Parameter optimization for automated concrete detection in image data

Several research studies have been recently initiated to investigate the use of construction site images for automated infrastructure inspection, progress monitoring, etc. In these studies, it is always necessary to extract material regions (concrete or steel) from the images. Existing methods made use of material's special color/texture ranges for material information retrieval, but they do not sufficiently discuss how to find these appropriate color/texture ranges. As a result, users have to define appropriate ones by themselves, which is difficult for those who do not have enough image processing background. This paper presents a novel method of identifying concrete material regions using machine learning techniques. Under the method, each construction site image is first divided into regions through image segmentation. Then, the visual features of each region are calculated and classified with a pre-trained classifier. The output value determines whether the region is composed of concrete or not. The method was implemented using C++ and tested over hundreds of construction site images. The results were compared with the manual classification ones to indicate the method's validity.

Electron spin resonance signature of the oxygen vacancy in HfO 2

The oxygen vacancy has been inferred to be the critical defect in HfO 2, responsible for charge trapping, gate threshold voltage instability, and Fermi level pinning for high work function gates, but it has never been conclusively identified. Here, the electron spin resonance g tensor parameters of the oxygen vacancy are calculated, using methods that do not over-estimate the delocalization of the defect wave function, to be g xx = 1.918, g yy = 1.926, g zz = 1.944, and are consistent with an observed spectrum. The defect undergoes a symmetry lowering polaron distortion to be localized mainly on a single adjacent Hf ion. © 2012 American Institute of Physics.