Computer Engineering Commons^™

Exploring The Use And Misuse Of Large Language Models (Llms), Hezekiah Paul D. Valdez

Master's Projects

Large Language Models (LLMs) have quickly gone from simple rule-based systems to complex knowledge bases capable of tackling many different tasks across a variety of fields. What began as an exercise in human-computer interaction has become the basis for artificial intelligence in a variety of mediums. When attached to larger systems, LLMs become generative assistants that can perform highly on human proficiency assessments and other benchmark skill assessments. This increase in proficiency has led these systems to be deployed in fields such as cybersecurity, business, and programming to help improve productivity and efficiency. However, such a wide availability has allowed …

Code Quality Enhancement: Evaluating Ai Code Generation With Software Metrics, Sirisha Krishna Murthy

Master's Projects

With the advancements in the stream of AI in the recent time and the evolution of Generative AI, it is a given that there is a need to effectively integrate AI into daily tasks, including Coding. When talking about Generative AI, one important thing to consider is prompting, which is that way to talk to the AI. Depending on specific needs and tasks the way we need to prompt AI can vary. With rapid development in the field, there are a lot of new benchmarks that evaluate the AI coders on correctness, but to effectively adapt AI into actual coding …

Enhancing Qwen2.5-Coder: A Deep Dive Into Fine-Tuning Using Peft For Superior Code Outputs, Lohith Nagaraja

Master's Projects

The main objective of this research is to improve the quality of software code that is produced by the Qwen2.5-Coder model specifically in terms of maintainability, complexity, and reliability. Our approach is going to be a more specific one that will involve the Parameter-Efficient Fine Tuning (PEFT) framework combined with quantization through Low-Rank Adaption (LoRA). This approach involves fine-tuning only some of the parameters of a model to make it suitable for software programming with the general structure of the model largely intact. In this paper, SonarQube is used as a tool to help quantify the improvements made to the …

Cross-Temporal Hierarchical Forecast Reconciliation Of Natural Gas Demand, Colin O. Quinn, George F. Corliss, Richard J. Povinelli

Electrical and Computer Engineering Faculty Research and Publications

Local natural gas distribution companies (LDCs) require accurate demand forecasts across various time periods, geographic regions, and customer class hierarchies. Achieving coherent forecasts across these hierarchies is challenging but crucial for optimal decision making, resource allocation, and operational efficiency. This work introduces a method that structures the gas distribution system into cross-temporal hierarchies to produce accurate and coherent forecasts. We apply our method to a case study involving three operational regions, forecasting at different geographical levels and analyzing both hourly and daily frequencies. Trained on five years of data and tested on one year, our model achieves a 10% reduction …

Compare And Contrast The Intent Of Hacking Vs Penetration Testing, Kehinde Alabi

Harrisburg University Other Works

No abstract provided.

Joint Learning Of Unknown Safety Constraints And Control Policies In Reinforcement Learning, Lunet Abiye Yifru

Graduate Theses, Dissertations, and Problem Reports (ETD)

Reinforcement learning (RL) has revolutionized decision-making across a wide range of domains over the past few decades. Yet, deploying RL policies in real-world scenarios presents the crucial challenge of ensuring safety. Traditional safe RL approaches have predominantly focused on incorporating predefined safety constraints into the policy learning process. However, this reliance on predefined safety constraints poses limitations in dynamic and unpredictable real-world settings where such constraints may not be available or sufficiently adaptable. Bridging this gap, we propose a novel approach that concurrently learns a safe RL control policy and identifies the unknown safety constraint parameters of a given environment. …

An Approach For Robotic Pollination That Utilizes Imitation Learning, Ronald Michael Butts Ii

Graduate Theses, Dissertations, and Problem Reports (ETD)

The global decline in pollinator populations poses a significant threat to agriculture, motivating the development of robotic pollination systems. Previous works demonstrated successful robotic pollination of bramble flowers using visual servoing; however, pollination was limited to specific flower orientations. As such, the objective of this work is to develop a robotic pollination system that is capable of pollinating a wider range of orientations.

This research introduces an imitation learning-based framework for robotic pollination that positions the manipulator to view chosen flowers in specific orientations. The developed model leverages object detection (YOLOv8) to identify individual flowers and a convolutional neural network …

Autonomous Object Search Planning In Large-Scale Environments, Matthew A. Collins

Graduate Theses, Dissertations, and Problem Reports (ETD)

The advancement of autonomous search holds significant promise for applications ranging from emergency response to planetary exploration. This thesis investigates strategies to enhance autonomous search performance in large-scale environments. The main contribution of this work is its practical application in real-world scenarios, where efficient search methods are essential for managing vast amounts of data, particularly in large environments. Effective search planning requires navigating complexities such as limited prior information and managing large state spaces, necessitating advanced strategies to plan with this limited information. Additionally, balancing exploration and exploitation is crucial for optimizing the search process, as it ensures thorough coverage …

Machine Learning Based Intrusion Detection Framework For Can Bus Vulnerabilities In Modern Vehicles, Obinna C. Agbo

Graduate Theses, Dissertations, and Problem Reports (ETD)

The Controller Area Network (CAN) bus is a crucial communication backbone in modern vehicles, connecting various Electronic Control Units (ECUs). However, inherent design weaknesses such as the lack of encryption and authentication make CAN networks vulnerable to cyber-attacks, including spoofing, Denial of Service (DoS), and fuzzing attacks. This thesis thoroughly evaluates these vulnerabilities and the limitations of existing security frameworks like Message Authentication Codes (MACs) and encryption, advocating for the adoption of Intrusion Detection Systems (IDS) as a more practical solution for CAN bus security. The proposed IDS leverages advanced machine learning techniques to accurately detect intrusions, even under complex …

Mechanism Design For Optimizing On-Chain Sell Order In Market Without Market Maker, Nico Pei

CMC Senior Theses

The absence of market makers alters the microstructure of the market. It’s difficult to get exposed to time-weighted prices in markets without market makers. In this paper, we delve into three mechanism designs – discrete gradual dutch auction, continuous gradual dutch auction, and variable rate gradual dutch auction – to study how to execute time-weighted sell orders on blockchain in a market without market makers. To make it simpler for readers to understand, we imagine an example of helping a close friend of Picasso to sell his 100 Picasso paintings in the next 10 years since 1970, with the private …

An Efficient And Trusted Deep Learning Framework For Real-Time Ppe Detection In Secure Iomt Environment, Anusha Verma

Browse all Theses and Dissertations

Occupationally-acquired infections impact thousands of healthcare workers (HCWs) in the U.S., with many cases preventable through proper use of personal protective equipment (PPE). This study seeks to develop a robust system to enhance PPE compliance and reduce infection risks among HCWs. The objectives of this thesis are twofold: (1) to create a hybrid machine learning model that combines object detection and keypoint detection to ensure correct donning and doffing of PPE, and (2) to design a real-time feedback system using LED indicators and a display interface to offer actionable guidance to HCWs during PPE usage. The goal is to optimize …

An Enhanced Real-Time Object Detection Of Helmets And License Plates Using A Lightweight Yolov8 Deep Learning Model, Mounika Thatikonda

Browse all Theses and Dissertations

Traffic surveillance and enforcement heavily depend on the real-time detection of helmets and license plates, particularly in high-density urban environments. This study presents a dynamic and optimized lightweight model, the proposed G-YOLOv8n, designed for resource constrained edge devices like the Raspberry Pi. By integrating the GhostNet module into the YOLOv8n architecture, this research achieves a nearly 50% reduction in model size and computational load, while maintaining comparable detection accuracy to the original YOLOv8n. These enhancements enable real-time processing capabilities crucial for traffic monitoring operations. The growing demand for real-time, low-power solutions in intelligent transportation systems necessitates lightweight, efficient detection models. …

The Graduation Walk: Pareto Optimization Of Degree Paths, Arianna Gail Sy Chaves

Masters Theses

"A student’s academic history, course availability at their institution, and the overall degree of difficulty of the schedule for each semester are all critical factors in their academic success and experience. This thesis proposes an advanced recommendation algorithm that considers these real and conflicting factors that are involved in identifying a prudent degree path - a semester-by-semester course schedule to graduation - for each student. The original contribution of this work is the use of weighted-sum multi-objective constraint programming to minimize an increased number of optimization criteria and identify Pareto-optimal degree paths to be recommended to the student. The conflicting …

A Lightweight Machine-Learning Framework For Enhancing Security In Iot Blockchain Networks, Charles Connor Rawlins

Doctoral Dissertations

"Blockchain is one of the fastest technologies that rivals the Internet in terms of adoption speed. This security method is applicable to data-centric environments for validating data in the presence of faults. However, traditional blockchain implementation introduces bottlenecks with computationally intense security measures to prevent malicious spam and resolve conflicts. This dissertation explores a new direction for blockchain technology that allows limited nodes, like IoT devices, to make independent decisions with compressed knowledge of past blockchain history through the use of machine-learning for active decisions (or the first machine-intelligent blockchain protocol). Proposing to introduce machine-intelligence into the rapidly evolving paradigm …

Deep Learning Techniques For Image Segmentation In Dermoscopic Skin Cancer Images, Norsang Lama

Doctoral Dissertations

"Melanoma is recognized as the most lethal type of skin cancer, responsible for a significant proportion of skin cancer-related deaths. However, early detection of melanoma is essential for successful treatment outcomes. Computer-aided skin cancer diagnosis tools can save lives by enabling earlier detection of skin cancer. Image segmentation is a crucial step in computer-aided diagnosis as it allows the detection of critical features or regions in an image. Thus, an accurate image segmentation method is necessary to create a more precise computer-aided diagnostic tool for skin cancer diagnosis. This dissertation includes investigating and developing deep learning techniques to improve image …

Improvements In Biomedical Image Analysis With Computational Intelligence And Data Fusion Techniques, Akanksha Maurya

Doctoral Dissertations

"An estimated 2 million new cases of basal cell carcinoma (BCC) are diagnosed each year in the United States, making it one of the most common skin cancers. Earlier detection of these cancers enables less invasive biopsies. Clinical detection consists of a preliminary visual observation of these skin lesions by an experienced dermatologist making it a specialized task highly dependent on their time, availability, and resources. Hence, there is a need for automating this process that can assist healthcare staff. In recent years, deep learning (DL) has been used extensively and successfully to diagnose different cancers in dermoscopic images. Telangiectasia …

การพยากรณ์จำนวนสายโทรศัพท์เข้าของศูนย์บริการข้อมูลทางโทรศัพท์แบบรายครึ่งชั่วโมง, กฤตชญา ประภารัตน์

Chulalongkorn University Theses and Dissertations (Chula ETD)

การวิจัยนี้มีวัตถุประสงค์เพื่อศึกษาโมเดลที่เหมาะสมสำหรับการพยากรณ์จำนวนสายโทรศัพท์เข้าของศูนย์บริการข้อมูลทางโทรศัพท์แบบรายครึ่งชั่วโมง โดยศูนย์บริการข้อมูลทางโทรศัพท์ หรือ Call Center มีบทบาทเป็นศูนย์รวมสายโทรเข้าและโทรออกของธุรกิจ ซึ่งเป็นช่องทางสำคัญในการตอบสนองความต้องการของลูกค้า ไม่ว่าจะเป็นการสอบถามข้อมูล การขอคำแนะนำ หรือแก้ปัญหาต่าง ๆ ศูนย์บริการข้อมูลทางโทรศัพท์จึงมีการจัดวางแผนกำลังคนรับสาย เพื่อให้สอดคล้องกับปริมาณสายโทรศัพท์ที่คาดว่าจะเข้ามา แต่ในบางครั้งการวางแผนจัดกำลังคนรับสายอาจต้องมีการปรับระหว่างวัน เนื่องจากจำนวนสายโทรเข้าอาจมีจำนวนมากกว่าหรือน้อยกว่าที่คาดการณ์ไว้ ซึ่งวิธีการเดิมที่บริษัทใช้ในการคำนวน อาจมีความคลาดเคลื่อน และไม่สามารถปรับตัวเลขได้ภายในระยะเวลาอันสั้น งานวิจัยนี้จึงนำเสนอการพยากรณ์จำนวนสายโทรศัพท์เข้าของศูนย์บริการข้อมูลทางโทรศัพท์แบบรายครึ่งชั่วโมง มีวัตถุประสงค์เพื่อพยากรณ์ปริมาณสายการโทรเข้าช่วงหลัง 10 น. เพื่อช่วยให้ฝ่ายวางแผนกำลังคนเห็นแนวโน้มปริมาณสายที่คาดว่าจะเข้ามา และตัดสินใจปรับแผนการจัดกำลังคนได้อย่างทันท่วงที โดยโมเดลจะจัดกลุ่มและพยากรณ์รูปแบบการกระจายตัวของปริมาณสายโทรเข้า และพยากรณ์จำนวนสายที่คาดว่าจะเข้ามา ผลการทดลองพบว่า โมเดลที่พัฒนาขึ้นมี MAPE อยู่ที่ 20.8% ซึ่งมีประสิทธิภาพดีกว่าวิธีการคำนวนเดิมของบริษัทที่มี MAPE อยู่ที่ 52.7%

Reinventing Integrated Photonic Devices And Circuits For High Performance Communication And Computing Applications, Venkata Sai Praneeth Karempudi

Theses and Dissertations--Electrical and Computer Engineering

The long-standing technological pillars for computing systems evolution, namely Moore's law and Von Neumann architecture, are breaking down under the pressure of meeting the capacity and energy efficiency demands of computing and communication architectures that are designed to process modern data-centric applications related to Artificial Intelligence (AI), Big Data, and Internet-of-Things (IoT). In response, both industry and academia have turned to 'more-than-Moore' technologies for realizing hardware architectures for communication and computing. Fortunately, Silicon Photonics (SiPh) has emerged as one highly promising ‘more-than-Moore’ technology. Recent progress has enabled SiPh-based interconnects to outperform traditional electrical interconnects, offering advantages like high bandwidth density, …

Detection Of Tooth Position By Yolov4 And Various Dental Problems Based On Cnn With Bitewing Radiograph, Kuo Chen Li, Yi-Cheng Mao, Mu-Feng Lin, Yi-Qian Li, Chiung-An Chen, Tsung-Yi Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Periodontitis is a high prevalence dental disease caused by bacterial infection of the bone that surrounds the tooth. Early detection and precision treatment can prevent more severe symptoms such as tooth loss. Traditionally, periodontal disease is identified and labeled manually by dental professionals. The task requires expertise and extensive experience, and it is highly repetitive and time-consuming. The aim of this study is to explore the application of AI in the field of dental medicine. With the inherent learning capabilities, AI exhibits remarkable proficiency in processing extensive datasets and effectively managing repetitive tasks. This is particularly advantageous in professions demanding …

Cybersecurity Awareness And Risks Among Students, Staff & Faculty At The University Of North Alabama, Jerry Lwamba

Theses

As individuals and organizations become significantly connected to the internet, cybercriminals find an increasing number of ways to exploit their targets (Broadhurst, 2018). This study explores the world of cybersecurity awareness of risks associated with online activities within the University of North Alabama (UNA). This research aims to assess the level of cybersecurity awareness of risks university stakeholders face, and to also understand behaviors that expose them to cybercrime and emphasize the urge for comprehensive cybersecurity best practices. When cybercriminals employ tactics such as spam, phishing, or spear phishing in their attempts to break into network systems, distribute malware, or …

Fake News Detection In Online Platforms, Elena Shushkevich

Doctoral

This thesis presents research conducted during a Ph.D. program at Technological University Dublin from 2020 to 2024. The objective of this research is to develop and evaluate effective methods for detecting and classifying fake news in social media and press, addressing the critical issue of misinformation in the digital age. The relevance of this study is underscored by the increasing prevalence of fake news and its potential societal impact, emphasizing the importance of advanced tools for identifying and mitigating misinformation.

Outdoor Navigation Support Using Machine Learning For People With Visual Impairment, Fatmaelzahraa Eltaher

Doctoral

Navigating urban environments is challenging for People with Visual Impairments (PVI). Many PVI opt to use navigation systems, such as Google Maps. However, current navigation systems often lack information crucial for PVI, such as the presence of traffic lights or roadworks. Knowing these features would enable PVI to understand their surroundings better and choose routes that suit their preferences. For instance, 79.6\% of PVI surveyed in this thesis prefer road junctions (termed intersections) controlled by traffic lights.

This research contributes to advancing navigation support for PVI in urban environments by focusing on annotating maps with useful environmental information. It proposes …

Towards Interpretable Propaganda Detection In News Text Through The Use Of Rhetorical Devices As Features, Kyle Hamilton

Doctoral

This doctoral thesis presents research undertaken towards interpretable propaganda detection in news text. Propaganda and mis/disinformation in the media have been identified in the social sciences literature as a major threat to the functioning of democratic society. While mis/disinformation is false by definition, propaganda is intended to modify the beliefs and behaviors of the information consumer but does not have to be false. Propagandistic text is often characterized by the use of rhetorical devices and linguistic style designed to exploit the reader’s cognitive and emotional biases. One way of counteracting this effect is by shifting the focus from “what” is …

Statically Controlled Synchronized Lane Architectures, Scott K. Pomerville

Dissertations, Master's Theses and Master's Reports

Modern superscalar processors dominate the field of computing. While dynamic execution allows for versatility in code, these processors are complex. Statically scheduled code has historically enabled simpler processor designs, but static scheduling cannot account for variables that are unknown at compile time. Furthermore, static scheduling has many inefficiencies, such as the need to insert a large number of nops for code in traditional Very Long Instruction Word (VLIW) processors. In this dissertation, we explore a novel architectural approach for statically scheduled code by breaking the code into several synchronous instruction streams. By representing code in a fundamentally new way, we …

How Can A Cloud Computing It Framework Be Created And Applied Effectively In The Online Printing Industry?, Stefan Meissner

Dissertations

This research aims to design a cloud computing IT framework for the online printing industry based on a detailed literature review, the development of proof of concepts (PoC), and the conduction of a focus group. The framework can be adopted by the online printing industry or by vendors of print-specific applications to optimize their products for the online printing industry. The author has been working in the online printing process optimization and automation since 2007. During this time, he got deep insight into many industry-specific applications, their architectural design, and their challenges being used in the context of online printing. …

On The Performance Of A Photonic Reconfigurable Electromagnetic Band Gap Antenna Array For 5g Applications, Taha A. Elwi, Fatma Taher, Bal S. Virdee, Mohammad Alibakhshikenari, Ignacio J.Garcia Zuazola, Astrit Krasniqi, Amna Shibib Kamel, Nurhan Turker Tokan, Salahuddin Khan, Naser Ojaroudi Parchin, Patrizia Livreri, Iyad Dayoub, Giovanni Pau, Sonia Aissa, Ernesto Limiti, Mohamed Fathy Abo Sree

All Works

In this paper, a reconfigurable Multiple-Input Multiple-Output (MIMO) antenna array is presented for 5G portable devices. The proposed array consists of four radiating elements and an Electromagnetic Band Gap (EBG) structure. Planar monopole radiating elements are employed in the array with Coplanar Waveguide Ports (CWPs). Each CWP is grounded on one side to a reflecting L-shaped structure that has an effect of improving the antenna's directivity. It is shown that by inductively connecting Minkowski fractal structure of 1^{st} order to the radiating element, the impedance matching is improved that results in enhancement in the array's bandwidth performance. The EBG structure …

Investigation And Implementation Of Miniaturized Microwave System For Linear Array Antenna Loaded With Omega Structures Planar Array, Ahmed F. Miligy, Fatma Taher, Mohamed Fathy Abo Sree, Sara Yehia Abdel Fatah, Thamer Alghamdi, Moath Alathbah

All Works

This paper investigates and implements a miniaturized microwave system for microstrip linear array antenna that operates in X-band (10.1 GHz), S-band (3.4 GHz) and C-band (5.6 GHz). The microwave system consists of three parts: a power divider, a directional coupler, and a matching network stub. These systems feed a linear array (16 elements) of patch antennas loaded with resonance planar omega structures array (160 elements) distributed in both patch (64 elements) and ground (96 elements) as the metamaterial structures for miniaturization purpose. The 1-to-2 divider feeds two directional couplers that act as phase shifters. The couplers fed a set of …

A New Cache Replacement Policy In Named Data Network Based On Fib Table Information, Mehran Hosseinzadeh, Neda Moghim, Samira Taheri, Nasrin Gholami

VMASC Publications

Named Data Network (NDN) is proposed for the Internet as an information-centric architecture. Content storing in the router’s cache plays a significant role in NDN. When a router’s cache becomes full, a cache replacement policy determines which content should be discarded for the new content storage. This paper proposes a new cache replacement policy called Discard of Fast Retrievable Content (DFRC). In DFRC, the retrieval time of the content is evaluated using the FIB table information, and the content with less retrieval time receives more discard priority. An impact weight is also used to involve both the grade of retrieval …

Resilience In The Wake Of Storms: Unveiling Spatiotemporal Mobility Dynamics Of Gulf Coast Communities Through Crowd-Sourced Data, Joswin Valerian Concessao

Computer Science and Engineering Theses - Archive

Flood events present substantial challenges for coastal communities, severely impacting public safety, transportation infrastructure, and overall livability. Tropical storms, hurricanes, and sea level rise can cause extensive damage to homes and critical systems, requiring costly and prolonged recovery efforts. Coastal transportation networks are particularly vulnerable to flooding, leading to road closures, increased congestion, restricted access to essential services, and long-term economic disruptions. Understanding the effects of flood events on mobility patterns is crucial for urban planning and effective disaster management.

This thesis utilizes motif analysis to examine transportation network disruptions and access patterns in Harrison County, Mississippi, during Hurricane Ida …

Observability-Aware Path Planning For Autonomous Navigation, Raymond Brink Neistat

Open Access Master's Theses

Terrain-Aided Navigation (TAN) is a popular method of localization for GPS-denied vehicles, particularly in the marine domain. There are many ways to perform TAN in a marine setting, such as Bathymetric SLAM (BSLAM) and Bayesian Filtering with the aid of an a priori map. These techniques have been studied extensively, but show an overall lack of rigorous observability analyses. Without nonlinear observability analyses, TAN practitioners do not have an analytical indicator to know which areas of terrain will provide opportunities for the best localization performance. This thesis reviews current developments in the endeavors of nonlinear observability analyses as well as …