The Diffusion Kernel Filter

A particle filter method is presented for the discrete-time filtering problem with nonlinear ItA ` stochastic ordinary differential equations (SODE) with additive noise supposed to be analytically integrable as a function of the underlying vector-Wiener process and time. The Diffusion Kernel Filter is arrived at by a parametrization of small noise-driven state fluctuations within branches of prediction and a local use of this parametrization in the Bootstrap Filter. The method applies for small noise and short prediction steps. With explicit numerical integrators, the operations count in the Diffusion Kernel Filter is shown to be smaller than in the Bootstrap Filter whenever the initial state for the prediction step has sufficiently few moments. The established parametrization is a dual-formula for the analysis of sensitivity to gaussian-initial perturbations and the analysis of sensitivity to noise-perturbations, in deterministic models, showing in particular how the stability of a deterministic dynamics is modeled by noise on short times and how the diffusion matrix of an SODE should be modeled (i.e. defined) for a gaussian-initial deterministic problem to be cast into an SODE problem. From it, a novel definition of prediction may be proposed that coincides with the deterministic path within the branch of prediction whose information entropy at the end of the prediction step is closest to the average information entropy over all branches. Tests are made with the Lorenz-63 equations, showing good results both for the filter and the definition of prediction.

The univalent polynomial of Suffridge as a summability kernel

A positive summability trigonometric kernel {K(n)(theta)}(infinity)(n=1) is generated through a sequence of univalent polynomials constructed by Suffridge. We prove that the convolution {K(n) * f} approximates every continuous 2 pi-periodic function f with the rate omega(f, 1/n), where omega(f, delta) denotes the modulus of continuity, and this provides a new proof of the classical Jackson`s theorem. Despite that it turns out that K(n)(theta) coincide with positive cosine polynomials generated by Fejer, our proof differs from others known in the literature.

Peat as a natural solid-phase for copper preconcentration and determination in a multicommuted flow system coupled to flame atomic absorption spectrometry

The physical and chemical characteristics of peat were assessed through measurement of pH, percentage of organic matter, cationic exchange capacity (CEC), elemental analysis, infrared spectroscopy and quantitative analysis of metals by ICP OES. Despite the material showed to be very acid in view of the percentage of organic matter, its CEC was significant, showing potential for retention of metal ions. This characteristic was exploited by coupling a peat mini-column to a flow system based on the multicommutation approach for the in-line copper concentration prior to flame atomic absorption spectrometric determination. Cu(II) ions were adsorbed at pH 4.5 and eluted with 0.50 mol L(-1) HNO(3). The influence of chemical and hydrodynamic parameters, such as sample pH, buffer concentration, eluent type and concentration, sample flow-rate and preconcentration time were investigated. Under the optimized conditions, a linear response was observed between 16 and 100 mu g L(-1), with a detection limit estimated as 3 mu g L(-1) at the 99.7% confidence level and an enrichment factor of 16. The relative standard deviation was estimated as 3.3% (n = 20). The mini-column was used for at least 100 sampling cycles without significant variation in the analytical response. Recoveries from copper spiked to lake water or groundwater as well as concentrates used in hemodialysis were in the 97.3-111 % range. The results obtained for copper determination in these samples agreed with those achieved by graphite furnace atomic absorption spectrometry (GFAAS) at the 95% confidence level. (C) 2009 Elsevier B.V. All rights reserved.