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- Genre: Academic theses
- Creators: Papandreou-Suppappola, Antonia
- Creators: Johnston, Carol
- Member of: Theses and Dissertations
Description
Recently, the use of zinc oxide (ZnO) nanowires as an interphase in composite materials has been demonstrated to increase the interfacial shear strength between carbon fiber and an epoxy matrix. In this research work, the strong adhesion between ZnO and carbon fiber is investigated to elucidate the interactions at the interface that result in high interfacial strength. First, molecular dynamics (MD) simulations are performed to calculate the adhesive energy between bare carbon and ZnO. Since the carbon fiber surface has oxygen functional groups, these were modeled and MD simulations showed the preference of ketones to strongly interact with ZnO, however, this was not observed in the case of hydroxyls and carboxylic acid. It was also found that the ketone molecules ability to change orientation facilitated the interactions with the ZnO surface. Experimentally, the atomic force microscope (AFM) was used to measure the adhesive energy between ZnO and carbon through a liftoff test by employing highly oriented pyrolytic graphite (HOPG) substrate and a ZnO covered AFM tip. Oxygen functionalization of the HOPG surface shows the increase of adhesive energy. Additionally, the surface of ZnO was modified to hold a negative charge, which demonstrated an increase in the adhesive energy. This increase in adhesion resulted from increased induction forces given the relatively high polarizability of HOPG and the preservation of the charge on ZnO surface. It was found that the additional negative charge can be preserved on the ZnO surface because there is an energy barrier since carbon and ZnO form a Schottky contact. Other materials with the same ionic properties of ZnO but with higher polarizability also demonstrated good adhesion to carbon. This result substantiates that their induced interaction can be facilitated not only by the polarizability of carbon but by any of the materials at the interface. The versatility to modify the magnitude of the induced interaction between carbon and an ionic material provides a new route to create interfaces with controlled interfacial strength.
ContributorsGalan Vera, Magdian Ulises (Author) / Sodano, Henry A (Thesis advisor) / Jiang, Hanqing (Committee member) / Solanki, Kiran (Committee member) / Oswald, Jay (Committee member) / Speyer, Gil (Committee member) / Arizona State University (Publisher)
Created2013
Increasing the efficiency of Doppler processing and backend processing in medical ultrasound systems
Description
Ultrasound imaging is one of the major medical imaging modalities. It is cheap, non-invasive and has low power consumption. Doppler processing is an important part of many ultrasound imaging systems. It is used to provide blood velocity information and is built on top of B-mode systems. We investigate the performance of two velocity estimation schemes used in Doppler processing systems, namely, directional velocity estimation (DVE) and conventional velocity estimation (CVE). We find that DVE provides better estimation performance and is the only functioning method when the beam to flow angle is large. Unfortunately, DVE is computationally expensive and also requires divisions and square root operations that are hard to implement. We propose two approximation techniques to replace these computations. The simulation results on cyst images show that the proposed approximations do not affect the estimation performance. We also study backend processing which includes envelope detection, log compression and scan conversion. Three different envelope detection methods are compared. Among them, FIR based Hilbert Transform is considered the best choice when phase information is not needed, while quadrature demodulation is a better choice if phase information is necessary. Bilinear and Gaussian interpolation are considered for scan conversion. Through simulations of a cyst image, we show that bilinear interpolation provides comparable contrast-to-noise ratio (CNR) performance with Gaussian interpolation and has lower computational complexity. Thus, bilinear interpolation is chosen for our system.
ContributorsWei, Siyuan (Author) / Chakrabarti, Chaitali (Thesis advisor) / Frakes, David (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2013
Description
The body is capable of regulating hunger in several ways. Some of these hunger regulation methods are innate, such as genetics, and some, such as the responses to stress and to the smell of food, are innate but can be affected by body conditions such as BMI and physical activity. Further, some hunger regulation methods stem from learned behaviors originating from cultural pressures or parenting styles. These latter regulation methods for hunger can be grouped into the categories: emotion, environment, and physical. The factors that regulate hunger can also influence the incidence of disordered eating, such as eating in the absence of hunger (EAH). Eating in the absence of hunger can occur in one of two scenarios, continuous EAH or beginning EAH. College students are at a particularly high risk for EAH and weight gain due to stress, social pressures, and the constant availability of energy dense and nutrient poor food options. The purpose of this study is to validate a modified EAH-C survey in college students and to discover which of the three latent factors (emotion, environment, physical) best predicts continual and beginning EAH. To do so, a modified EAH-C survey, with additional demographic components, was administered to students at a major southwest university. This survey contained two questions, one each for continuing and beginning EAH, regarding 14 factors related to emotional, physical, or environmental reasons that may trigger EAH. The results from this study revealed that the continual and beginning EAH surveys displayed good internal consistency reliability. We found that for beginning and continuing EAH, although emotion is the strongest predictor of EAH, all three latent factors are significant predictors of EAH. In addition, we found that environmental factors had the greatest influence on an individual's likelihood to continue to eat in the absence of hunger. Due to statistical abnormalities and differing numbers of factors in each category, we were unable to determine which of the three factors exerted the greatest influence on an individual's likelihood to begin eating in the absence of hunger. These results can be utilized to develop educational tools aimed at reducing EAH in college students, and ultimately reducing the likelihood for unhealthy weight gain and health complications related to obesity.
ContributorsGoett, Taylor (Author) / Johnston, Carol (Thesis advisor) / Lee, Chong (Committee member) / Lespron, Christy (Committee member) / Arizona State University (Publisher)
Created2013
Description
In October, 2009, participants of the Arizona Special Supplemental Nutrition Program for Women, Infants and Children (WIC) began receiving monthly Cash Value Vouchers (CVV) worth between six and 10 dollars towards the purchase of fresh fruits and vegetables. Data from the Arizona Department of Health Services (ADHS) showed CVV redemption rates in the first two years of the program were lower than the national average of 77% redemption. In response, the ADHS WIC Food List was expanded to also include canned and frozen fruits and vegetables. More recent data from ADHS suggest that redemption rates are improving, but variably exist among different WIC sub-populations. The purpose of this project was to identify themes related to the ease or difficulty of WIC CVV use amongst different categories of low-redeeming WIC participants. A total of 8 focus groups were conducted, four at a clinic in each of two Valley cities: Surprise and Mesa. Each of the four focus groups comprised one of four targeted WIC participant categories: pregnant, postpartum, breastfeeding, and children with participation ranging from 3-9 participants per group. Using the general inductive approach, recordings of the focus groups were transcribed, hand-coded and uploaded into qualitative analysis software resulting in four emergent themes including: interactions and shopping strategies, maximizing WIC value, redemption issues, and effect of rule change. Researchers identified twelve different subthemes related to the emergent theme of interactions and strategies to improve their experience, including economic considerations during redemption. Barriers related to interactions existed that made their purchase difficult, most notably anger from the cashier and other shoppers. However, participants made use of a number of strategies to facilitate WIC purchases or extract more value out of WIC benefits, such as pooling their CVV. Finally, it appears that the fruit and vegetable rule change was well received by those who were aware of the change. These data suggest a number of important avenues for future research, including verifying these themes are important within a larger, representative sample of Arizona WIC participants, and exploring strategies to minimize barriers identified by participants, such as use of electronic benefits transfer-style cards (EBT).
ContributorsBertmann, Farryl M. W (Author) / Wharton, Christopher (Christopher Mack), 1977- (Thesis advisor) / Ohri-Vachaspati, Punam (Committee member) / Johnston, Carol (Committee member) / Hampl, Jeffrey (Committee member) / Dixit-Joshi, Sujata (Committee member) / Barroso, Cristina (Committee member) / Arizona State University (Publisher)
Created2013
Description
"Sensor Decade" has been labeled on the first decade of the 21st century. Similar to the revolution of micro-computer in 1980s, sensor R&D; developed rapidly during the past 20 years. Hard workings were mainly made to minimize the size of devices with optimal the performance. Efforts to develop the small size devices are mainly concentrated around Micro-electro-mechanical-system (MEMS) technology. MEMS accelerometers are widely published and used in consumer electronics, such as smart phones, gaming consoles, anti-shake camera and vibration detectors. This study represents liquid-state low frequency micro-accelerometer based on molecular electronic transducer (MET), in which inertial mass is not the only but also the conversion of mechanical movement to electric current signal is the main utilization of the ionic liquid. With silicon-based planar micro-fabrication, the device uses a sub-micron liter electrolyte droplet sealed in oil as the sensing body and a MET electrode arrangement which is the anode-cathode-cathode-anode (ACCA) in parallel as the read-out sensing part. In order to sensing the movement of ionic liquid, an imposed electric potential was applied between the anode and the cathode. The electrode reaction, I_3^-+2e^___3I^-, occurs around the cathode which is reverse at the anodes. Obviously, the current magnitude varies with the concentration of ionic liquid, which will be effected by the movement of liquid droplet as the inertial mass. With such structure, the promising performance of the MET device design is to achieve 10.8 V/G (G=9.81 m/s^2) sensitivity at 20 Hz with the bandwidth from 1 Hz to 50 Hz, and a low noise floor of 100 ug/sqrt(Hz) at 20 Hz.
ContributorsLiang, Mengbing (Author) / Yu, Hongyu (Thesis advisor) / Jiang, Hanqing (Committee member) / Kozicki, Micheal (Committee member) / Arizona State University (Publisher)
Created2013
Description
Distributed inference has applications in a wide range of fields such as source localization, target detection, environment monitoring, and healthcare. In this dissertation, distributed inference schemes which use bounded transmit power are considered. The performance of the proposed schemes are studied for a variety of inference problems. In the first part of the dissertation, a distributed detection scheme where the sensors transmit with constant modulus signals over a Gaussian multiple access channel is considered. The deflection coefficient of the proposed scheme is shown to depend on the characteristic function of the sensing noise, and the error exponent for the system is derived using large deviation theory. Optimization of the deflection coefficient and error exponent are considered with respect to a transmission phase parameter for a variety of sensing noise distributions including impulsive ones. The proposed scheme is also favorably compared with existing amplify-and-forward (AF) and detect-and-forward (DF) schemes. The effect of fading is shown to be detrimental to the detection performance and simulations are provided to corroborate the analytical results. The second part of the dissertation studies a distributed inference scheme which uses bounded transmission functions over a Gaussian multiple access channel. The conditions on the transmission functions under which consistent estimation and reliable detection are possible is characterized. For the distributed estimation problem, an estimation scheme that uses bounded transmission functions is proved to be strongly consistent provided that the variance of the noise samples are bounded and that the transmission function is one-to-one. The proposed estimation scheme is compared with the amplify and forward technique and its robustness to impulsive sensing noise distributions is highlighted. It is also shown that bounded transmissions suffer from inconsistent estimates if the sensing noise variance goes to infinity. For the distributed detection problem, similar results are obtained by studying the deflection coefficient. Simulations corroborate our analytical results. In the third part of this dissertation, the problem of estimating the average of samples distributed at the nodes of a sensor network is considered. A distributed average consensus algorithm in which every sensor transmits with bounded peak power is proposed. In the presence of communication noise, it is shown that the nodes reach consensus asymptotically to a finite random variable whose expectation is the desired sample average of the initial observations with a variance that depends on the step size of the algorithm and the variance of the communication noise. The asymptotic performance is characterized by deriving the asymptotic covariance matrix using results from stochastic approximation theory. It is shown that using bounded transmissions results in slower convergence compared to the linear consensus algorithm based on the Laplacian heuristic. Simulations corroborate our analytical findings. Finally, a robust distributed average consensus algorithm in which every sensor performs a nonlinear processing at the receiver is proposed. It is shown that non-linearity at the receiver nodes makes the algorithm robust to a wide range of channel noise distributions including the impulsive ones. It is shown that the nodes reach consensus asymptotically and similar results are obtained as in the case of transmit non-linearity. Simulations corroborate our analytical findings and highlight the robustness of the proposed algorithm.
ContributorsDasarathan, Sivaraman (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Reisslein, Martin (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
Electrical neural activity detection and tracking have many applications in medical research and brain computer interface technologies. In this thesis, we focus on the development of advanced signal processing algorithms to track neural activity and on the mapping of these algorithms onto hardware to enable real-time tracking. At the heart of these algorithms is particle filtering (PF), a sequential Monte Carlo technique used to estimate the unknown parameters of dynamic systems. First, we analyze the bottlenecks in existing PF algorithms, and we propose a new parallel PF (PPF) algorithm based on the independent Metropolis-Hastings (IMH) algorithm. We show that the proposed PPF-IMH algorithm improves the root mean-squared error (RMSE) estimation performance, and we demonstrate that a parallel implementation of the algorithm results in significant reduction in inter-processor communication. We apply our implementation on a Xilinx Virtex-5 field programmable gate array (FPGA) platform to demonstrate that, for a one-dimensional problem, the PPF-IMH architecture with four processing elements and 1,000 particles can process input samples at 170 kHz by using less than 5% FPGA resources. We also apply the proposed PPF-IMH to waveform-agile sensing to achieve real-time tracking of dynamic targets with high RMSE tracking performance. We next integrate the PPF-IMH algorithm to track the dynamic parameters in neural sensing when the number of neural dipole sources is known. We analyze the computational complexity of a PF based method and propose the use of multiple particle filtering (MPF) to reduce the complexity. We demonstrate the improved performance of MPF using numerical simulations with both synthetic and real data. We also propose an FPGA implementation of the MPF algorithm and show that the implementation supports real-time tracking. For the more realistic scenario of automatically estimating an unknown number of time-varying neural dipole sources, we propose a new approach based on the probability hypothesis density filtering (PHDF) algorithm. The PHDF is implemented using particle filtering (PF-PHDF), and it is applied in a closed-loop to first estimate the number of dipole sources and then their corresponding amplitude, location and orientation parameters. We demonstrate the improved tracking performance of the proposed PF-PHDF algorithm and map it onto a Xilinx Virtex-5 FPGA platform to show its real-time implementation potential. Finally, we propose the use of sensor scheduling and compressive sensing techniques to reduce the number of active sensors, and thus overall power consumption, of electroencephalography (EEG) systems. We propose an efficient sensor scheduling algorithm which adaptively configures EEG sensors at each measurement time interval to reduce the number of sensors needed for accurate tracking. We combine the sensor scheduling method with PF-PHDF and implement the system on an FPGA platform to achieve real-time tracking. We also investigate the sparsity of EEG signals and integrate compressive sensing with PF to estimate neural activity. Simulation results show that both sensor scheduling and compressive sensing based methods achieve comparable tracking performance with significantly reduced number of sensors.
ContributorsMiao, Lifeng (Author) / Chakrabarti, Chaitali (Thesis advisor) / Papandreou-Suppappola, Antonia (Thesis advisor) / Zhang, Junshan (Committee member) / Bliss, Daniel (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2013
Description
This research examines the current challenges of using Lamb wave interrogation methods to localize fatigue crack damage in a complex metallic structural component subjected to unknown temperatures. The goal of this work is to improve damage localization results for a structural component interrogated at an unknown temperature, by developing a probabilistic and reference-free framework for estimating Lamb wave velocities and the damage location. The methodology for damage localization at unknown temperatures includes the following key elements: i) a model that can describe the change in Lamb wave velocities with temperature; ii) the extension of an advanced time-frequency based signal processing technique for enhanced time-of-flight feature extraction from a dispersive signal; iii) the development of a Bayesian damage localization framework incorporating data association and sensor fusion. The technique requires no additional transducers to be installed on a structure, and allows for the estimation of both the temperature and the wave velocity in the component. Additionally, the framework of the algorithm allows it to function completely in an unsupervised manner by probabilistically accounting for all measurement origin uncertainty. The novel algorithm was experimentally validated using an aluminum lug joint with a growing fatigue crack. The lug joint was interrogated using piezoelectric transducers at multiple fatigue crack lengths, and at temperatures between 20°C and 80°C. The results showed that the algorithm could accurately predict the temperature and wave speed of the lug joint. The localization results for the fatigue damage were found to correlate well with the true locations at long crack lengths, but loss of accuracy was observed in localizing small cracks due to time-of-flight measurement errors. To validate the algorithm across a wider range of temperatures the electromechanically coupled LISA/SIM model was used to simulate the effects of temperatures. The numerical results showed that this approach would be capable of experimentally estimating the temperature and velocity in the lug joint for temperatures from -60°C to 150°C. The velocity estimation algorithm was found to significantly increase the accuracy of localization at temperatures above 120°C when error due to incorrect velocity selection begins to outweigh the error due to time-of-flight measurements.
ContributorsHensberry, Kevin (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2013
Description
There are several visual dimensions of food that can affect food intake, example portion size, color, and variety. This dissertation elucidates the effect of number of pieces of food on preference and amount of food consumed in humans and motivation for food in animals. Chapter 2 Experiment 1 showed that rats preferred and also ran faster for multiple pieces (30, 10 mg pellets) than an equicaloric, single piece of food (300 mg) showing that multiple pieces of food are more rewarding than a single piece. Chapter 2 Experiment 2 showed that rats preferred a 30-pellet food portion clustered together rather than scattered. Preference and motivation for clustered food pieces may be interpreted based on the optimal foraging theory that animals prefer foods that can maximize energy gain and minimize the risk of predation. Chapter 3 Experiment 1 showed that college students preferred and ate less of a multiple-piece than a single-piece portion and also ate less in a test meal following the multiple-piece than single-piece portion. Chapter 3 Experiment 2 replicated the results in Experiment 1 and used a bagel instead of chicken. Chapter 4 showed that college students given a five-piece chicken portion scattered on a plate ate less in a meal and in a subsequent test meal than those given the same portion clustered together. This is consistent with the hypothesis that multiple pieces of food may appear like more food because they take up a larger surface area than a single-piece portion. All together, these studies show that number and surface area occupied by food pieces are important visual cues determining food choice in animals and both food choice and intake in humans.
ContributorsBajaj, Devina (Author) / Phillips, Elizabeth D. (Thesis advisor) / Cohen, Adam (Committee member) / Johnston, Carol (Committee member) / Bimonte-Nelson, Heather A. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Woven fabric composite materials are widely used in the construction of aircraft engine fan containment systems, mostly due to their high strength to weight ratios and ease of implementation. The development of a predictive model for fan blade containment would provide great benefit to engine manufactures in shortened development cycle time, less risk in certification and fewer dollars lost to redesign/recertification cycles. A mechanistic user-defined material model subroutine has been developed at Arizona State University (ASU) that captures the behavioral response of these fabrics, namely Kevlar® 49, under ballistic loading. Previously developed finite element models used to validate the consistency of this material model neglected the effects of the physical constraints imposed on the test setup during ballistic testing performed at NASA Glenn Research Center (NASA GRC). Part of this research was to explore the effects of these boundary conditions on the results of the numerical simulations. These effects were found to be negligible in most instances. Other material models for woven fabrics are available in the LS-DYNA finite element code. One of these models, MAT234: MAT_VISCOELASTIC_LOOSE_FABRIC (Ivanov & Tabiei, 2004) was studied and implemented in the finite element simulations of ballistic testing associated with the FAA ASU research. The results from these models are compared to results obtained from the ASU UMAT as part of this research. The results indicate an underestimation in the energy absorption characteristics of the Kevlar 49 fabric containment systems. More investigation needs to be performed in the implementation of MAT234 for Kevlar 49 fabric. Static penetrator testing of Kevlar® 49 fabric was performed at ASU in conjunction with this research. These experiments are designed to mimic the type of loading experienced during fan blade out events. The resulting experimental strains were measured using a non-contact optical strain measurement system (ARAMIS).
ContributorsFein, Jonathan (Author) / Rajan, Subramaniam D. (Thesis advisor) / Mobasher, Barzin (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2012