Engineering Commons^™

Insights Into Cellular Evolution: Temporal Deep Learning Models And Analysis For Cell Image Classification, Xinran Zhao

Master's Theses

Understanding the temporal evolution of cells poses a significant challenge in developmental biology. This study embarks on a comparative analysis of various machine-learning techniques to classify cell colony images across different timestamps, thereby aiming to capture dynamic transitions of cellular states. By performing Transfer Learning with state-of-the-art classification networks, we achieve high accuracy in categorizing single-timestamp images. Furthermore, this research introduces the integration of temporal models, notably LSTM (Long Short Term Memory Network), R-Transformer (Recurrent Neural Network enhanced Transformer) and ViViT (Video Vision Transformer), to undertake this classification task to verify the effectiveness of incorporating temporal features into the classification …

Brunet: Disruption-Tolerant Tcp And Decentralized Wi-Fi For Small Systems Of Vehicles, Nicholas Brunet

Master's Theses

Reliable wireless communication is essential for small systems of vehicles. However, for small-scale robotics projects where communication is not the primary goal, programmers frequently choose to use TCP with Wi-Fi because of their familiarity with the sockets API and the widespread availability of Wi-Fi hardware. However, neither of these technologies are suitable in their default configurations for highly mobile vehicles that experience frequent, extended disruptions. BRUNET (BRUNET Really Useful NETwork) provides a two-tier software solution that enhances the communication capabilities for Linux-based systems. An ad-hoc Wi-Fi network permits decentralized peer-to-peer and multi-hop connectivity without the need for dedicated network infrastructure. …

Decentralized Machine Learning On Blockchain: Developing A Federated Learning Based System, Nikhil Sridhar

Master's Theses

Traditional Machine Learning (ML) methods usually rely on a central server to per-
form ML tasks. However, these methods have problems like security risks, data
storage issues, and high computational demands. Federated Learning (FL), on the
other hand, spreads out the ML process. It trains models on local devices and then
combines them centrally. While FL improves computing and customization, it still
faces the same challenges as centralized ML in security and data storage.

This thesis introduces a new approach combining Federated Learning and Decen-
tralized Machine Learning (DML), which operates on an Ethereum Virtual Machine
(EVM) compatible blockchain. The …

Contextually Dynamic Quest Generation Using In-Session Player Information In Mmorpg, Shangwei Lin

Master's Theses

Massively multiplayer online role-playing games (MMORPGs) are one of the most

popular genres in video games that combine massively multiplayer online genres with

role-playing gameplay. MMORPGs’ featured social interaction and forms of level pro-

gression through quest completion are the core for gaining players’ attention. Varied

and challenging quests play an essential part in retaining that attention. However,

well-crafted content takes much longer to develop with human efforts than it does to

consume, and the dominant procedural content generation models for quests suffer

from the drawback of being incompatible with dynamic world changes and the feeling

of repetition over time. …

Analysis And Usage Of Natural Language Features In Success Prediction Of Legislative Testimonies, Marine Cossoul

Master's Theses

Committee meetings are a fundamental part of the legislative process in which
constituents, lobbyists, and legislators alike can speak on proposed bills at the
local and state level. Oftentimes, unspoken “rules” or standards are at play in
political processes that can influence the trajectory of a bill, leaving constituents
without a political background at an inherent disadvantage when engaging with
the legislative process. The work done in this thesis aims to explore the extent to
which the language and phraseology of a general public testimony can influence a
vote, and examine how this information can be used to promote civic …

Shelfaware: Accelerating Collaborative Awareness With Shelf Crdt, John C. Waidhofer

Master's Theses

Collaboration has become a key feature of modern software, allowing teams to work together effectively in real-time while in different locations. In order for a user to communicate their intention to several distributed peers, computing devices must exchange high-frequency updates with transient metadata like mouse position, text range highlights, and temporary comments. Current peer-to-peer awareness solutions have high time and space complexity due to the ever-expanding logs that each client must maintain in order to ensure robust collaboration in eventually consistent environments. This paper proposes an awareness Conflict-Free Replicated Data Type (CRDT) library that provides the tooling to support an …

Group-Invariant Reinforcement Learning, Fnu Ankur

Master's Theses

Our work introduces a way to learn an optimal reinforcement learning agent accompanied by intrinsic properties of the environment. The extracted properties helps the agent to extrapolate the learning to unseen states efficiently. Out of all the various types of properties, we are intrigued towards equivariant and invariant properties, which essentially translates to symmetry. Contrary to many approaches, we do not assume the symmetry, rather learn them, making the approach agnostic to the environment and the property. The learned properties offers multiple perspective of the environment to exploit it to benefit decision making while interacting with the environment. By building …

Automatic Presentation Slide Generation Using Llms, Tanya Gupta

Master's Theses

Presentation slides are widely used for conveying information in academic and professional contexts. However, manual slide creation can be time-consuming. Our research focuses on automated slide generation, specifically for scientific research papers. Automating the creation of presentation slides for scientific documents is a rather novel task and hence, there’s limited training data available and there also exists the token constraints of language models like BERT, with a maximum sequence length of 512 tokens. In this study, we fine-tune large language models, including Longformer-Encoder-Decoder (supporting sequences up to 16,834 tokens) and BIGBIRD-Pegasus (supporting sequences up to 4,096 tokens). We tackle this …

Detecting The Onion Routing Traffic In Real-Time By Using Reinforcement Learning, Dazhou Liu

Master's Theses

Anonymous networks have been popularly utilized to protect user anonymity and facilitate network security for a decade. However, such networks have been a platform for adversarial affairs and various network attacks including suspicious traffic generators. As a result, detecting anonymous network traffic is one critical task to defend a network against unpredictable attacks. Many new methods using machine learning and deep learning techniques have been proposed. However, many of them rely heavily on a vast amount of labeled data and have complicated architectures. Since network traffic always fluctuates under different network environments, those techniques may degrade in performance due to …

Intrinsic Motivation By The Principles Of Non-Linear Dynamical Systems, Phu C. Nguyen

Master's Theses

The design of appropriate control rules for the stabilization of dynamical systems can require quite substantial domain knowledge. Modern AI methodologies, such as Reinforcement Learning, are often used to mitigate the need for such knowledge. However, these can be slow and often rely on at least some hand-designed reward structure, and thus human input, to be more effective. Here, we propose an alternative route to construct rewards requiring only minimal domain knowledge, essentially relying on the structure of the dynamical system itself. For this, we use truncated Lyapunov exponents as rewards to calculate the stabilizing controller from samples. Concretely, the …

Controllability-Constrained Deep Neural Network Models For Enhanced Control Of Dynamical Systems, Suruchi Sharma

Master's Theses

Control of a dynamical system without the knowledge of dynamics is an important and challenging task. Modern machine learning approaches, such as deep neural networks (DNNs), allow for the estimation of a dynamics model from control inputs and corresponding state observation outputs. Such data-driven models are often utilized for the derivation of model-based controllers. However, in general, there are no guarantees that a model represented by DNNs will be controllable according to the formal control-theoretical meaning of controllability, which is crucial for the design of effective controllers. This often precludes the use of DNN-estimated models in applications, where formal controllability …

Deep Learning In Ai Medical Imaging For Stroke Diagnosis, James Mario Guzman

Master's Theses

Enhancing medical imaging stroke diagnosis applications with artificial intelligence (AI) tools to determine lesion volume, location and clinical metadata is vital toward guiding patient treatment and procedure. A major hardship in developing stroke diagnosis AI tools is the scarcity of publicly available clinical 3D stroke datasets. Through working with Johns Hopkins University, University of Michigan’s ICPSR data repository and SJSU research, we gained access to potentially the largest 3D MRI stroke dataset with clinical metadata annotated by neuroradiologists known as ICPSR 38464. With the ICPSR 38464 dataset recently being available through institutional review board (IRB) approval or exemption, we were …

A Design Of A Digital Lockout Tagout System With Machine Learning, Brandon H. Chen

Master's Theses

Lockout Tagout (LOTO) is a safety procedure instated by the Occupational Safety and Health Administration (OSHA) when doing maintenance on dangerous machinery and hazardous power sources. In this procedure, authorized workers shut off the machinery and use physical locks and tags to prevent operation during maintenance. LOTO has been the industry standard for 32 years since it was instantiated, being used in many different industries such as industrial work, mining, and agriculture. However, LOTO is not without its issues. The LOTO procedure requires employees to be trained and is prone to human error. As well, there is a clash between …

Identifying And Minimizing Underspecification In Breast Cancer Subtyping, Jonathan Cheuk-Kiu Tang

Master's Theses

In the realm of biomedical technology, both accuracy and consistency are crucial to the development and deployment of these tools. While accuracy is easy to measure, consistency metrics are not so simple to measure, especially in the scope of biomedicine where prediction consistency can be difficult to achieve. Typically, biomedical datasets contain a significantly larger amount of features compared to the amount of samples, which goes against ordinary data mining practices. As a result, predictive models may fail to find valid pathways for prediction during training on such datasets. This concept is known as underspecification.

Underspecification has been more accepted …

Panodepth – Panoramic Monocular Depth Perception Model And Framework, Adley K. Wong

Master's Theses

Depth perception has become a heavily researched area as companies and researchers are striving towards the development of self-driving cars. Self-driving cars rely on perceiving the surrounding area, which heavily depends on technology capable of providing the system with depth perception capabilities. In this paper, we explore developing a single camera (monocular) depth prediction model that is trained on panoramic depth images. Our model makes novel use of transfer learning efficient encoder models, pre-training on a larger dataset of flat depth images, and optimizing the model for use with a Jetson Nano. Additionally, we present a training and optimization framework …

A Nano-Drone Safety Architecture, Connor J. Sexton

Master's Theses

As small-form factor drones grow more intelligent, they increasingly require more sophisticated capabilities to record sensor data and system state, ensuring safe and improved operation. Already regulations for black boxes, electronic data recorders (EDRs), for determining liabilities and improving the safety of large-form factor autonomous vehicles are becoming established. Conventional techniques use hardened memory storage units that conserve all sensor (visual) and system operational state; and N-way redundant models for detecting uncertainty in system operation. For small-form factor drones, which are highly limited by weight, power, and computational resources, these techniques become increasingly prohibitive. In this paper, we propose a …

Strainer: State Transcript Rating For Informed News Entity Retrieval, Thomas M. Gerrity

Master's Theses

Over the past two decades there has been a rapid decline in public oversight of state and local governments. From 2003 to 2014, the number of journalists assigned to cover the proceedings in state houses has declined by more than 30\%. During the same time period, non-profit projects such as Digital Democracy sought to collect and store legislative bill and hearing information on behalf of the public. More recently, AI4Reporters, an offshoot of Digital Democracy, seeks to actively summarize interesting legislative data.

This thesis presents STRAINER, a parallel project with AI4Reporters, as an active data retrieval and filtering system for …

Accelerating Graphics Rendering On Risc-V Gpus, Joshua Simpson

Master's Theses

Graphics Processing Units (GPUs) are commonly used to accelerate massively parallel workloads across a wide range of applications from machine learning to cryptocurrency mining. The original application for GPUs, however, was to accelerate graphics rendering which remains popular today through video gaming and video rendering. While GPUs began as fixed function hardware with minimal programmability, modern GPUs have adopted a design with many programmable cores and supporting fixed function hardware for rasterization, texture sampling, and render output tasks. This balance enables GPUs to be used for general purpose computing and still remain adept at graphics rendering. Previous work at the …

Viability And Implementation Of A Vector Cryptography Extension For Risc-V, Jonathan W. Skelly

Master's Theses

RISC-V is an open-source instruction-set architecture (ISA) forming the basis of thousands of commercial and experimental microprocessors. The Scalar Cryptography extension ratified in December 2021 added scalar instructions that target common hashing and encryption algorithms, including SHA2 and AES. The next step forward for the RISC-V ISA in the field of cryptography and digital security is the development of vector cryptography instructions.

This thesis examines if it is viable to add vector implementations of existing RISC-V scalar cryptography instructions to the existing vector instruction format, and what improvements they can make to the execution of SHA2 and AES algorithms. Vector …

A Study Of Grammar-Based Fuzzing Approaches, Ziwei Wu

Master's Theses

Fuzzing is the process of finding security vulnerabilities in code by creating inputs that will activate the exploits. Grammar-based fuzzing uses a grammar, which represents the syntax of all inputs a target program will accept, allowing the fuzzer to create well-formed complex inputs. This thesis conducts an in-depth study on two blackbox grammar-based fuzzing methods, GLADE and Learn&Fuzz, on their performance and usability to the average user. The blackbox fuzzer Radamsa was also used to compare fuzzing effectiveness. From our results in fuzzing PDF objects, GLADE beats both Radamsa and Learn&Fuzz in terms of coverage and pass rate. XML inputs …

Specialized Named Entity Recognition For Breast Cancer Subtyping, Griffith Scheyer Hawblitzel

Master's Theses

The amount of data and analysis being published and archived in the biomedical research community is more than can feasibly be sifted through manually, which limits the information an individual or small group can synthesize and integrate into their own research. This presents an opportunity for using automated methods, including Natural Language Processing (NLP), to extract important information from text on various topics. Named Entity Recognition (NER), is one way to automate knowledge extraction of raw text. NER is defined as the task of identifying named entities from text using labels such as people, dates, locations, diseases, and proteins. There …

Improving Relation Extraction From Unstructured Genealogical Texts Using Fine-Tuned Transformers, Carloangello Parrolivelli

Master's Theses

Though exploring one’s family lineage through genealogical family trees can be insightful to developing one’s identity, this knowledge is typically held behind closed doors by private companies or require expensive technologies, such as DNA testing, to uncover. With the ever-booming explosion of data on the world wide web, many unstructured text documents, both old and new, are being discovered, written, and processed which contain rich genealogical information. With access to this immense amount of data, however, entails a costly process whereby people, typically volunteers, have to read large amounts of text to find relationships between people. This delays having genealogical …

Low-Cost Uav Swarm For Real-Time Object Detection Applications, Joel Valdovinos Miranda

Master's Theses

With unmanned aerial vehicles (UAVs), also known as drones, becoming readily available and affordable, applications for these devices have grown immensely. One type of application is the use of drones to fly over large areas and detect desired entities. For example, a swarm of drones could detect marine creatures near the surface of the ocean and provide users the location and type of animal found. However, even with the reduction in cost of drone technology, such applications result costly due to the use of custom hardware with built-in advanced capabilities. Therefore, the focus of this thesis is to compile an …

A Research Framework And Initial Study Of Browser Security For The Visually Impaired, Elaine Lau, Zachary Peterson

Master's Theses

The growth of web-based malware and phishing attacks has catalyzed significant advances in the research and use of interstitial warning pages and modals by a browser prior to loading the content of a suspect site. These warnings commonly use visual cues to attract users' attention, including specialized iconography, color, and an absence of buttons to communicate the importance of the scenario. While the efficacy of visual techniques has improved safety for sighted users, these techniques are unsuitable for blind and visually impaired users. This is likely not due to a lack of interest or technical capability by browser manufactures, where …

Impact Of Teaching Practices And Communication Climates On Participation In Computer Science Education, Jackie Krone

Master's Theses

One way to understand teaching is to view it as a people process rather than a presentation of knowledge. It follows that the role of an educator often extends beyond the primary subject matter and into the realm of classroom management. With this in mind, our research aimed to capture the various teaching practices, participation patterns, and communication climates that occur in virtual computer science classrooms. We sought to answer the following research questions related to virtual computer science classrooms at our institution: Who participates in virtual computer science classrooms, and is participation proportional to student demographics? Is there any …

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 …

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 …

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 …