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- All Subjects: Machine Learning
- All Subjects: Entrepreneurship
- Creators: Barrett, The Honors College
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
- Status: Published
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
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
After freelancing on my own for the past year and a half, I have realized that one of the biggest obstacles to college entrepreneurs is a fear or apprehension to sales. As a computer science major trying to sell my services, I discovered very quickly that I had not been prepared for the difficulty of learning sales. Sales get a bad rap and very often is the last thing that young entrepreneurs want to try, but the reality is that sales is oxygen to a company and a required skill for an entrepreneur. Due to this, I compiled all of my knowledge into an e-book for young entrepreneurs starting out to learn how to open up a conversation with a prospect all the way to closing them on the phone. Instead of starting from scratch like I did, college entrepreneurs can learn the bare basics of selling their own services, even if they are terrified of sales and what it entails. In this e-book, there are tips that I have learned to deal with my anxiety about sales such as taking the pressure off of yourself and prioritizing listening more than pitching. Instead of trying to teach sales expecting people to be natural sales people, this e-book takes the approach of helping entrepreneurs that are terrified of sales and show them how they can cope with this fear and still close a client. In the future, I hope young entrepreneurs will have access to more resources that handle this fear and make it much easier for them to learn it by themselves. This e-book is the first step.
ContributorsMead, Kevin Tyler (Author) / Sebold, Brent (Thesis director) / Kruse, Gabriel (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This study explores the best known practices of small businesses from different entrepreneurs' perspectives and attempts to address the question: are there consistencies between different entrepreneurs' approaches to establishing and growing a business? Ten entrepreneurs from a variety of business types (product and service) were interviewed using a consistent question template that asked questions regarding financing, business strategy (and scalability), interpersonal forces, innate qualities, partnerships, and resources. The primary overlaps between these businesses are with regard to the confluence between personal risk and business strategy, the risk of working with friends and family, the capacity to scale relative to special content knowledge or process knowledge, and partnerships
etworking.
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ContributorsCole, Chandler William (Author) / Kellso, James (Thesis director) / Gilmore, Bruce (Committee member) / Department of Supply Chain Management (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Women dominate in terms of purchasing power and spending. They hold 60 percent of all US personal income, and those aged 50 years or older have a combined net worth of approximately $19 trillion. Of this group, women between 50 and 70 years old, in particular, are the biggest spenders (Barmann, 2014). More important than their spending power, however, is how satisfied (or dissatisfied) they are with their current purchases. Though women make 85 percent of all consumer purchases, 91 percent of women say, "...advertisers don't understand them," (Barmann, 2014). This makes sense, considering that 50 percent of the products marketed to men are actually purchased by women (Barmann, 2014). Successfully targeting women, especially Baby Boomers (women between 52 and 70 years old), would be a lucrative endeavor, and to better understand the unmet needs of that demographic, exploratory research was needed. In-depth interviews of Baby Boomer women reveals a problem that \u2014 even on a macro level \u2014 has gone unresolved, and has perhaps worsened, throughout written history: the Generation Gap (Bengtson, 1970). To illustrate the depth of the problem, there exist starkly different impressions of younger generations, namely Millennials (born between 1980 and 1995). According to The New Generation Gap by Neil Howe and William Strauss (1992), Baby Boomers view Millennials as unintelligent, entitled "pleasure beasts." In Millennials Rising, also by Howe and Strauss (2000), Millennials are characterized as a generation that is, "...beginning to manifest a wide array of positive social habits that older Americans no longer associate with youth, including a new focus on teamwork, achievement, modesty, and good conduct." These contradictory opinions further support the substantial misunderstanding between generations that surfaced during in-depth interviews. Using the results of in-depth interviews and follow-up questions for idea validation, this thesis presents a potential method for "closing the gap." The goal of this business offering is not to homogenize older and younger generations of women; the goal is to cultivate empathy and connection \u2014 Intergenerational Cohesion \u2014 between them.
ContributorsSeefus, Cole Hawk Gillette (Author) / Gray, Nancy (Thesis director) / Giard, Jacques (Committee member) / Department of Management (Contributor) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Aventure is the newest contemporary luxury men and women’s apparel and accessories boutique in Arizona. The boutique will be located in Downtown Scottsdale, specifically in McKellips Plaza conveniently located near Scottsdale Fashion Square. Aventure is the first of its kind in the Phoenix Metropolitan Area, providing Millennial fashion fanatics with a destination that fills the empty void in the city’s growing fashion scene. At Aventure, we bridge the gap between pop culture, streetwear, and high-fashion. Through our mantra ‘Redefining the Luxury Retail Experience,’ we aim is to promote expression of one’s self to the fullest extent through style.
‘Aventure,’ which means “experience” or “adventure” in French, defines this upscale boutique and its essence of inclusion. This store does not aim to be your traditional retailer; instead, Aventure aims to build a community within and around the store for individuals with similar styles and passion for fashion. At the moment, the city of Scottsdale (and the Metro Phoenix area as a whole) does not have its own identity in the fashion world. There is no reason why Metro Phoenix cannot, with time, become more recognized in the global fashion community. With an array of exclusive luxury merchandise and an urban atmosphere, Aventure aims to pioneer the Valley’s establishment on the national high-end fashion scene.
The boutique is a result of the vision of its founder Ahmed Imam. Ahmed is a graduating Honors student at Arizona State Univeristy’s W.P. Carey School of Business, pursuing concurrent degrees in Finance and Business Entrepreneurship. Having been passionate about fashion for as long as he can remember, Ahmed will leverage his connections to the industry and excellent understanding of the Metro Phoenix market to turn Aventure into a hallmark of the community. Through his professional experience and educational background, Ahmed also brings the necessary knowledge and skills to the table to effectively run a startup.
The retail industry is experiencing steady growth, with the luxury goods sector expected to perform very well in the coming years. Using market-based sales forecasting, Aventure is estimated to break even by the third year of operations. Sales are expected to grow 20 percent after Year 1, and grow 5 percent thereafter. Net operating income of $83,643 is estimated in Year 1, growing to $141,351 by the end of Year 3. Overall, total startup expenses are estimated to be $206,574, made up of investments from owners and a term loan from Bank of America. The owner investment will be used to cover capital equipment, location, and administrative expenses. These include furniture, equipment, machinery, rent, utility, legal and accounting fees, prepaid insurance, and other expenses. The majority of the term loan will be used to finance opening inventory and advertising expenses, with the rest going towards cash on the balance sheet to ensure liquidity.
‘Aventure,’ which means “experience” or “adventure” in French, defines this upscale boutique and its essence of inclusion. This store does not aim to be your traditional retailer; instead, Aventure aims to build a community within and around the store for individuals with similar styles and passion for fashion. At the moment, the city of Scottsdale (and the Metro Phoenix area as a whole) does not have its own identity in the fashion world. There is no reason why Metro Phoenix cannot, with time, become more recognized in the global fashion community. With an array of exclusive luxury merchandise and an urban atmosphere, Aventure aims to pioneer the Valley’s establishment on the national high-end fashion scene.
The boutique is a result of the vision of its founder Ahmed Imam. Ahmed is a graduating Honors student at Arizona State Univeristy’s W.P. Carey School of Business, pursuing concurrent degrees in Finance and Business Entrepreneurship. Having been passionate about fashion for as long as he can remember, Ahmed will leverage his connections to the industry and excellent understanding of the Metro Phoenix market to turn Aventure into a hallmark of the community. Through his professional experience and educational background, Ahmed also brings the necessary knowledge and skills to the table to effectively run a startup.
The retail industry is experiencing steady growth, with the luxury goods sector expected to perform very well in the coming years. Using market-based sales forecasting, Aventure is estimated to break even by the third year of operations. Sales are expected to grow 20 percent after Year 1, and grow 5 percent thereafter. Net operating income of $83,643 is estimated in Year 1, growing to $141,351 by the end of Year 3. Overall, total startup expenses are estimated to be $206,574, made up of investments from owners and a term loan from Bank of America. The owner investment will be used to cover capital equipment, location, and administrative expenses. These include furniture, equipment, machinery, rent, utility, legal and accounting fees, prepaid insurance, and other expenses. The majority of the term loan will be used to finance opening inventory and advertising expenses, with the rest going towards cash on the balance sheet to ensure liquidity.
ContributorsImam, Ahmed Mohamed (Author) / Ostrom, Amy (Thesis director) / Schlacter, John (Committee member) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The Confessions of a College Entrepreneur is an honors project with the goal of revealing the business and marketing strategies that Charles Crawford used to create multiple successful companies. It's a collection of personal stories, book notes, millionaire interviews, and experiences that Charles had over the past 4 years of intense business experience and research across multiple industries. Charles wants college students and business owners to succeed in business ventures and life in general. This creative thesis project is the map for how to do just that.
ContributorsCrawford, Charles Joseph (Author) / Budolfson, Arthur (Thesis director) / Giles, Charles (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor)
Created2014-12
Description
Epilepsy affects numerous people around the world and is characterized by recurring seizures, prompting the ability to predict them so precautionary measures may be employed. One promising algorithm extracts spatiotemporal correlation based features from intracranial electroencephalography signals for use with support vector machines. The robustness of this methodology is tested through a sensitivity analysis. Doing so also provides insight about how to construct more effective feature vectors.
ContributorsMa, Owen (Author) / Bliss, Daniel (Thesis director) / Berisha, Visar (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2015-05