Matching Items (209)
Description
Unrestricted Mexican exports of sugar into the U.S. is considered the most pressing issue facing the U.S. sugar industry. The goal of this dissertation is to analyze the trade of sugar between Mexico and the U.S. as well as analyze additional primary issues confronting the U.S. sugar industry. Chapters 1 and 2 provide an introduction to the U.S. sugar industry. Chapters 3 through 6 develop trade models which analyze sugar trade between Mexico and the U.S. The trade models estimate how NAFTA, USDA sugar forecast errors and Mexican ownership of twenty percent of the Mexican sugar industry each impact U.S. producer surplus and Mexican welfare. Results validate that U.S. producer surplus and in some instances Mexican welfare were decreased by full implementation of NAFTA. U.S. producer surplus and Mexican welfare were decreased due to USDA sugar production forecasting errors. U.S. producer surplus would be increased if the Mexican government did not own twenty percent of Mexican sugar production. Using an online choice experiment, Chapter 7 assesses U.S. consumers' preferences and willingness to pay (WTP) for imported and genetically modified (GM) labeled sugar and sugar in soft drinks. Results indicate that consumers prefer bags of sugar and soft drinks labeled as "Not GM". Furthermore, consumers prefer sugar from Canada and the U.S. over sugar from Mexico, Brazil and the Philippines. Evidence is also provided that participants are more likely to choose actual products in the choice set rather than the "none of these" options when controlling for hypothetical bias by using consequentiality techniques. A non-hypothetical experimental auction was used in Chapter 8 to determine consumers' WTP for soft drinks labeled with sweetener and calorie information and analyzed the role of taste panels in an experimental auction. Results indicate that sugar is consumers' most preferred sweetener and calorie labeling is ineffective at influencing consumers to choose healthier soft drinks. Including taste in an experimental auction caused significant reductions in consumers' WTP for all soft drinks. Chapter 9 concludes by summarizing the results of this dissertation and discussing the future challenges facing the U.S. sugar industry.
ContributorsLewis, Karen Elizabeth (Author) / Schmitz, Troy (Thesis advisor) / Grebitus, Carola (Committee member) / Manfredo, Mark (Committee member) / Ketcham, Andrea (Committee member) / Arizona State University (Publisher)
Created2014
Description
The Dodd-Frank Act was created to promote financial stability in the United States. However, no one is quite sure what it is yet. While action had to be taken and Dodd-Frank has some positives, Dodd-Frank, as it is deciphered today, has severe drawbacks. Since Dodd-Frank is only in its infancy, it is difficult to form an interim conclusion about its effects on agricultural lending at this point. After passing Dodd-Frank in 2010, the government began trying to figure out what it means. Four years later, they are still trying and are about half way through making the rules. This law essentially replaces Glass-Steagall, which was repealed several years ago. Many believe repealing Glass-Steagall was a big reason for the financial collapse of 2008. While Glass-Steagall was a short, easily understood document, Dodd Frank adds many more regulations and pages. This creates a long, bulky, confusing law that seems to be extremely tough to comprehend legally or as a banker. In this study, I try to balance the positives and negatives of Dodd-Frank to understand if it is more detrimental or beneficial to agricultural lending. While we find that Dodd-Frank does help keep banks from some of the risky investments that many believe led to the financial crisis, the added paperwork, compliance costs, and strain it puts on small banks can be worrisome. I interviewed several agriculture-lending professionals who regularly deal with the rules and regulations of Dodd-Frank to discover the impact the new law has on banks, their customers, and the economy as a whole. These interviews give insight into what Dodd-Frank means to the agriculture-lending market and what changes have had to occur since the law was passed. These interviews demonstrate that Dodd-Frank is largely looked down upon by the banking industry. The professionals interviewed are very experienced. After the extensive research, interviews, and discoveries that came of this study, it was concluded that Dodd-Frank seems to hurt the lending industry much more than it helps. One major concern is the strain Dodd-Frank puts on small banks and how it makes "too big to fail" banks even bigger.
ContributorsBettencourt, Bradley D (Author) / Thor, Eric (Thesis advisor) / Manfredo, Mark (Committee member) / Englin, Jeff (Committee member) / Arizona State University (Publisher)
Created2014
Descriptionyour words
ContributorsWang, Dan, M.S (Author) / Grebitus, Carola (Thesis advisor) / Schroeter, Christiane (Committee member) / Manfredo, Mark (Committee member) / Hughner, Renee (Committee member) / Arizona State University (Publisher)
Created2014
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
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