flávio Jorge Ponzoni. Spectral Mixture for Remote Sensing. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02017-0.

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4

Lin, Li, He Guoqi y United States. National Aeronautics and Space Administration., eds. Nonlinear spectral mixture modeling of lunar multispectral: Implications for lateral transport. [Washington, DC: National Aeronautics and Space Administration, 1997.

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5

Wehrmeyer, Joseph A. Temperature and mixture fraction profiles in counterflow diffusion flames using linewise Raman imaging. Washington, D. C: AIAA, 1995.

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6

So odd a mixture: Along the autistic spectrum in Pride and prejudice. London: Jessica Kingsley Publishers, 2007.

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7

United States. National Aeronautics and Space Administration., ed. NONLINEAR SPECTRAL MIXTURE MODELING OF LUNAR MULTISPECTRAL: IMPLICATIONS FOR LATERAL TRANSPORT... NASA/CR-1998-208863... SEP. 10, 1999. [S.l: s.n., 2000.

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8

Ruckebusch, Cyril. Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Superresolution Imaging. Elsevier Science & Technology Books, 2016.

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9

Ruckebusch, Cyril. Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging. Elsevier, 2016.

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10

Resolving Spectral Mixtures - With Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging. Elsevier, 2016. http://dx.doi.org/10.1016/c2015-0-00401-4.

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11

Shimabukuro, Yosio Edemir y Flávio Jorge Ponzoni. Spectral Mixture for Remote Sensing: Linear Model and Applications. Springer, 2018.

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12

An Image Fusion Algorithm for Spatially Enhancing Spectral Mixture Maps. Storming Media, 1997.

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13

Relationship of soil, grass, and bedrock over the Kaweah serpentinite melange through spectral mixture analysis of AVIRIS data. [Washington, DC: National Aeronautics and Space Administration, 1991.

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14

Attwood, Anthony y Phyllis Ferguson-Bottomer. So Odd a Mixture: Along the Autistic Spectrum in 'Pride and Prejudice'. Kingsley Publishers, Jessica, 2007.

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15

Bottomer, Phyllis Ferguson. So Odd a Mixture: Along the Autistic Spectrum in 'Pride and Prejudice'. Jessica Kingsley Publishers, 2007.

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16

National Aeronautics and Space Administration (NASA) Staff. Relationships of Soil, Grass, and Bedrock over the Kaweah Serpentine Melange Through Spectral Mixture Analysis of Aviris Data. Independently Published, 2018.

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17

Pernet, Bruno, ed. Larval Feeding: Mechanisms, Rates, and Performance in Nature. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786962.003.0007.

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Larvae of many marine invertebrates must capture and ingest particulate food in order to develop to metamorphosis. These larvae use only a few physical processes to capture particles, but implement these processes using diverse morphologies and behaviors. Detailed understanding of larval feeding mechanism permits investigators to make predictions about feeding performance, including the size spectrum of particles larvae can capture and the rates at which they can capture them. In nature, larvae are immersed in complex mixtures of edible particles of varying size, density, flavor, and nutritional quality, as well as many particles that are too large to ingest. Concentrations of all of these components vary on fine temporal and spatial scales. Mechanistic models linking larval feeding mechanism to performance can be combined with data on food availability in nature and integrated into broader bioenergetics models to yield increased understanding of the biology of larvae in complex natural habitats.
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