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- Creators: Computer Science and Engineering Program
- Creators: Ira A. Fulton Schools of Engineering
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Description
Conflict minerals are those that are taken from violent, militia controlled mines in areas like the Democratic Republic of the Congo and used in technology such as laptops, cellphones, and computers. They are then sold to fund bloody wars that have been raging for years. The issue of conflict minerals continue to rise as technology advances. To combat the issue, the Dodd-Frank Act was implemented in the U.S. in 2010. The act requires companies listed on the stock exchange to report information on possible conflict mineral usage. However, there is a large discrepancy in the compliance levels between many similar technology companies subject to the Dodd-Frank Act. This paper addresses the factors driving compliance through the use of a theory testing method known as pattern matching, and attempts to answer why such similar companies have such different compliance levels. The pattern matching technique looks to test the applicability of theories based on what they theorize will happen and what actually happens in a given scenario. In this instance, the theories know as general deterrence, institutional, and stakeholder theory were put to the test in order to identify the factors driving compliance levels with conflict mineral policies. Both general deterrence and stakeholder theory were able to adequately match their theorized outcomes of conflict mineral compliance with actual observed outcomes. However, general deterrence theory more adequately explained the differences in compliance levels between similar companies. This information has implications on the policy side of the issue, as it reveals a way to more effectively drive up compliance levels by increasing disincentives and penalties in accordance with general deterrence theory.
ContributorsMeyer, Matthew Jacob (Author) / Kull, Thomas (Thesis director) / Wood, Reed (Committee member) / Department of Supply Chain Management (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
As the IoT (Internet of Things) market continues to grow, Company X needs to find a way to penetrate the market and establish larger market share. The problem with Company X's current strategy and cost structure lies in the fact that the fastest growing portion of the IoT market is microcontrollers (MCUs). As Company X currently holds its focus in manufacturing microprocessors (MPUs), the current manufacturing strategy is not optimal for entering competitively into the MCU space. Within the MCU space, the companies that are competing the best do not utilize such high level manufacturing processes because these low cost products do not demand them. Given that the MCU market is largely untested by Company X and its products would need to be manufactured at increasingly lower costs, it runs the risk of over producing and holding obsolete inventory that is either scrapped or sold at or below cost. In order to eliminate that risk, we will explore alternative manufacturing strategies for Company X's MCU products specifically, which will allow for a more optimal cost structure and ultimately a more profitable Internet of Things Group (IoTG). The IoT MCU ecosystem does not require the high powered technology Company X is currently manufacturing and therefore, Company X loses large margins due to its unnecessary leading technology. Since cash is king, pursuing a fully external model for MCU design and manufacturing processes will generate the highest NPV for Company X. It also will increase Company X's market share, which is extremely important given that every tech company in the world is trying to get its hands into the IoT market. It is possible that in ten to thirty years down the road, Company X can manufacture enough units to keep its products in-house, but this is not feasible in the foreseeable future. For now, Company X should focus on the cost market of MCUs by driving its prices down while maintaining low costs due to the variables of COGS and R&D given in our fully external strategy.
ContributorsKadi, Bengimen (Co-author) / Peterson, Tyler (Co-author) / Langmack, Haley (Co-author) / Quintana, Vince (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Supply Chain Management (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Department of Marketing (Contributor) / School of Accountancy (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This is a creative thesis project on the topic of the third party logistics industry, and the improvements that are possible through the implementation of goods to person technologies. The scope of the project entails the relationship between Company X, which is a third party logistics provider, and Company Y, a major toy retailer. This thesis identifies current trends for the third party logistics industry such as rising operating costs and average savings achieved through these business relationships. After identifying the negative trends that Company X is vulnerable to such as high human resources costs, and cost of quality issues. Given the findings derived from industry data, a final recommendation was settled on to improve productivity and ultimately reduce the use of temporary labor for Company X. The implementation of a goods to person technology solution provides the opportunity to reduce hours of operation, man hours, as well as direct and indirect costs such as labor. Research has proven that firms operating in the retail industry rely heavily on temporary labor to handle the seasonal demand brought by the holidays, thus this recommendation could be applied to a variety of operations. The data compiled throughout this thesis have major implications for the third party logistics industry and achieving long term profitability in operations management.
ContributorsFonseca, Tanner (Author) / Printezis, Antonios (Thesis director) / Kellso, James (Committee member) / Department of Supply Chain Management (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Company X has developed RealSenseTM technology, a depth sensing camera that provides machines the ability to capture three-dimensional spaces along with motion within these spaces. The goal of RealSense was to give machines human-like senses, such as knowing how far away objects are and perceiving the surrounding environment. The key issue for Company X is how to commercialize RealSense's depth recognition capabilities. This thesis addresses the problem by examining which markets to address and how to monetize this technology. The first part of the analysis identified potential markets for RealSense. This was achieved by evaluating current markets that could benefit from the camera's gesture recognition, 3D scanning, and depth sensing abilities. After identifying seven industries where RealSense could add value, a model of the available, addressable, and obtainable market sizes was developed for each segment. Key competitors and market dynamics were used to estimate the portion of the market that Company X could capture. These models provided a forecast of the discounted gross profits that could be earned over the next five years. These forecasted gross profits, combined with an examination of the competitive landscape and synergistic opportunities, resulted in the selection of the three segments thought to be most profitable to Company X. These segments are smart home, consumer drones, and automotive. The final part of the analysis investigated entrance strategies. Company X's competitive advantages in each space were found by examining the competition, both for the RealSense camera in general and other technologies specific to each industry. Finally, ideas about ways to monetize RealSense were developed by exploring various revenue models and channels.
ContributorsDunn, Nicole (Co-author) / Boudreau, Thomas (Co-author) / Kinzy, Chris (Co-author) / Radigan, Thomas (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / WPC Graduate Programs (Contributor) / Department of Psychology (Contributor) / Department of Finance (Contributor) / School of Accountancy (Contributor) / Department of Economics (Contributor) / School of Mathematical and Statistical Science (Contributor) / W. P. Carey School of Business (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This paper presents work that was done to create a system capable of facial expression recognition (FER) using deep convolutional neural networks (CNNs) and test multiple configurations and methods. CNNs are able to extract powerful information about an image using multiple layers of generic feature detectors. The extracted information can be used to understand the image better through recognizing different features present within the image. Deep CNNs, however, require training sets that can be larger than a million pictures in order to fine tune their feature detectors. For the case of facial expression datasets, none of these large datasets are available. Due to this limited availability of data required to train a new CNN, the idea of using naïve domain adaptation is explored. Instead of creating and using a new CNN trained specifically to extract features related to FER, a previously trained CNN originally trained for another computer vision task is used. Work for this research involved creating a system that can run a CNN, can extract feature vectors from the CNN, and can classify these extracted features. Once this system was built, different aspects of the system were tested and tuned. These aspects include the pre-trained CNN that was used, the layer from which features were extracted, normalization used on input images, and training data for the classifier. Once properly tuned, the created system returned results more accurate than previous attempts on facial expression recognition. Based on these positive results, naïve domain adaptation is shown to successfully leverage advantages of deep CNNs for facial expression recognition.
ContributorsEusebio, Jose Miguel Ang (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The constant evolution of technology has greatly shifted the way in which we gain knowledge information. This, in turn, has an affect on how we learn. Long gone are the days where students sit in libraries for hours flipping through numerous books to find one specific piece of information. With the advent of Google, modern day students are able to arrive at the same information within 15 seconds. This technology, the internet, is reshaping the way we learn. As a result, the academic integrity policies that are set forth at the college level seem to be outdated, often prohibiting the use of technology as a resource for learning. The purpose of this paper is to explore why exactly these resources are prohibited. By contrasting a subject such as Computer Science with the Humanities, the paper explores the need for the internet as a resource in some fields as opposed to others. Taking a look at the knowledge presented in Computer Science, the course structure, and the role that professors play in teaching this knowledge, this thesis evaluates the epistemology of Engineering subjects. By juxtaposing Computer Science with the less technology reliant humanities subjects, it is clear that one common policy outlining academic integrity does not suffice for an entire university. Instead, there should be amendments made to the policy specific to each subject, in order to best foster an environment of learning at the university level. In conclusion of this thesis, Arizona State University's Academic Integrity Policy is analyzed and suggestions are made to remove ambiguity in the language of the document, in order to promote learning at the university.
ContributorsMohan, Sishir Basavapatna (Author) / Brake, Elizabeth (Thesis director) / Martin, William (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The purpose of this project was to construct and write code for a vehicle to take advantage of the benefits of combining stepper motors with mecanum wheels. This process involved building the physical vehicle, designing a custom PCB for the vehicle, writing code for the onboard microprocessor, and implementing motor control algorithms.
ContributorsDavis, Severin Jan (Author) / Burger, Kevin (Thesis director) / Vannoni, Greg (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
This thesis, entitled "A Community Perspective on Alcohol Education," was conducted over a ten month period during the Spring 2014 and Fall 2014 semesters, composed by Christopher Stuller and Nicholas Schmitzer. The research involved interviewing twelve professionals from Arizona State University and the City of Tempe to gather a holistic view on alcohol education and alcohol safety as it involves the students at ASU. Upon completion of the interviews, recommendations were made regarding areas of improvement for alcohol education and alcohol safety at Arizona State University. These recommendations range from creating a mandatory alcohol education class to passing a Guardian Angel Law to creating a national network of alcohol education best practices. Through this thesis, the authors hope to prevent future alcohol related injuries, deaths, and tragedies. For the final display of this thesis a website was created. For the ease of reading, all information has been presented in text format.
ContributorsSchmitzer, Nicholas (Co-author) / Stuller, Christopher (Co-author) / Koretz, Lora (Thesis director) / Scott Lynch, Jacquelyn (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / School of Accountancy (Contributor) / Department of Supply Chain Management (Contributor)
Created2014-12
Description
This project was centered around designing a processor model (using the C programming language) based on the Coldfire computer architecture that will run on third party software known as Open Virtual Platforms. The end goal is to have a fully functional processor that can run Coldfire instructions and utilize peripheral devices in the same way as the hardware used in the embedded systems lab at ASU. This project would cut down the substantial amount of time students spend commuting to the lab. Having the processor directly at their disposal would also encourage them to spend more time outside of class learning the hardware and familiarizing themselves with development on an embedded micro-controller. The model will be accurate, fast and reliable. These aspects will be achieved through rigorous unit testing and use of the OVP platform which provides instruction accurate simulations at hundreds of MIPS (million instructions per second) for the specified model. The end product was able to accurately simulate a subset of the Coldfire instructions at very high rates.
ContributorsDunning, David Connor (Author) / Burger, Kevin (Thesis director) / Meuth, Ryan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-12
Description
The purpose of this thesis is to explore if any correlation exists between the proposed components of happiness with overall self-perceived happiness. This thesis also explores how introversion and extraversion, gender, and working status affects the proposed components of happiness for college students and how their happiness influences engagement, motivation, preference of organizational culture, and the activities that they engage in. This research was gathered from secondary sources and a survey that was given to undergraduate students at Arizona State University. We found that well-being, gratitude, achievement, psychological empowerment, and affection contribute to both extraverts and introverts' happiness. In addition, we found that extraverts reported higher means than introverts in each factor; including happiness in general and what contributes to it. Contrary to popular belief, our research shows that autonomy either had no correlation or negatively correlates with happiness. In addition, we found that both extraverts and introverts participate in social and nonsocial activities rather than solely on their expected type of activity. Our research also shows that females reported higher means than males on gratitude, achievement, and autonomy. One significant implication of this study is that it can help individuals to better understand themselves and people they interact with.
ContributorsVasquez, Delia (Co-author) / Lopez, Miguel (Co-author) / LePine, Marcie (Thesis director) / Arce, Alma (Committee member) / Barrett, The Honors College (Contributor) / Department of Supply Chain Management (Contributor) / Department of Psychology (Contributor) / School of Accountancy (Contributor)
Created2014-12