Signal Phase Recovery and Unwrapping

Description
The need to recover a signal from incomplete or corrupted measurements is a central challenge in signal processing. A particular problem of this type is recovery of a signal after its Fourier magnitude or its Fourier phase is lost. This problem

The need to recover a signal from incomplete or corrupted measurements is a central challenge in signal processing. A particular problem of this type is recovery of a signal after its Fourier magnitude or its Fourier phase is lost. This problem has a rich history that originated in the field of x-ray crystallography and continues to be of substantial interest in molecular imaging and numerous other applications. It has been observed that Fourier phase is typically more important in representing recognizable features of one-dimensional signals (e.g., audio waveforms) and two-dimensional signals, such as images. Classical experiments illustrating this observation are reproduced in this thesis, and practical iterative algorithms for recovering a signal from either its phase or magnitude are demonstrated. Unsurprisingly, it is typically more difficult to compensate for the loss of phase information, and recovery of a signal from its Fourier magnitude is seen to be less effective than recovery from its Fourier phase. A partitioning method is introduced to improve image recovery from magnitude information, and the phase unwrapping problem for one-dimensional signals is discussed briefly.
Date Created
2024-05
Agent

Machine Learning Methods for Defect Analysis in Semiconductor Manufacturing

Description
Semiconductor manufacturing produces a variety of particle defects on wafer surfaces, each defect having its own topology. Statistical trends among these topologies can be discovered by using unsupervised machine learning techniques such as K-means clustering. By employing four (4) different

Semiconductor manufacturing produces a variety of particle defects on wafer surfaces, each defect having its own topology. Statistical trends among these topologies can be discovered by using unsupervised machine learning techniques such as K-means clustering. By employing four (4) different heuristics, the K-means algorithm can be optimized to generate clusters of defect images that are well separated and highly congruent to the features extracted from the images. The result is the formation of clusters that demonstrate a high degree of qualitative similarity among the topologies of all the defects in the cluster. Further study should confirm which exact features are selected by the model by comparing trends in chemical or procedural analyses.
Date Created
2024-05
Agent

MMS Electrical Design: Aiding Chronic Fatigue Syndrome Research

Description

This project revolves around the enhancement of an existing data collection device utilized for patient monitoring within the framework of the leadership of Shad Roundy's team. The initial deployment involved a 10-Axis Internal Measurement Unit (IMU) sourced from MetaMotionS (MMS)

This project revolves around the enhancement of an existing data collection device utilized for patient monitoring within the framework of the leadership of Shad Roundy's team. The initial deployment involved a 10-Axis Internal Measurement Unit (IMU) sourced from MetaMotionS (MMS) for comprehensive data acquisition from patients at University of Utah’s Downtown Behavioral Health Clinic (BHC). The primary objective transitioned towards optimizing the device's functionality, particularly addressing challenges related to limited battery life, device size, and data transfer efficiency. A systematic approach was undertaken to address these challenges, involving meticulous research into alternative batteries, with the CL 582728 identified as a promising solution capable of extending the device's operational lifespan to around one month. Additionally, the initiative aimed at refining data collection processes through real-time transmission facilitated by Raspberry Pi devices at BHC via Bluetooth, leveraging the energy-efficient Nordic Semiconductor nRF52840 Bluetooth chip. The project also entailed intricate circuit design endeavors utilizing Autodesk Eagle, with reference to a model provided by MMS. Despite encountering programming challenges for the microcontroller, the groundwork was laid for a conceptual solution, with plans to delegate the programming task to a team member possessing advanced expertise. Though the device has yet to be fabricated, the design is near completion.

Date Created
2024-05
Agent

Examining Observational Signatures and Theoretical Models of Dark Matter

Description
The vast majority of matter found within the universe is from the dark sector composed of 75% dark energy and 20% dark matter. While the accelerated expansion rate of the universe is attributable to dark energy, dark matter is fundamentally

The vast majority of matter found within the universe is from the dark sector composed of 75% dark energy and 20% dark matter. While the accelerated expansion rate of the universe is attributable to dark energy, dark matter is fundamentally defined as an unknown substance that interacts gravitationally with its surroundings. The research presented here investigates the methods derived from observational signatures to construct theoretical models of dark matter.
Date Created
2024-05
Agent

A Comprehensive Study on Object Detection Technology for Small-Scale, Low-Power Motion Based Applications

Description
The thesis explores the avenues of machine learning principles in object detection using TensorFlow 2 Object Detection API Libraries for implementation. Integrating object detection capabilities into ESP-32 cameras can enhance functionality in the capstone dragster application and potential applications, such

The thesis explores the avenues of machine learning principles in object detection using TensorFlow 2 Object Detection API Libraries for implementation. Integrating object detection capabilities into ESP-32 cameras can enhance functionality in the capstone dragster application and potential applications, such as autonomous robots. The research implements the TensorFlow 2 Object Detection API, a widely used framework for training and deploying object detection models. By leveraging the pre-trained models available in the API, the system can detect a wide range of objects with high accuracy and speed. Fine-tuning these models using a custom dataset allows us to enhance their performance in detecting specific objects of interest. Experiments to identify strengths and weaknesses of each model's implementation before and after training using similar images were evaluated The thesis also explores the potential limitations and challenges of deploying object detection on real-time ESP-32 cameras, such as limited computational resources, costs, and power constraints. The results obtained from the experiments demonstrate the feasibility and effectiveness of implementing object detection on ESP-32 cameras using the TensorFlow2 Object Detection API. The system achieves satisfactory accuracy and real-time processing capabilities, making it suitable for various practical applications. Overall, this thesis provides a foundation for further advancements and optimizations in the integration of object detection capabilities into small, low-power devices such as ESP-32 cameras and a crossroad to explore its applicability for other image-capturing and processing devices in industrial, automotive, and defense sectors of industry.
Date Created
2024-05
Agent

Modeling SKYSURF Completeness Data for Comparison to the Hubble Space Telescope Exposure Time Calculator

Description
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

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.
Date Created
2024-05
Agent

Meditations on the Mary-El: A Spiritual Journey through Tarot

Description
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

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.
Date Created
2024-05
Agent

Rodgers and Hammerstein's Oklahoma! Reimagined for the Jazz Idiom

Description

In the early history of jazz, many of the songs that were popularized by jazz musicians became known as jazz standards, and these songs remain a central component of the jazz repertoire today. Many of these jazz standards were adapted

In the early history of jazz, many of the songs that were popularized by jazz musicians became known as jazz standards, and these songs remain a central component of the jazz repertoire today. Many of these jazz standards were adapted from early Broadway musicals and revues. Rodgers and Hammerstein's Oklahoma! is widely considered to be the most significant musical in the history of Broadway theater. Its innovative blending of song, dance, and a cohesive dramatic story has profoundly influenced the structure of Broadway musicals to this day. However, none of the songs from this show have risen to the status of a jazz standard, and many appear to have not been adapted to the jazz idiom at all. In my Barrett Honors creative project, I have reimagined and arranged nine songs from the original Rodgers and Hammerstein production. I then led a jazz quintet through two months of rehearsals, culminating in a performance of the work for my senior jazz performance recital. A link to the performance at the ASU School of Music Recital Hall is included here: https://www.youtube.com/watch?v=jeOs4muj12M

Date Created
2024-05
Agent