Tensor Smoothing Research Articles

Implementation of a finite difference frequency domain mode solver incorporating subpixel smoothing.

Finite difference frequency domain (FDFD) mode solvers are straightforward to implement but can suffer from slow convergence when applied to high-contrast refractive index structures. In this work, we show how subpixel smoothing can improve the convergence properties of a full-vectorial FDFD mode solver. Based on a standard Yee grid, we formulate a generalized eigenbproblem whose solutions provide the modes of the waveguides taking into account the tensor nature of the effective dielectric constant. We investigate the convergence of the proposed FDFD mode solver in several cases including a step index fiber, a microsctuctured fiber, and a cylindrical plasmonic waveguide. The results show that tensor smoothing can significantly improve the convergence of the solver, thus allowing the use of less dense grids in the calculations. Our implementation is freely available on the web under an open-source licence.

Modelling temporal genetic and spatio‐temporal residual effects for high‐throughput phenotyping data*

SummaryHigh‐throughput phenomics data are being collected in both the laboratory and the field. The data are often collected at many time points and there may be spatial variation in the laboratory or field that impacts on the growth of the plants, and that may influence the traits of interest. Modelling the genetic effects is of primary interest in such studies, but these effects might be biased if non‐genetic effects present in the experiment are ignored. With data that are collected both in time and space, there may be a need to jointly model these multi‐dimensional non‐genetic effects. Thus both modelling of genetic effects over time and non‐genetic effects over time and space in a one‐stage analysis is considered. An experiment that involves field phenomics data with four dimensions, two in space and two in time, provides the vehicle to examine the models. Factor analytic (FA) models are often used for genetic effects for different environments to provide reliable estimates of genetic variances and correlations. As the time dimension defines the environments, FA models are examined for the phenomics data. Reduced rank tensor smoothing splines are presented as a possible approach for modelling the spatio‐temporal effects, although an additional term is included for heterogeneity over the two time dimensions. This approach is feasible, although very time‐consuming. The process of model selection for the genetic effects is presented including tests, information criteria and diagnostics. Comparisons of more simplistic models are made with the reduced rank tensor spline. This also shows the interplay between the genetic and residual models in model selection.

Surface Fairing towards Regular Principal Curvature Line Networks

AbstractFreeform surfaces whose principal curvature line network is regularly distributed, are essential to many real applications like CAD modeling, architecture design, and industrial fabrication. However, most designed surfaces do not hold this nice property because it is hard to enforce such constraints in the design process. In this paper, we present a novel method for surface fairing which takes a regular distribution of the principal curvature line network on a surface as an objective. Our method first removes the high‐frequency signals from the curvature tensor field of an input freeform surface by a novel rolling guidance tensor filter, which results in a more regular and smooth curvature tensor field, then deforms the input surface to match the smoothed field as much as possible. As an application, we solve the problem of approximating freeform surfaces with regular principal curvature line networks, discretized by quadrilateral meshes. By introducing the circular or conical conditions on the quadrilateral mesh to guarantee the existence of discrete principal curvature line networks, and minimizing the approximate error to the original surface and improving the fairness of the quad mesh, we obtain a regular discrete principal curvature line network that approximates the original surface. We evaluate the efficacy of our method on various freeform surfaces and demonstrate the superiority of the rolling guidance tensor filter over other tensor smoothing techniques. We also utilize our method to generate high‐quality circular/conical meshes for architecture design and cyclide spline surfaces for CAD modeling.

Diffusion tensor smoothing through weighted Karcher means.

Diffusion tensor magnetic resonance imaging (MRI) quantifies the spatial distribution of water Diffusion at each voxel on a regular grid of locations in a biological specimen by Diffusion tensors- 3 × 3 positive definite matrices. Removal of noise from DTI is an important problem due to the high scientific relevance of DTI and relatively low signal to noise ratio it provides. Leading approaches to this problem amount to estimation of weighted Karcher means of Diffusion tensors within spatial neighborhoods, under various metrics imposed on the space of tensors. However, it is unclear how the behavior of these estimators varies with the magnitude of DTI sensor noise (the noise resulting from the thermal e!ects of MRI scanning) as well as the geometric structure of the underlying Diffusion tensor neighborhoods. In this paper, we combine theoretical analysis, empirical analysis of simulated DTI data, and empirical analysis of real DTI scans to compare the noise removal performance of three kernel-based DTI smoothers that are based on Euclidean, log-Euclidean, and affine-invariant metrics. The results suggest, contrary to conventional wisdom, that imposing a simplistic Euclidean metric may in fact provide comparable or superior noise removal, especially in relatively unstructured regions and/or in the presence of moderate to high levels of sensor noise. On the contrary, log-Euclidean and affine-invariant metrics may lead to better noise removal in highly structured anatomical regions, especially when the sensor noise is of low magnitude. These findings emphasize the importance of considering the interplay of sensor noise magnitude and tensor field geometric structure when assessing Diffusion tensor smoothing options. They also point to the necessity for continued development of smoothing methods that perform well across a large range of scenarios.

On Generalized Cross Validation for Tensor Smoothing Splines

The natural tensor-product smoothing spline is one of the methods of choice for fitting noisy data given on a grid. A generalized cross-validation procedure for automatic selection of the smoothing parameter in the method is introduced. It is shown that as in the well-known univariate and thin plate spline cases, the method selects the parameter in an asymptotically optimal way. Computational aspects of the method are also discussed, and a numerical example is presented. The method developed here can also be extended to complete and periodic tensor-product smoothing splines.