Pavement rutting is one of the major asphalt pavement surface distresses affecting pavement structure integrity and driving safety and is also a required performance measure specified in the Highway Performance Monitoring System (HPMS). Manual rutting measurement is still conducted by many state Departments of Transportation (DOTs), like Georgia DOT; however, it is time-consuming, labor-intensive, and dangerous. Although point-based rut bar systems have been developed and utilized by state DOTs to measure rutting conditions, they often underestimate rut depth measurements. There is an urgent need to develop an automated method to accurately and reliably measure rutting conditions. With the advance of sensing technology, emerging 3D line laser imaging technology is capable of collecting high-resolution 3D range data at highway speed (e.g., 100 km/h) and, therefore, holds a great potential for accurately and repeatedly measuring pavement rutting condition. The main contribution of this research includes a methodology, along with a series of methods and procedures, for the first time, developed utilizing emerging 3D line laser imaging technology to improve existing 1D rut depth measurement accuracy and repeatability and to measure additional 2D and 3D rutting characteristics. These methods and procedures include: (1) a threshold-based outlier removal method employing the multivariate adaptive regression splines (MARS) technique to remove outliers caused by non-rutting features, such as wide transverse cracks and potholes; (2) a modified topological-ordering-based segment clustering (MTOSC) method to optimally partition the continuous roadway network into segments with uniform rutting condition; (3) an overlapping-reducing heuristic method to solve large-scale segmentation problems; (4) a network-level rutting condition assessment procedure for analyzing 3D range data to statistically interpret the pavement rutting condition in support of network-level pavement management decisions; (5) an isolated rut detection method to determine the termini, maximum depth, and volume of isolated ruts in support of project-level maintenance operations. Comprehensive experimental tests were conducted in the laboratory and the field to validate the accuracy and repeatability of 1D rut depth obtained using the 3D range data. Experimental tests were also conducted in the laboratory to validate the accuracy of 3D rut volume. Case studies were conducted on one interstate highway (I-95), two state routes (SR 275 and SR 67), and one local road (Benton Blvd.) to demonstrate the capability of the developed methods and procedures. The results of experimental tests and case studies show that the proposed methodology is promising for improving the rutting measurement accuracy and reliability. This research is one of the initial effort in studying the applicability of this emerging sensing technology in pavement management. And the outcomes of this research will play a key role in advancing state DOTs’ existing pavement rutting condition assessment practices.

Jointed Plain Concrete Pavements (JPCP) are some the most popular forms of concrete pavement that are used in the state of Georgia. Each year the Georgia Department of Transportation (GDOT) inspects and surveys their highways to determine what condition the pavement is in and if any rehabilitation is required to maintain the integrity of the highway. These annual surveys include the JPCP and the key concrete pavement characteristics that are used to determine the condition of the JPCP are the faulting at the joints and the roughness of the section. Since it is well known that concrete will exhibit slight movement when subjected to thermal variations it is possible that the these minor movements could have an impact on the measured slab properties used to rate the JPCP section. The focus of this research is to develop a methodology to use three dimensional technologies to capture JPCP surface data under a variety of thermal conditions, to develop a procedure to collect and analyze concrete temperature data, to develop a method to analyze the surface data and how to correlate all of the data that was collected. Three test sites were chosen that covered a total of 6 test sections that were composed of 25 slabs and 26 joints each. This provided a total of 150 slabs and 156 joints that were used for analysis. A single slab was selected as a test specimen to install thermal logging devices into so that the temperature distributions through the slab could be investigated. Three positions were monitored to determine if the position that the temperature gradient was measured was critical. It was found that the temperature followed a similar trend for all of the positions with the profiles being slightly shifted from each other. It was also concluded that the temperature in the bottom of the slab was approximately the same as the temperature in the base. It was discovered that the maximum positive temperature gradient occurred simultaneously with the maximum ambient air temperature and the maximum surface temperature. The results showed that the surface temperature followed a trend similar to the ambient air temperature. However the surface temperature was greater throughout the day. The faulting analysis results indicated that out of the 156 joints inspected only 15 showed a variation in the average faulting that was greater than the 0.5 mm (0.02 in) accuracy of the sensors used to collect the JPCP surface data. Further investigation revealed that there was no clear trend between the temperature change and the average faulting variation. It was concluded that if there was a change in the average faulting due to temperature it is smaller than what can be depicted by the sensing vehicle and it is less than the 1 mm (0.04 in) measurement accuracy that is specified in the American Association of State Highway and Transportation Officials (AASHTO) R36-04 specification which governs the accuracy requirements for automated faulting measurement methods. The International Roughness Index (IRI) was the method used to measure the roughness on each test site for each data collection run. This resulted in 336 IRI values that were inspected to determine whether there was an impact from the temperature variations. The IRI results showed that the roughness of the test sections did vary through the day. After it was found that the IRI did vary through the day the IRI distributions were compared to the temperature distribution and 7 out of the 12 distributions studied showed a weak correlation between the temperature and the IRI. The amount of variation in the IRI was not quantified because the exact accuracy of the IRI values attained from the sensing vehicle was unknown. However it was attempted to validate the system and determine the accuracy but one of the validation test sections showed disappointing results while the other two showed promising results. Further research is required to fully evaluate the sensing vehicles ability and accuracy when measuring the IRI. A procedure was also developed to extract the longitudinal and transverse curvature of the concrete pavement slabs. Three test slabs were selected at one of the test sites and curvature results were generated using the developed procedure. The curvature results were visually and quantitatively assessed. The visual analysis indicated that the curvature profiles measured by the 3D line lasers did change throughout the data collection, but the patterns did not follow what was expected and a correlation could not be created with the temperature. The quantitative results for the longitudinal curvature revealed that one of the slabs did show a pattern that followed the temperature changes during the data collection, but it did show as much as 4.65 mm (0.183 in) of change between consecutive data collection runs. The longitudinal curvature results for the other two slabs did not show a trend and exhibited unlikely changes in the curvature measured between consecutive data collection runs, which in some instances the deviation was as much as 12.09 mm (0.480 in). For the transverse curvature one of the slabs indicated that the curvature did not change during the data collection, while the other two showed sudden changes as high as 2.16 mm (0.085 in) between consecutive data collection runs. The developed procedure is only preliminary and needs to be further evaluated and refined for it to be able to adequately measure the curvature of as slab. The results also need to be verified using actual measured ground truth curvatures to determine the validity of using the developed procedure and the 3D line laser data to measure the curvature of concrete slabs. Once the procedure is proven to produce reliable results it should be compared to other curvature computation methods, such as those that utilize road profilers or LIDARs, to determine which method is the best.

Meteorological satellite images at visible and infra-red wavelengths are an invaluable source of information on cloud systems because of their extensive coverage of the whole of the Earth's surface, providing data in areas that are only sparsely monitored, if at all, by other means. Although this information has been used subjectively by forecasters for many years, the lack of automatic, quantitative analysis techniques largely prevents its assimilation into numerical weather prediction (NWP) models, which are the basis of all modern weather forecasting. This thesis investigates the use of neural network techniques for the analysis of the images in order to make fuller use of the available data. The recognition of a particular type of cloud is dependent on the determination of a set of features from the satellite image spectral bands that will give discriminating information. This feature extraction and selection process is dealt with in detail, and a feature selection process based on the radial basis function (RBF) neural network is presented. The high-dimensional feature space is visualized on a two-dimensional plane by the use of three techniques: the Kohonen map, the Sammon map, and a recently-developed technique known as the Generative Topographic Mapping (GTM). Classification results using a multi-layer perceptron (MLP) and an RBF neural network are presented. The results of independently classifying each pixel in an image are compared with a method that makes use of contextual information, the Markov Random Field (MRF) model. The limitations of the pixel-based approach are highlighted, and a region-based approach is presented that enables the definition of large-scale regions of uniform cloud type. Two segmentation methods are used, the active contour (or snake) model, and the more recentlydeveloped level set technique. The latter method was found to provide many benefits over the former. The region-based approach will facilitate the assimilation of cloud system information into NWP models in the future.

The objective of this research is a fast circular synthetic aperture radar (F-CSAR) algorithm. Slow-time imaging distinguishes synthetic aperture radar (SAR) from its predecessor imaging radars. SAR slow-time imaging is strongly rooted in Huygens-Fresnel principle and Kirchhoff's approximation based scalar diffraction theory. Slant-plane SAR Green's function and resultant Fourier integral, unlike some Fourier integrals, cannot be analyzed using residue theory from complex analysis and Cauchy-Riemann equations yield analyticity. The asymptotic expansion of 1D and 2D Fourier integrals renders a decomposition of the Green's function leading to SAR data focusing. The research unveils Fraunhofer diffraction patterns in 2D aperture synthesis formulation corresponding to various aperture shapes including circular aperture that appears to be an optimum aperture shape from the mathematical condition in the asymptotic expansion. It is shown that these diffraction patterns may be used for refocusing of defocused images. F-CSAR algorithm is demonstrated using Householder transform recently shown to have improved error bounds and stability. Research is also carried out into various interpolation schemes. Backprojection implementation of CSAR is compared to F-CSAR and elevation coverage renders 3D reconstruction. F-CSAR is also demonstrated using GTRI T-72 tank turntable data.

Bibliography: p. 269-276. / xxx, 306 p. : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / The main objective of this thesis is to create a significant advance in the area of solid-state imaging via the research of an image sensor that can be ultimately integrated with high-speed gallium arsenide (GaAs) processing circuitry on a common substrate chip. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1997

Bibliography: leaves xvii-xxvi / 1 v. (various foliations) : ill ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1985

This thesis is concerned with exploiting the native optical coherence tomography (OCT) contrast mechanism in new ways and with a new contrast mechanism, in both cases to enhance the information content of the tomographic image. Through experiments in microsphere solutions, we show that static speckle contains information about local particle density when the effective number of scatterers in the OCT resolution volume is less than about five. This potentially provides contrast enhancement in OCT images based on local scatterer density, and we discuss the experimental conditions suited to utilising this in biological tissue. We also describe the corrupting effects of multiple scattering, a ubiquitous phenomenon in OCT, on the information content of the static speckle. Consequently, we detail the development of polarisation-based metrics for characterising multiple scattering in OCT images of solid biological tissues. We exploit a detection scheme used for polarisation-sensitive contrast for a new purpose. We present experiments demonstrating the behaviour of these metrics in liquid phantoms, and in biological tissues, ranging from homogeneous non-birefringent to highly heterogeneous and birefringent samples. We discuss the conditions under which these metrics could be used to characterise the relative contribution of single and multiple scattering and, thus, aid in the study of penetration depth limits in OCT. We present a study of a new contrast mechanism - dynamic elastography which seeks to determine the dynamic mechanical properties of tissues. We present a framework for describing the OCT signal in samples undergoing vibrations, and perform experiments at vibration frequencies in the order of tens to hundreds of Hertz, to confirm the theory, and demonstrate the modes of measurement possible with this technique. These modes of measurement, including acoustic amplitude-sweep and frequency-sweep, could provide new information about the local mechanical properties of a sample. We describe a technological advancement enabling, in principle, measurements of local tissue refractive index contrast much deeper within a sample, than is possible with conventional OCT imaging. The design is based on measurement of the optical path length through tissue filling a fixed-width channel situated at the tip of a needle. The needle design and calibration is presented, as well as measurements of scattering phantoms and various biological tissues. This design potentially enables the use of refractive index-based contrast enhancement in the guidance of breast biopsy procedures.

Thesis (Ph. D.)--University of Wyoming, 2005. / Title from PDF title page (viewed on Nov. 1, 2007). Includes bibliographical references.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 118-124).

Thesis (Ph.D.)--University of Wollongong, 2006. / Typescript. Includes bibliographical references: leaf 225-237.