Matching Items (61)
Description
The purpose of this project was to program a Raspberry Pi to be able to play music from both local storage on the Pi and from internet radio stations such as Pandora. The Pi also needs to be able to play various types of file formats, such as mp3 and FLAC. Finally, the project is also to be driven by a mobile app running on a smartphone or tablet. To achieve this, a client server design was employed where the Raspberry Pi acts as the server and the mobile app is the client. The server functionality was achieved using a Python script that listens on a socket and calls various executables that handle the different formats of music being played. The client functionality was achieved by programming an Android app in Java that sends encoded commands to the server, which the server decodes and begins playing the music that command dictates. The designs for both the client and server are easily extensible and allow for any future modifications to the project to be easily made.
ContributorsStorto, Michael Olson (Author) / Burger, Kevin (Thesis director) / Meuth, Ryan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
Students learn in various ways \u2014 visualization, auditory, memorizing, or making analogies. Traditional lecturing in engineering courses and the learning styles of engineering students are inharmonious causing students to be at a disadvantage based on their learning style (Felder & Silverman, 1988). My study analyzes the traditional approach to learning coding skills which is unnatural to engineering students with no previous exposure and examining if visual learning enhances introductory computer science education. Visual and text-based learning are evaluated to determine how students learn introductory coding skills and associated problem solving skills. My study was conducted to observe how the two types of learning aid the students in learning how to problem solve as well as how much knowledge can be obtained in a short period of time. The application used for visual learning was Scratch and Repl.it was used for text-based learning. Two exams were made to measure the progress made by each student. The topics covered by the exam were initialization, variable reassignment, output, if statements, if else statements, nested if statements, logical operators, arrays/lists, while loop, type casting, functions, object orientation, and sorting. Analysis of the data collected in the study allow us to observe whether the traditional method of teaching programming or block-based programming is more beneficial and in what topics of introductory computer science concepts.
ContributorsVidaure, Destiny Vanessa (Author) / Meuth, Ryan (Thesis director) / Yang, Yezhou (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Even in the largest public university in the country, computer related degrees such as Computer Science, Computer Systems Engineering and Software Engineering have low enrollment rates and high dropout rates. This is interesting because the careers that require these degrees are marketed as the highest paying and most powerful. The goal of this project was to find out what the students of Arizona State University (ASU) thought about these majors and why they did or did not pick them. A total of 206 students were surveyed from a variety of sources including upper level classes, lower level classes and Barrett, the Honors College. Survey questions asked why the students picked their current major, if they had a previous major and why did they switch, and if the students had considered one of the three computer related degrees. Almost all questions were open ended, meaning the students did not have multiple choice answers and instead could write as short or as long of a response as needed. Responses were grouped based on a set of initial hypotheses and any emerging trends. These groups were displayed in several different bar graphs broken down by gender, grade level and category of student (stayed in a computer related degree, left one, joined one or picked a non-computer related degree). Trends included students of all grade levels picking their major because they were passionate or interested in the subject. This may suggest that college students are set in their path and will not switch majors easily. Students also reported seeing computer related degrees as too difficult and intimidating. However, given the low (when compared to all of ASU) number of students surveyed, the conclusions and trends given cannot be representative of ASU as a whole. Rather, they are just representative of this sample population. Further work on this study, if time permitted, would be to try to survey more students and question some of the trends established to find more specific answers.
ContributorsMeza, Edward L (Author) / Meuth, Ryan (Thesis director) / Miller, Phillip (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
This thesis dives into the world of machine learning by attempting to create an application that will accurately predict whether or not a sneaker will resell at a profit. To begin this study, I first researched different machine learning algorithms to determine which would be best for this project. After ultimately deciding on using an artificial neural network, I then moved on to collecting data, using StockX and Twitter. StockX is a platform where individuals can post and resell shoes, while also providing statistics and analytics about each pair of shoes. I used StockX to retrieve data about the actual shoe, which involved retrieving data for the network feature variables: gender, brand, and retail price. Additionally, I also retrieved the data for the average deadstock price for each shoe, which describes what the mean price of new, unworn shoes are selling for on StockX. This data was used with the retail price data to determine whether or not a shoe has been, on average, selling for a profit. I used Twitter’s API to retrieve links to different shoes on StockX along with retrieving the number of favorites and retweets each of those links had. These metrics were used to account for ‘hype’ of the shoe, with shoes traditionally being more profitable the larger the hype surrounding them. After preprocessing the data, I trained the model using a randomized 80% of the data. On average, the model had about a 65-70% accuracy range when tested with the remaining 20% of the data. Once the model was optimized, I saved it and uploaded it to a web application that took in user input for the five feature variables, tested the datapoint using the model, and outputted the confidence in whether or not the shoe would generate a profit.
From a technical perspective, I used Python for the whole project, while also using HTML/CSS for the front-end of the application. As for key packages, I used Keras, an open source neural network library to build the model; data preprocessing was done using sklearn’s various subpackages. All charts and graphs were done using data visualization libraries matplotlib and seaborn. These charts provided insight as to what the final dataset looked like. They showed how the brand distribution is relatively close to what it should be, while the gender distribution was heavily skewed. Future work on this project would involve expanding the dataset, automating the entirety of the data retrieval process, and finally deploying the project on the cloud for users everywhere to use the application.
From a technical perspective, I used Python for the whole project, while also using HTML/CSS for the front-end of the application. As for key packages, I used Keras, an open source neural network library to build the model; data preprocessing was done using sklearn’s various subpackages. All charts and graphs were done using data visualization libraries matplotlib and seaborn. These charts provided insight as to what the final dataset looked like. They showed how the brand distribution is relatively close to what it should be, while the gender distribution was heavily skewed. Future work on this project would involve expanding the dataset, automating the entirety of the data retrieval process, and finally deploying the project on the cloud for users everywhere to use the application.
ContributorsShah, Shail (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The Internet has made it possible to exchange information at a rapid rate. With this extraordinary ability, media companies and various other organizations have been able to communicate thoughts and information to an extremely large audience. As a result, news subscribers are overwhelmed with biased information, which makes it very easy to be misinformed. Unfortunately, there is currently no way to stay truly informed without spending countless hours searching the Internet for different viewpoints and ultimately using that information to formulate a sound understanding. This project (nicknamed "Newsie") solves this problem by providing news subscribers with many news sources to every topic, thereby saving them time and ultimately paving a way to a more informed society. Since one of the main goals of this project is to provide information to the largest number of people, Newsie is designed with availability in mind. Unsurprisingly, the most accessible method of communication is the Internet \u2014 more specifically, a website. Users will be able to access Newsie via a webpage, and easily view to most recent headlines with their corresponding articles from several sources. Another goal of the project is to classify different articles and sources based on their bias. After reading articles, users will be able to vote on their biases. This provides a crowdsourced method of determining bias.
ContributorsAlimov, Robert Joseph (Author) / Meuth, Ryan (Thesis director) / Franceschini, Enos (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
Cryptocurrencies have become one of the most fascinating forms of currency and economics due to their fluctuating values and lack of centralization. This project attempts to use machine learning methods to effectively model in-sample data for Bitcoin and Ethereum using rule induction methods. The dataset is cleaned by removing entries with missing data. The new column is created to measure price difference to create a more accurate analysis on the change in price. Eight relevant variables are selected using cross validation: the total number of bitcoins, the total size of the blockchains, the hash rate, mining difficulty, revenue from mining, transaction fees, the cost of transactions and the estimated transaction volume. The in-sample data is modeled using a simple tree fit, first with one variable and then with eight. Using all eight variables, the in-sample model and data have a correlation of 0.6822657. The in-sample model is improved by first applying bootstrap aggregation (also known as bagging) to fit 400 decision trees to the in-sample data using one variable. Then the random forests technique is applied to the data using all eight variables. This results in a correlation between the model and data of 9.9443413. The random forests technique is then applied to an Ethereum dataset, resulting in a correlation of 9.6904798. Finally, an out-of-sample model is created for Bitcoin and Ethereum using random forests, with a benchmark correlation of 0.03 for financial data. The correlation between the training model and the testing data for Bitcoin was 0.06957639, while for Ethereum the correlation was -0.171125. In conclusion, it is confirmed that cryptocurrencies can have accurate in-sample models by applying the random forests method to a dataset. However, out-of-sample modeling is more difficult, but in some cases better than typical forms of financial data. It should also be noted that cryptocurrency data has similar properties to other related financial datasets, realizing future potential for system modeling for cryptocurrency within the financial world.
ContributorsBrowning, Jacob Christian (Author) / Meuth, Ryan (Thesis director) / Jones, Donald (Committee member) / McCulloch, Robert (Committee member) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
DescriptionFresh15 is an iOS application geared towards helping college students eat healthier. This is based on a user's preferences of price range, food restrictions, and favorite ingredients. Our application also considers the fact that students may have to order their ingredients online since they don't have access to transportation.
ContributorsBailey, Reece (Co-author) / Fallah-Adl, Sarah (Co-author) / Meuth, Ryan (Thesis director) / McDaniel, Troy (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Machine learning for analytics has exponentially increased in the past few years due to its ability to identify hidden insights in data. It also has a plethora of applications in healthcare ranging from improving image recognition in CT scans to extracting semantic meaning from thousands of medical form PDFs. Currently in the BioElectrical Systems and Technology Lab, there is a biosensor in development that retrieves and analyzes data manually. In a proof of concept, this project uses the neural network architecture to automatically parse and classify a cardiac disease data set as well as explore health related factors impacting cardiac disease in patients of all ages.
ContributorsMurella, Akhila Sainagaki (Author) / Blain-Christen, Jennifer (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of the Arts, Media and Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This project is a full integrated development environment implementing the LEGv8 assembly language standard, to be used in classroom settings. The LEGv8 assembly language is defined by the ARM edition of "Computer Organization and Design: The Hardware/Software Interface" by David A. Patterson and John L. Hennessy as a more approachable alternative to the full ARMv8 instruction set. The MIPS edition of that same book is used in the Computer Organization course at ASU. This class makes heavy use of the "MARS" MIPS simulator, which allows students to write and run their own MIPS assembly programs. Writing assembly language programs is a key component of the course, as assembly programs have many design difficulties as compared to a high-level language. This project is a fork of the MARS project. The interface and functionality remain largely the same aside from the change to supporting the LEGv8 syntax and instruction set. Faculty used to the MARS environment from teaching Computer Organization should only have to adjust to the new language standard, as the editor and environment will be familiar. The available instructions are basic arithmetic/logical operations, memory interaction, and flow control. Both floating-point and integer operations are supported, with limited support of conditional execution. Only branches can be conditionally executed, per LEGv8. Directives remain in the format supported by MARS, as documentation on ARM-style directives is both sparse and agreeable to this standard. The operating system functions supported by the MARS simulator also remain, as there is no generally standardized requirements for operating system interactions.
ContributorsWhite, Josiah Jeremiah (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
The Internet of Things has spread Wi-Fi connectivity to household and business devices everywhere. It is important that we understand IoT's risks and capabilities as its popularity continues to grow, and that we recognize new and exciting uses for it. In this project, the ESP8266 Wi-Fi controller, powered by a lithium battery, is used to transmit messages from a user's browser or mobile phone to an OLED display. The ESP8266 is a system on a chip (SOC) which boasts impressive features such as full TCP/IP stack, 1 MB of flash memory, and a 32-bit CPU. A web server is started on the ESP8266 which listens at a specific port and relays any strings from the client back to the display, acting as a simple notification system for a busy individual such as a professor. The difficulties with this project stemmed from the security protocol of Arizona State University's Wi-Fi network and from the limitations of the Wi-Fi chip itself. Several solutions are suggested, such as utilizing a personal cellular broadband router and polling a database for stored strings through a service such as Data.Sparkfun.com.
ContributorsKovatcheva, Simona Kamenova (Author) / Burger, Kevin (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12