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.
Displaying 1 - 10 of 199
Filtering by
- Creators: Spanias, Andreas
- Creators: Shen, Wei
- Creators: Kaloush, Kamil
Description
In recent years, an increase of environmental temperature in urban areas has raised many concerns. These areas are subjected to higher temperature compared to the rural surrounding areas. Modification of land surface and the use of materials such as concrete and/or asphalt are the main factors influencing the surface energy balance and therefore the environmental temperature in the urban areas. Engineered materials have relatively higher solar energy absorption and tend to trap a relatively higher incoming solar radiation. They also possess a higher heat storage capacity that allows them to retain heat during the day and then slowly release it back into the atmosphere as the sun goes down. This phenomenon is known as the Urban Heat Island (UHI) effect and causes an increase in the urban air temperature. Many researchers believe that albedo is the key pavement affecting the urban heat island. However, this research has shown that the problem is more complex and that solar reflectivity may not be the only important factor to evaluate the ability of a pavement to mitigate UHI. The main objective of this study was to analyze and research the influence of pavement materials on the near surface air temperature. In order to accomplish this effort, test sections consisting of Hot Mix Asphalt (HMA), Porous Hot Mix asphalt (PHMA), Portland Cement Concrete (PCC), Pervious Portland Cement Concrete (PPCC), artificial turf, and landscape gravels were constructed in the Phoenix, Arizona area. Air temperature, albedo, wind speed, solar radiation, and wind direction were recorded, analyzed and compared above each pavement material type. The results showed that there was no significant difference in the air temperature at 3-feet and above, regardless of the type of the pavement. Near surface pavement temperatures were also measured and modeled. The results indicated that for the UHI analysis, it is important to consider the interaction between pavement structure, material properties, and environmental factors. Overall, this study demonstrated the complexity of evaluating pavement structures for UHI mitigation; it provided great insight on the effects of material types and properties on surface temperatures and near surface air temperature.
ContributorsPourshams-Manzouri, Tina (Author) / Kaloush, Kamil (Thesis advisor) / Wang, Zhihua (Thesis advisor) / Zapata, Claudia E. (Committee member) / Mamlouk, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given parameter. It is frequently implemented using wireless sensor networks. There have been several studies on optimizing power allocation in wireless sensor networks used for distributed estimation, the vast majority of which assume linear radio-frequency amplifiers. Linear amplifiers are inherently inefficient, so in this dissertation nonlinear amplifiers are examined to gain efficiency while operating distributed sensor networks. This research presents a method to boost efficiency by operating the amplifiers in the nonlinear region of operation. Operating amplifiers nonlinearly presents new challenges. First, nonlinear amplifier characteristics change across manufacturing process variation, temperature, operating voltage, and aging. Secondly, the equations conventionally used for estimators and performance expectations in linear amplify-and-forward systems fail. To compensate for the first challenge, predistortion is utilized not to linearize amplifiers but rather to force them to fit a common nonlinear limiting amplifier model close to the inherent amplifier performance. This minimizes the power impact and the training requirements for predistortion. Second, new estimators are required that account for transmitter nonlinearity. This research derives analytically and confirms via simulation new estimators and performance expectation equations for use in nonlinear distributed estimation. An additional complication when operating nonlinear amplifiers in a wireless environment is the influence of varied and potentially unknown channel gains. The impact of these varied gains and both measurement and channel noise sources on estimation performance are analyzed in this paper. Techniques for minimizing the estimate variance are developed. It is shown that optimizing transmitter power allocation to minimize estimate variance for the most-compressed parameter measurement is equivalent to the problem for linear sensors. Finally, a method for operating distributed estimation in a multipath environment is presented that is capable of developing robust estimates for a wide range of Rician K-factors. This dissertation demonstrates that implementing distributed estimation using nonlinear sensors can boost system efficiency and is compatible with existing techniques from the literature for boosting efficiency at the system level via sensor power allocation. Nonlinear transmitters work best when channel gains are known and channel noise and receiver noise levels are low.
ContributorsSantucci, Robert (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioðlu, Cihan (Committee member) / Bakkaloglu, Bertan (Committee member) / Tsakalis, Kostas (Committee member) / Arizona State University (Publisher)
Created2013
Description
Vehicle type choice is a significant determinant of fuel consumption and energy sustainability; larger, heavier vehicles consume more fuel, and expel twice as many pollutants, than their smaller, lighter counterparts. Over the course of the past few decades, vehicle type choice has seen a vast shift, due to many households making more trips in larger vehicles with lower fuel economy. During the 1990s, SUVs were the fastest growing segment of the automotive industry, comprising 7% of the total light vehicle market in 1990, and 25% in 2005. More recently, due to rising oil prices, greater awareness to environmental sensitivity, the desire to reduce dependence on foreign oil, and the availability of new vehicle technologies, many households are considering the use of newer vehicles with better fuel economy, such as hybrids and electric vehicles, over the use of the SUV or low fuel economy vehicles they may already own. The goal of this research is to examine how vehicle miles traveled, fuel consumption and emissions may be reduced through shifts in vehicle type choice behavior. Using the 2009 National Household Travel Survey data it is possible to develop a model to estimate household travel demand and total fuel consumption. If given a vehicle choice shift scenario, using the model it would be possible to calculate the potential fuel consumption savings that would result from such a shift. In this way, it is possible to estimate fuel consumption reductions that would take place under a wide variety of scenarios.
ContributorsChristian, Keith (Author) / Pendyala, Ram M. (Thesis advisor) / Chester, Mikhail (Committee member) / Kaloush, Kamil (Committee member) / Ahn, Soyoung (Committee member) / Arizona State University (Publisher)
Created2013
Description
Autonomous vehicle control systems utilize real-time kinematic Global Navigation Satellite Systems (GNSS) receivers to provide a position within two-centimeter of truth. GNSS receivers utilize the satellite signal time of arrival estimates to solve for position; and multipath corrupts the time of arrival estimates with a time-varying bias. Time of arrival estimates are based upon accurate direct sequence spread spectrum (DSSS) code and carrier phase tracking. Current multipath mitigating GNSS solutions include fixed radiation pattern antennas and windowed delay-lock loop code phase discriminators. A new multipath mitigating code tracking algorithm is introduced that utilizes a non-symmetric correlation kernel to reject multipath. Independent parameters provide a means to trade-off code tracking discriminant gain against multipath mitigation performance. The algorithm performance is characterized in terms of multipath phase error bias, phase error estimation variance, tracking range, tracking ambiguity and implementation complexity. The algorithm is suitable for modernized GNSS signals including Binary Phase Shift Keyed (BPSK) and a variety of Binary Offset Keyed (BOC) signals. The algorithm compensates for unbalanced code sequences to ensure a code tracking bias does not result from the use of asymmetric correlation kernels. The algorithm does not require explicit knowledge of the propagation channel model. Design recommendations for selecting the algorithm parameters to mitigate precorrelation filter distortion are also provided.
ContributorsMiller, Steven (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Heating of asphalt during production and construction causes the volatilization and oxidation of binders used in mixes. Volatilization and oxidation causes degradation of asphalt pavements by increasing the stiffness of the binders, increasing susceptibility to cracking and negatively affecting the functional and structural performance of the pavements. Degradation of asphalt binders by volatilization and oxidation due to high production temperature occur during early stages of pavement life and are known as Short Term Aging (STA). Elevated temperatures and increased exposure time to elevated temperatures causes increased STA of asphalt. The objective of this research was to investigate how elevated mixing temperatures and exposure time to elevated temperatures affect aging and stiffening of binders, thus influencing properties of the asphalt mixtures. The study was conducted in two stages. The first stage evaluated STA effect of asphalt binders. It involved aging two Performance Graded (PG) virgin asphalt binders, PG 76-16 and PG 64-22 at two different temperatures and durations, then measuring their viscosities. The second stage involved evaluating the effects of elevated STA temperature and time on properties of the asphalt mixtures. It involved STA of asphalt mixtures produced in the laboratory with the PG 64-22 binder at mixing temperatures elevated 25OF above standard practice; STA times at 2 and 4 hours longer than standard practices, and then compacted in a gyratory compactor. Dynamic modulus (E*) and Indirect Tensile Strength (IDT) were measured for the aged mixtures for each temperature and duration to determine the effect of different aging times and temperatures on the stiffness and fatigue properties of the aged asphalt mixtures. The binder test results showed that in all cases, there was increased viscosity. The results showed the highest increase in viscosity resulted from increased aging time. The results also indicated that PG 64-22 was more susceptible to elevated STA temperature and extended time than the PG 76-16 binders. The asphalt mixture test results confirmed the expected outcome that increasing the STA and mixing temperature by 25oF alters the stiffness of mixtures. Significant change in the dynamic modulus mostly occurred at four hour increase in STA time regardless of temperature.
ContributorsLolly, Rubben (Author) / Kaloush, Kamil (Thesis advisor) / Bearup, Wylie (Committee member) / Zapata, Claudia (Committee member) / Mamlouk, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
The field of education has been immensely benefited by major breakthroughs in technology. The arrival of computers and the internet made student-teacher interaction from different parts of the world viable, increasing the reach of the educator to hitherto remote corners of the world. The arrival of mobile phones in the recent past has the potential to provide the next paradigm shift in the way education is conducted. It combines the universal reach and powerful visualization capabilities of the computer with intimacy and portability. Engineering education is a field which can exploit the benefits of mobile devices to enhance learning and spread essential technical know-how to different parts of the world. In this thesis, I present AJDSP, an Android application evolved from JDSP, providing an intuitive and a easy to use environment for signal processing education. AJDSP is a graphical programming laboratory for digital signal processing developed for the Android platform. It is designed to provide utility; both as a supplement to traditional classroom learning and as a tool for self-learning. The architecture of AJDSP is based on the Model-View-Controller paradigm optimized for the Android platform. The extensive set of function modules cover a wide range of basic signal processing areas such as convolution, fast Fourier transform, z transform and filter design. The simple and intuitive user interface inspired from iJDSP is designed to facilitate ease of navigation and to provide the user with an intimate learning environment. Rich visualizations necessary to understand mathematically intensive signal processing algorithms have been incorporated into the software. Interactive demonstrations boosting student understanding of concepts like convolution and the relation between different signal domains have also been developed. A set of detailed assessments to evaluate the application has been conducted for graduate and senior-level undergraduate students.
ContributorsRanganath, Suhas (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013
Description
Image resolution limits the extent to which zooming enhances clarity, restricts the size digital photographs can be printed at, and, in the context of medical images, can prevent a diagnosis. Interpolation is the supplementing of known data with estimated values based on a function or model involving some or all of the known samples. The selection of the contributing data points and the specifics of how they are used to define the interpolated values influences how effectively the interpolation algorithm is able to estimate the underlying, continuous signal. The main contributions of this dissertation are three fold: 1) Reframing edge-directed interpolation of a single image as an intensity-based registration problem. 2) Providing an analytical framework for intensity-based registration using control grid constraints. 3) Quantitative assessment of the new, single-image enlargement algorithm based on analytical intensity-based registration. In addition to single image resizing, the new methods and analytical approaches were extended to address a wide range of applications including volumetric (multi-slice) image interpolation, video deinterlacing, motion detection, and atmospheric distortion correction. Overall, the new approaches generate results that more accurately reflect the underlying signals than less computationally demanding approaches and with lower processing requirements and fewer restrictions than methods with comparable accuracy.
ContributorsZwart, Christine M. (Author) / Frakes, David H (Thesis advisor) / Karam, Lina (Committee member) / Kodibagkar, Vikram (Committee member) / Spanias, Andreas (Committee member) / Towe, Bruce (Committee member) / Arizona State University (Publisher)
Created2013
Description
Laboratory assessment of crack resistance and propagation in asphalt concrete is a difficult task that challenges researchers and engineers. Several fracture mechanics based laboratory tests currently exist; however, these tests and subsequent analysis methods rely on elastic behavior assumptions and do not consider the time-dependent nature of asphalt concrete. The C* Line Integral test has shown promise to capture crack resistance and propagation within asphalt concrete. In addition, the fracture mechanics based C* parameter considers the time-dependent creep behavior of the materials. However, previous research was limited and lacked standardized test procedure and detailed data analysis methods were not fully presented. This dissertation describes the development and refinement of the C* Fracture Test (CFT) based on concepts of the C* line integral test. The CFT is a promising test to assess crack propagation and fracture resistance especially in modified mixtures. A detailed CFT test protocol was developed based on a laboratory study of different specimen sizes and test conditions. CFT numerical simulations agreed with laboratory results and indicated that the maximum horizontal tensile stress (Mode I) occurs at the crack tip but diminishes at longer crack lengths when shear stress (Mode II) becomes present. Using CFT test results and the principles of time-temperature superposition, a crack growth rate master curve was successfully developed to describe crack growth over a range of test temperatures. This master curve can be applied to pavement design and analysis to describe crack propagation as a function of traffic conditions and pavement temperatures. Several plant mixtures were subjected to the CFT and results showed differences in resistance to crack propagation, especially when comparing an asphalt rubber mixture to a conventional one. Results indicated that crack propagation is ideally captured within a given range of dynamic modulus values. Crack growth rates and C* prediction models were successfully developed for all unmodified mixtures in the CFT database. These models can be used to predict creep crack propagation and the C* parameter when laboratory testing is not feasible. Finally, a conceptual approach to incorporate crack growth rate and the C* parameter into pavement design and analysis was presented.
ContributorsStempihar, Jeffrey (Author) / Kaloush, Kamil (Thesis advisor) / Witczak, Matthew (Committee member) / Mamlouk, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
Motion capture using cost-effective sensing technology is challenging and the huge success of Microsoft Kinect has been attracting researchers to uncover the potential of using this technology into computer vision applications. In this thesis, an upper-body motion analysis in a home-based system for stroke rehabilitation using novel RGB-D camera - Kinect is presented. We address this problem by first conducting a systematic analysis of the usability of Kinect for motion analysis in stroke rehabilitation. Then a hybrid upper body tracking approach is proposed which combines off-the-shelf skeleton tracking with a novel depth-fused mean shift tracking method. We proposed several kinematic features reliably extracted from the proposed inexpensive and portable motion capture system and classifiers that correlate torso movement to clinical measures of unimpaired and impaired. Experiment results show that the proposed sensing and analysis works reliably on measuring torso movement quality and is promising for end-point tracking. The system is currently being deployed for large-scale evaluations.
ContributorsDu, Tingfang (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Rikakis, Thanassis (Committee member) / Arizona State University (Publisher)
Created2012
Description
Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire data space and not just the samples considered. By exploiting the relation of dictionary learning to 1-D subspace clustering, a multilevel dictionary learning algorithm is developed, and it is shown to outperform conventional sparse models in compressed recovery, and image denoising. Theoretical aspects of learning such as algorithmic stability and generalization are considered, and ensemble learning is incorporated for effective large scale learning. In addition to building strategies for efficiently implementing 1-D subspace clustering, a discriminative clustering approach is designed to estimate the unknown mixing process in blind source separation. By exploiting the non-linear relation between the image descriptors, and allowing the use of multiple features, sparse methods can be made more effective in recognition problems. The idea of multiple kernel sparse representations is developed, and algorithms for learning dictionaries in the feature space are presented. Using object recognition experiments on standard datasets it is shown that the proposed approaches outperform other sparse coding-based recognition frameworks. Furthermore, a segmentation technique based on multiple kernel sparse representations is developed, and successfully applied for automated brain tumor identification. Using sparse codes to define the relation between data samples can lead to a more robust graph embedding for unsupervised clustering. By performing discriminative embedding using sparse coding-based graphs, an algorithm for measuring the glomerular number in kidney MRI images is developed. Finally, approaches to build dictionaries for local sparse coding of image descriptors are presented, and applied to object recognition and image retrieval.
ContributorsJayaraman Thiagarajan, Jayaraman (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013