Computer Engineering Commons^™

Performance Of Inline Compression With Software Caching For Reducing The Memory Footprint In Pysdc, Emily Lattanzio

All Theses

The volume of data required for High Performance Computing (HPC) applications is growing faster than the memory storage available to store the required data, leading to performance bottlenecks in transferring data. Whether sending data from main memory to computation nodes or between parallel processes during runtime, the more data there is to send, the longer it will take to for that data to be sent from one location to the next. Hence the need for inline data compression, which reduces the amount of allocated memory needed by storing the largest data structures in a compressed format and decompressing/recompressing single variables …

Modality Distillation Using A Sam-Guided Multimodal Teacher For Unimodal Wildfire Segmentation And Temperature Prediction, Michael N. Marinaccio

All Theses

Wildfires are one of the world’s most devastating natural disasters that affect the environment, communities, and more critically, humans that live in and around those communities. Due to the threat of large-scale destruction in landscapes and human inhabited areas, it has become increasingly more important to develop wildfire detection, management, and suppression strategies to mitigate and prevent these negative outcomes. Wildfire research encompasses many different areas. Most notably, the development of communication, navigation, remote sensing, and monitoring systems. In wildfire monitoring, limitations discovered in-ground and satellite observation have shifted the focus toward Unmanned Aerial Vehicle (UAV) based wildfire research, which …

Design Considerations Of A Gpu, Nicholas M. Devilliers

Electrical Engineering and Computer Science Undergraduate Honors Theses

With the current era of AI technology, the era of single instruction multiple data has become an increasingly viable solution to accelerate training. The problem is that while software to use GPUs and other hardware accelerators, designing GPUs and ASIC devices has become increasingly more expensive and there aren’t great examples of generic GPUs that anyone can use and modify. In this thesis, there are four design considerations that will be discussed and how they affect the result of a generic GPU. The four considerations that were talked about in the thesis are, word width, arithmetic type, number of stages, …

Ntier Code Generator, Mohammed Qattan

Electronic Theses, Projects, and Dissertations

This project presents a software tool designed to automate the generation of standardized code for all layers of an n-tier architecture, including the Database Layer, Data Access Layer (DAL), and Business Logic Layer (BLL). By employing object-oriented principles and parsing the database structure, the tool ensures modularity, scalability, and maintainability. It efficiently formats code templates for CRUD operations, enhancing development efficiency and consistency, while streamlining database interactions and enforcing business rules.

This project presents an innovative software tool designed to automate the generation of standardized code for all layers of an n-tier architecture, including the Database Layer, Data Access Layer …

Virtual Makeup And Technology Integration, Vishwa Bhatt

Electronic Theses, Projects, and Dissertations

The Virtual Makeup Streamlit application presents an advanced approach to digital cosmetic try-on by allowing users to apply makeup to their facial images in real time. This project uses computer vision and web technologies to create an interactive and user-friendly platform that capitalizes on the increasing popularity of virtual try-on solutions in the cosmetics industry.

At its core, the system uses effective facial detection and semantic segmentation techniques to recognize and separate facial areas such as lips and hair. Techniques such as U-Net and Resnet, and Midepipe are used to create accurate segmentation masks, which are essential for accurate makeup …

Lightlink: Visible Light Communication For Batteryless Devices With Variable Computational Load, Charles Phillips

All Theses

Visible Light Communication (VLC) devices have been experimentally proven to work as a suitable communication medium for batteryless devices. However, the effects of practical load have yet to be fully explored. To that end, we have developed LightLink, a new MAC and PHY layer protocol for VLC within batteryless devices, and have studied various ways that computational load can affect transmission accuracy in realistic scenarios. Our key findings point us towards an adaptive VLC reception system based on inferred environmental variables.

Blockchain-Integrated Version Control For Secure And Transparent Software Supply Chains, Iwinosa W. Aideyan

All Theses

The software supply chain encompasses all stages of software development and delivery from initial coding and version control to integration and deployment. As development environments become increasingly distributed and reliant on external dependencies, ensuring the integrity, auditability, and consistency of code changes has become a pressing challenge. Traditional version control systems like Git, while effective for collaboration and tracking revisions, do not inherently provide tamper-evident commit histories. Features such as history rewriting (e.g., git rebase, git push --force) can be exploited to manipulate commit logs without detection, posing risks in security-sensitive domains. This thesis proposes a blockchain-integrated version control framework …

The Role Of Ai In Enhancing Teamwork, Resilience And Decision-Making: Review Of Recent Developments, Satyadhar Joshi

Harrisburg University Other Works

This paper explores the transformative impact of artificial intelligence (AI) on organizational teamwork, decision-making, and resilience. This paper furthur reviews recent literature on the integration of Artificial Intelligence (AI) in various organizational functions, focusing on its impact on innovation management, leadership paradigms, and organizational resilience. We provide groundwork required to enhance frameworks that can integrate cognitive scaffolding with antifragile team dynamics, employing behavioral economics and neurocognitive principles. We introduce methodologies for enhancing team resilience through adaptive AI systems, cross-training interventions, and pre-mortem simulation techniques. The framework addresses key challenges in confirmation bias mitigation, cultural dimension alignment, and vigilance decrement prevention. …

Human-Ai Teaming For Academic Performance Analysis, Kendarius Ward, Elise Hernandez, Tom Antony, Md Abdullah Al Hafiz Khan, Kazi Aminul Islam, Abm Adnan Azmee

Symposium of Student Scholars

Educators today often work with students who are struggling academically, but limited time and resources make it difficult to uncover the root causes and provide timely assistance. Artificial intelligence (AI) is a growing, viable tool for analyzing large datasets and solving problems in different domains; however, human expertise is required to enhance the AI model’s performance. This study will utilize human-AI teaming to assess student performance based on factors such as their academic involvement, hours spent studying, and grade-point-average, among others. These findings will help instructors better grasp each student's academic needs. By incorporating humans into the AI pipeline, we …

Scalable Distributed Ai: Low-Cost, High-Performance Computing With Jetson Nano And Dask, Kesava Manikanta Chirumamilla

ATU Scholars Symposium

No abstract provided.

Digital Thread: Bridging Macro–Micro Services In System-Of-Systems, Joshit Mohanty

Graduate Student Government Association Research Conference

Organizations and industries increasingly rely on distributed services in decentralized environments—ranging from large-scale, system-of-system architectures to fine-grained, agent-based microservices. While this distributed paradigm offers flexibility and innovation, it presents critical challenges such as interoperability gaps, inconsistent data formats, and a lack of holistic oversight. Traditional integration approaches, including ad-hoc middleware or enterprise service buses, tend to solve these issues reactively. As a result, technical debt accumulates, stakeholder misalignments persist, and scaling to new demands becomes complex.

This research proposes digital thread (DT) as the unifying framework to create an authoritative source of truth: a continuous flow of information across the …

Meta-Clustering For Specialized Language Models: Enhancing Contextual Adaptation And Mitigating Hallucinations In Diverse Healthcare Environments, Joshit Mohanty, Vaishali Vaishali, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Large Language Models (LLMs) have significantly advanced conversational AI by enabling dialogic information-seeking and task execution across diverse domains. However, their extensive parameters and broad domain scope lead to “data hallucinations.” These shortcomings are particularly evident in dynamic and diverse environments like India’s healthcare sector, where myriad languages, regional practices, and cultural nuances demand specialized, localized expertise rather than one-size-fits-all generalist models. This paper introduces a meta-clustering framework that integrates Distilled Language Models (DLMs) and Small/Specialized Language Models (SLMs) with meta-learning principles to address these limitations. By drawing on evidence from works such as MedHalu and Med-HALT, the framework seeks …

Leadership In The Age Of Ai: Review Of Quantitative Models And Visualization For Managerial Decision-Making, Satyadhar Joshi

Harrisburg University Other Works

This paper offers a comprehensive review of existing literature on the intersection of Artificial Intelligence (AI) and leadership, drawing on both theoretical insights and practical implementations. By analyzing scholarly publications from the past two years (2023-2025), the review traces emerging patterns in how AI technologies are being integrated into leadership practices. Key themes include the growing relevance of learning-based systems for adaptive decision-making and the application of attention-based models to improve responsiveness in dynamic environments. The review also addresses ethical dimensions of AI-enabled leadership, emphasizing the need to balance algorithmic efficiency with human judgment and oversight. Concerns around transparency, psychological …

Evaluating Hyper-V Vs Proxmox: Performance Comparison For Virtualization*, Ivan Vakal, Edwin Regalado

Campus Research Month

Virtual environments play a significant role in the IT industry, with many companies relying on this technology. With VMware’s increasing licensing costs following its acquisition by Broadcom, many businesses are seeking alternative virtualization solutions. This study evaluates the performance of Proxmox and Hyper-V by implementing a three-node high-availability cluster for each platform and conducting benchmarking tests on CPU performance, storage efficiency, and network throughput. Our results indicate that Hyper-V performs better with Windows-based virtual machines, while Proxmox demonstrates superior performance with Linux-based workloads. Additionally, Proxmox offers a more user-friendly cluster setup, whereas Hyper-V requires greater technical expertise.

Comparing Ai And Human Self-Assessments In Memorization Performance*, Meg Ermer, Abishur Moses-Pakkianathan

Campus Research Month

Many students in higher education use flashcard applications for learning large amounts of information in limited amounts of time. Many of these applications rely on spaced-repetition algorithms for memorization, which are proven to be more efficient than traditional study methods. We compared the effects of studying with a spaced-repetition application that utilizes a NLU model to calculate a user's understanding of material against the effects of studying with a spaced-repetition model that did not use NLU. We used our results to determine if replacing the self-assessment component of flashcard studying applications with a NLU model led to better memorization and …

Aiops–Driven Adaptive Anomaly Detection In Evolving Cloud Environments Using Transfer Learning, Mayur Shivakumar

Master's Theses

As cloud-based microservice architectures have become the foundation of contempo- rary enterprise solutions, performance interference, wherein co-located services com- pete for shared resources, remains a significant challenge. This phenomenon, often referred to as the noisy neighbor problem, manifests when one workload unexpect- edly increases the CPU, memory, disk I/O, or network consumption, resulting in latency spikes or throughput degradation for other services. While existing isolation mechanisms (e.g., cgroups and QoS policies) provide some mitigation, they rarely prevent contention entirely, particularly in dynamic, rapidly evolving environments with frequent code deployments.

This thesis proposes an AIOps-driven adaptive anomaly detection framework that integrates …

Comparative Performance Analysis Of Cryptographic Workloads Across Cloud Providers: A Multi-Language Study On Faas And Iaas Platforms Dataset, Jeremiah Webb

Doctoral Dissertations and Master's Theses

Cloud computing has become a relatively new paradigm for the delivery of compute resources, with key management services (KMS) playing a crucial role in securely handling cryptographic operations in the cloud. This paper presents the microbenchmark of cloud cryptographic workloads, including SHA HMAC generation, AES encryption/decryption, ECC signature/verification, and RSA encryption/decryption, across Function-as-a-Service (FaaS) and Infrastructure-as-a-Service (IaaS) in conjunction with KMS offerings from Ama- zon Web Services (AWS) and Microsoft Azure to conduct a comparative performance analysis. The methodology involves the AWS Cloud Development Kit (CDK) and the Bicep language to deploy AWS Lambda Functions and Azure Functions, respectively, to …

Ai, Blockchain, And Autonomous Innovation : Charting The Future Of Intelligent Enterprises, Shubham Gupta

Harrisburg University Other Works

In today’s digital economy, artificial intelligence (AI) and blockchain are twin forces driving transformative change. AI and blockchain each rose to prominence on their own, but together they hold the promise of revolutionizing how businesses operate and create value. AI systems can analyze massive datasets, automate complex decisions, and even mimic human learning and reasoning. Blockchain technology, on the other hand, enables secure and tamper-proof transactions by distributing records across a network, ensuring transparency and trust without relying on a central authority. The convergence of these technologies is ushering in new possibilities for automation, smarter decision-making, and secure digital transactions …

On The Provenance Of Software Systems: Automating Software Traceability With Knowledge Graph And Large Language Model Synergy, Tyler Procko

Doctoral Dissertations and Master's Theses

The present dissertation delineates a system that enables those engaged in software development to automatically generate and maintain project life cycle provenance. All projects are implemented and made manifest with the development of artifacts, e.g., papers, code files, etc. Tools exist to accelerate artifact creation, but little focus is paid to the processes that produce them. In terms of Ontology, or, from Ancient Greek, the study of being, the two most basic entities in reality are Continuant and Occurrent, or, roughly, “Artifact” and “Process”. This dissertation posits that for any created artifact, its process of creation, i.e., its life …

Accurate And Scalable Control-Flow Differential Analysis On System Traces, Yuta Nakamura

College of Computing and Digital Media Dissertations

Debugging and understanding system behavior pose technical challenges, often necessitating the comparison of two audited execution traces. Although provenance systems execution traces, the audited traces at most enable causal analysis within a single known execution. As a result, utilizing provenance systems differential analysis thus for debugging and reasoning is a challenging task. This thesis addresses the challenge of using provenance in debugging by developing accurate and scalable methods for differential analysis of system provenance. Our approach emphasizes the importance of knowing the application’s provenance graph structure and embedding this graph structure information within traces to conduct a precise differential analysis …

A Beginner’S Guide To Artificial Intelligence (Ai) And Generative Ai (Gen Ai) For Small Businesses, Stanley Mierzwa, Iassen Christov

Center for Cybersecurity

Generative Artificial Intelligence (GenAI) guide for small businesses.

Support and funding for this effort was received from the United States Small Business Administration (Contract 73351023C0016) for this activity and research. Formulated and ultimately created as a result of this grant was the New Jersey Cybersecurity Regional Cluster (NJCRC), which included the partners of NTouch-BCT Strategies, Covenant Business Concepts, and the Kean University Center for Cybersecurity. As part of the free cybersecurity risk assessments offered to small businesses in the community, organizations were introduced to GenAI through a demonstration, with the key information provided in this booklet.

Artificial Intelligence In Decision-Making: Literature Review, Najm A. Kh. Alhatimi Aleessawi, Leila Djaghrouri

Journal of the Association of Arab Universities for Research in Higher Education مجلة اتحاد الجامعات العربية للبحوث في التعليم العالي

In the fast-changing world of artificial intelligence (AI), the relationship between technology and decision-making has become a central area of study. Over the past five years, numerous papers have been published examining how AI methods are applied to decision-making processes across various industries. This article aims to highlight the key potential of artificial intelligence to enhance decision-making. It does so by systematically reviewing the literature on the role of AI in improving decision-making, particularly studies published between 2020 and 2024. The review consolidates the main findings from articles in renowned databases such as Google Scholar, Scopus, and IEEE Xplore, offering …

Efficacy Of Immersive Virtual Reality Gameplay In Environmental Attitude Change: The Case Of Abandoned Offshore Oil Platforms In Santa Barbara, Arun Prasad Srinivasan Manoharan

Master's Theses

Public perception plays an important role in shaping conservation policies and decisions, especially in contested environmental spaces. Offshore oil platforms, historically viewed as environmental hazards, have been found to serve as marine habitats that support diverse marine life. However, public perception remains largely negative, influenced by concerns over pollution from past oil spill accidents. Traditional environmental education methods, such as lectures and documentaries, often fail to engage audiences effectively or shift entrenched opinions. This study explores the efficacy of immersive Virtual Reality (VR) gameplay in changing environmental attitudes, specifically in the context of abandoned offshore oil platforms in Santa Barbara, …

Are Emojis The New Words? A Sentiment Analysis Of Social Media Brand Conversations, Yashodhan Karulkar, Dev T. Vora, Siddharth Vaddepalli, Yash Thakur

Journal of International Technology and Information Management

Emojis have become an increasingly important aspect of consumer-brand interactions in the Indian subcontinent. However, the impact of emoji use on brand image and mental health remains underexplored, particularly in emerging economies like India, where structured research on this topic is limited. To address this gap, the present study analyzes over 4,600 consumer tweets related to 19 prominent brands across eleven industries. Using VADER sentiment analysis, the research develops a metric to assess consumer sentiment and brand engagement in relation to emoji usage. The findings indicate that effective integration of emojis contributes to positive consumer sentiment and enhanced brand engagement. …

Modeling Of Analog-To-Digital Converter In Signal Processing, Ravshan Aliev, A.U. Djalilov

Chemical Technology, Control and Management

This article is devoted to the study of the modeling process of analog-to-digital converters (ADCs) that process signals, one of the main parts of control system elements and devices. As we know, ADCs are an important part of modern control systems. During the research, the main stages of analog signal conversion were analyzed, i.e. discretization, quantization, coding. A classification of analog-to-digital conversion methods was made and the advantages and disadvantages of each were identified. Also, the characteristics and parameters of ADC were studied, their impact on ADCs performance was evaluated, and it was determined that certain characteristics should be taken …

Machine Learning And Shap Interpretability For Chronic Disease Understanding, Nnaemeka Charles Igwe, Khandaker Mamun Ahmed

SDSU Data Science Symposium

Non-communicable diseases (NCDs), such as diabetes, are major global health concerns influenced by various health parameters and lifestyle choices. Traditional methods struggle to efficiently predict and manage these conditions due to the complexity and diversity of medical data. There is a need to leverage machine learning algorithms and modern computational tools to accurately predict diabetes, improve diagnosis, and provide actionable insights for better healthcare outcomes. In this project we study the application of machine learning methods for predicting NCDs such as diabetes. Moreover, we leverage hyperparameter tuning techniques for model development and SHapley Additive exPlanation (SHAP) for results interpretations and …

Design And Implementation Of Uvm-Based Verification Framework For Deep Learning Accelerators, Randa Ahmed Hussein Aboudeif

Theses and Dissertations

Recent advancements in deep learning (DL) have made hardware accelerators, known as deep learning accelerators (DLAs), a preferred solution for numerous high-performance computing (HPC) applications, including speech recognition, computer vision, and image classification. DLAs are composed of hundreds of parallel processing engines to speed up computations and can gain access to pre-trained networks from the cloud or through on-chip memory to implement the DNN inference process. DLA verification is becoming an important and challenging phase. The verification process is required to handle the complex DLA design. Moreover, the reliability of DLAs is critical for assessment as they are involved in …

Interfaces Gráficas Y Género: Impacto En Discursos Normativos, Marco V. Ferruzca, Paulo C. Portilla, Juan Villegas, Román A. Mora

GDI. Revista de investigación de Género, Diseño e Innovación

El diseño de interfaces gráficas de usuario generalmente considera una reflexión sobre propiedades vinculadas a aspectos visuales como el color, las imágenes, la tipografía, etc. Asimismo, algunos aspectos del usuario se ponen también a consideración como su edad, cultura, experiencia, género, entre otros. Sin embargo, la noción de género sólo se reduce a un binarismo para definir si la interfaz gráfica encaja en la categoría mujer u hombre. Muy pocos investigadores se han detenido a profundizar en el discurso heteronormativo que puede desprenderse de la interpretación de significado de una interfaz gráfica como consecuencia de la interrelación entre usuariogénerocomputadora. El …

An Evaluation Of Reinforcement Learning Algorithms In Video Game Development, Isaac Lockwood

Masters Theses & Specialist Projects

Reinforcement Learning (RL) has demonstrated substantial promise for creating adaptive, responsive AI in complex environments such as video games. Yet despite growing academic interest, industry adoption remains limited due to computational overhead, reward-design challenges, and unpredictable AI behaviors. This thesis investigates how RL algorithms—specifically Advantage Actor-Critic (A2C), Deep Q-Network (DQN), and Proximal Policy Optimization (PPO)—can be applied to three different genres of video games. Those being first-person shooter (fps), fighting, and strategy.

Through a combination of scenario-based experimentation and comprehensive analysis, this work explores the feasibility and design considerations crucial for integrating RL-driven AI into commercial games. Key factors examined …

Ai In Higher Ed, Where Are We Now?: Insights From The 2025 Educause Ai Landscape Study, Angela Neria, Jeff Burns

Posters

Curious about how higher education is really using AI? Wondering what’s next for AI policies, workforce impacts, and leadership strategies? The 2025 EDUCAUSE AI Landscape Study has the answers! Based on fresh data from institutions across higher ed, this study highlights key trends, challenges, and opportunities in AI adoption. Stop by our poster session to get a quick snapshot of where AI stands today—and where it’s headed. Let’s talk about what these findings mean for PSU and the future of AI in higher education!