188 resultados para Scanning Mobility Particle S
Resumo:
The molecular ordering of coronene (C24H12) obtained by vacuum-deposition onto predominantly Ag(111) on mica has been investigated using the scanning tunnelling microscope. Real-space topographic images reveal that in certain regions we obtain layer-by-layer ordered growth of the molecules on this substrate which agrees with previous indirect measurements (the growth did not display this ordering in other regions). In our experiments on the ordered regions, we observe the best imaging contrast at a voltage bias of -0.28 V which may correspond to a resonant tunnelling process through the molecules. © 1995.
Resumo:
The structure and chemistry of the interface between a Si(111) substrate and an AlN(0001) thin film grown by metalorganic vapor phase epitaxy have been investigated at a subnanometer scale using high-angle annular dark field imaging and electron energy-loss spectroscopy. 〈1120̄〉AlN ∥ 〈110〉Si and 〈0001〉AlN ∥ 〈111〉 Si epitaxial relations were observed and an Al-face polarity of the AlN thin film was determined. Despite the use of Al deposition on the Si surface prior to the growth, an amorphous interlayer of composition SiNx was identified at the interface. Mechanisms leading to its formation are discussed. © 2010 American Institute of Physics.
Resumo:
A study has been performed of the erosion of aluminium by silica sand particles at a velocity of 4.5 m s-1, both air-borne and in the form of a water-borne slurry. Measurements made under similar experimental conditions show that slurry erosion proceeds at a rate several times that of air-borne erosion, the ratio of the two rates depending strongly on the angle of impact. Sand particles become embedded into the metal surface during air-borne particle erosion, forming a composite layer of metal and silica, and provide the major cause of the difference in wear rate. The embedded particles giving rise to surface hardening and a significant reduction in the erosion rate. Embedment of erodent particles was not observed during slurry erosion. Lubrication of the impacting interfaces by water appears to have minimal effect on the wear of aluminium by slurry erosion.
Resumo:
Increasing the field of view of a holographic display while maintaining adequate image size is a difficult task. To address this problem, we designed a system that tessellates several sub-holograms into one large hologram at the output. The sub-holograms we generate is similar to a kinoform but without the paraxial approximation during computation. The sub-holograms are loaded onto a single spatial light modulator consecutively and relayed to the appropriate position at the output through a combination of optics and scanning reconstruction light. We will review the method of computer generated hologram and describe the working principles of our system. Results from our proof-of-concept system are shown to have an improved field of view and reconstructed image size. ©2009 IEEE.
Resumo:
Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. In many applications it may be necessary to compute the sensitivity, or derivative, of the optimal filter with respect to the static parameters of the state-space model; for instance, in order to obtain maximum likelihood model parameters of interest, or to compute the optimal controller in an optimal control problem. In Poyiadjis et al. [2011] an original particle algorithm to compute the filter derivative was proposed and it was shown using numerical examples that the particle estimate was numerically stable in the sense that it did not deteriorate over time. In this paper we substantiate this claim with a detailed theoretical study. Lp bounds and a central limit theorem for this particle approximation of the filter derivative are presented. It is further shown that under mixing conditions these Lp bounds and the asymptotic variance characterized by the central limit theorem are uniformly bounded with respect to the time index. We demon- strate the performance predicted by theory with several numerical examples. We also use the particle approximation of the filter derivative to perform online maximum likelihood parameter estimation for a stochastic volatility model.
Resumo:
We design a particle interpretation of Feynman-Kac measures on path spaces based on a backward Markovian representation combined with a traditional mean field particle interpretation of the flow of their final time marginals. In contrast to traditional genealogical tree based models, these new particle algorithms can be used to compute normalized additive functionals "on-the-fly" as well as their limiting occupation measures with a given precision degree that does not depend on the final time horizon. We provide uniform convergence results with respect to the time horizon parameter as well as functional central limit theorems and exponential concentration estimates. Our results have important consequences for online parameter estimation for non-linear non-Gaussian state-space models. We show how the forward filtering backward smoothing estimates of additive functionals can be computed using a forward only recursion.