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Alejandro Ribeiro - One of the best experts on this subject based on the ideXlab platform.

  • d map distributed maximum a Posteriori Probability estimation of dynamic systems
    IEEE Transactions on Signal Processing, 2013
    Co-Authors: Felicia Y Jakubiec, Alejandro Ribeiro
    Abstract:

    This paper develops a framework for the estimation of a time-varying random signal using a distributed sensor network. Given a continuous time model sensors collect noisy observations and produce local estimates according to the discrete time equivalent system defined by the sampling period of observations. Estimation is performed using a maximum a Posteriori Probability estimator (MAP) within a given window of interest. To mediate the incorporation of information from other sensors we introduce Lagrange multipliers to penalize the disagreement between neighboring estimates. We show that the resulting distributed (D)-MAP algorithm is able to track dynamical signals with a small error. This error is characterized in terms of problem constants and vanishes with the sampling time as long as the log-likelihood function which is assumed to be log-concave satisfies a smoothness condition. We implement the D-MAP algorithm for a linear and a nonlinear system model to show that the performance corroborates with theoretical findings.

  • distributed maximum a Posteriori Probability estimation for tracking of dynamic systems
    Asilomar Conference on Signals Systems and Computers, 2012
    Co-Authors: Felicia Y Jakubiec, Alejandro Ribeiro
    Abstract:

    We present a framework for the estimation of time-varying random signals with wireless sensor networks. Given a continuous time model, sensors collect noisy observations according to the discrete-time equivalent system defined by the sampling period of observations. Estimation is performed locally using a maximum a Posteriori Probability estimator (MAP) within a time window. To incorporate information from neighboring sensors we introduce Lagrange multipliers to penalize the disagreement between estimates. We show that the distributed (D-)MAP algorithm is able to track dynamical signals with an error characterized in terms of problem constants. This error vanishes with the sampling period if the log-likelihood function satisfies a smoothness condition.

  • distributed maximum a Posteriori Probability estimation of dynamic systems with wireless sensor networks
    International Conference on Acoustics Speech and Signal Processing, 2012
    Co-Authors: Felicia Y Jakubiec, Alejandro Ribeiro
    Abstract:

    This paper develops a framework for the estimation of a time-varying random signal using a wireless sensor network. Given a continuous time model, sensors collect noisy observations and produce local estimates according to the discrete-time equivalent system defined by the sampling period of observations. Estimation is performed using a maximum a Posteriori Probability estimator (MAP) within a given window of interest. To mediate the incorporation of information from other sensors we introduce Lagrange multipliers to penalize the disagreement between neighboring estimates. We show that the resulting distributed (D-)MAP algorithm is able to track dynamical signals with a small error. This error is characterized in terms of problem constants and vanishes with the sampling time as long as the log-likelihood function satisfies a smoothness condition.

  • ACSCC - Distributed maximum a Posteriori Probability estimation for tracking of dynamic systems
    2012 Conference Record of the Forty Sixth Asilomar Conference on Signals Systems and Computers (ASILOMAR), 2012
    Co-Authors: Felicia Y Jakubiec, Alejandro Ribeiro
    Abstract:

    We present a framework for the estimation of time-varying random signals with wireless sensor networks. Given a continuous time model, sensors collect noisy observations according to the discrete-time equivalent system defined by the sampling period of observations. Estimation is performed locally using a maximum a Posteriori Probability estimator (MAP) within a time window. To incorporate information from neighboring sensors we introduce Lagrange multipliers to penalize the disagreement between estimates. We show that the distributed (D-)MAP algorithm is able to track dynamical signals with an error characterized in terms of problem constants. This error vanishes with the sampling period if the log-likelihood function satisfies a smoothness condition.

  • ICASSP - Distributed maximum a Posteriori Probability estimation of dynamic systems with wireless sensor networks
    2012 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2012
    Co-Authors: Felicia Y Jakubiec, Alejandro Ribeiro
    Abstract:

    This paper develops a framework for the estimation of a time-varying random signal using a wireless sensor network. Given a continuous time model, sensors collect noisy observations and produce local estimates according to the discrete-time equivalent system defined by the sampling period of observations. Estimation is performed using a maximum a Posteriori Probability estimator (MAP) within a given window of interest. To mediate the incorporation of information from other sensors we introduce Lagrange multipliers to penalize the disagreement between neighboring estimates. We show that the resulting distributed (D-)MAP algorithm is able to track dynamical signals with a small error. This error is characterized in terms of problem constants and vanishes with the sampling time as long as the log-likelihood function satisfies a smoothness condition.

Felicia Y Jakubiec - One of the best experts on this subject based on the ideXlab platform.

  • d map distributed maximum a Posteriori Probability estimation of dynamic systems
    IEEE Transactions on Signal Processing, 2013
    Co-Authors: Felicia Y Jakubiec, Alejandro Ribeiro
    Abstract:

    This paper develops a framework for the estimation of a time-varying random signal using a distributed sensor network. Given a continuous time model sensors collect noisy observations and produce local estimates according to the discrete time equivalent system defined by the sampling period of observations. Estimation is performed using a maximum a Posteriori Probability estimator (MAP) within a given window of interest. To mediate the incorporation of information from other sensors we introduce Lagrange multipliers to penalize the disagreement between neighboring estimates. We show that the resulting distributed (D)-MAP algorithm is able to track dynamical signals with a small error. This error is characterized in terms of problem constants and vanishes with the sampling time as long as the log-likelihood function which is assumed to be log-concave satisfies a smoothness condition. We implement the D-MAP algorithm for a linear and a nonlinear system model to show that the performance corroborates with theoretical findings.

  • distributed maximum a Posteriori Probability estimation for tracking of dynamic systems
    Asilomar Conference on Signals Systems and Computers, 2012
    Co-Authors: Felicia Y Jakubiec, Alejandro Ribeiro
    Abstract:

    We present a framework for the estimation of time-varying random signals with wireless sensor networks. Given a continuous time model, sensors collect noisy observations according to the discrete-time equivalent system defined by the sampling period of observations. Estimation is performed locally using a maximum a Posteriori Probability estimator (MAP) within a time window. To incorporate information from neighboring sensors we introduce Lagrange multipliers to penalize the disagreement between estimates. We show that the distributed (D-)MAP algorithm is able to track dynamical signals with an error characterized in terms of problem constants. This error vanishes with the sampling period if the log-likelihood function satisfies a smoothness condition.

  • distributed maximum a Posteriori Probability estimation of dynamic systems with wireless sensor networks
    International Conference on Acoustics Speech and Signal Processing, 2012
    Co-Authors: Felicia Y Jakubiec, Alejandro Ribeiro
    Abstract:

    This paper develops a framework for the estimation of a time-varying random signal using a wireless sensor network. Given a continuous time model, sensors collect noisy observations and produce local estimates according to the discrete-time equivalent system defined by the sampling period of observations. Estimation is performed using a maximum a Posteriori Probability estimator (MAP) within a given window of interest. To mediate the incorporation of information from other sensors we introduce Lagrange multipliers to penalize the disagreement between neighboring estimates. We show that the resulting distributed (D-)MAP algorithm is able to track dynamical signals with a small error. This error is characterized in terms of problem constants and vanishes with the sampling time as long as the log-likelihood function satisfies a smoothness condition.

  • ACSCC - Distributed maximum a Posteriori Probability estimation for tracking of dynamic systems
    2012 Conference Record of the Forty Sixth Asilomar Conference on Signals Systems and Computers (ASILOMAR), 2012
    Co-Authors: Felicia Y Jakubiec, Alejandro Ribeiro
    Abstract:

    We present a framework for the estimation of time-varying random signals with wireless sensor networks. Given a continuous time model, sensors collect noisy observations according to the discrete-time equivalent system defined by the sampling period of observations. Estimation is performed locally using a maximum a Posteriori Probability estimator (MAP) within a time window. To incorporate information from neighboring sensors we introduce Lagrange multipliers to penalize the disagreement between estimates. We show that the distributed (D-)MAP algorithm is able to track dynamical signals with an error characterized in terms of problem constants. This error vanishes with the sampling period if the log-likelihood function satisfies a smoothness condition.

  • ICASSP - Distributed maximum a Posteriori Probability estimation of dynamic systems with wireless sensor networks
    2012 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2012
    Co-Authors: Felicia Y Jakubiec, Alejandro Ribeiro
    Abstract:

    This paper develops a framework for the estimation of a time-varying random signal using a wireless sensor network. Given a continuous time model, sensors collect noisy observations and produce local estimates according to the discrete-time equivalent system defined by the sampling period of observations. Estimation is performed using a maximum a Posteriori Probability estimator (MAP) within a given window of interest. To mediate the incorporation of information from other sensors we introduce Lagrange multipliers to penalize the disagreement between neighboring estimates. We show that the resulting distributed (D-)MAP algorithm is able to track dynamical signals with a small error. This error is characterized in terms of problem constants and vanishes with the sampling time as long as the log-likelihood function satisfies a smoothness condition.

Bunpei Irie - One of the best experts on this subject based on the ideXlab platform.

  • an a Posteriori Probability calculation method for analytic word recognition applicable to address recognition
    International Conference on Document Analysis and Recognition, 2009
    Co-Authors: Tomoyuki Hamamura, Takuma Akagi, Bunpei Irie
    Abstract:

    In this paper, we propose a novel calculation method of an “a Posteriori” Probability for analytic word recognition.The method is suitable for address recognition tasks. Our previous method needs calculation over all words in a lexicon,while the proposed method only needs calculation on the concerned word. In the address recognition task, lexicon size becomes very large and only a part of it can be handled. Hence, the previous method cannot be used, and the proposed method is convenient. Experimental results show that the proposed method guarantees high precision of Probability calculation.

  • ICDAR - An A Posteriori Probability Calculation Method for Analytic Word Recognition Applicable to Address Recognition
    2009 10th International Conference on Document Analysis and Recognition, 2009
    Co-Authors: Tomoyuki Hamamura, Takuma Akagi, Bunpei Irie
    Abstract:

    In this paper, we propose a novel calculation method of an “a Posteriori” Probability for analytic word recognition.The method is suitable for address recognition tasks. Our previous method needs calculation over all words in a lexicon,while the proposed method only needs calculation on the concerned word. In the address recognition task, lexicon size becomes very large and only a part of it can be handled. Hence, the previous method cannot be used, and the proposed method is convenient. Experimental results show that the proposed method guarantees high precision of Probability calculation.

  • an analytic word recognition algorithm using a Posteriori Probability
    International Conference on Document Analysis and Recognition, 2007
    Co-Authors: Tomoyuki Hamamura, Takuma Akagi, Bunpei Irie
    Abstract:

    Word recognition algorithms are classified into two major groups. One is an "analytic" approach of recognizing individual characters, while the other is a "holistic" approach dealing with an entire word image. In the former approach, matching scores used to be calculated using heuristic functions, such as an average of confidence values on character recognition. In some non-heuristic studies, a stochastic evaluation function is employed, which is a ratio between an "a Posteriori" Probability and an "a priori" Probability ("a Posteriori" Probability ratio). In this research, a new evaluation function is proposed, which is an improvement of "a Posteriori" Probability ratio. A result of an experiment using real images shows 9.1% improvement on handwritten word recognition.

  • ICDAR - An Analytic Word Recognition Algorithm Using a Posteriori Probability
    Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2, 2007
    Co-Authors: Tomoyuki Hamamura, Takuma Akagi, Bunpei Irie
    Abstract:

    Word recognition algorithms are classified into two major groups. One is an "analytic" approach of recognizing individual characters, while the other is a "holistic" approach dealing with an entire word image. In the former approach, matching scores used to be calculated using heuristic functions, such as an average of confidence values on character recognition. In some non-heuristic studies, a stochastic evaluation function is employed, which is a ratio between an "a Posteriori" Probability and an "a priori" Probability ("a Posteriori" Probability ratio). In this research, a new evaluation function is proposed, which is an improvement of "a Posteriori" Probability ratio. A result of an experiment using real images shows 9.1% improvement on handwritten word recognition.

Lin Zhong - One of the best experts on this subject based on the ideXlab platform.

  • rejection based on a Posteriori Probability estimated by mlp with application for mandarin voice dialer on asic
    International Conference on Acoustics Speech and Signal Processing, 2000
    Co-Authors: Lin Zhong
    Abstract:

    High performance Mandarin voice dialer is much more difficult than its English counterpart to achieve, especially on inexpensive hardware as ASIC. One way to improve its performance is to incorporate rejecters into the system. In our study, an MLP based postprocessor, an a Posteriori Probability estimator, is applied after HMM Viterbi recognition. Poor utterances, which are recognized by HMMs but have low a Posteriori Probability, are then rejected. Rejecting 4.9% of all the testing utterances, the MLP rejector boosts the HMM-based system's single digit accuracy from 97.1% to 99.6% for the Mandarin voice dialer, a ten-syllable speaker independent task. The performance is better than those of rejection based on linear discrimination, anti-digit models or likelihood ratio.

  • ICASSP - Rejection based on a Posteriori Probability estimated by MLP with application for Mandarin voice dialer on ASIC
    2000 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.00CH37100), 1
    Co-Authors: Lin Zhong, Jia Liu, Runsheng Liu
    Abstract:

    High performance Mandarin voice dialer is much more difficult than its English counterpart to achieve, especially on inexpensive hardware as ASIC. One way to improve its performance is to incorporate rejecters into the system. In our study, an MLP based postprocessor, an a Posteriori Probability estimator, is applied after HMM Viterbi recognition. Poor utterances, which are recognized by HMMs but have low a Posteriori Probability, are then rejected. Rejecting 4.9% of all the testing utterances, the MLP rejector boosts the HMM-based system's single digit accuracy from 97.1% to 99.6% for the Mandarin voice dialer, a ten-syllable speaker independent task. The performance is better than those of rejection based on linear discrimination, anti-digit models or likelihood ratio.

Lichun Chen - One of the best experts on this subject based on the ideXlab platform.

  • A three‐step maximum a Posteriori Probability method for InSAR data inversion of coseismic rupture with application to the 14 April 2010 Mw 6.9 Yushu, China, earthquake
    Journal of Geophysical Research: Solid Earth, 2013
    Co-Authors: Jianbao Sun, Zheng-kang Shen, Roland Bürgmann, Min Wang, Lichun Chen
    Abstract:

    [1] We develop a three-step maximum a Posteriori Probability method for coseismic rupture inversion, which aims at maximizing the a posterior Probability density function (PDF) of elastic deformation solutions of earthquake rupture. The method originates from the fully Bayesian inversion and mixed linear-nonlinear Bayesian inversion methods and shares the same posterior PDF with them, while overcoming difficulties with convergence when large numbers of low-quality data are used and greatly improving the convergence rate using optimization procedures. A highly efficient global optimization algorithm, adaptive simulated annealing, is used to search for the maximum of a posterior PDF (“mode” in statistics) in the first step. The second step inversion approaches the “true” solution further using the Monte Carlo inversion technique with positivity constraints, with all parameters obtained from the first step as the initial solution. Then slip artifacts are eliminated from slip models in the third step using the same procedure of the second step, with fixed fault geometry parameters. We first design a fault model with 45° dip angle and oblique slip, and produce corresponding synthetic interferometric synthetic aperture radar (InSAR) data sets to validate the reliability and efficiency of the new method. We then apply this method to InSAR data inversion for the coseismic slip distribution of the 14 April 2010 Mw 6.9 Yushu, China earthquake. Our preferred slip model is composed of three segments with most of the slip occurring within 15 km depth and the maximum slip reaches 1.38 m at the surface. The seismic moment released is estimated to be 2.32e+19 Nm, consistent with the seismic estimate of 2.50e+19 Nm.

  • a three step maximum a Posteriori Probability method for insar data inversion of coseismic rupture with application to the 14 april 2010 mw 6 9 yushu china earthquake
    Journal of Geophysical Research, 2013
    Co-Authors: Jianbao Sun, Zheng-kang Shen, Roland Bürgmann, Min Wang, Lichun Chen
    Abstract:

    [1] We develop a three-step maximum a Posteriori Probability method for coseismic rupture inversion, which aims at maximizing the a posterior Probability density function (PDF) of elastic deformation solutions of earthquake rupture. The method originates from the fully Bayesian inversion and mixed linear-nonlinear Bayesian inversion methods and shares the same posterior PDF with them, while overcoming difficulties with convergence when large numbers of low-quality data are used and greatly improving the convergence rate using optimization procedures. A highly efficient global optimization algorithm, adaptive simulated annealing, is used to search for the maximum of a posterior PDF (“mode” in statistics) in the first step. The second step inversion approaches the “true” solution further using the Monte Carlo inversion technique with positivity constraints, with all parameters obtained from the first step as the initial solution. Then slip artifacts are eliminated from slip models in the third step using the same procedure of the second step, with fixed fault geometry parameters. We first design a fault model with 45° dip angle and oblique slip, and produce corresponding synthetic interferometric synthetic aperture radar (InSAR) data sets to validate the reliability and efficiency of the new method. We then apply this method to InSAR data inversion for the coseismic slip distribution of the 14 April 2010 Mw 6.9 Yushu, China earthquake. Our preferred slip model is composed of three segments with most of the slip occurring within 15 km depth and the maximum slip reaches 1.38 m at the surface. The seismic moment released is estimated to be 2.32e+19 Nm, consistent with the seismic estimate of 2.50e+19 Nm.