Dual-Band, Polarization-Insensitive, Ultrathin and Flexible Metamaterial Absorber Based on High-Order Magnetic Resonance

We demonstrate a dual-band, polarization-insensitive, ultrathin and flexible metamaterial absorber (MA), based on high-order magnetic resonance. By exploiting a flexible polyimide substrate, the thickness of MA came to be 1/148 of the working wavelength. The absorption performance of the proposed structure was investigated for both planar and bending models. In the case of the planar model, a single peak was achieved at a frequency of 4.3 GHz, with an absorption of 98%. Furthermore, additional high-order absorption peaks were obtained by the bending structure on a cylindrical surface, while the fundamental peak with a high absorption was maintained well. Our work might be useful for the realization and the development of future devices, such as emitters, detectors, sensors, and energy converters.

Iterative multilinear optimization for planar model fitting under geometric constraints

Planes are the core geometric models present everywhere in the three-dimensional real world. There are many examples of manual constructions based on planar patches: facades, corridors, packages, boxes, etc. In these constructions, planar patches must satisfy orthogonal constraints by design (e.g. walls with a ceiling and floor). The hypothesis is that by exploiting orthogonality constraints when possible in the scene, we can perform a reconstruction from a set of points captured by 3D cameras with high accuracy and a low response time. We introduce a method that can iteratively fit a planar model in the presence of noise according to three main steps: a clustering-based unsupervised step that builds pre-clusters from the set of (noisy) points; a linear regression-based supervised step that optimizes a set of planes from the clusters; a reassignment step that challenges the members of the current clusters in a way that minimizes the residuals of the linear predictors. The main contribution is that the method can simultaneously fit different planes in a point cloud providing a good accuracy/speed trade-off even in the presence of noise and outliers, with a smaller processing time compared with previous methods. An extensive experimental study on synthetic data is conducted to compare our method with the most current and representative methods. The quantitative results provide indisputable evidence that our method can generate very accurate models faster than baseline methods. Moreover, two case studies for reconstructing planar-based objects using a Kinect sensor are presented to provide qualitative evidence of the efficiency of our method in real applications.

A simplified planar model for geosynthetics reinforced composite foundation subjected to vertical load

A simplified planar model for geosynthetics reinforced composite foundation under large-scale loading was established with a new consolidation analysis. The cushion was modeled by modified Pasternak model and the reaction of pile and subsoil was modeled by Winkler model. The effect of geosynthetics layer was directly considered as an elastic cable and the subsoil was divided into numerous columns with only vertical drainage. The solution was obtained by a finite difference based iterative scheme. The feasibility of the model was demonstrated by a case study. Then a parameter study was executed to analyze the effect of several influential factors. The results showed that there is a critical pile –to-pitch ratio that makes the increase of the stiffness of the geosynthetic material the most conducive to deformation control.