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- All Subjects: Python
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
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
Description
The most important task for a beginning computer science student, in order for them to succeed in their future studies, is to learn to be able to understand code. One of the greatest indicators of student success in beginning programming courses is the ability to read code and predict its output, as this shows that the student truly understands what each line of code is doing. Yet few tools available to students today focus on helping students to improve their ability to read code. The goal of the random Python program generator is to give students a tool to practice this important skill.
The program writes randomly generated, syntactically correct Python 3 code in order to provide students infinite examples from which to study. The end goal of the project is to create an interactive tool where beginning programming students can click a button to generate a random code snippet, check if what they predict the output to be is correct, and get an explanation of the code line by line. The tool currently lacks a front end, but it currently is able to write Python code that includes assignment statements, delete statements, if statements, and print statements. It supports boolean, float, integer, and string variable types.
The program writes randomly generated, syntactically correct Python 3 code in order to provide students infinite examples from which to study. The end goal of the project is to create an interactive tool where beginning programming students can click a button to generate a random code snippet, check if what they predict the output to be is correct, and get an explanation of the code line by line. The tool currently lacks a front end, but it currently is able to write Python code that includes assignment statements, delete statements, if statements, and print statements. It supports boolean, float, integer, and string variable types.
ContributorsDiLorenzo, Kaitlyn (Author) / Meuth, Ryan (Thesis director) / Miller, Phillip (Committee member) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Artistic expression can be made more accessible through the use of technological interfaces such as auditory analysis, generative artificial intelligence models, and simplification of complicated systems, providing a way for human driven creativity to serve as an input that allow users to creatively express themselves. Studies and testing were done with industry standard performance technology and protocols to create an accessible interface for creative expression. Artificial intelligence models were created to generate art based on simple text inputs. Users were then invited to display their creativity using the software, and a comprehensive performance showcased the potential of the system for artistic expression.
ContributorsPardhe, Joshua (Author) / Lim, Kang Yi (Co-author) / Meuth, Ryan (Thesis director) / Brian, Jennifer (Committee member) / Hermann, Kristen (Committee member) / Barrett, The Honors College (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Watts College of Public Service & Community Solut (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
Artistic expression can be made more accessible through the use of technological interfaces such as auditory analysis, generative artificial intelligence models, and simplification of complicated systems, providing a way for human driven creativity to serve as an input that allow users to creatively express themselves. Studies and testing were done with industry standard performance technology and protocols to create an accessible interface for creative expression. Artificial intelligence models were created to generate art based on simple text inputs. Users were then invited to display their creativity using the software, and a comprehensive performance showcased the potential of the system for artistic expression.
ContributorsLim, Kang Yi (Author) / Pardhe, Joshua (Co-author) / Meuth, Ryan (Thesis director) / Brian, Jennifer (Committee member) / Hermann, Kristen (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05