To reduce the cost of silicon solar cells and improve their efficiency, it is crucial to identify and understand the defects limiting the electrical performance in silicon wafers. Bulk defects in semiconductors produce discrete energy levels within the bandgap and may act as recombination centers. This project investigates the viability of using machine learning for characterizing bulk defects in Silicon by using a Random Forest Regressor to extract the defect energy level and capture cross section ratios for a simulated Molybdenum defect and experimental Silicon Vacancy defect. Additionally, a dual convolutional neural network is used to classify the defect energy level in the upper or lower half bandgap.

This honors thesis explores the potential use of LoRa technology for detecting moisture in a diaper. Tests of both onboard and external humidity sensors coupled with LoRa transmission are incredibly promising. The potential scale of the final device also shows much promise, measuring smaller than a U.S. dime. However, the estimated cost for producing these proof-of-concept units in bulk is $19.41 per unit. While this is believed to be a pessimistic estimate of the price, the cost of production remains too high regardless for large-scale implementation. The thesis concludes by emphasizing the need for further research and development to optimize the design and reduce the cost of production. Despite the limitations imposed by price, the idea of using LoRa in detecting moisture in a diaper remains intriguing and promising, however, RFID technology has many advantages, such as size, cost, and passive power features.

This honors thesis report aims to propose a sustainable long-term solution for providing off-grid solar energy to rural communities that lack the necessary grid energy infrastructure. With this in mind, we aim to establish the framework and documentation for people to be able to build and maintain their own off-grid solar power systems. Due to recent incentives for clean energy both nationwide and statewide, the team will discuss the current renewable energy market and the future growth potential of residential solar energy systems, which includes off-grid or remote solar. This discussion will include comparing pre-built solar systems currently offered for purchase against the proposed design outlined in this report. Notably, the outlined design has been made with an emphasis on system sustainability, low initial cost, reliability, ease of manufacturing/maintenance, and material selection. Lastly, the team will discuss the project’s approach to documentation with a user manual draft to ensure the system's long-term sustainability and troubleshooting. Although the efforts of this project have increased over time, this project remains active within the ASU EWB chapter, meaning that not all aspects described throughout this report are fully complete as future work will continue to optimize and improve the system. A rural community in northern Arizona, will be used as an example to understand a rural community's needs for designing a solar panel system that provides sufficient energy for a single household. The project was completed in collaboration with Arizona State University’s Engineering Projects In Community Service (EPICS) program and Engineers Without Borders (EWB) chapter. Both these organizations aim to connect ASU students to the professional mentors and resources they need to design and implement low-cost, small-scale, easily replicated, and sustainable engineering projects.

Visual impairment is a significant challenge that affects millions of people worldwide. Access to written text, such as books, documents, and other printed materials, can be particularly difficult for individuals with visual impairments. In order to address this issue, our project aims to develop a text-to-Braille and speech translating device that will help people with visual impairments to access written text more easily and independently.

As more electric vehicles (EVs) are adopted, users need a solution to meet their expectations when it comes to Level 2 EV Charging (EVC). Currently, Adaptive Charging (AC) Techniques are used in multi-unit, public, settings. In the future, AC should be utilized to provide an optimized charging experience for the EV user in a single-unit residential application. In this experiment, an Electric Vehicle simulation tool was created using Python. A training dataset was generated from Alternative Fuels and Data Center (EVI-Pro) using charging data from Phoenix, Arizona. Similarly, the utility price plan chosen for this exercise was SRP Electric Vehicle Price plan. This will be the cost-basis for the thesis. There were four cases that were evaluated by the simulation tool. (1) Utility Guided Scheduling (2) Automatic Scheduling (3) Off-Site Enablement (4) Bidirectional enablement. These use-cases are some of the critical problems facing EV users when it comes to charging at home. Each of these scenarios and algorithms were proven to save the user money in their daily bill. Overall, the user will need some sort of weighted scenario that considers all four cases to provide the best solution to the user. All four scenarios support the use of Adaptive Charging techniques in residential level 2 electric vehicle chargers. By applying these techniques, the user can save up to 90% on their energy bill while offsetting the energy grid during peak hours. The adaptive charging techniques applied in this thesis are critical to the adoption of the next generation electric vehicles. Users need to be enabled to use the latest and greatest technology. In the future, individuals can use this report as a baseline to use an Artificial Intelligence model to make an educated case-by-case decision to deal with the variability of the data.