The Experts below are selected from a list of 1950 Experts worldwide ranked by ideXlab platform
Folker Meyer - One of the best experts on this subject based on the ideXlab platform.
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the biological Observation Matrix biom format or how i learned to stop worrying and love the ome ome
GigaScience, 2012Co-Authors: Daniel Mcdonald, Jose C Clemente, Justin Kuczynski, Jai Ram Rideout, Jesse Stombaugh, Doug Wendel, Andreas Wilke, Susan M Huse, John Hufnagle, Folker MeyerAbstract:We present the Biological Observation Matrix (BIOM, pronounced “biome”) format: a JSON-based file format for representing arbitrary Observation by sample contingency tables with associated sample and Observation metadata. As the number of categories of comparative omics data types (collectively, the “ome-ome”) grows rapidly, a general format to represent and archive this data will facilitate the interoperability of existing bioinformatics tools and future meta-analyses. The BIOM file format is supported by an independent open-source software project (the biom-format project), which initially contains Python objects that support the use and manipulation of BIOM data in Python programs, and is intended to be an open development effort where developers can submit implementations of these objects in other programming languages. The BIOM file format and the biom-format project are steps toward reducing the “bioinformatics bottleneck” that is currently being experienced in diverse areas of biological sciences, and will help us move toward the next phase of comparative omics where basic science is translated into clinical and environmental applications. The BIOM file format is currently recognized as an Earth Microbiome Project Standard, and as a Candidate Standard by the Genomic Standards Consortium.
Dario Calderon-alvarez - One of the best experts on this subject based on the ideXlab platform.
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Optimal Filtering for Incompletely Measured Polynomial Systems with Multiplicative Noise
Circuits Systems and Signal Processing, 2008Co-Authors: Michael Basin, Dario Calderon-alvarezAbstract:In this paper, the optimal filtering problem for polynomial system states with polynomial multiplicative noise over linear Observations with an arbitrary, not necessarily invertible, Observation Matrix is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. Thus, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. A transformation of the Observation equation is introduced to reduce the original problem to the previously solved one with an invertible Observation Matrix. The procedure for obtaining a closed system of the filtering equations for any polynomial state with polynomial multiplicative noise over linear Observations is then established, which yields the explicit closed form of the filtering equations in the particular cases of linear and bilinear state equations. In an example, the performance of the designed optimal filter is verified against those of the optimal filter for a quadratic state with a state-independent noise and a conventional extended Kalman---Bucy filter.
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Optimal filtering for incompletely measured polynomial states with multiplicative noise
2008 American Control Conference, 2008Co-Authors: Michael Basin, Dario Calderon-alvarezAbstract:In this paper, the optimal filtering problem for polynomial system states with polynomial multiplicative noise over linear Observations with an arbitrary, not necessarily invertible, Observation Matrix is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. A transformation of the Observation equation is introduced to reduce the original problem to the previously solved one with an invertible Observation Matrix. The procedure for obtaining a closed system of the filtering equations for any polynomial state with polynomial multiplicative noise over linear Observations is then established, which yields the explicit closed form of the filtering equations in the particular cases of linear and bilinear state equations. In the example, performance of the designed optimal filter is verified against the optimal filter for a quadratic state with a state-independent noise and a conventional extended Kalman-Bucy filter.
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Optimal filtering for incompletely measured polynomial states over linear Observations
International Journal of Adaptive Control and Signal Processing, 2008Co-Authors: Michael Basin, Dario Calderon-alvarez, Mikhail SkliarAbstract:In this paper, the optimal filtering problem for polynomial system states over linear Observations with an arbitrary, not necessarily invertible, Observation Matrix is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. A transformation of the Observation equation is introduced to reduce the original problem to the previously solved one with an invertible Observation Matrix. The procedure for obtaining a closed system of the filtering equations for any polynomial state over linear Observations is then established, which yields the explicit closed form of the filtering equations in the particular case of a third-order state equation. In the example, performance of the designed optimal filter is verified against a conventional extended Kalman–Bucy filter. Copyright © 2007 John Wiley & Sons, Ltd.
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Optimal Filtering for Incompletely Measured Polynomial States with
2008Co-Authors: Michael Basin, Dario Calderon-alvarezAbstract:In this paper, the optimal filtering problem for polynomial system states with polynomial multiplicative noise over linear Observations with an arbitrary, not necessarily invertible, Observation Matrix is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. A transformation of the Observation equation is introduced to reduce the original problem to the previously solved one with an invertible Observation Matrix. The procedure for obtaining a closed system of the filtering equations for any polynomial state with polynomial multiplicative noise over linear Observations is then established, which yields the explicit closed form of the filtering equations in the particular cases of linear and bilinear state equations. In the example, performance of the designed optimal filter is verified against the optimal filter for a quadratic state with a state-independent noise and a conventional extended Kalman-Bucy filter.
Daniel Mcdonald - One of the best experts on this subject based on the ideXlab platform.
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the biological Observation Matrix biom format or how i learned to stop worrying and love the ome ome
GigaScience, 2012Co-Authors: Daniel Mcdonald, Jose C Clemente, Justin Kuczynski, Jai Ram Rideout, Jesse Stombaugh, Doug Wendel, Andreas Wilke, Susan M Huse, John Hufnagle, Folker MeyerAbstract:We present the Biological Observation Matrix (BIOM, pronounced “biome”) format: a JSON-based file format for representing arbitrary Observation by sample contingency tables with associated sample and Observation metadata. As the number of categories of comparative omics data types (collectively, the “ome-ome”) grows rapidly, a general format to represent and archive this data will facilitate the interoperability of existing bioinformatics tools and future meta-analyses. The BIOM file format is supported by an independent open-source software project (the biom-format project), which initially contains Python objects that support the use and manipulation of BIOM data in Python programs, and is intended to be an open development effort where developers can submit implementations of these objects in other programming languages. The BIOM file format and the biom-format project are steps toward reducing the “bioinformatics bottleneck” that is currently being experienced in diverse areas of biological sciences, and will help us move toward the next phase of comparative omics where basic science is translated into clinical and environmental applications. The BIOM file format is currently recognized as an Earth Microbiome Project Standard, and as a Candidate Standard by the Genomic Standards Consortium.
M. Gautier - One of the best experts on this subject based on the ideXlab platform.
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Refined Instrumental Variable method for non-linear dynamic identification of robots
2010Co-Authors: A. Janot, P. O. Vandanjon, M. GautierAbstract:The identification of the dynamic parameters of robot is based on the use of the inverse dynamic identification model which is linear with respect to the parameters. This model is sampled while the robot is tracking “exciting” trajectories, in order to get an over determined linear system. The linear least squares solution of this system calculates the estimated parameters. The efficiency of this method has been proved through the experimental identification of a lot of prototypes and industrial robots. However, this method needs joint torque and position measurements and the estimation of the joint velocities and accelerations through the bandpass filtering of the joint position at high sample rate. So, the Observation Matrix is noisy. Moreover identification process takes place when the robot is controlled by feedback. These violations of assumption imply that the LS estimator is not consistent. This paper focuses on the Refined Instrumental Variable (RIV) approach to over-come this problem of noisy Observation Matrix. This technique is applied to a 2 degrees of freedom (DOF) prototype devel-oped by the IRCCyN Robotic team.
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Identification of robots dynamics with the Instrumental Variable method
2009 IEEE International Conference on Robotics and Automation, 2009Co-Authors: A. Janot, P. O. Vandanjon, M. GautierAbstract:The identification of the dynamic parameters of robot is based on the use of the inverse dynamic model which is linear with respect to the parameters. This model is sampled while the robot is tracking ldquoexcitingrdquo trajectories, in order to get an over determined linear system. The linear least squares solution of this system calculates the estimated parameters. The efficiency of this method has been proved through the experimental identification of a lot of prototypes and industrial robots. However, this method needs joint torque and position measurements and the estimation of the joint velocities and accelerations through the pass band filtering of the joint position at high sample rate. So, the Observation Matrix is noisy. Moreover identification process takes place when the robot is controlled by feedback. These violations of assumption imply that the LS solution is biased. The Simple Refined Instrumental Variable (SRIV) approach deals with this problem of noisy Observation Matrix and can be statistically optimal. This paper focuses on this technique which will be applied to a 2 degrees of freedom (DOF) prototype developed by the IRCCyN Robotic team.
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ICRA - Identification of robots dynamics with the Instrumental Variable method
2009 IEEE International Conference on Robotics and Automation, 2009Co-Authors: A. Janot, P. O. Vandanjon, M. GautierAbstract:The identification of the dynamic parameters of robot is based on the use of the inverse dynamic model which is linear with respect to the parameters. This model is sampled while the robot is tracking “exciting” trajectories, in order to get an over determined linear system. The linear least squares solution of this system calculates the estimated parameters. The efficiency of this method has been proved through the experimental identification of a lot of prototypes and industrial robots. However, this method needs joint torque and position measurements and the estimation of the joint velocities and accelerations through the pass band filtering of the joint position at high sample rate. So, the Observation Matrix is noisy. Moreover identification process takes place when the robot is controlled by feedback. These violations of assumption imply that the LS solution is biased. The Simple Refined Instrumental Variable (SRIV) approach deals with this problem of noisy Observation Matrix and can be statistically optimal. This paper focuses on this technique which will be applied to a 2 degrees of freedom (DOF) prototype developed by the IRCCyN Robotic team.
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Using the Instrumental Variable Method for Robots Identification
IFAC Proceedings Volumes, 2009Co-Authors: A. Janot, P. O. Vandanjon, M. GautierAbstract:Abstract The identification of the dynamic parameters of robot is based on the use of the inverse dynamic model which is linear with respect to the parameters. This model is sampled while the robot is tracking “exciting” trajectories in order to get an over determined linear system. The linear least squares solution of this system calculates the estimated parameters. The efficiency of this method has been proved through the experimental identification of a lot of prototypes and industrial robots. However, this method needs joint torque and position measurements and the estimation of the joint velocities and accelerations through the pass band filtering of the joint position at high sample rate. So, the Observation Matrix is noisy. Moreover identification process takes place when the robot is controlled by feedback. These violations of assumption imply a theoretical biased LS solution which can be avoided with prefiltering data and tracking “exciting” trajectories. The Simple Refined Instrumental Variable (SRIV) approach deals with this problem of noisy Observation Matrix and can be statistically optimal. This paper focuses on this technique which will be applied to a 2 degrees of freedom (DOF) prototype developed by the IRCCyN Robotic team.
Michael Basin - One of the best experts on this subject based on the ideXlab platform.
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Optimal Filtering for Incompletely Measured Polynomial Systems with Multiplicative Noise
Circuits Systems and Signal Processing, 2008Co-Authors: Michael Basin, Dario Calderon-alvarezAbstract:In this paper, the optimal filtering problem for polynomial system states with polynomial multiplicative noise over linear Observations with an arbitrary, not necessarily invertible, Observation Matrix is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. Thus, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. A transformation of the Observation equation is introduced to reduce the original problem to the previously solved one with an invertible Observation Matrix. The procedure for obtaining a closed system of the filtering equations for any polynomial state with polynomial multiplicative noise over linear Observations is then established, which yields the explicit closed form of the filtering equations in the particular cases of linear and bilinear state equations. In an example, the performance of the designed optimal filter is verified against those of the optimal filter for a quadratic state with a state-independent noise and a conventional extended Kalman---Bucy filter.
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Optimal filtering for incompletely measured polynomial states with multiplicative noise
2008 American Control Conference, 2008Co-Authors: Michael Basin, Dario Calderon-alvarezAbstract:In this paper, the optimal filtering problem for polynomial system states with polynomial multiplicative noise over linear Observations with an arbitrary, not necessarily invertible, Observation Matrix is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. A transformation of the Observation equation is introduced to reduce the original problem to the previously solved one with an invertible Observation Matrix. The procedure for obtaining a closed system of the filtering equations for any polynomial state with polynomial multiplicative noise over linear Observations is then established, which yields the explicit closed form of the filtering equations in the particular cases of linear and bilinear state equations. In the example, performance of the designed optimal filter is verified against the optimal filter for a quadratic state with a state-independent noise and a conventional extended Kalman-Bucy filter.
-
Optimal filtering for incompletely measured polynomial states over linear Observations
International Journal of Adaptive Control and Signal Processing, 2008Co-Authors: Michael Basin, Dario Calderon-alvarez, Mikhail SkliarAbstract:In this paper, the optimal filtering problem for polynomial system states over linear Observations with an arbitrary, not necessarily invertible, Observation Matrix is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. A transformation of the Observation equation is introduced to reduce the original problem to the previously solved one with an invertible Observation Matrix. The procedure for obtaining a closed system of the filtering equations for any polynomial state over linear Observations is then established, which yields the explicit closed form of the filtering equations in the particular case of a third-order state equation. In the example, performance of the designed optimal filter is verified against a conventional extended Kalman–Bucy filter. Copyright © 2007 John Wiley & Sons, Ltd.
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Optimal Filtering for Incompletely Measured Polynomial States with
2008Co-Authors: Michael Basin, Dario Calderon-alvarezAbstract:In this paper, the optimal filtering problem for polynomial system states with polynomial multiplicative noise over linear Observations with an arbitrary, not necessarily invertible, Observation Matrix is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. A transformation of the Observation equation is introduced to reduce the original problem to the previously solved one with an invertible Observation Matrix. The procedure for obtaining a closed system of the filtering equations for any polynomial state with polynomial multiplicative noise over linear Observations is then established, which yields the explicit closed form of the filtering equations in the particular cases of linear and bilinear state equations. In the example, performance of the designed optimal filter is verified against the optimal filter for a quadratic state with a state-independent noise and a conventional extended Kalman-Bucy filter.
