Extraction of object image features with gradation contour

Image retrieval using features has been used in previous studies including shape, color, texture, but these features are lagging. With the selection of high-level features with contours, this research is done with the hypothesis that images on objects can also be subjected to representations that are commonly used in natural images. Considering the above matters, we need to research the feature extraction of object images using gradation contour. From the results of the gradation contour test results, there is linearity between the results of accuracy with the large number of images tested. Therefore, it can be said that the influence of the number of images will affect the accuracy of classification. The use of contour gradation can be accepted and treated equally in all image types, so there is no more differentiation between image features. The complexity of the image does not affect the method of extracting features that are only used uniquely by an image. From the results of testing the polynomial coefficient savings data as a result of the gradation contour, the highest result is 81.40% with the highest number of categories and the number of images tested in the category is also higher

Characterization and prediction of permanent deformation properties of unbound granular materials for Pavement ME Design

The objective of this study is to characterize and predict the permanent deformation properties of unbound granular materials (UGMs) for Pavement ME Design. First, laboratory repeated load triaxial (RLT) tests are conducted on the UGMs from 11 quarries in Texas to measure the permanent strain curves. The shakedown theory is applied to evaluate the permanent deformation behavior of the selected UGMs. It is found that using Werkmeister's criteria to define the shakedown range boundaries is not suitable for the selected UGMs. Under this circumstance, new criteria are proposed to redefine the shakedown range boundaries for the flexible base materials in Texas. The new criteria are consistent with the current Texas flexible base specification in terms of aggregate classification. Second, the mechanistic-empirical design guide (MEPDG) model is used to determine the permanent deformation properties of the selected UGMs on the basis of the measured permanent strain curves. The determined permanent deformation properties are assigned as target values for the development of permanent deformation prediction models. Third, a series of performance-related base course properties are used to comprehensively characterize the UGMs, which include the dry density, moisture content, aggregate gradation, morphological properties, percent fines content, and methylene blue value. These performance-related base course properties are assigned as the inputs of the permanent deformation prediction models. Fourth, a multiple regression analysis is conducted to develop the prediction models for permanent deformation properties using these performance-related properties. The developed models are capable of accurately predicting the permanent deformation properties of UGMs. Compared to other prediction models (e.g., simple indicators-based models and Pavement ME Design models), the developed models have the highest prediction accuracy. It is also found that the Pavement ME model-predicted permanent strains are much lower than those measured from the RLT tests. This demonstrates that the current Pavement ME Design software substantially underestimates the rutting that occurs in base course. Finally, the developed prediction models are validated by comparing the predicted and measured permanent strains of other four base materials. The obtained R-squared value of 0.81 indicates that the developed models have a desirable accuracy in the prediction of permanent deformation properties of UGMs

Size and Shape Determination of Riprap and Large-sized Aggregates Using Field Imaging

Riprap rock and large-sized aggregates are extensively used in transportation, geotechnical, and hydraulic engineering applications. Traditional methods for assessing riprap categories based on particle weight may involve subjective visual inspection and time-consuming manual measurements. Aggregate imaging and segmentation techniques can efficiently characterize riprap particles for their size and morphological/shape properties to estimate particle weights. Particle size and morphological/shape characterization ensure the reliable and sustainable use of all aggregate skeleton materials at quarry production lines and construction sites. Aggregate imaging systems developed to date for size and shape characterization, however, have primarily focused on measurement of separated or non-overlapping aggregate particles. This research study presents an innovative approach for automated segmentation and morphological analyses of stockpile aggregate images based on deep-learning techniques. As a project outcome, a portable, deployable, and affordable field-imaging system is envisioned to estimate volumes of individual riprap rocks for field evaluation. A state-of-the-art object detection and segmentation framework is used to train an image-segmentation kernel from manually labeled 2D riprap images in order to facilitate automatic and user-independent segmentation of stockpile aggregate images. The segmentation results show good agreement with ground-truth validation, which entailed comparing the manual labeling to the automatically segmented images. A significant improvement to the efficiency of size and morphological analyses conducted on densely stacked and overlapping particle images is achieved. The algorithms are integrated into a software application with a user-friendly Graphical User Interface (GUI) for ease of operation. Based on the findings of this study, this stockpile aggregate image analysis program promises to become an efficient and innovative application for field-scale and in-place evaluations of aggregate materials. The innovative imaging-based system is envisioned to provide convenient, reliable, and sustainable solutions for the on-site quality assurance/quality control (QA/QC) tasks related to riprap rock and large-sized aggregate material characterization and classification.IDOT-R27-182Ope

Three-dimensional camouflage:exploiting photons to conceal form

Many animals have a gradation of body color, termed “countershading,” where the areas that are typically exposed to more light are darker. One hypothesis is that this patterning enhances visual camouflage by making the retinal image of the animal match that of the background, a fundamentally two-dimensional theory. More controversially, countershading may also obliterate cues to three-dimensional (3D) shape delivered by shading. Despite relying on distinct cognitive mechanisms, these two potential functions hitherto have been amalgamated in the literature. It has previously not been possible to validate either hypothesis empirically, because there has been no general theory of optimal countershading that allows quantitative predictions to be made about the many environmental parameters involved. Here we unpack the logical distinction between using countershading for background matching and using it to obliterate 3D shape. We use computational modeling to determine the optimal coloration for the camouflage of 3D shape. Our model of 3D concealment is derived from the physics of light and informed by perceptual psychology: we simulate a 3D world that incorporates naturalistic lighting environments. The model allows us to predict countershading coloration for terrestrial environments, for any body shape and a wide range of ecologically relevant parameters. The approach can be generalized to any light distribution, including those underwater

Applicability of satellite remote sensing for detection and monitoring of coal strip mining activities

The author has identified the following significant results. Large areas covered by orbital photography allows the user to estimate the acreage of strip mining activity from a few frames. Infrared photography both in color and in black and white transparencies was found to be the best suited for this purpose

Technology transfer-transportation

Problems in the public transportation industry and refining methods for decreasing the time gap between the development and the marketing of new technology are considered. Eight NASA innovations are either being adapted for use on highways, railways, or rapid transit, or are already entering the marketplace. Chronologies for three of these programs are provided

Hybrid Rugosity Mesostructures (HRMs) for fast and accurate rendering of fine haptic detail

The haptic rendering of surface mesostructure (fine relief features) in dense triangle meshes requires special structures, equipment, and high sampling rates for detailed perception of rugged models. Low cost approaches render haptic texture at the expense of fidelity of perception. We propose a faster method for surface haptic rendering using image-based Hybrid Rugosity Mesostructures (HRMs), paired maps with per-face heightfield displacements and normal maps, which are layered on top of a much decimated mesh, effectively adding greater surface detail than actually present in the geometry. The haptic probe’s force response algorithm is modulated using the blended HRM coat to render dense surface features at much lower costs. The proposed method solves typical problems at edge crossings, concave foldings and texture transitions. To prove the wellness of the approach, a usability testbed framework was built to measure and compare experimental results of haptic rendering approaches in a common set of specially devised meshes, HRMs, and performance tests. Trial results of user testing evaluations show the goodness of the proposed HRM technique, rendering accurate 3D surface detail at high sampling rates, deriving useful modeling and perception thresholds for this technique.Peer ReviewedPostprint (published version

Utilizing Aggregates Characteristics to Minimize Cement Content in Portland Cement Concrete

Aggregate, the main constituent of concrete, constitutes 60 to 80% of the total volume of concrete. Proper selection of the type and particle size distribution of the aggregates affects the workability and the hardened properties of the concrete. There are two main reasons for increasing the amount of aggregates in concrete. The first is that cement is more expensive than aggregate, so using more aggregate reduces the cost of producing concrete. The second is that most of the durability problems, e.g. shrinkage and freezing and thawing or hardened concrete, are caused by cement. Generally, concrete shrinkage increases with increase in cement content; aggregates, on the other hand, reduce shrinkage and provide more volume stability. Furthermore, cement production is a key source of carbon dioxide (CO2) emissions, and reducing its usage should be a goal for concrete production. Various projects have explored methods of minimizing cement in concrete; among the most common of those is replacing cement with cementitious and pozzolanic materials such as fly ash.Aggregates Foundation for Technology, Research, and Education (AFTRE)Civil, Architectural, and Environmental Engineerin