The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform

T.h. Mareci - One of the best experts on this subject based on the ideXlab platform.

  • A constrained variational principle for direct estimation and smoothing of the diffusion tensor field from complex DWI
    IEEE Transactions on Medical Imaging, 2004
    Co-Authors: Zhizhou Wang, B.c. Vemuri, Y. Chen, T.h. Mareci
    Abstract:

    In this paper, we present a novel constrained variational principle for simultaneous smoothing and estimation of the diffusion tensor field from complex valued diffusion-weighted images (DWI). The constrained variational principle involves the minimization of a regularization term of L/sup p/ norms, subject to a nonlinear inequality constraint on the data. The data term we employ is the original Stejskal-Tanner equation instead of the Linearized Version usually employed in literature. The complex valued nonlinear form leads to a more accurate (when compared to the Linearized Version) estimate of the tensor field. The inequality constraint requires that the nonlinear least squares data term be bounded from above by a known tolerance factor. Finally, in order to accommodate the positive definite constraint on the diffusion tensor, it is expressed in terms of Cholesky factors and estimated. The constrained variational principle is solved using the augmented Lagrangian technique in conjunction with the limited memory quasi-Newton method. Experiments with complex-valued synthetic and real data are shown to depict the performance of our tensor field estimation and smoothing algorithm.

  • Simultaneous smoothing and estimation of the tensor field from diffusion tensor MRI
    2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2003. Proceedings., 2003
    Co-Authors: Zhizhou Wang, B.c. Vemuri, Y. Chen, T.h. Mareci
    Abstract:

    Diffusion tensor magnetic resonance imaging (DT-MRI) is a relatively new imaging modality in the field of medical imaging. This modality of imaging allows one to capture the structural connectivity if any between functionally meaningful regions for example, in the brain. The data however can be noisy and requires restoration. In this paper, we present a unified model for simultaneous smoothing and estimation of diffusion tensor field from DT-MRI. The diffusion tensor field is estimated directly from the raw data with L/sup P/ smoothness and positive definiteness constraints. The data term we employ is from the original Stejskal-Tanner equation instead of the Linearized Version as usually done in literature. In addition, we use Cholesky decomposition to ensure positive definiteness of the diffusion tensor. The unified model is discretized and solved numerically using limited memory quasi-Newton method. Both synthetic and real data experiments are shown to depict the algorithm performance.

Zhizhou Wang - One of the best experts on this subject based on the ideXlab platform.

  • A constrained variational principle for direct estimation and smoothing of the diffusion tensor field from complex DWI
    IEEE Transactions on Medical Imaging, 2004
    Co-Authors: Zhizhou Wang, B.c. Vemuri, Y. Chen, T.h. Mareci
    Abstract:

    In this paper, we present a novel constrained variational principle for simultaneous smoothing and estimation of the diffusion tensor field from complex valued diffusion-weighted images (DWI). The constrained variational principle involves the minimization of a regularization term of L/sup p/ norms, subject to a nonlinear inequality constraint on the data. The data term we employ is the original Stejskal-Tanner equation instead of the Linearized Version usually employed in literature. The complex valued nonlinear form leads to a more accurate (when compared to the Linearized Version) estimate of the tensor field. The inequality constraint requires that the nonlinear least squares data term be bounded from above by a known tolerance factor. Finally, in order to accommodate the positive definite constraint on the diffusion tensor, it is expressed in terms of Cholesky factors and estimated. The constrained variational principle is solved using the augmented Lagrangian technique in conjunction with the limited memory quasi-Newton method. Experiments with complex-valued synthetic and real data are shown to depict the performance of our tensor field estimation and smoothing algorithm.

  • Simultaneous smoothing and estimation of the tensor field from diffusion tensor MRI
    2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2003. Proceedings., 2003
    Co-Authors: Zhizhou Wang, B.c. Vemuri, Y. Chen, T.h. Mareci
    Abstract:

    Diffusion tensor magnetic resonance imaging (DT-MRI) is a relatively new imaging modality in the field of medical imaging. This modality of imaging allows one to capture the structural connectivity if any between functionally meaningful regions for example, in the brain. The data however can be noisy and requires restoration. In this paper, we present a unified model for simultaneous smoothing and estimation of diffusion tensor field from DT-MRI. The diffusion tensor field is estimated directly from the raw data with L/sup P/ smoothness and positive definiteness constraints. The data term we employ is from the original Stejskal-Tanner equation instead of the Linearized Version as usually done in literature. In addition, we use Cholesky decomposition to ensure positive definiteness of the diffusion tensor. The unified model is discretized and solved numerically using limited memory quasi-Newton method. Both synthetic and real data experiments are shown to depict the algorithm performance.

Miroslav Krstic - One of the best experts on this subject based on the ideXlab platform.

  • boundary observer for output feedback stabilization of thermal fluid convection loop
    IEEE Transactions on Control Systems and Technology, 2010
    Co-Authors: Rafael Vazquez, Miroslav Krstic
    Abstract:

    In this paper, we consider a 2-D model of thermal fluid convection that exhibits the prototypical Rayleigh-Bernard convective instability. The fluid is enclosed between two cylinders, heated from above, and cooled from below, which makes its motion unstable for a large enough Rayleigh number. We design an stabilizing output feedback boundary control law for a realistic collocated setup, with actuation and measurements located at the outer boundary. Actuation is through rotation (direct velocity actuation) and heat flux (heating or cooling) of the outer cylinder, while measurements of friction and temperature are obtained at the same boundary. Though only a Linearized Version of the plant is considered in the design, an extensive closed loop simulation study of the nonlinear model shows that our design works for reasonably large initial conditions. A highly accurate approximation to the control kernels and observer output injection gains is found in closed form.

  • Explicit Output Feedback Stabilization of a Thermal Convection Loop by Continuous Backstepping and Singular Perturbations
    2007 American Control Conference, 2007
    Co-Authors: Rafael Vazquez, Miroslav Krstic
    Abstract:

    An output feedback feedback boundary control law that stabilizes fluid flow in a 2D thermal convection loop is presented. The fluid is enclosed between two cylinders, heated from above and cooled from below, which makes its motion unstable for a large enough Rayleigh number. We consider a collocated setup, with actuation and measurements located at the outer boundary. Actuation is through rotation (direct velocity actuation) and heat flux (heating or cooling) of the outer cylinder, while measurements of friction and temperature are available at the same boundary. The design is based on a combination of singular perturbation theory and backstepping output feedback control design for parabolic PDEs. Stability is proved by Lyapunov method. Though only a Linearized Version of the plant is considered in the design, an extensive closed loop simulation study of the nonlinear model shows that the result holds for reasonably large initial conditions. A highly accurate approximation to the control and observer output injection kernels is found in closed form.

  • Thermal convection loop control by continuous backstepping and singular perturbations
    Proceedings of the 2005 American Control Conference 2005., 2005
    Co-Authors: R. Vasquez, Miroslav Krstic
    Abstract:

    A state feedback boundary control law that stabilizes fluid flow in a 2D thermal convection loop is presented. The fluid is enclosed between two cylinders, heated from above and cooled from below, which makes its motion unstable for a large enough Rayleigh number. The actuation is at the boundary through rotation (direct velocity actuation) and heat flux (heating or cooling) of the outer boundary. The design is a new approach for this kind of a coupled PDE problem, based on a combination of singular perturbation theory and the backstepping method for infinite dimensional linear systems. Stability is proved by Lyapunov method. Though only a Linearized Version of the plant is considered in the design, an extensive closed loop simulation study of the nonlinear PDE model shows that the result holds for reasonably large initial conditions. A highly accurate approximation to the control law is found in closed form.

D. Sandri - One of the best experts on this subject based on the ideXlab platform.

  • On a FEM method for a Linearized Version of the Oldroyd’s problem
    Computer Methods in Applied Mechanics and Engineering, 2002
    Co-Authors: D. Sandri
    Abstract:

    Abstract We present a study of a finite element method (FEM) for the approximation of a Linearized Version of the Oldroyd’s problem. The method, is based on a work in [ZAMM 65 (1985) 449] concerning the continuous problem, on a splitting of the momentum equation and on a modification of the streamline upwind Petrov Galerkin method in [Comput. Meth. Appl. Mech. Engrg. 45 (1984) 285]. It is also formally connected to the modified elastic viscous split stress method in [J. Non-Newtonian Fluid Mech. 60 (1995) 27]. This method allows us to improve some numerical results expected for more standard methods, in particular, we obtain convergence results in the case of a null Newtonian fraction of the viscosity.

  • on a fem method for a Linearized Version of the oldroyd s problem
    Computer Methods in Applied Mechanics and Engineering, 2002
    Co-Authors: D. Sandri
    Abstract:

    Abstract We present a study of a finite element method (FEM) for the approximation of a Linearized Version of the Oldroyd’s problem. The method, is based on a work in [ZAMM 65 (1985) 449] concerning the continuous problem, on a splitting of the momentum equation and on a modification of the streamline upwind Petrov Galerkin method in [Comput. Meth. Appl. Mech. Engrg. 45 (1984) 285]. It is also formally connected to the modified elastic viscous split stress method in [J. Non-Newtonian Fluid Mech. 60 (1995) 27]. This method allows us to improve some numerical results expected for more standard methods, in particular, we obtain convergence results in the case of a null Newtonian fraction of the viscosity.

B.c. Vemuri - One of the best experts on this subject based on the ideXlab platform.

  • A constrained variational principle for direct estimation and smoothing of the diffusion tensor field from complex DWI
    IEEE Transactions on Medical Imaging, 2004
    Co-Authors: Zhizhou Wang, B.c. Vemuri, Y. Chen, T.h. Mareci
    Abstract:

    In this paper, we present a novel constrained variational principle for simultaneous smoothing and estimation of the diffusion tensor field from complex valued diffusion-weighted images (DWI). The constrained variational principle involves the minimization of a regularization term of L/sup p/ norms, subject to a nonlinear inequality constraint on the data. The data term we employ is the original Stejskal-Tanner equation instead of the Linearized Version usually employed in literature. The complex valued nonlinear form leads to a more accurate (when compared to the Linearized Version) estimate of the tensor field. The inequality constraint requires that the nonlinear least squares data term be bounded from above by a known tolerance factor. Finally, in order to accommodate the positive definite constraint on the diffusion tensor, it is expressed in terms of Cholesky factors and estimated. The constrained variational principle is solved using the augmented Lagrangian technique in conjunction with the limited memory quasi-Newton method. Experiments with complex-valued synthetic and real data are shown to depict the performance of our tensor field estimation and smoothing algorithm.

  • Simultaneous smoothing and estimation of the tensor field from diffusion tensor MRI
    2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2003. Proceedings., 2003
    Co-Authors: Zhizhou Wang, B.c. Vemuri, Y. Chen, T.h. Mareci
    Abstract:

    Diffusion tensor magnetic resonance imaging (DT-MRI) is a relatively new imaging modality in the field of medical imaging. This modality of imaging allows one to capture the structural connectivity if any between functionally meaningful regions for example, in the brain. The data however can be noisy and requires restoration. In this paper, we present a unified model for simultaneous smoothing and estimation of diffusion tensor field from DT-MRI. The diffusion tensor field is estimated directly from the raw data with L/sup P/ smoothness and positive definiteness constraints. The data term we employ is from the original Stejskal-Tanner equation instead of the Linearized Version as usually done in literature. In addition, we use Cholesky decomposition to ensure positive definiteness of the diffusion tensor. The unified model is discretized and solved numerically using limited memory quasi-Newton method. Both synthetic and real data experiments are shown to depict the algorithm performance.