Dispersion curves are essential to any guided-wave-related project. The Semi-Analytical Finite Element (SAFE) method has become the conventional way to compute dispersion curves for homogeneous waveguides. However, only recently has a general SAFE formulation for commercial and open-source software become available, meaning that until now SAFE analyses have been variable and more time consuming than desirable. Likewise, the Floquet boundary conditions enable analysis of waveguides with periodicity and have been an integral part of the development of metamaterials. In fact, we have found the use of Floquet boundary conditions to be an extremely powerful tool for homogeneous waveguides, too. The nuances of using periodic boundary conditions for homogeneous waveguides that do not exhibit periodicity are discussed. Comparisons between this method and SAFE are made for selected homogeneous waveguide applications. The COMSOL Multiphysics software is used for the results shown, but any standard finite element software that can implement Floquet periodicity (user-defined or built-in) should suffice. Finally, we identify a number of complex waveguides for which dispersion curves can be found with relative ease by using the periodicity inherent to the Floquet boundary conditions.

This article focuses on the fusion of flaw indications from multi-sensor nondestructive materials testing. Because each testing method makes use of a different physical principle, a multi-method approach has the potential of effectively differentiating actual defect indications from the many false alarms, thus enhancing detection reliability. In this study, we propose a new technique for aggregating scattered two- or three-dimensional sensory data. Using a density-based approach, the proposed method explicitly addresses localization uncertainties such as registration errors. This feature marks one of the major of advantages of this approach over pixel-based image fusion techniques. We provide guidelines on how to set all the key parameters and demonstrate the technique’s robustness. Finally, we apply our fusion approach to experimental data and demonstrate its capability to locate small defects by substantially reducing false alarms under conditions where no single-sensor method is adequate.