Matching Items (10)
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- All Subjects: Python
- Creators: Computer Science and Engineering Program
- Resource Type: Text
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
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
Next year, Arizona State University is launching a chatbot that will place their knowledge and services right into the palms of their students’ hands. Currently named Sunny, this virtual assistant will be able to answer questions regarding all aspects of college life, from orientation to housing, financial aid, schedules, intramurals, and more. Over the last semester, I have met with members of the Sunny development team to discuss their design and implementation plans. With their information plus a bit of outside research, I was able to combine several frameworks and technologies to build a prototype for Sunny. Prototypes allow developers to evaluate their designs early on, giving them ample time to make any necessary adjustments. I am confident that the Sunny development team will be able to learn from my basic implementation, from its triumphs and failures, to create the best possible chatbot for the students attending Arizona State University.
ContributorsGrossnickle, Brandon Michael (Co-author) / Grossnickle, Brandon (Co-author) / Balasooriya, Janaka (Thesis director) / Gray, Bobby (Committee member) / Longie, Joel (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
Agent Based modeling has been used in computer science to simulate complex phenomena. The introduction of Agent Based Models into the field of economics (Agent Based Computational Economics ACE) is not new, however work on making model environments simpler to design for individuals without a background in computer science or computer engineering is a constantly evolving topic. The issue is a trade off of how much is handled by the framework and how much control the modeler has, as well as what tools exist to allow the user to develop insights from the behavior of the model. The solutions looked at in this thesis are the construction of a simplified grammar for model construction, the design of an economic based library to assist in ACE modeling, and examples of how to construct interactive models.
ContributorsAnderson, Brandon David (Author) / Bazzi, Rida (Thesis director) / Kuminoff, Nicolai (Committee member) / Roberts, Nancy (Committee member) / Computer Science and Engineering Program (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
When creating computer vision applications, it is important to have a clear image of what is represented such that further processing has the best representation of the underlying data. A common factor that impacts image quality is blur, caused either by an intrinsic property of the camera lens or by introducing motion while the camera’s shutter is capturing an image. Possible solutions for reducing the impact of blur include cameras with faster shutter speeds or higher resolutions; however, both of these solutions require utilizing more expensive equipment, which is infeasible for instances where images are already captured. This thesis discusses an iterative solution for deblurring an image using an alternating minimization technique through regularization and PSF reconstruction. The alternating minimizer is then used to deblur a sample image of a pumpkin field to demonstrate its capabilities.
ContributorsSmith, Zachary (Author) / Espanol, Malena (Thesis director) / Ozcan, Burcin (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2023-05
Description
Machine learning has a near infinite number of applications, of which the potential has yet to have been fully harnessed and realized. This thesis will outline two departments that machine learning can be utilized in, and demonstrate the execution of one methodology in each department. The first department that will be described is self-play in video games, where a neural model will be researched and described that will teach a computer to complete a level of Super Mario World (1990) on its own. The neural model in question was inspired by the academic paper “Evolving Neural Networks through Augmenting Topologies”, which was written by Kenneth O. Stanley and Risto Miikkulainen of University of Texas at Austin. The model that will actually be described is from YouTuber SethBling of the California Institute of Technology. The second department that will be described is cybersecurity, where an algorithm is described from the academic paper “Process Based Volatile Memory Forensics for Ransomware Detection”, written by Asad Arfeen, Muhammad Asim Khan, Obad Zafar, and Usama Ahsan. This algorithm utilizes Python and the Volatility framework to detect malicious software in an infected system.
ContributorsBallecer, Joshua (Author) / Yang, Yezhou (Thesis director) / Luo, Yiran (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05
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
Description
In this work, we explore the potential for realistic and accurate generation of hourly traffic volume with machine learning (ML), using the ground-truth data of Manhattan road segments collected by the New York State Department of Transportation (NYSDOT). Specifically, we address the following question– can we develop a ML algorithm that generalizes the existing NYSDOT data to all road segments in Manhattan?– by introducing a supervised learning task of multi-output regression, where ML algorithms use road segment attributes to predict hourly traffic volume. We consider four ML algorithms– K-Nearest Neighbors, Decision Tree, Random Forest, and Neural Network– and hyperparameter tune by evaluating the performances of each algorithm with 10-fold cross validation. Ultimately, we conclude that neural networks are the best-performing models and require the least amount of testing time. Lastly, we provide insight into the quantification of “trustworthiness” in a model, followed by brief discussions on interpreting model performance, suggesting potential project improvements, and identifying the biggest takeaways. Overall, we hope our work can serve as an effective baseline for realistic traffic volume generation, and open new directions in the processes of supervised dataset generation and ML algorithm design.
ContributorsOtstot, Kyle (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
Phishing is one of most common and effective attack vectors in modern cybercrime. Rather than targeting a technical vulnerability in a computer system, phishing attacks target human behavioral or emotional tendencies through manipulative emails, text messages, or phone calls. Through PyAntiPhish, I attempt to create my own version of an anti-phishing solution, through a series of experiments testing different machine learning classifiers and URL features. With an end-goal implementation as a Chromium browser extension utilizing Python-based machine learning classifiers (those available via the scikit-learn library), my project uses a combination of Python, TypeScript, Node.js, as well as AWS Lambda and API Gateway to act as a solution capable of blocking phishing attacks from the web browser.
ContributorsYang, Branden (Author) / Osburn, Steven (Thesis director) / Malpe, Adwith (Committee member) / Ahn, Gail-Joon (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05