Engineering Commons^™

Generalized Model To Enable Zero-Shot Imitation Learning For Versatile Robots, Yongshuai Wu

Master's Theses

The rapid advancement in Deep Learning (DL), especially in Reinforcement Learning (RL) and Imitation Learning (IL), has positioned it as a promising approach for a multitude of autonomous robotic systems. However, the current methodologies are predominantly constrained to singular setups, necessitating substantial data and extensive training periods. Moreover, these methods have exhibited suboptimal performance in tasks requiring long-horizontal maneuvers, such as Radio Frequency Identification (RFID) inventory, where a robot requires thousands of steps to complete.

In this thesis, we address the aforementioned challenges by presenting the Cross-modal Reasoning Model (CMRM), a novel zero-shot Imitation Learning policy, to tackle long-horizontal robotic …

Brain Computer Interface-Based Drone Control Using Gyroscopic Data From Head Movements, Ikaia Cacha Melton

Honors College Theses

This research explores the potential of using gyroscopic data from a person’s head movement to control a DJI Tello quadcopter via a Brain-Computer Interface (BCI). In this study, over 100 gyroscopic recordings capturing the X, Y and Z columns (formally known as GyroX, GyroY, GyroZ) between 4 volunteers with the Emotiv Epoc X headset were collected. The Emotiv Epoc X data captured (left, right, still, and forward) head movements of each participant associated with the DJI Tello quadcopter navigation. The data underwent thorough processing and analysis, revealing distinctive patterns in charts using Microsoft Excel. A Python condition algorithm was then …

Evaluating The Effect Of Noise On Secure Quantum Networks, Karthick Anbalagan

Master's Theses

This thesis focuses on examining the resilience of secure quantum networks to environmental noise. Specifically, we evaluate the effectiveness of two well-known quantum key distribution (QKD) protocols: the Coherent One-Way (COW) protocol and Kak’s Three-Stage protocol (Kak06). The thesis systematically evaluates these protocols in terms of their efficiency, operational feasibility, and resistance to noise, thereby contributing to the progress of secure quantum communications. Using simulations, this study evaluates the protocols in realistic scenarios that include factors such as noise and decoherence. The results illustrate each protocol’s relative benefits and limitations, highlighting the three-stage protocol’s superior security characteristics, resistance to interference, …

Machine Learning For Intrusion Detection Into Unmanned Aerial System 6g Networks, Faisal Alrefaei

Doctoral Dissertations and Master's Theses

Progress in the development of wireless network technology has played a crucial role in the evolution of societies and provided remarkable services over the past decades. It remotely offers the ability to execute critical missions and effective services that meet the user's needs. This advanced technology integrates cyber and physical layers to form cyber-physical systems (CPS), such as the Unmanned Aerial System (UAS), which consists of an Unmanned Aerial Vehicle (UAV), ground network infrastructure, communication link, etc. Furthermore, it plays a crucial role in connecting objects to create and develop the Internet of Things (IoT) technology. Therefore, the emergence of …

Simulating Information And Communication Applications In Employee Interaction Network Models, Matthew Kanter

Student Research Submissions

Information and communication technology (ICT) use has been identified throughout its development and evolution with the Internet boom as a net positive tool for most employees and organizations in the working world. Only recently have studies regarding employees’ well-being begun to come to the forefront of research regarding these rapidly evolving technologies, however these are important issues to discuss in the context of work-life boundary management, emotional exhaustion, overwhelming stress levels, and moral disengagement among other employee well-being dimensions. To explore how employees’ well being might be influenced by ICT use, this study conducted a quantitative survey and analyzed a …

A Smart Hybrid Enhanced Recommendation And Personalization Algorithm Using Machine Learning, Aswin Kumar Nalluri

Electronic Theses, Projects, and Dissertations

In today’s age of streaming services, the effectiveness and precision of recommendation systems are crucial in improving user satisfaction. This project introduces the Smart Hybrid Enhanced Recommendation and Personalization Algorithm (SHERPA) a cutting-edge machine learning approach aimed at transforming how movie suggestions are made. By combining Term Frequency Inverse Document Frequency (TF-IDF) for content based filtering and Alternating Squares (ALS) with Weighted Regularization for filtering SHERPA offers a sophisticated method for delivering tailored recommendations.

The algorithm underwent evaluation using a dataset that included over 50 million ratings from 480,000 Netflix users encompassing 17,000 movie titles. The performance of SHERPA was …

Summonable Construction Delivery Robot, Kevin M. Lewis

Honors Capstones

In many different construction industries, there is a need for tools, parts, and other necessary items to be transported quickly and efficiently over various types of terrain. Human resources have often been used to address these needs, which can become very time and cost inefficient over long periods. The design proposal here is aimed at addressing this need by developing an autonomous outdoor mobile robot based on a quadrupedal robot design. This approach differs by incorporating a wheeled and quadrupedal hybrid actuation system that provides terrain negotiation and speed at the appropriate times. The team uses Robot Operating System (ROS) …

Ai-Powered Information Retrieval In Meeting Records And Transcripts Enhancing Efficiency And User Experience, Srushti Nitin Ghadge

Theses and Dissertations

This study compares the traditional search methods, which is to search from video recordings of the meetings by moving the slider back and forth or by keyword search in transcripts versus integrated AI video plus transcript search. Based on the previous test results, we introduced some human-centric design features to the AI and built a new enhanced AI search tool for information retrieval. For search technique efficiency testing, the method had two set of experiments. The first results of the experiment showed that AI-based search algorithms were more accurate and faster than conventional search approaches. Participants were also happier with …

Effectiveness Of Cnn-Lstm Models Used For Apple Stock Forecasting, Ethan White

Electronic Theses, Projects, and Dissertations

This culminating experience project investigates the effectiveness of convolutional neural networks mixed with long short-term memory (CNN-LSTM) models, and an ensemble method, extreme gradient boosting (XGBoost), in predicting closing stock prices. This quantitative analysis utilizes recent AAPL stock data from the NASDAQ index. The chosen research questions (RQs) are: RQ1. What are the optimal hyperparameters for CNN-LSTM models in stock price forecasting? RQ2. What is the best architecture for CNN-LSTM models in this context? RQ3. How can ensemble techniques like XGBoost effectively enhance the predictions of CNN-LSTM models for stock price forecasting?

The research questions were answered through a thorough …

Automatic Speech Recognition For Air Traffic Control Using Convolutional Lstm, Sakshi Nakashe

Electronic Theses, Projects, and Dissertations

The need for automatic speech recognition in air traffic control is critical as it enhances the interaction between the computer and human. Speech recognition helps to automatically transcribe the communication between the pilots and the air traffic controllers, which reduces the time taken for administrative tasks. This project aims to provide improvement to the Automatic Speech Recognition (ASR) system for air traffic control by investigating the impact of convolution LSTM model on ASR as suggested by previous studies. The research questions are: (Q1) Comparing the performance of ConvLSTM with other conventional models, how does ConvLSTM perform with respect to recognizing …

Investigating Autonomous Ground Vehicles For Weed Elimination, Abraham Mitchell

Computer Science and Computer Engineering Undergraduate Honors Theses

The management of weeds in crop fields is a continuous agricultural problem. The use of herbicides is the most common solution, but herbicidal resistance decreases effectiveness, and the use of herbicides has been found to have severe adverse effects on human health and the environment. The use of autonomous drone systems for weed elimination is an emerging solution, but challenges in GPS-based localization and navigation can impact the effectiveness of these systems. The goal of this thesis is to evaluate techniques for minimizing localization errors of drones as they attempt to eliminate weeds. A simulation environment was created to model …

Automated Brain Tumor Classifier With Deep Learning, Venkata Sai Krishna Chaitanya Kandula

Electronic Theses, Projects, and Dissertations

Brain Tumors are abnormal growth of cells within the brain that can be categorized as benign (non-cancerous) or malignant (cancerous). Accurate and timely classification of brain tumors is crucial for effective treatment planning and patient care. Medical imaging techniques like Magnetic Resonance Imaging (MRI) provide detailed visualizations of brain structures, aiding in diagnosis and tumor classification^[8].

In this project, we propose a brain tumor classifier applying deep learning methodologies to automatically classify brain tumor images without any manual intervention. The classifier uses deep learning architectures to extract and classify brain MRI images. Specifically, a Convolutional Neural Network (CNN) …

Cultural Awareness Application, Bharat Gupta

Electronic Theses, Projects, and Dissertations

In an increasingly interconnected global landscape, cultural awareness and competency have become indispensable skills for individuals and organizations alike. This paper introduces a pioneering cultural awareness application, grounded in the Cultural Orientation Model—a comprehensive framework devised by Dr. Walker [8]to guide individuals in understanding, appreciating, and effectively engaging with diverse cultures. The application encompasses ten primary dimensions, each representing fundamental aspects of social life shared by members of any socio-cultural environment. Through a combination of cultural education, interactive learning, guidance on cultural etiquette, and integration of cultural events, the application aims to foster empathy, tolerance, and effective cross-cultural communication skills. …

Recommendation System Using Machine Learning For Fertilizer Prediction, Durga Rajesh Bommireddy

Electronic Theses, Projects, and Dissertations

This project presents the development of a sophisticated machine-learning model aimed at enhancing agricultural productivity by predicting the optimal fertilizer suited to specific crop requirements. Leveraging a diverse set of features including soil color, pH levels, rainfall, temperature, and crop type, our model offers tailored recommendations to farmers. Three powerful algorithms, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and XG-Boost, were implemented to facilitate the prediction process. Through comprehensive experimentation and evaluation, we assessed the performance of each algorithm in accurately predicting the best fertilizer for maximizing crop yield. The project not only contributes to the advancement of machine …

Understanding Timing Error Characteristics From Overclocked Systolic Multiply-Accumulate Arrays In Fpgas, Andrew S. Chamberlin

All Graduate Theses and Dissertations, Fall 2023 to Present

Artificial Intelligence (AI) is one of the biggest fields of research for computer hardware right now. Hardware accelerators are chips (such as graphics cards) that are purpose built to be the best at a specific type of operation. AI hardware accelerators are a growing field of research. Part of hardware in general is a digital clock that controls the pace at which computations occur. If this clock runs too quickly, the hardware won't have enough time to finish its computation. We call that a timing error. This paper focuses on studying the characteristics of timing errors in a small custom …

Diegetic Sonification For Low Vision Gamers, Jhané Dawes

Master's Theses

There are not many games designed for all players that provide accommodations for low vision users. This means that low vision users may not get to engage with the gaming community in the same way as their sighted peers. In this thesis, I explore how diegetic sonification can be used as a tool to support these low vision gamers in the typical gaming environment. I asked low vision players to engage with a prototype game level with two diegetic sonification techniques applied, without the use of their corrective lenses. I found that participants had more enjoyment and experienced less difficulty …

Side Channel Detection Of Pc Rootkits Using Nonlinear Phase Space, Rebecca Clark

Honors Theses

Cyberattacks are increasing in size and scope yearly, and the most effective and common means of attack is through malicious software executed on target devices of interest. Malware threats vary widely in terms of behavior and impact and, thus, effective methods of detection are constantly being sought from the academic research community to offset both volume and complexity. Rootkits are malware that represent a highly feared threat because they can change operating system integrity and alter otherwise normally functioning software. Although normal methods of detection that are based on signatures of known malware code are the standard line of defense, …

Building Software At Scale: Understanding Productivity As A Product Of Software Engineering Intrinsic Factors, Gauthier Ingende Wa Boway

Master's Theses

During our education at KSU, we have learned about various factors that affect productivity such as schedule, budget, and risks, but those are often controlled outside of what we could learn as software engineering principles, patterns, or practices. On top of that, other off-work factors such as health conditions, emotional distress, or political climate, just to name a few, could drastically affect the productivity of a software engineering team. We see a demarcation between those factors that affect productivity in software engineering but are not inherent to the discipline itself, which we call resistance factors, and the factors that are …

Enhancing Cyber Resilience: Development, Challenges, And Strategic Insights In Cyber Security Report Websites Using Artificial Inteligence, Pooja Sharma

Harrisburg University Dissertations and Theses

In an era marked by relentless cyber threats, the imperative of robust cyber security measures cannot be overstated. This thesis embarks on an in-depth exploration of the historical trajectory and contemporary relevance of penetration testing methodologies, elucidating their evolution from nascent origins to indispensable tools in the cyber security arsenal. Moreover, it undertakes the ambitious task of conceptualizing and implementing a cyber security report website, meticulously designed to fortify cyber resilience in the face of ever-evolving threats in the digital realm.

The research journey commences with an insightful examination of the historical antecedents of penetration testing, tracing its genesis in …

The Role Of Attention Mechanisms In Enhancing Transparency And Interpretability Of Neural Network Models In Explainable Ai, Bhargav Kotipalli

Harrisburg University Dissertations and Theses

In the rapidly evolving field of artificial intelligence (AI), deep learning models' interpretability

and reliability are severely hindered by their complexity and opacity. Enhancing the

transparency and interpretability of AI systems for humans is the primary objective of the

emerging field of explainable AI (XAI). The attention mechanisms at the heart of XAI's work

are based on human cognitive processes. Neural networks can now dynamically focus on

relevant parts of the input data thanks to these mechanisms, which enhances interpretability

and performance. This report covers in-depth talks of attention mechanisms in neural networks

within XAI, as well as an analysis …

Decoding The Future: Integration Of Artificial Intelligence In Web Development, Dhiraj Choithramani

Harrisburg University Dissertations and Theses

The thesis explores AI's profound impact on web development, particularly in front-end and back-end processes. AI revolutionizes UI prototyping by automating design creation, enhancing both efficiency and aesthetics. It also aids in code review, content generation, and process flow experimentation, streamlining development workflows. Through AI-driven tools like GitHub's Copilot and Wix ADI, developers benefit from coding assistance and innovative design capabilities. Despite some challenges, AI's evolving role promises to reshape web development, offering unprecedented efficiency and user-centric solutions.

Enhancing Mobile App User Experience: A Deep Learning Approach For System Design And Optimization, Deepesh Haryani

Harrisburg University Dissertations and Theses

This paper presents a comprehensive framework for enhancing user experience in mobile applications through the integration of deep learning systems. The proposed system design encompasses various components, including data collection and preprocessing, model development and training, integration with mobile applications, dataset management service, model training service, model serving, hyperparameter optimization, metadata and artifact store, and workflow orchestration. Each component is meticulously designed with a focus on scalability, efficiency, isolation, and critical analysis. Innovative design principles are employed to ensure seamless integration, usability, and automation. Additionally, the paper discusses distributed training service design, advanced optimization techniques, and decision criteria for hyperparameter …

State Omniscience For Cooperative Local Catalog Maintenance Of Close Proximity Satellite Systems, Chris Hays

Doctoral Dissertations and Master's Theses

Resiliency in multi-agent system navigation is reliant on the inherent ability of the system to withstand, overcome, or recover from adverse conditions and disturbances. In large part, resiliency is achieved through reducing the impact of critical failure points to the success and/or performance of the system. In this view, decentralized multi-agent architectures have become an attractive solution for multi-agent navigation, but decentralized architectures place the burden of information acquisition directly on the agents themselves. In fact, the design of distributed estimators has been a growing interest to enable complex multi-sensor/multi-agent tasks. In such scenarios, it is important that each local …

Enhancing Information Architecture With Machine Learning For Digital Media Platforms, Taylor N. Mietzner

Honors College Theses

Modern advancements in machine learning are transforming the technological landscape, including information architecture within user experience design. With the unparalleled amount of user data generated on online media platforms and applications, an adjustment in the design process to incorporate machine learning for categorizing the influx of semantic data while maintaining a user-centric structure is essential. Machine learning tools, such as the classification and recommendation system, need to be incorporated into the design for user experience and marketing success. There is a current gap between incorporating the backend modeling algorithms and the frontend information architecture system design together. The aim of …

Implementation Of Path Planning Methods To Detect And Avoid Gps Signal Degradation In Urban Environments, Ayush Raminedi

Doctoral Dissertations and Master's Theses

In the modern world, various missions are being carried out under the assistance of autonomous flight vehicles due to their ability to operate in a wide range of flight conditions. Regardless, these autonomous vehicles are prone to GPS signal loss in urban environments due to obstructions that cause scintillation, multi-path, and shadowing. These effects that decrease the GPS functionality can deteriorate the accuracy of GPS positioning causing losses in signal tracking leading to a decrease in navigation performance. These effects are modeled into the simulation environment and are used as part of the path planning algorithm to provide better navigation …

Cyber Attacks Against Industrial Control Systems, Adam Kardorff

LSU Master's Theses

Industrial Control Systems (ICS) are the foundation of our critical infrastructure, and allow for the manufacturing of the products we need. These systems monitor and control power plants, water treatment plants, manufacturing plants, and much more. The security of these systems is crucial to our everyday lives and to the safety of those working with ICS. In this thesis we examined how an attacker can take control of these systems using a power plant simulator in the Applied Cybersecurity Lab at LSU. Running experiments on a live environment can be costly and dangerous, so using a simulated environment is the …

Protecting Return Address Integrity For Risc-V Via Pointer Authentication, Yuhe Zhao

Masters Theses

Embedded systems based on lightweight microprocessors are becoming more prevalent in various applications. However, the security of them remains a significant challenge due to the limited resources and exposure to external threats. Especially, some of these devices store sensitive data and control critical devices, making them high-value targets for attackers. Software security is particularly important because attackers can easily access these devices on the internet and obtain control of them by injecting malware.

Return address (RA) hijacking is a common software attack technique used to compromise control flow integrity (CFI) by manipulating memory, such as return-to-libc attacks. Several methods have …

Blockchain Design For A Secure Pharmaceutical Supply Chain, Zhe Xu

Masters Theses

In the realm of pharmaceuticals, particularly during the challenging times of the COVID-19 pandemic, the supply chain for drugs has faced significant strains. The increased demand for vaccines and therapeutics has revealed critical weaknesses in the current drug supply chain management systems. If not addressed, these challenges could lead to severe societal impacts, including the rise of counterfeit medications and diminishing trust in government authorities.

The study identified that more than the current strategies, such as the Drug Supply Chain Security Act (DSCSA) in the U.S., which focuses on unique authentication and traceability codes for prescription drugs, is needed to …

Extracting Dnn Architectures Via Runtime Profiling On Mobile Gpus, Dong Hyub Kim

Masters Theses

Due to significant investment, research, and development efforts over the past decade, deep neural networks (DNNs) have achieved notable advancements in classification and regression domains. As a result, DNNs are considered valuable intellectual property for artificial intelligence providers. Prior work has demonstrated highly effective model extraction attacks which steal a DNN, dismantling the provider’s business model and paving the way for unethical or malicious activities, such as misuse of personal data, safety risks in critical systems, or spreading misinformation. This thesis explores the feasibility of model extraction attacks on mobile devices using aggregated runtime profiles as a side-channel to leak …

An Efficient Privacy-Preserving Framework For Video Analytics, Tian Zhou

Doctoral Dissertations

With the proliferation of video content from surveillance cameras, social media, and live streaming services, the need for efficient video analytics has grown immensely. In recent years, machine learning based computer vision algorithms have shown great success in various video analytic tasks. Specifically, neural network models have dominated in visual tasks such as image and video classification, object recognition, object detection, and object tracking. However, compared with classic computer vision algorithms, machine learning based methods are usually much more compute-intensive. Powerful servers are required by many state-of-the-art machine learning models. With the development of cloud computing infrastructures, people are able …