Non-Destructive Infrared Evaluation of Thermo-Physical Parameters in Bamboo Specimens

The estimation of heat conduction properties has considerable importance in the characterization of bamboo with respect to its potential use as an alternative construction material. Even though traditional methods such as hot plates have successfully measured thermal parameters, like thermal diffusivity and conductivity in bamboo samples, it is still necessary to transform the cylindrical bamboo specimen into a piece with special geometry and size. This requirement makes this method impractical in applications where several bamboo specimens need to be measured in their original cylindrical shape. This paper presents the estimation of thermo-physical parameters k and ρ c p in Guadua angustifolia kunth (Guadua a.k.) bamboo through nonlinear least square optimization and infrared thermography. A sensitivity analysis was carried out to determine how the temperature on the bamboo surface is affected by changes in the convection coefficient h, thermal conductivity k, and volumetric heat capacity ρ c p . In spite of the nonlinearity and high correlation in the parameters of the inverse heat conduction problem (IHCP), the estimation of such parameters is robust and consistent with those reported in the literature

Graph Learning Based on Signal Smoothness Representation for Homogeneous and Heterogeneous Change Detection

International audienceGraph-based methods are promising approaches for traditional and modern techniques in change detection (CD) applications. Nonetheless, some graph-based approaches omit the existence of useful priors that account for the structure of a scene, and the inter- and intra-relationships between the pixels are analyzed. To address this issue, in this article, we propose a framework for CD based on graph fusion and driven by graph signal smoothness representation. In addition to modifying the graph learning stage, in the proposed model, we apply a Gaussian mixture model for superpixel segmentation (GMMSP) as a downsampling module to reduce the computational cost required to learn the graph of the entire images. We carry out tests on 14 real cases of natural disasters, farming, and construction. The dataset contains homogeneous cases with multispectral (MS) and synthetic aperture radar (SAR) images, along with heterogeneous cases that include MS/SAR images. We compare our approach against probabilistic thresholding, unsupervised learning, deep learning, and graph-based methods. In terms of Cohen’s kappa coefficient, our proposed model based on graph signal smoothness representation outperformed state-of-the-art approaches in ten out of 14 datasets