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.

Towards improving cluster-based feature selection with a simplified silhouette filter

This paper proposes a filter-based algorithm for feature selection. The filter is based on the partitioning of the set of features into clusters. The number of clusters, and consequently the cardinality of the subset of selected features, is automatically estimated from data. The computational complexity of the proposed algorithm is also investigated. A variant of this filter that considers feature-class correlations is also proposed for classification problems. Empirical results involving ten datasets illustrate the performance of the developed algorithm, which in general has obtained competitive results in terms of classification accuracy when compared to state of the art algorithms that find clusters of features. We show that, if computational efficiency is an important issue, then the proposed filter May be preferred over their counterparts, thus becoming eligible to join a pool of feature selection algorithms to be used in practice. As an additional contribution of this work, a theoretical framework is used to formally analyze some properties of feature selection methods that rely on finding clusters of features. (C) 2011 Elsevier Inc. All rights reserved.

A Floating-point Extended Kalman Filter Implementation for Autonomous Mobile Robots

Localization and Mapping are two of the most important capabilities for autonomous mobile robots and have been receiving considerable attention from the scientific computing community over the last 10 years. One of the most efficient methods to address these problems is based on the use of the Extended Kalman Filter (EKF). The EKF simultaneously estimates a model of the environment (map) and the position of the robot based on odometric and exteroceptive sensor information. As this algorithm demands a considerable amount of computation, it is usually executed on high end PCs coupled to the robot. In this work we present an FPGA-based architecture for the EKF algorithm that is capable of processing two-dimensional maps containing up to 1.8 k features at real time (14 Hz), a three-fold improvement over a Pentium M 1.6 GHz, and a 13-fold improvement over an ARM920T 200 MHz. The proposed architecture also consumes only 1.3% of the Pentium and 12.3% of the ARM energy per feature.

Aerosol inorganic composition at a tropical site: Discrepancies between filter-based sampling and a semi-continuous method

The concentrations of the water-soluble inorganic aerosol species, ammonium (NH4+), nitrate (NO3-), chloride (Cl-), and sulfate (SO42-), were measured from September to November 2002 at a pasture site in the Amazon Basin (Rondnia, Brazil) (LBA-SMOCC). Measurements were conducted using a semi-continuous technique (Wet-annular denuder/Steam-Jet Aerosol Collector: WAD/SJAC) and three integrating filter-based methods, namely (1) a denuder-filter pack (DFP: Teflon and impregnated Whatman filters), (2) a stacked-filter unit (SFU: polycarbonate filters), and (3) a High Volume dichotomous sampler (HiVol: quartz fiber filters). Measurements covered the late dry season (biomass burning), a transition period, and the onset of the wet season (clean conditions). Analyses of the particles collected on filters were performed using ion chromatography (IC) and Particle-Induced X-ray Emission spectrometry (PIXE). Season-dependent discrepancies were observed between the WAD/SJAC system and the filter-based samplers. During the dry season, when PM2.5 (D-p <= 2.5 mu m) concentrations were similar to 100 mu g m(-3), aerosol NH4+ and SO42- measured by the filter-based samplers were on average two times higher than those determined by the WAD/SJAC. Concentrations of aerosol NO3- and Cl- measured with the HiVol during daytime, and with the DFP during day- and nighttime also exceeded those of the WAD/SJAC by a factor of two. In contrast, aerosol NO3- and Cl- measured with the SFU during the dry season were nearly two times lower than those measured by the WAD/SJAC. These differences declined markedly during the transition period and towards the cleaner conditions during the onset of the wet season (PM2.5 similar to 5 mu g m(-3)); when filter-based samplers measured on average 40-90% less than the WAD/SJAC. The differences were not due to consistent systematic biases of the analytical techniques, but were apparently a result of prevailing environmental conditions and different sampling procedures. For the transition period and wet season, the significance of our results is reduced by a low number of data points. We argue that the observed differences are mainly attributable to (a) positive and negative filter sampling artifacts, (b) presence of organic compounds and organosulfates on filter substrates, and (c) a SJAC sampling efficiency of less than 100%.

Mice and larvae tracking using a particle filter with an auto-adjustable observation model

This paper proposes a novel way to combine different observation models in a particle filter framework. This, so called, auto-adjustable observation model, enhance the particle filter accuracy when the tracked objects overlap without infringing a great runtime penalty to the whole tracking system. The approach has been tested under two important real world situations related to animal behavior: mice and larvae tracking. The proposal was compared to some state-of-art approaches and the results show, under the datasets tested, that a good trade-off between accuracy and runtime can be achieved using an auto-adjustable observation model. (C) 2009 Elsevier B.V. All rights reserved.

A note on a practical relationship between filter coefficients and scaling and wavelet functions of Discrete Wavelet Transforms

In this paper, the relationship between the filter coefficients and the scaling and wavelet functions of the Discrete Wavelet Transform is presented and exemplified from a practical point-of-view. The explanations complement the wavelet theory, that is well documented in the literature, being important for researchers who work with this tool for time-frequency analysis. (c) 2011 Elsevier Ltd. All rights reserved.

Baking Dalarna's biking cake : Collaboration as a means for destination competitiveness, a case study of “Biking Dalarna"

In the contemporary tourism industry, the competitive game is between destinations. Tourism operations struggle to remain competitive on the international market and their success depends to a large extent on other complementary and competing tourism organizations at the destination. It is the sum of the total tourism offerings at the destination which determines its attractiveness. This research explores tourism collaboration process as a means of generating destination competitiveness. The focus of the research is on the enhancing factors which contribute to the success of the collaboration and to the development of quality tourism products. The research studies the case of Biking Dalarna, a collaboration of different organizations at five biking destinations in Dalarna, Sweden. Its purpose is to develop biking tourism in the region and to make Dalarna into Sweden’s leading biking destination. It is a qualitative research; the empirical data was collected through in depth interviews with representatives of six Biking Dalarna member organizations. The qualitative data collected from the participants provides inside look into the members reflections and experience of collaborating. The findings of this research demonstrate how collaboration has improved the biking product in Dalarna and promoted solutions to development problems. The research finds the good relationship between the collaborating actors and the involvement and leadership of the regional tourism management organization as the most contributing factors to the success of Biking Dalarna. The research also suggests that a third desired outcome of collaboration, improved marketing attributes was yet to be achieved in the case of Biking Dalarna.

Filter-tipped Libraries

Is it possible to say something positive about Internet filtering in libraries and not have everyone, including your mother, call you a wild-eyed, hidebound, neo-Nazi bashi-bazouk? No, of course not, but I'm going to try to anyway.

Localization of a mobile robot based on odometry and natural landmarks using extended kalman filter

SANTANA, André M.; SOUZA, Anderson A. S.; BRITTO, Ricardo S.; ALSINA, Pablo J.; MEDEIROS, Adelardo A. D. Localization of a mobile robot based on odometry and natural landmarks using extended Kalman Filter. In: INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, 5., 2008, Funchal, Portugal. Proceedings... Funchal, Portugal: ICINCO, 2008.

Efficacy evaluation of a commercial neem cake for control of Haematobia irritans on Nelore cattle

Muita atenção tem sido dada ao desenvolvimento de inseticidas vegetais buscando-se um efetivo controle de ectoparasitas de bovinos, sem prejudicar animais, consumidores e meio ambiente. Este estudo, realizado de abril a julho de 2008, na Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) Pecuária Sudeste, em São Carlos, SP, Brasil, avaliou a eficácia de uma torta comercial de nim (Azadirachta indica) no controle da mosca-dos-chifres (Haematobia irritans) em bovinos. A torta de nim, misturada ao sal mineral na concentração de 2%, foi fornecida a 20 vacas Nelore, durante nove semanas, e sua eficácia foi monitorada através de contagens semanais nos grupos tratado e controle. Infestações individuais foram registradas por meio de fotos digitais em todos os animais de ambos os grupos, e o número de moscas foi, posteriormente, quantificado com o auxílio de um sistema de análise de imagem computadorizado. A quantificação dos componentes da torta de nim, por cromatografia líquida, revelou a presença de azadiractina (421 mg.kg-1) e 3-tigloyl-azadirachtol (151 mg.kg-1). A adição da torta de nim a 2% reduziu o consumo de sal mineral em cerca de 22%. O tratamento com torta de nim a 2% não reduziu as infestações por mosca-dos-chifres em bovinos durante as nove semanas do estudo.

Avaliação de meios filtrantes primários em filtro contínuo de tambor rotativo a vácuo para lodo de caldo de cana

Este estudo teve o objetivo de avaliar o comportamento de treze tipos de meios filtrantes primários desenvolvidos para uso na filtração a vácuo de lodo de caldo de cana, simulando as operações de formação e desidratação da torta em filtros contínuos de tambor rotativo a vácuo, empregados nas indústrias de açúcar e álcool do Brasil. Para tanto, foi desenvolvida uma planta-piloto anexa ao filtro de tambor rotativo a vácuo, na qual foram realizados todos os ensaios, com o objetivo de refletir a realidade das variáveis operacionais durante uma safra sucroalcooleira. Os resultados são apresentados, comparando-se as taxas de filtração, variando a pressão de formação da torta, temperatura e concentração de auxiliar filtrante, mostrando ao usuário um novo caminho para o melhoramento quantitativo e qualitativo, sem aumentar a área nominal da unidade de filtração.

A Discussion Related to Orbit Determination Using Nonlinear Sigma Point Kalman Filter

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Unscented Kalman Filter Applied to the Spacecraft Attitude Estimation with Euler Angles

The aim of this work is to test an algorithm to estimate, in real time, the attitude of an artificial satellite using real data supplied by attitude sensors that are on board of the CBERS-2 satellite (China Brazil Earth Resources Satellite). The real-time estimator used in this work for attitude determination is the Unscented Kalman Filter. This filter is a new alternative to the extended Kalman filter usually applied to the estimation and control problems of attitude and orbit. This algorithm is capable of carrying out estimation of the states of nonlinear systems, without the necessity of linearization of the nonlinear functions present in the model. This estimation is possible due to a transformation that generates a set of vectors that, suffering a nonlinear transformation, preserves the same mean and covariance of the random variables before the transformation. The performance will be evaluated and analyzed through the comparison between the Unscented Kalman filter and the extended Kalman filter results, by using real onboard data.