12,344 research outputs found
Plugin procedure in segmentation and application to hyperspectral image segmentation
In this article we give our contribution to the problem of segmentation with
plug-in procedures. We give general sufficient conditions under which plug in
procedure are efficient. We also give an algorithm that satisfy these
conditions. We give an application of the used algorithm to hyperspectral
images segmentation. Hyperspectral images are images that have both spatial and
spectral coherence with thousands of spectral bands on each pixel. In the
proposed procedure we combine a reduction dimension technique and a spatial
regularisation technique. This regularisation is based on the mixlet
modelisation of Kolaczyck and Al
Two loop detection mechanisms: a comparison
In order to compare two loop detection mechanisms we describe two calculi for theorem proving in intuitionistic propositional logic. We call them both MJ Hist, and distinguish between them by description as `Swiss' or `Scottish'. These calculi combine in different ways the ideas on focused proof search of Herbelin and Dyckhoff & Pinto with the work of Heuerding emphet al on loop detection. The Scottish calculus detects loops earlier than the Swiss calculus but at the expense of modest extra storage in the history. A comparison of the two approaches is then given, both on a theoretic and on an implementational level
The Weierstrass subgroup of a curve has maximal rank
We show that the Weierstrass points of the generic curve of genus over an
algebraically closed field of characteristic 0 generate a group of maximal rank
in the Jacobian
Gaussian process model based predictive control
Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimization of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark
Adaptive, cautious, predictive control with Gaussian process priors
Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a k-step horizon. The expected value of a quadratic cost function is minimised, over this prediction horizon, without ignoring the variance of the model predictions. The general method and its main features are illustrated on a simulation example
Gaussian Process priors with uncertain inputs? Application to multiple-step ahead time series forecasting
We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. k-step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form y t = f(Yt-1 ,..., Yt-L ), the prediction of y at time t + k is based on the point estimates of the previous outputs. In this paper, we show how, using an analytical Gaussian approximation, we can formally incorporate the uncertainty about intermediate regressor values, thus updating the uncertainty on the current prediction
Origin of Second Harmonic Generation from individual Silicon Nanowires
We investigate Second Harmonic Generation from individual silicon nanowires
and study the influence of resonant optical modes on the far-field nonlinear
emission. We find that the polarization of the Second Harmonic has a
size-dependent behavior and explain this phenomenon by a combination of
different surface and bulk nonlinear susceptibility contributions. We show that
the Second Harmonic Generation has an entirely different origin, depending on
whether the incident illumination is polarized parallel or perpendicularly to
the nanowire axis. The results open perspectives for further geometry-based
studies on the origin of Second Harmonic Generation in nanostructures of
high-index centrosymmetric semiconductors.Comment: 7 Pages, 4 Figures + 3 Pages, 6 Figures in Appendi
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