ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Nighttime visibility of pavement markings is provided by glass beads embedded into the striping surface. The glass beads take light from the vehicle headlamps and reflect it back to the driver. This phenomenon is known as retroreflection. Literature suggests that the amount of the bead embedded into the striping surface has a profound impact on the intensity of the retroreflected light. In order to gain insight into how the glass beads provide retroreflection, an experiment was carried out to produce paint stripes with glass beads and measure the retroreflection. Samples were created at various application rates and embedment depths, in an attempt to verify the optimal embedment and observe the effect of application rate on retroreflection. The experiment was conducted using large, airport quality beads and small, road quality beads. Image analysis was used to calculate the degree to which beads were embedded and in an attempt to quantify bead distribution on the stripe surface. The results from the large beads showed that retroreflection was maximized when the beads were embedded approximately seventy percent by bead volume. The results also showed that as the application rate increased, the retroreflection increased, up to a point and then decreased. A model was developed to estimate the retroreflectivity given the amount of beads, bead spacing, and distribution of bead embedment. Results from the small beads were less conclusive, but did demonstrate that the larger beads are better at providing retroreflection. Avenues for future work in this area were identified as the experiment was conducted.
The activity-based approach to travel demand analysis and modeling, which has been developed over the past 30 years, has received tremendous success in transportation planning and policy analysis issues, capturing the multi-way joint relationships among socio-demographic, economic, land use characteristics, activity participation, and travel behavior. The development of synthesizing population with an array of socio-demographic and socio-economic attributes has drawn remarkable attention due to privacy and cost constraints in collecting and disclosing full scale data. Although, there has been enormous progress in producing synthetic population, there has been less progress in the development of population evolution modeling arena to forecast future year population. The objective of this dissertation is to develop a well-structured full-fledged demographic evolution modeling system, capturing migration dynamics and evolution of person level attributes, introducing the concept of new household formations and apprehending the dynamics of household level long-term choices over time. A comprehensive study has been conducted on demography, sociology, anthropology, economics and transportation engineering area to better understand the dynamics of evolutionary activities over time and their impacts in travel behavior. This dissertation describes the methodology and the conceptual framework, and the development of model components. Demographic, socio-economic, and land use data from American Community Survey, National Household Travel Survey, Census PUMS, United States Time Series Economic Dynamic data and United States Center for Disease Control and Prevention have been used in this research. The entire modeling system has been implemented and coded using programming language to develop the population evolution module named `PopEvol' into a computer simulation environment. The module then has been demonstrated for a portion of Maricopa County area in Arizona to predict the milestone year population to check the accuracy of forecasting. The module has also been used to evolve the base year population for next 15 years and the evolutionary trend has been investigated.
This work proposes an integrated method for simultaneously tracking multiple neural sources using the probability hypothesis density particle filter (PPHDF) and reducing the effect of artifacts using feature extraction and stochastic modeling. Unique time-frequency features are first extracted using matching pursuit decomposition for both neural activity and artifact signals.
The features are used to model probability density functions for each signal type using Gaussian mixture modeling for use in the PPHDF neural tracking algorithm. The probability density function of the artifacts provides information to the tracking algorithm that can help reduce the probability of incorrectly estimating the dynamically varying number of current dipole sources and their corresponding neural activity localization parameters. Simulation results demonstrate the effectiveness of the proposed algorithm in increasing the tracking accuracy performance for multiple dipole sources using recordings that have been contaminated by artifacts.
dynamic state estimation problem whose complexity is intensified
under low signal-to-noise ratio (SNR) or high clutter conditions.
This is important, for example, when tracking
multiple, closely spaced targets moving in the same direction such as a
convoy of low observable vehicles moving through a forest or multiple
targets moving in a crisscross pattern. The SNR in
these applications is usually low as the reflected signals from
the targets are weak or the noise level is very high.
An effective approach for detecting and tracking a single target
under low SNR conditions is the track-before-detect filter (TBDF)
that uses unthresholded measurements. However, the TBDF has only been used to
track a small fixed number of targets at low SNR.
This work proposes a new multiple target TBDF approach to track a
dynamically varying number of targets under the recursive Bayesian framework.
For a given maximum number of
targets, the state estimates are obtained by estimating the joint
multiple target posterior probability density function under all possible
target
existence combinations. The estimation of the corresponding target existence
combination probabilities and the target existence probabilities are also
derived. A feasible sequential Monte Carlo (SMC) based implementation
algorithm is proposed. The approximation accuracy of the SMC
method with a reduced number of particles is improved by an efficient
proposal density function that partitions the multiple target space into a
single target space.
The proposed multiple target TBDF method is extended to track targets in sea
clutter using highly time-varying radar measurements. A generalized
likelihood function for closely spaced multiple targets in compound Gaussian
sea clutter is derived together with the maximum likelihood estimate of
the model parameters using an iterative fixed point algorithm.
The TBDF performance is improved by proposing a computationally feasible
method to estimate the space-time covariance matrix of rapidly-varying sea
clutter. The method applies the Kronecker product approximation to the
covariance matrix and uses particle filtering to solve the resulting dynamic
state space model formulation.
This study investigates the mastic level structure of asphalt concrete containing RAP materials. Locally sourced RAP material was screened and sieved to separate the coated fines (passing #200) from the remaining sizes. These binder coated fines were mixed with virgin filler at proportions commensurate with 0%, 10%, 30%, 50% and 100% RAP dosage levels. Mastics were prepared with these blended fillers and a PG 64-22 binder at a filler content of 27% by volume. Rheological experiments were conducted on the resulting composites as well as the constituents, virgin binder, solvent extracted RAP binder. The results from the dynamic modulus experiments showed an expected increase in stiffness with increase in dosage levels. These results were used to model the hypothesized structure of the composite. The study presented discusses the different micromechanical models employed, their applicability and suitability to correctly predict the blended mastic composite. The percentage of blending between virgin and RAP binder estimated using Herve and Zaoui model decreased with increase in RAP content.