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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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 vastly growing field of supercomputing is in dire need of a new measurement system to optimize JMRAM (Josephson junction magnetoresistive random access memory) devices. To effectively measure these devices, an ultra-low-noise, low cost cryogenic dipping probe with a dynamic voltage range is required. This dipping probe has been designed by ASU with <100 nVp-p noise, <10 nV offsets, 10 pV to 16 mV voltage range, and negligible thermoelectric drift. There is currently no other research group or company that can currently match both these low noise levels and wide voltage range. Two different dipping probes can be created with these specifications: one for high-use applications and one for low-use applications. The only difference between these probes is the outer shell; the high-use application probe has a shell made of G-10 fiberglass for a higher price, and the low-use application probe has a shell made of AISI 310 steel for a lower price. Both types of probes can be assembled in less than 8 hours for less than $2,500, requiring only soldering expertise. The low cost and short time to create these probes makes wide profit margins possible. The market for these cryogenic dipping probes is currently untapped, as most research groups and companies that use these probes build their own, which allows for rapid business growth. These potential consumers can be easily reached by marketing these probes at superconducting conferences. After several years of selling >50 probes, mass production can easily become possible by hiring several technicians, and still maintaining wide profit margins.
ContributorsHudson, Brooke Ashley (Author) / Adams, James (Thesis director) / Anwar, Shahriar (Committee member) / Materials Science and Engineering Program (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The goal of the paper was to examine the fatigue mechanisms of polymers and silicone based elastomers. The mechanisms of fatigue due to crazing: the alignment of polymer chains to the stress axis, and shear banding: the localized orientation of the polymer by the shear stresses from two planes, were discussed in depth in this paper. Crazing only occurs in tensile stress, is initiated on the surface of the material, and only occurs in brittle polymers. Crazing also accounts for a 40-60% decrease in density, causing localized weakening of the material and a concentration in stress. This is due to a decrease in effective cross sectional area. The mechanism behind discontinuous growth bands was also discussed to be the cause of cyclic crazing. Shear banding only occurs in ductile polymers and can result in the failure of polymers via necking. Furthermore, the high fatigue resistance of silicone elastomers was discussed in this paper. This conclusion was made because of the lack of fatigue mechanisms (crazing, discontinuous growth bands, and shears banding) in the observed elastomer's microstructure after the samples had undergone fatigue tests. This was done through an analysis of room temperature vulcanized silicone adhesives, a heat-curing silicone elastomer, and a self-curing transparent silicone rubber. Fatigue of room temperature vulcanized silicon was observed, however this was reasoned to be the failure of the adhesion of the elastomer to the steel substrate instead of the microstructure itself. Additionally, the significance of fatigue in real world applications was discussed using SouthWest Airlines Flight 812 as an example.
ContributorsWong, Christopher Stanley (Author) / Adams, James (Thesis director) / Krause, Stephen (Committee member) / Anwar, Shahriar (Committee member) / Materials Science and 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
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
Description
The purpose of this honors project is to analyze the difference between different powder separation techniques, and their suitability for my capstone project – ‘Effect of Powder Reuse on DMLS (Direct Metal Laser Sintering) Product Integrity’. Due to the nature of my capstone project, my group needs to characterize foreign contaminants in IN 718 (Ni-based superalloy) powder with a mean diameter around 40um. In order to clearly analyze the contaminants and recycle useful IN 718 powders, powder separation is favorable since the filtered samples will be much easier to characterize rather than inspect all the powders at once under microscope. By conducting literature review, I found that powder separation is commonly used in Geology, and Chemistry department. To screen which combination of techniques could be the best for my project, I have consulted several research specialists, obtained adequate knowledge about powder separation. Accordingly, I will summarize the pros and cons of each method with regard the specific project that I am working on, and further explore the impacts of each method under economical, societal, and environmental considerations. Several powder separation techniques will be discussed in details in the following sections, including water elutriation, settling column, magnetic separation and centrifugation. In addition to these methods, sieving, water tabling and panning will be briefly introduced. After detailed comparison, I found that water elutriation is the most efficient way to purity IN718 powder for reuse purpose, and recovery rate is as high as 70%, which could result in a significant reduction in the manufacturing cost for Honeywell since currently Honeywell only use virgin powders to build parts, and 90% of the leftover powders are discarded.
ContributorsLuo, Zheyu (Author) / Adams, James (Thesis director) / Tasooji, Amaneh (Committee member) / Materials Science and Engineering Program (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05