Search results for "position"

showing 10 items of 6771 documents

MOCVD deposition of YSZ on stainless steels

2003

Abstract Yttria stabilized zirconia was deposited on stainless steel using the metal–organic chemical vapor deposition (MOCVD) technique, from β-diketonate precursors. The variation of the evaporation temperatures of yttrium and zirconium precursor allowed to control the level of Y within the film. Over the temperature range 125–150 °C, the Y content increased from 2.5 to 17.6 at.%. X-ray diffraction (XRD) analyses evidenced tetragonal phase of zirconia when the Y content was below 8 at.%, and cubic phase for higher concentration. Sputtered neutral mass spectrometry (SNMS) profiles confirmed that the control and stability of Y precursor temperature were of major importance to guarantee the …

ZirconiumMaterials scienceMetallurgyAnalytical chemistryGeneral Physics and Astronomychemistry.chemical_elementSurfaces and InterfacesGeneral ChemistryChemical vapor depositionYttriumAtmospheric temperature rangeCondensed Matter PhysicsSurfaces Coatings and FilmsTetragonal crystal systemchemistryCubic zirconiaMetalorganic vapour phase epitaxyYttria-stabilized zirconiaApplied Surface Science
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Mechanical properties of aluminum, zirconium, hafnium and tantalum oxides and their nanolaminates grown by atomic layer deposition

2015

ABSTRACT The mechanical properties of two different metal oxide nanolaminates comprised of Ta 2 O 5 and Al 2 O 3 , HfO 2 or ZrO 2 , grown on soda–lime glass substrate by atomic layer deposition, were investigated. Ta 2 O 5 and Al 2 O 3 layers were amorphous, whereas ZrO 2 and HfO 2 possessed crystalline structure. Thickness of single oxide layers was varied between 2.5 and 15 nm. The total thickness of the laminate structures was in the range of 160–170 nm. The hardness values of single layer oxides on glass ranged from 6.7 GPa (Ta 2 O 5 ) to 9.5 GPa (Al 2 O 3 ). Corresponding elastic moduli were 96 GPa and 101 GPa. The hardnesses of laminates were in the range of 6.8–7.8 GPa and elastic mo…

ZirconiumMaterials scienceMetallurgyTantalumOxidechemistry.chemical_elementSurfaces and InterfacesGeneral ChemistrySubstrate (electronics)Condensed Matter PhysicsSurfaces Coatings and FilmsAmorphous solidAtomic layer depositionchemistry.chemical_compoundchemistryAluminiumMaterials ChemistryComposite materialThin filmSurface and Coatings Technology
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Chemical Bath Deposition as a Simple Way to Grow Isolated and Coalesced ZnO Nanorods for Light-Emitting Diodes Fabrication

2018

A way to grow and characterize isolated and coalesced ZnO nanorods on $p$ -GaN/sapphire structure is presented. Chemical bath deposition can be used to grow ZnO nanorods of device-quality, simply controlling the duration time of the growth process and the concentration of the nutrient solution in the bath. Increasing the duration of the process, as well as the concentration of the solution, leads to compact and sound layers instead of separated nanorods. However, too high concentrations stop the growth process. Light-emitting diodes fabricated on these ZnO-p-GaN heterostructure have a peak of electroluminescence at 400 nm and exhibit interesting electrical and optical properties. Optical po…

ZnO nanorodMaterials scienceFabricationRenewable Energy Sustainability and the Environmentbusiness.industryEnergy Engineering and Power TechnologyZnO-p-GaN heterojunction-based LEDComputer Science Applications1707 Computer Vision and Pattern RecognitionHeterojunctionElectroluminescenceSettore ING-INF/01 - ElettronicaIndustrial and Manufacturing Engineeringlaw.inventionchemical bath depositionComputer Networks and CommunicationArtificial IntelligencelawSapphireOptoelectronicsNanorodbusinessInstrumentationLayer (electronics)Chemical bath depositionLight-emitting diode2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI)
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Coalescence of ZnO nanorods grown by chemical bath deposition

2018

In this work, a way to grow isolated and coalesced ZnO nanorods on p-GaN/sapphire structure is presented. Chemical bath deposition [1],[2] was used to grow ZnO nanorods of device-quality on a p-GaN/n-GaN/sapphire template, simply controlling the duration time of the growth process and the concentration of the nutrient solution in the bath. Several p-GaN templates were soaked in a nutrient solution, prepared with different concentration of zinc nitrate hexahydrate (Sigma-Aldrich, reagent grade 98%) and hexamethylenetetramine (Alfa Aesar, ACS 99%) in deionized water, while being heated at a temperature of 80 °C for a period varying from 8 to 25 hours; then, the samples were left in the soluti…

ZnO nanorods chemical bath depositionSettore ING-INF/01 - Elettronica
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Extensions of the witness method to characterize under-, over- and well-constrained geometric constraint systems

2011

International audience; This paper describes new ways to tackle several important problems encountered in geometric constraint solving, in the context of CAD, and which are linked to the handling of under- and over-constrained systems. It presents a powerful decomposition algorithm of such systems. Our methods are based on the witness principle whose theoretical background is recalled in a first step. A method to generate a witness is then explained. We show that having a witness can be used to incrementally detect over-constrainedness and thus to compute a well-constrained boundary system. An algorithm is introduced to check if anchoring a given subset of the coordinates brings the number …

[ INFO.INFO-MO ] Computer Science [cs]/Modeling and SimulationBoundary (topology)Witness configuration020207 software engineeringContext (language use)CAD02 engineering and technologyW-decompositionComputer Graphics and Computer-Aided DesignWitness[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationIndustrial and Manufacturing EngineeringComputer Science ApplicationsConstraint (information theory)symbols.namesakeTransformation groupJacobian matrix and determinant0202 electrical engineering electronic engineering information engineeringsymbolsGeometric constraints solving020201 artificial intelligence & image processingFinite setAlgorithmAlgorithmsMathematics
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Discrimination of coral reflectance spectra in the Red Sea

2002

Benthic populations can potentially be mapped from remotely acquired spectral imagery, provided that they have distinctive reflectance signatures. We examined the spectral reflectance characteristics of 14 genera of Red Sea coral using a submersible spectroradiometer. Coral spectra varied quantitatively and qualitatively over the depth interval 5–20 m. Tissue pigment content had a larger effect on reflectance than colony morphology. Ten coral genera could be discriminated with a statistical probability of 52% on the basis of their absolute reflectance. Six groups of two to three coral genera could be discriminated with a probability of 60% on the basis of their rates of change in reflectanc…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing010504 meteorology & atmospheric sciences[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingCoral0211 other engineering and technologies02 engineering and technologyAquatic Science01 natural sciencesSpectral line[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing14. Life underwaterComputingMilieux_MISCELLANEOUS021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensinggeographygeography.geographical_feature_categoryfungiPigment compositionCoral reefReflectivityWavelengthSpectroradiometerBenthic zone[SDE]Environmental SciencesEnvironmental science[SDE.BE]Environmental Sciences/Biodiversity and Ecology
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Spatial correction in dynamic photon emission by affine transformation matrix estimation

2014

International audience; Photon emission microscopy and Time Resolved Imaging have proved their efficiency for defect localization on VLSI. A common process to find defect candidate locations is to draw a comparison between acquisitions on a normally working device and a faulty one. In order to be accurate and meaningful, this method requires that the acquisition scene remains the same between the two parts. In practice, it can be difficult to set. In this paper, a method to correct position by affine matrix transformation is suggested. It is based on image features detection, description and matching and affine transformation estimation.

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingMatching (graph theory)Computer science[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingPosition (vector)020204 information systems0202 electrical engineering electronic engineering information engineeringComputer vision[SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingVery-large-scale integrationHarris affine region detectorbusiness.industryProcess (computing)Affine shape adaptationTransformation (function)020201 artificial intelligence & image processing[ SPI.NANO ] Engineering Sciences [physics]/Micro and nanotechnologies/MicroelectronicsArtificial intelligenceAffine transformationbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Kolmogorov Superposition Theorem and Wavelet Decomposition for Image Compression

2009

International audience; Kolmogorov Superposition Theorem stands that any multivariate function can be decomposed into two types of monovariate functions that are called inner and external functions: each inner function is associated to one dimension and linearly combined to construct a hash-function that associates every point of a multidimensional space to a value of the real interval $[0,1]$. These intermediate values are then associated by external functions to the corresponding value of the multidimensional function. Thanks to the decomposition into monovariate functions, our goal is to apply this decomposition to images and obtain image compression. We propose a new algorithm to decomp…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing010102 general mathematicsMathematical analysisWavelet transform02 engineering and technologyFunction (mathematics)[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingSuperposition theorem01 natural sciencesWavelet packet decompositionWavelet[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]Dimension (vector space)[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingPoint (geometry)0101 mathematics[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingImage compressionMathematics
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Asserting the Precise Position of 3D and Multispectral Acquisition Systems for Multisensor Registration Applied to Cultural Heritage Analysis

2012

International audience; We present a novel method to register multispectral acquisitions on a 3D model. The method is based on the external tracking of the acquisition systems using close-range photogrammetric techniques: multiple calibrated cameras simultaneously observe the successive acquisition systems in use. The views from these cameras are used to precisely determine the position of each acquisition system. All datasets can then be projected in the same coordinate system. The registration is thus independent from the quality and content of the data. This method is well suited to the study of cultural heritage or any other application where we do not wish to place targets on the objec…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceCoordinate systemMultispectral image02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingclose range photogrammetryTracking (particle physics)multispectral acquisitions[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingPosition (vector)0202 electrical engineering electronic engineering information engineeringComputer vision[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing2d-3d registrationbusiness.industry020207 software engineeringcultural heritageObject (computer science)Pipeline (software)optical calibrationCultural heritage3d digitizationPhotogrammetry020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Kolmogorov Superposition Theorem and Its Application to Multivariate Function Decompositions and Image Representation

2008

International audience; In this paper, we present the problem of multivariate function decompositions into sums and compositions of monovariate functions. We recall that such a decomposition exists in the Kolmogorov's superposition theorem, and we present two of the most recent constructive algorithms of these monovariate functions. We first present the algorithm proposed by Sprecher, then the algorithm proposed by Igelnik, and we present several results of decomposition for gray level images. Our goal is to adapt and apply the superposition theorem to image processing, i.e. to decompose an image into simpler functions using Kolmogorov superpositions. We synthetise our observations, before …

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingImage processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologySuperposition theorem01 natural sciences[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[ INFO.INFO-TI ] Computer Science [cs]/Image ProcessingComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION0202 electrical engineering electronic engineering information engineeringApplied mathematics0101 mathematics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingMathematicsDiscrete mathematicsSignal processingArtificial neural network010102 general mathematicsApproximation algorithmSpline (mathematics)[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]Kolmogorov structure function[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]020201 artificial intelligence & image processingHypercube[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing2008 IEEE International Conference on Signal Image Technology and Internet Based Systems
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