'Spectral information' diaporamas de présentation

Hyperspectral Imagery (HSI) Dimensionality Reduction

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Instrumentation Concepts Ground-based Optical Telescopes

Instrumentation Concepts Ground-based Optical Telescopes. Keith Taylor (IAG/USP) Aug-Nov, 2008. IAG-USP (Keith Taylor). Aug-Sep, 2008. Imaging Fourier Transform Spectrographs (IFTS). FTS = Michelson Interferometer: IFTS = Imaging IFTS over solid angle, ?. Beam-splitter produces 2 arms;

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Speech Parametrisation

Speech Parametrisation. Compact encoding of information in speech Accentuates important info Attempts to eliminate irrelevant information Accentuates stable info Attempts to eliminate factors which tend to vary most across utterances (and speakers). Frames.

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Study of Foregrounds and Limitations to EoR Detection

Study of Foregrounds and Limitations to EoR Detection. Nithyanandan Thyagarajan N. Udaya Shankar Ravi Subrahmanyan (Raman Research Institute). 1. HI Power Spectrum. Statistical detections seem feasible Forms a key science of SKA precursors & pathfinders MWA LOFAR GMRT LWA PAPER.

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Can Color Detect Cancer?

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Understanding the Origin of Solar Flares

Understanding the Origin of Solar Flares. Joseph M. Davila (GSFC). Can We Confirm Observationally Some Basic Flare Assumptions?. Flares originate from energy stored in the corona Reconnection in the corona releases this stored energy

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Thickness map [nm] of the poly-Si layer of a poly-Si/SiO 2 /Si multi-layer structure

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Temporal masking of spectrally reduced speech: psychoacoustical experiments and links with ASR

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Vegetation Enhancements (continued)

Vegetation Enhancements (continued). Statistical and Feature Space Transformations. Learning objectives. What is a feature space and how are they constructed? Where are plants and soils found in feature space? How can we use feature space to enhance vegetation?

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IFTSUV: an Imaging Fourier Transform Spectrometer in the UV

IFTSUV: an Imaging Fourier Transform Spectrometer in the UV. Anne Millard, 3 rd year PhD student, directed by P. Lemaire and J.-C. Vial. Scientific objectives. Add spectral information to 2D images

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Random Matrix Theory in a nut shell.

3. Spectral statistics. Random Matrix Theory in a nut shell. Eigenvalues distribution:. Graphs: the spectrum and the spectral statistics. GUE. GOE. The random G (V,d) ensemble. Spectral 2-points correlations:. ( mapping the spectrum on the unit circle). Why do random graphs display the

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Multispectral laser scanning: an overview

Multispectral laser scanning: an overview. Mark Danson & Fadal Sasse Ecosystems and Environment Research Centre, School of Environment and Life Sciences, University of Salford, Manchester, UK @ SalcaSalford. Rachel Gaulton (U of Newcastle) Sanna Kaasalainen (FGI).

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Spectral ranges in IR Spectroscopy

Spectral ranges in IR Spectroscopy. NIR. MIR. FIR. 15.000 cm -1. 4.000 cm -1. 400 cm -1. 5 cm -1. What is IR-spectroscopy?. Interaction of light and matter. Excitation of vibrations. Types of vibrations example: water. stretching vibrations:. deformation vibration.

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DCTC/DCSC Features

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Feature Based Fusion of Multi-Sensor Data West State University PI: John A. Doe Portfolio/Operational Problem: HSI/Fusion.

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