Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Electrical Engineering Program
- Creators: Mokwa, Michael
Description
We apply a Bayesian network-based approach for determining the structure of consumers' brand concept maps, and we further extend this approach in order to provide a precise delineation of the set of cognitive variations of that brand concept map structure which can simultaneously coexist within the data. This methodology can operate with nonlinear as well as linear relationships between the variables, and utilizes simple Likert-style marketing survey data as input. In addition, the method can operate without any a priori hypothesized structures or relations among the brand associations in the model. The resulting brand concept map structures delineate directional (as opposed to simply correlational) relations among the brand associations, and differentiates between the predictive and the diagnostic directions within each link. Further, we determine a Bayesian network-based link strength measure, and apply it to a comparison of the strengths of the connections between different semantic categories of brand association descriptors, as well as between different strategically important drivers of brand differentiation. Finally, we apply a precise form of information propagation through the predictive and diagnostic links within the network in order to evaluate the effect of introducing new information to the brand concept network. This overall methodology operates via a factorization of the joint distribution of the brand association variables via conditional independence properties and an application of the causal Markov condition, and as such, it represents an alternative approach to correlation-based structural determination methods. By using conditional independence as a core structural construct, the methods utilized here are especially well- suited for determining and analyzing asymmetric or directional beliefs about brand or product attributes. This methodology builds on the pioneering Brand Concept Mapping approach of Roedder John et al. (2006). Similar to that approach, the Bayesian network-based method derives the specific link-by-link structure among a brand's associations, and also allows for a precise quantitative determination of the likely effects that manipulation of specific brand associations will have upon other strategically important associations within that brand image. In addition, the method's precise informational semantics and specific structural measures allow for a greater understanding of the structure of these brand associations.
ContributorsBrownstein, Steven Alan (Author) / Reingen, Peter (Thesis advisor) / Kumar, Ajith (Committee member) / Mokwa, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
Convergent products are products that offer multiple capabilities from different product categories. For example, a smartphone acts as an internet browser, personal assistant, and telephone. Marketers are constantly considering the value of adding new functionalities to these convergent products. This work examines convergent products in terms of the hedonic and utilitarian value they provide along with whether the addition is related to the base product, revealing complex and nuanced interactions. This work contributes to marketing theory by advancing knowledge in the convergent products and product design literatures, specifically by showing how hedonic and utilitarian value and addition relatedness interact to impact the evaluation of convergent goods and services. Looking at a greater complexity of convergent product types also helps to resolve prior conflicting findings in the convergent products and hedonic and utilitarian value literatures. Additionally, this work examines the role of justification in convergent products, showing how different additions can help consumers to justify the evaluation of a convergent product. A three-item measure for justification was developed for this research, and can be used by future researchers to better understand the effects of justification in consumption. This work is also the first to explicitly compare effects between convergent goods and convergent services. Across two experiments, it is found that these two products types (convergent goods versus convergent services) are evaluated differently. For convergent goods, consumers evaluate additions based on anticipated practicality/productivity and on how easily they are justified. For convergent services, consumers evaluate additions based on perceptions of performance risk associated with the convergent service, which stems from the intangibility of these services. The insights gleaned from the research allow specific recommendations to be made to managers regarding convergent offerings. This research also examines the applicability of hedonic and utilitarian value to a special type of advertising appeal: reward appeals. Reward appeals are appeals that focus on peripheral benefits from purchasing or using a product, such as time or money savings, and make suggestions on how to use these savings. This work examines potential interactions between reward appeals and other common advertising elements: social norms information and role clarity messaging.
ContributorsEaton, Kathryn Karnos (Author) / Bitner, Mary Jo (Thesis advisor) / Olsen, G. Douglas (Thesis advisor) / Mokwa, Michael (Committee member) / Arizona State University (Publisher)
Created2012
Description
Breast microcalcifications are a potential indicator of cancerous tumors. Current visualization methods are either uncomfortable or impractical. Impedance measurement studies have been performed, but not in a clinical setting due to a low sensitivity and specificity. We are hoping to overcome this challenge with the development of a highly accurate impedance probe on a biopsy needle. With this technique, microcalcifications and the surrounding tissue could be differentiated in an efficient and comfortable manner than current techniques for biopsy procedures. We have developed and tested a functioning prototype for a biopsy needle using bioimpedance sensors to detect microcalcifications in the human body. In the final prototype a waveform generator sends a sin wave at a relatively low frequency(<1KHz) into the pre-amplifier, which both stabilizes and amplifies the signal. A modified howland bridge is then used to achieve a steady AC current through the electrodes. The voltage difference across the electrodes is then used to calculate the impedance being experienced between the electrodes. In our testing, the microcalcifications we are looking for have a noticeably higher impedance than the surrounding breast tissue, this spike in impedance is used to signal the presence of the calcifications, which are then sampled for examination by radiology.
ContributorsWen, Robert Bobby (Co-author) / Grula, Adam (Co-author) / Vergara, Marvin (Co-author) / Ramkumar, Shreya (Co-author) / Kozicki, Michael (Thesis director) / Ranjani, Kumaran (Committee member) / School of Molecular Sciences (Contributor) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Leveraging Machine Learning and Wireless Sensing for Robot Localization - Location Variance Analysis
Description
Object localization is used to determine the location of a device, an important aspect of applications ranging from autonomous driving to augmented reality. Commonly-used localization techniques include global positioning systems (GPS), simultaneous localization and mapping (SLAM), and positional tracking, but all of these methodologies have drawbacks, especially in high traffic indoor or urban environments. Using recent improvements in the field of machine learning, this project proposes a new method of localization using networks with several wireless transceivers and implemented without heavy computational loads or high costs. This project aims to build a proof-of-concept prototype and demonstrate that the proposed technique is feasible and accurate.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
ContributorsChang, Roger (Co-author) / Kann, Trevor (Co-author) / Alkhateeb, Ahmed (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.
ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Buck converters are a class of switched-mode power converters often used to step down DC input voltages to a lower DC output voltage. These converters naturally produce a current and voltage ripple at their output due to their switching action. Traditional methods of reducing this ripple have involved adding large discrete inductors and capacitors to filter the ripple, but large discrete components cannot be integrated onto chips. As an alternative to using passive filtering components, this project investigates the use of active ripple cancellation to reduce the peak output ripple. Hysteretic controlled buck converters were chosen for their simplicity of design and fast transient response. The proposed cancellation circuits sense the output ripple of the buck converter and inject an equal ripple exactly out of phase with the sensed ripple. Both current-mode and voltage-mode feedback loops are simulated, and the effectiveness of each cancellation circuit is examined. Results show that integrated active ripple cancellation circuits offer a promising substitute for large discrete filters.
ContributorsWang, Ziyan (Author) / Bakkaloglu, Bertan (Thesis director) / Kitchen, Jennifer (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
This creative project thesis involves electronic music composition and production, and it uses some elements of algorithmic music composition (through recurrent neural networks). Algorithmic composition techniques are used here as a tool in composing the pieces, but are not the main focus. Thematically, this project explores the analogy between artificial neural networks and neural activity in the brain. This project consists of three short pieces, each exploring these concept in different ways.
ContributorsKarpur, Ajay (Author) / Suzuki, Kotoka (Thesis director) / Ingalls, Todd (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The Phoenix CubeSat is a 3U Earth imaging CubeSat which will take infrared (IR) photos of cities in the United Stated to study the Urban Heat Island Effect, (UHI) from low earth orbit (LEO). It has many different components that need to be powered during the life of its mission. The only power source during the mission will be its solar panels. It is difficult to calculate power generation from solar panels by hand because of the different orientations the satellite will be positioned in during orbit; therefore, simulation will be used to produce power generation data. Knowing how much power is generated is integral to balancing the power budget, confirming whether there is enough power for all the components, and knowing whether there will be enough power in the batteries during eclipse. This data will be used to create an optimal design for the Phoenix CubeSat to accomplish its mission.
ContributorsBarakat, Raymond John (Author) / White, Daniel (Thesis director) / Kitchen, Jennifer (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
This thesis examines Endgame, a gaming themed bar and restaurant located in the heart of Tempe, Arizona on Mill Avenue. The business serves regular bar fare and offers a wide selection of video games for its customers to play and enjoy. Recently Endgame recognized that it was unsatisfied with its current revenue stream, prompting this investigative study. Upon completing this project, three business problems that are limiting Endgame's revenue growth were identified. The issues identified were: food sales, visibility/access, and alcohol sales. To better understand each of these issues a study was conducted in the form of ethnography research and a survey was distributed to Endgame's target market. Two instances of observational research were conducted and a survey was distributed to 400+ students in the W. P. Carey School of Business. The data collected revealed underlying sentiments about Endgame's food/beverage service and issues related to locating the bar. This investigation revealed that ordering food and beverages at Endgame is difficult and not a straight forward process. This led to a set of recommendations related to creating an efficient and simple ordering process. The study also showed that Endgame (which is on the second floor of a building) lacks the appropriate signage to indicate its location. Using this information, recommendations were made for Endgame to create additional signage near stairs and elevators to indicate their location. The research also revealed a general lack of consumer awareness in relation to alcoholic beverages that contributed to low sales. This led to a strategy to revitalize Endgame's marketing campaign and a redesign of their beverage menu. Outside of the three business problems found during observational research, several other areas were examined in the survey at the request of Endgame's management. These areas revealed additional understandings into consumer behavior and feelings towards Endgame. These customer insights along with the recommendations given in this paper will be used by Endgame to increase their overall business revenues.
ContributorsPaplham, Tyler James (Author) / Eaton, John (Thesis director) / Mokwa, Michael (Committee member) / Department of Information Systems (Contributor) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
In competitive Taekwondo, Electronic Body Protectors (EBPs) are used to register hits made by players during sparring. EBPs are comprised of three main components: chest guard, foot sock, and headgear. This equipment interacts with each other through the use of magnets, electric sensors, transmitters, and a receiver. The receiver is connected to a computer programmed with software to process signals from the transmitter and determine whether or not a competitor scored a point. The current design of EBPs, however, have numerous shortcomings, including sensing false positives, failing to register hits, costing too much, and relying on human judgment. This thesis will thoroughly delineate the operation of the current EBPs used and discuss research performed in order to eliminate these weaknesses.
ContributorsSpell, Valerie Anne (Author) / Kozicki, Michael (Thesis director) / Kitchen, Jennifer (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05