Digital Commons Network^™

Optimizing A Virtual Human Platform For Depression/Suicide Ideation Identification For The American Soldier, Christina M. Monahan

Master's Theses

Suicide surpassed homicide to be the second leading cause of death among people 10-24 years old in the United States \cite{1}. This statistic is alarming especially when combined with the more than eight distinctly different types of clinical depression among society today \cite{2}. To further complicate this health crisis, let’s consider the current worldwide isolating pandemic often referred to as COVID-19 that has spanned 12 months. It is more important than ever to consider how we can get ahead of the crisis by identifying the symptoms as they set in and more importantly ahead of the decision to commit suicide. …

Wi-Fi Sensing: Device-Free In-Zone Object Movement Detection, Nicholas P. Schnorr

Master's Theses

Wi-Fi Sensing is becoming a prominent field with a wide range of potential applications. Using existing hardware on a wireless network such as access points, cell phones, and smart home devices, important information can be inferred about the current physical environment. Through the analysis of Channel State Information collected in the Neighborhood Discovery Protocol process, the wireless network can detect disturbances in Wi-Fi signals when the physical environment changes. This results in a system that can sense motion within the Wi-Fi network, allowing for movement detection without any wearable devices.

The goal of this thesis is to answer whether Wi-Fi …

An Analysis Of Camera Configurations And Depth Estimation Algorithms For Triple-Camera Computer Vision Systems, Jared Peter-Contesse

Master's Theses

The ability to accurately map and localize relevant objects surrounding a vehicle is an important task for autonomous vehicle systems. Currently, many of the environmental mapping approaches rely on the expensive LiDAR sensor. Researchers have been attempting to transition to cheaper sensors like the camera, but so far, the mapping accuracy of single-camera and dual-camera systems has not matched the accuracy of LiDAR systems. This thesis examines depth estimation algorithms and camera configurations of a triple-camera system to determine if sensor data from an additional perspective will improve the accuracy of camera-based systems. Using a synthetic dataset, the performance of …

Subnational Map Of Poverty Generated From Remote-Sensing Data In Africa: Using Machine Learning Models And Advanced Regression Methods For Poverty Estimation, Lionel N. Hanke

Master's Theses

According to the 2020 poverty estimates from the World Bank, it is estimated that 9.1% - 9.4% of the global population lived on less than $1.90 per day. It is estimated that the Covid-19 pandemic further aggravated the issue by pushing more than 1% of the global population below the international poverty line of $1.90 per day (WorldBank, 2020). To provide help and formulate effective measures, poverty needs to be located as exact as possible. For this purpose, it was investigated whether regression methods with aggregated remote-sensing data could be used to estimate poverty in Africa. Therefore, five distinct regression …

Snr: Software Library For Introductory Robotics, Spencer F. Shaw

Master's Theses

This thesis introduces "SNR," a Python library for programming robotic systems in the context of introductory robotics courses. Greater demand for roboticists has pressured educational institutions to expand robotics curricula. Students are now more likely to take robotics courses earlier and with less prior programming experience. Students may be attempting to simultaneously learn a systems programming language, a library API, and robotics concepts. SNR is written purely in Python to present familiar semantics, eliminating one of these learning curves. Industry standard robotics libraries such as ROS often require additional build tools and configuration languages. Students in introductory courses frequently lack …

A Method For Monitoring Operating Equipment Effectiveness With The Internet Of Things And Big Data, Carl D. Hays Iii

Master's Theses

The purpose of this paper was to use the Overall Equipment Effectiveness productivity formula in plant manufacturing and convert it to measuring productivity for forklifts. Productivity for a forklift was defined as being available and picking up and moving containers at port locations in Seattle and Alaska. This research uses performance measures in plant manufacturing and applies them to mobile equipment in order to establish the most effective means of analyzing reliability and productivity. Using the Internet of Things to collect data on fifteen forklift trucks in three different locations, this data was then analyzed over a six-month period to …

Using Pitch Tipping For Baseball Pitch Prediction, Brian Ishii

Master's Theses

Data Analytics and technology have changed baseball as we know it. From the increase in defensive shifts to teams using cameras in the outfield to steal signs, teams will try anything to win. One way to gain an edge in baseball is to figure out what pitches a pitcher will pitch. Pitch prediction is a popular task to try to accomplish with all the data that baseball provides. Most methods involve using situational data like the ball and strike count. In this paper, we try a different method of predicting pitch type by only looking at the pitcher's pose in …

A Power System-Based Iot Network For Remote Sensing Applications, Dominic Gaiero

Master's Theses

Cities around the world are facing increasingly significant challenges, including rapid urbanization, resource management, and environmental threats. In California for example, wildfires present an ever-growing threat that gravely harms people, destroys communities, and causes billions of dollars in damages. The task of addressing these environmental threats and many other challenges is greatly aided with widespread data collection and real-time inference. However, as IoT networks scale and require more energy for near-data analytics, the IoT endpoints require more power and complexity, limiting their deployment. Additionally, deploying endpoints in remote locations creates further challenges with higher reliability and communication constraints. In this …

Scalable Cognitive Radio Network Testbed In Real Time, Kevin Z. Yu

Master's Theses

Modern society places an increasingly high demand on data transmission. Much of that data transmission takes place through communication over the frequency spectrum. The channels on the spectrum are limited resources. Researchers realize that at certain times of day some channels are overloaded, while others are not being fully utilized. A spectrum management system may be beneficial to remedy this efficiency issue. One of the proposed systems, Cognitive Radio Network (CRN), has progressed over the years thanks to studies on a wide range of subjects, including geolocation, data throughput rate, and channel handoff selection algorithm, which provide fundamental support for …

Dependencyvis: Helping Developers Visualize Software Dependency Information, Nathan Lui

Master's Theses

The use of dependencies have been increasing in popularity over the past decade, especially as package managers such as JavaScript's npm has made getting these packages a simple command to run. However, while incidents such as the left-pad incident has increased awareness of how vulnerable relying on these packages are, there is still some work to be done when it comes to getting developers to take the extra research step to determine if a package is up to standards. Finding metrics of different packages and comparing them is always a difficult and time consuming task, especially since potential vulnerabilities are …

A Study Of Implementation Methodologies For Distributed Real Time Collaboration, Lauren A. Craft

Master's Theses

Collaboration drives our world and is almost unavoidable in the programming industry. From higher education to the top technological companies, people are working together to drive discovery and innovation. Software engineers must work with their peers to accomplish goals daily in their workplace. When working with others there are a variety of tools to choose from such as Google Docs, Google Colab and Overleaf. Each of the aforementioned collaborative tools utilizes the Operational Transform (OT) technique in order to implement their real time collaboration functionality. Operational transform is the technique seen amongst most if not all major collaborative tools in …

Automating Deep-Sea Video Annotation, Hanson Egbert

Master's Theses

As the world explores opportunities to develop offshore renewable energy capacity, there will be a growing need for pre-construction biological surveys and post-construction monitoring in the challenging marine environment. Underwater video is a powerful tool to facilitate such surveys, but the interpretation of the imagery is costly and time-consuming. Emerging technologies have improved automated analysis of underwater video, but these technologies are not yet accurate or accessible enough for widespread adoption in the scientific community or industries that might benefit from these tools.

To address these challenges, prior research developed a website that allows to: (1) Quickly play and annotate …

Energy Efficient Computing Using Scalable General Purpose Analog Processors, Ethan Paul Palisoc De Guzman

Master's Theses

Due to fundamental physical limitations, conventional digital circuits have not been able to scale at the pace expected from Moore’s law. In addition, computationally intensive applications such as neural networks and computer vision demand large amounts of energy from digital circuits. As a result, energy efficient alternatives are needed in order to provide continued performance scaling. Analog circuits have many well known benefits: the ability to store more information onto a single wire and efficiently perform mathematical operations such as addition, subtraction, and differential equation solving. However, analog computing also comes with drawbacks such as its sensitivity to process variation …

A Deep Learning-Based Automatic Object Detection Method For Autonomous Driving Ships, Ojonoka Erika Atawodi

Master's Theses

An important feature of an Autonomous Surface Vehicles (ASV) is its capability of automatic object detection to avoid collisions, obstacles and navigate on their own.

Deep learning has made some significant headway in solving fundamental challenges associated with object detection and computer vision. With tremendous demand and advancement in the technologies associated with ASVs, a growing interest in applying deep learning techniques in handling challenges pertaining to autonomous ship driving has substantially increased over the years.

In this thesis, we study, design, and implement an object recognition framework that detects and recognizes objects found in the sea. We first curated …