Design Guide for Subgrades and Subbases

Iowa roadway engineers can help extend pavement life-spans by building stable and properly drained subgrade and subbase layers

Co doping induced structural and optical properties of sol-gel prepared ZnO thin films

Cataloged from PDF version of article.The preparation conditions for Co doping process into the ZnO structure were studied by the ultrasonic spray pyrolysis technique. Structural and optical properties of the Co:ZnO thin films as a function of Co concentrations were examined. It was observed that hexagonal wurtzite structure of ZnO is dominant up to the critical value, and after the value, the cubic structural phase of the cobalt oxide appears in the X-ray diffraction patterns. Every band-edge of Co:ZnO films shifts to the lower energies and all are confirmed with the PL measurements. Co substitution in ZnO lattice has been proved by the optical transmittance measurement which is observed as the loss of transmission appearing in specific region due to Co2+ characteristic transitions. © 2014 Elsevier B.V. All rights reserved

iMAPPER: Interaction-guided Scene Mapping from Monocular Videos

Next generation smart and augmented reality systems demand a computational understanding of monocular footage that captures humans in physical spaces to reveal plausible object arrangements and human-object interactions. Despite recent advances, both in scene layout and human motion analysis, the above setting remains challenging to analyze due to regular occlusions that occur between objects and human motions. We observe that the interaction between object arrangements and human actions is often strongly correlated, and hence can be used to help recover from these occlusions. We present iMapper, a data-driven method to identify such human-object interactions and utilize them to infer layouts of occluded objects. Starting from a monocular video with detected 2D human joint positions that are potentially noisy and occluded, we first introduce the notion of interaction-saliency as space-time snapshots where informative human-object interactions happen. Then, we propose a global optimization to retrieve and fit interactions from a database to the detected salient interactions in order to best explain the input video. We extensively evaluate the approach, both quantitatively against manually annotated ground truth and through a user study, and demonstrate that iMapper produces plausible scene layouts for scenes with medium to heavy occlusion. Code and data are available on the project page

{CurveFusion}: {R}econstructing Thin Structures from {RGBD} Sequences

We introduce CurveFusion, the first approach for high quality scanning of thin structures at interactive rates using a handheld RGBD camera. Thin filament-like structures are mathematically just 1D curves embedded in R^3, and integration-based reconstruction works best when depth sequences (from the thin structure parts) are fused using the object's (unknown) curve skeleton. Thus, using the complementary but noisy color and depth channels, CurveFusion first automatically identifies point samples on potential thin structures and groups them into bundles, each being a group of a fixed number of aligned consecutive frames. Then, the algorithm extracts per-bundle skeleton curves using L1 axes, and aligns and iteratively merges the L1 segments from all the bundles to form the final complete curve skeleton. Thus, unlike previous methods, reconstruction happens via integration along a data-dependent fusion primitive, i.e., the extracted curve skeleton. We extensively evaluate CurveFusion on a range of challenging examples, different scanner and calibration settings, and present high fidelity thin structure reconstructions previously just not possible from raw RGBD sequences

Investigating the Heat Generation Efficiency of Electrically-Conductive Asphalt Mastic Using Infrared Thermal Imaging

One of the emerging technologies for producing sustainable ice-and snow-free pavements is the use of electrically-conductive surface courses, e.g., electrically-conductive asphalt concrete (ECAC) that can melt ice and snow through resistive heating. Modifying the mastic in asphalt concrete with electrically-conductive materials is a promising approach for producing high-quality ECAC. The objective of this study is to evaluate electrical conductivity and heat generation efficiency of electrically-conductive asphalt mastic (ECAM) specimens at a below-freezing temperature—simulating the harsh weather conditions in North America during the wintertime. To this end, asphalt mastic was electrically modified with carbon fiber (CF) at varying volume contents. The ECAM specimens were then powered by 60V AC during a time window of 10 minutes so that their heat generation capacity could be characterized through infrared thermography (IRT). Based on the resistivity measurements and thermal data analysis, the most reasonable CF content enabling rapid heat-generating ECAM was identified; this has future implications with respect to achieving efficient highway, bridge, and airport pavement operations during wintertime