Using Wide Field Camera 3 (WFC3) data from the Hubble Space Telescope (HST) archival project "SKYSURF", we model completeness with respect to the exposure time and background of an image. This is accomplished by adding simulated objects with varying magnitudes and sizes into these HST images, and determining the matching rate for each set of parameters. The fifty percent completeness results then can be compared to the Exposure Time Calculator (ETC), in order to assess the differences between it and our analysis of the archive data. We find that for larger objects and exposures the ETC predicts higher completeness magnitudes, while for smaller objects, the ETC predicts lower magnitudes.

The Mary-El tarot deck is famous in the tarot community for its intense spiritual power and esoteric imagery. By analyzing its major arcana and investigating the symbology featured therein, for purposes of making the deck accessible to others, I discover a rich world of flowing energies and underlying transcendence. I've used writing to document my journey and discoveries of the internal self. I present these writings as my thesis, and I demonstrate my understanding of the cards through tarot readings.

Every year, Arizona mobile home residents suffer hundreds of fatalities and severe illnesses due to the effects of extreme heat within their homes exacerbated by high energy costs, a lack of energy-efficient infrastructure, and underlying socio-economic issues. Many of these deaths and severe illnesses can be prevented via active monitoring and reporting of temperature and humidity data from these living spaces. The team will design, build, test, and implement a Heat Warning Detection System (HWDS) to mitigate heat-related illnesses and deaths. The HWDS will detect when temperature and humidity levels have reached a dangerous threshold and will issue notifications to the emergency contacts of the resident over SMS and/or email. This will allow for timely preventative measures to be taken to ensure the safety of the resident. The team will investigate the ideal threshold to notify the mobile home residents. HWDS will require minimal user interaction. Apart from the initial physical installation of the device, the user will have to provide a list of emergency contacts that they would like the system to notify in the event that HWDS detects dangerous conditions in their residence. By deploying prototypes of HWDS to volunteer participant homes, we will be able to validate the functionality of the system as well as the usability of the physical device by homeowners. HWDS provides homeowners and their loved ones with the opportunity to take preventative measures before being exposed to conditions that could potentially have more severe implications. In the spirit of promoting accessibility and prevention among the most vulnerable communities in Greater Phoenix, our team partners with the Knowledge Exchange for Resilience at ASU (KER) to interface with organizations such as the Arizona Association of Manufactured Home, RV & Park Model Owners (AAMHO) to promote legislation and subsidies aimed towards making solutions such as ours more financially viable for the communities that need it most.

Recent satellite and remote sensing innovations have led to an eruption in the amount and variety of geospatial ice data available to the public, permitting in-depth study of high-definition ice imagery and digital elevation models (DEMs) for the goal of safe maritime navigation and climate monitoring. Few researchers have investigated texture in optical imagery as a predictive measure of Arctic sea ice thickness due to its cloud pollution, uniformity, and lack of distinct features that make it incompatible with standard feature descriptors. Thus, this paper implements three suitable ice texture metrics on 1640 Arctic sea ice image patches, namely (1) variance pooling, (2) gray-level co-occurrence matrices (GLCMs), and (3) textons, to assess the feasibly of a texture-based ice thickness regression model. Results indicate that of all texture metrics studied, only one GLCM statistic, namely homogeneity, bore any correlation (0.15) to ice freeboard.

Power generation through heat to electrical energy conversion for space applications faces distinct challenges not encountered in terrestrial settings, where Rankine and Brayton cycles have traditionally been predominant. The unique environment of space necessitates the adoption of either static converters, leveraging solid-state physics, or closed-cycle dynamic converters. While thermoelectric generators have historically been the primary choice for heat-to-electrical energy conversion in space applications, their relatively low efficiencies and limited scope for enhancement pose significant challenges as the power demands of space missions increase. This necessitates the exploration of alternative power generation methodologies to meet the evolving requirements. This thesis provides a comprehensive analysis of various power conversion technologies for space applications, focusing on the comparative study of static and dynamic converters, with a particular emphasis on Stirling converters. Other power systems discussed include thermoelectric, thermophotovoltaic, thermionic, and Brayton converters. Through comparative analysis, the research identifies the most promising converters for future space applications.

Machine learning has been increasingly integrated into several new areas, namely those related to vision processing and language learning models. These implementations of these processes in new products have demanded increasingly more expensive memory usage and computational requirements. Microcontrollers can lower this increasing cost. However, implementation of such a system on a microcontroller is difficult and has to be culled appropriately in order to find the right balance between optimization of the system and allocation of resources present in the system. A proof of concept that these algorithms can be implemented on such as system will be attempted in order to find points of contention of the construction of such a system on such limited hardware, as well as the steps taken to enable the usage of machine learning onto a limited system such as the general purpose MSP430 from Texas Instruments.

Quantum entanglement, a phenomenon first introduced in the realm of quantum mechanics by the famous Einstein-Podolsky-Rosen (EPR) paradox, has intrigued physicists and philosophers alike for nearly a century. Its implications for the nature of reality, particularly its apparent violation of local realism, have sparked intense debate and spurred numerous experimental investigations. This thesis presents a comprehensive examination of quantum entanglement with a focus on probing its non-local aspects. Central to this thesis is the development of a detailed project document outlining a proposed experimental approach to investigate the non-local nature of quantum entanglement. Drawing upon recent advancements in quantum technology, including the manipulation and control of entangled particles, the proposed experiment aims to rigorously test the predictions of quantum mechanics against the framework of local realism. The experimental setup involves the generation of entangled particle pairs, such as photons or ions, followed by the precise manipulation of their quantum states. By implementing a series of carefully designed measurements on spatially separated entangled particles, the experiment seeks to discern correlations that defy explanation within a local realistic framework.