The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Anthony G. Cohn - One of the best experts on this subject based on the ideXlab platform.
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Real-Time Hyperbola Recognition and Fitting in GPR Data
IEEE Transactions on Geoscience and Remote Sensing, 2017Co-Authors: Qingxu Dou, Lijun Wei, Derek R. Magee, Anthony G. CohnAbstract:The problem of automatically recognizing and fitting Hyperbolae from ground-penetrating radar (GPR) images is addressed, and a novel technique computationally suitable for real-time on-site application is proposed. After preprocessing of the input GPR images, a novel thresholding method is applied to separate the regions of interest from background. A novel column-connection clustering (C3) algorithm is then applied to separate the regions of interest from each other. Subsequently, a machine learnt model is applied to identify hyperbolic signatures from outputs of the C3 algorithm, and a Hyperbola is fitted to each such signature with an orthogonal-distance Hyperbola fitting algorithm. The novel clustering algorithm C3 is a central component of the proposed system, which enables the identification of hyperbolic signatures and Hyperbola fitting. Only two features are used in the machine learning algorithm, which is easy to train using a small set of training data. An orthogonal-distance Hyperbola fitting algorithm for “south-opening” Hyperbolae is introduced in this work, which is more robust and accurate than algebraic Hyperbola fitting algorithms. The proposed method can successfully recognize and fit hyperbolic signatures with intersections with others, hyperbolic signatures with distortions, and incomplete hyperbolic signatures with one leg fully or largely missed. As an additional novel contribution, formulas to compute an initial “south-opening” Hyperbola directly from a set of given points are derived, which make the system more efficient. The parameters obtained by fitting Hyperbolae to hyperbolic signatures are very important features; they can be used to estimate the location and size of the related target objects and the average propagation velocity of the electromagnetic wave in the medium. The effectiveness of the proposed system is tested on both synthetic and real GPR data.
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probabilistic robust Hyperbola mixture model for interpreting ground penetrating radar data
International Joint Conference on Neural Network, 2010Co-Authors: Huanhuan Chen, Anthony G. CohnAbstract:This paper proposes a probabilistic robust Hyperbola mixture model based on a classification expectation maximization algorithm and applies this algorithm to Ground Penetrating Radar (GPR) spatial data interpretation. Previous work tackling this problem using the Hough transform or neural networks for identifying GPR Hyperbolae are unsuitable for on-site applications owing to their computational demands and the difficulties of getting sufficient appropriate training data for neural network based approaches. By incorporating a robust Hyperbola fitting algorithm based on orthogonal distance into the probabilistic mixture model, the proposed algorithm can identify the Hyperbolae in GPR data in real time and also calculate the depth and the size of the buried utility pipes. The number of the Hyperbolae can be determined by conducting model selection using a Bayesian information criterion. The experimental results on both the synthetic/simulated and real GPR data show the effectiveness of this algorithm.
Juan B Arellano - One of the best experts on this subject based on the ideXlab platform.
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surfing the Hyperbola equations of the steady state farquhar von caemmerer berry c3 leaf photosynthesis model what can a theoretical analysis of their oblique asymptotes and transition points tell us
Bulletin of Mathematical Biology, 2020Co-Authors: Jon Mirandaapodaca, Emilio L Marcosbarbero, Rosa Morcuende, Juan B ArellanoAbstract:The asymptotes and transition points of the net CO2 assimilation (A/Ci) rate curves of the steady-state Farquhar-von Caemmerer-Berry (FvCB) model for leaf photosynthesis of C3 plants are examined in a theoretical study, which begins from the exploration of the standard equations of Hyperbolae after rotating the coordinate system. The analysis of the A/Ci quadratic equations of the three limitation states of the FvCB model-abbreviated as Ac, Aj and Ap-allows us to conclude that their oblique asymptotes have a common slope that depends only on the mesophyll conductance to CO2 diffusion (gm). The limiting values for the transition points between any two states of the three limitation states c, j and p do not depend on gm, and the results are therefore valid for rectangular and non-rectangular Hyperbola equations of the FvCB model. The analysis of the variation of the slopes of the asymptotes with gm casts doubts about the fulfilment of the steady-state conditions, particularly, when the net CO2 assimilation rate is inhibited at high CO2 concentrations. The application of the theoretical analysis to extended steady-state FvCB models, where the Hyperbola equations of Ac, Aj and Ap are modified to accommodate nitrogen assimilation and amino acids export via the photorespiratory pathway, is also discussed.
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Surfing the Hyperbola Equations of the Steady-State Farquhar–von Caemmerer–Berry C_3 Leaf Photosynthesis Model: What Can a Theoretical Analysis of Their Oblique Asymptotes and Transition Points Tell Us?
Bulletin of Mathematical Biology, 2019Co-Authors: Jon Miranda-apodaca, Rosa Morcuende, Emilio L. Marcos-barbero, Juan B ArellanoAbstract:The asymptotes and transition points of the net CO_2 assimilation ( A / C _i) rate curves of the steady-state Farquhar–von Caemmerer–Berry (FvCB) model for leaf photosynthesis of C_3 plants are examined in a theoretical study, which begins from the exploration of the standard equations of Hyperbolae after rotating the coordinate system. The analysis of the A / C _i quadratic equations of the three limitation states of the FvCB model—abbreviated as A _c, A _j and A _p—allows us to conclude that their oblique asymptotes have a common slope that depends only on the mesophyll conductance to CO_2 diffusion ( g _m). The limiting values for the transition points between any two states of the three limitation states c, j and p do not depend on g _m, and the results are therefore valid for rectangular and non-rectangular Hyperbola equations of the FvCB model. The analysis of the variation of the slopes of the asymptotes with g _m casts doubts about the fulfilment of the steady-state conditions, particularly, when the net CO_2 assimilation rate is inhibited at high CO_2 concentrations. The application of the theoretical analysis to extended steady-state FvCB models, where the Hyperbola equations of A _c, A _j and A _p are modified to accommodate nitrogen assimilation and amino acids export via the photorespiratory pathway, is also discussed.
Qingxu Dou - One of the best experts on this subject based on the ideXlab platform.
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Real-Time Hyperbola Recognition and Fitting in GPR Data
IEEE Transactions on Geoscience and Remote Sensing, 2017Co-Authors: Qingxu Dou, Lijun Wei, Derek R. Magee, Anthony G. CohnAbstract:The problem of automatically recognizing and fitting Hyperbolae from ground-penetrating radar (GPR) images is addressed, and a novel technique computationally suitable for real-time on-site application is proposed. After preprocessing of the input GPR images, a novel thresholding method is applied to separate the regions of interest from background. A novel column-connection clustering (C3) algorithm is then applied to separate the regions of interest from each other. Subsequently, a machine learnt model is applied to identify hyperbolic signatures from outputs of the C3 algorithm, and a Hyperbola is fitted to each such signature with an orthogonal-distance Hyperbola fitting algorithm. The novel clustering algorithm C3 is a central component of the proposed system, which enables the identification of hyperbolic signatures and Hyperbola fitting. Only two features are used in the machine learning algorithm, which is easy to train using a small set of training data. An orthogonal-distance Hyperbola fitting algorithm for “south-opening” Hyperbolae is introduced in this work, which is more robust and accurate than algebraic Hyperbola fitting algorithms. The proposed method can successfully recognize and fit hyperbolic signatures with intersections with others, hyperbolic signatures with distortions, and incomplete hyperbolic signatures with one leg fully or largely missed. As an additional novel contribution, formulas to compute an initial “south-opening” Hyperbola directly from a set of given points are derived, which make the system more efficient. The parameters obtained by fitting Hyperbolae to hyperbolic signatures are very important features; they can be used to estimate the location and size of the related target objects and the average propagation velocity of the electromagnetic wave in the medium. The effectiveness of the proposed system is tested on both synthetic and real GPR data.
Lijun Wei - One of the best experts on this subject based on the ideXlab platform.
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Real-Time Hyperbola Recognition and Fitting in GPR Data
IEEE Transactions on Geoscience and Remote Sensing, 2017Co-Authors: Qingxu Dou, Lijun Wei, Derek R. Magee, Anthony G. CohnAbstract:The problem of automatically recognizing and fitting Hyperbolae from ground-penetrating radar (GPR) images is addressed, and a novel technique computationally suitable for real-time on-site application is proposed. After preprocessing of the input GPR images, a novel thresholding method is applied to separate the regions of interest from background. A novel column-connection clustering (C3) algorithm is then applied to separate the regions of interest from each other. Subsequently, a machine learnt model is applied to identify hyperbolic signatures from outputs of the C3 algorithm, and a Hyperbola is fitted to each such signature with an orthogonal-distance Hyperbola fitting algorithm. The novel clustering algorithm C3 is a central component of the proposed system, which enables the identification of hyperbolic signatures and Hyperbola fitting. Only two features are used in the machine learning algorithm, which is easy to train using a small set of training data. An orthogonal-distance Hyperbola fitting algorithm for “south-opening” Hyperbolae is introduced in this work, which is more robust and accurate than algebraic Hyperbola fitting algorithms. The proposed method can successfully recognize and fit hyperbolic signatures with intersections with others, hyperbolic signatures with distortions, and incomplete hyperbolic signatures with one leg fully or largely missed. As an additional novel contribution, formulas to compute an initial “south-opening” Hyperbola directly from a set of given points are derived, which make the system more efficient. The parameters obtained by fitting Hyperbolae to hyperbolic signatures are very important features; they can be used to estimate the location and size of the related target objects and the average propagation velocity of the electromagnetic wave in the medium. The effectiveness of the proposed system is tested on both synthetic and real GPR data.
Derek R. Magee - One of the best experts on this subject based on the ideXlab platform.
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Real-Time Hyperbola Recognition and Fitting in GPR Data
IEEE Transactions on Geoscience and Remote Sensing, 2017Co-Authors: Qingxu Dou, Lijun Wei, Derek R. Magee, Anthony G. CohnAbstract:The problem of automatically recognizing and fitting Hyperbolae from ground-penetrating radar (GPR) images is addressed, and a novel technique computationally suitable for real-time on-site application is proposed. After preprocessing of the input GPR images, a novel thresholding method is applied to separate the regions of interest from background. A novel column-connection clustering (C3) algorithm is then applied to separate the regions of interest from each other. Subsequently, a machine learnt model is applied to identify hyperbolic signatures from outputs of the C3 algorithm, and a Hyperbola is fitted to each such signature with an orthogonal-distance Hyperbola fitting algorithm. The novel clustering algorithm C3 is a central component of the proposed system, which enables the identification of hyperbolic signatures and Hyperbola fitting. Only two features are used in the machine learning algorithm, which is easy to train using a small set of training data. An orthogonal-distance Hyperbola fitting algorithm for “south-opening” Hyperbolae is introduced in this work, which is more robust and accurate than algebraic Hyperbola fitting algorithms. The proposed method can successfully recognize and fit hyperbolic signatures with intersections with others, hyperbolic signatures with distortions, and incomplete hyperbolic signatures with one leg fully or largely missed. As an additional novel contribution, formulas to compute an initial “south-opening” Hyperbola directly from a set of given points are derived, which make the system more efficient. The parameters obtained by fitting Hyperbolae to hyperbolic signatures are very important features; they can be used to estimate the location and size of the related target objects and the average propagation velocity of the electromagnetic wave in the medium. The effectiveness of the proposed system is tested on both synthetic and real GPR data.
