Computer Engineering Commons^™

Toward Strategy Identification And Subtask Decomposition In Task Exploration, Tom Odem

Master's Projects

This research builds on work in anticipatory human-machine interaction, a subfield of human-machine interaction where machines can facilitate advantageous interactions by anticipating a user’s future state. The aim of this research is to further a machine’s understanding of user knowledge, skill, and behavior in pursuit of implicit coordination. A task explorer pipeline was developed that uses clustering techniques, paired with factor analysis and string edit distance, to automatically identify key global and local strategies that are used to complete tasks. Global strategies identify generalized sets of actions used to complete tasks, while local strategies identify sequences that used those sets …

Comparing Funders' Altruism Versus Self-Interest: Leveraging The Context Of Crisis, Dan Liu, Guangzhi Shang, Cynthia Fan Yang

Journal of International Technology and Information Management

While reward-based crowdfunding has widespread popularity, the motivations driving funders, balancing self-interest and altruism, have remained puzzling. Prior research has been constrained by examination methods and produced mixed findings regarding the weight of altruism versus self-interest among funders. Our study takes a fresh perspective, delving into funder behavior amid a major crisis—the tumultuous backdrop of the COVID-19 pandemic. Our findings reveal that funders not only display an increased willingness to contribute but also significantly amplify their contributions, particularly to projects in crisis-affected regions, irrespective of external incentives like rewards. This underscores the prevalence of altruistic motives among funders in challenging …

Understanding The Determinants Of Blockchain Adoption: An Empirical Study, Amarpreet Kohli, Nihar Kumthekar, Piyush Shah, Rebecca Jauch

Journal of International Technology and Information Management

Blockchain technology (BT) has the potential to enhance security and robustness of transactions through a distributed ledger bookkeeping process. This study employs technology-organization-environment (TOE) framework and threat-rigidity theory (TRT) to examine whether perceived disruption caused by COVID-19 pandemic significantly impacted the adoption of BT, and inclination to adopt BT in the US. The COVID-19 pandemic provided a unique backdrop, as it affected businesses across all industries, sizes, and geographies. Results show a non-significant effect of perceived pandemic disruption on the current stage of BT adoption and intention to adopt BT. However, disruption readiness positively influences the current stage of BT …

Comparing Vr And Tv In Nigeria And The U.S.: Impacts On Empathy, Engagement, And Enjoyment, Jinhee Yoo, Eugene A. Ohu, Radosław Mącik

Journal of International Technology and Information Management

This study (N = 170), involving participants from Nigeria and the U.S., investigated how different technologies (TV and VR) affect users' empathy (α = .93), engagement (α = .93), enjoyment (α = .93), preferences, and likelihood of technology use. Participants watched an animated documentary titled “Is Anna OK?” at two different time points, utilizing VR (Oculus Rift S) and TV, following which they completed measuring empathy, engagement, enjoyment, device preference, and usage likelihood. Analysis via one-way ANOVA and chi-square tests revealed that VR users reported significantly higher empathy and enjoyment compared to TV viewers, particularly on second viewing. Combining both …

Faux Capabilities: A Novel Approach For Code Analysis, Tanay Godse

Master's Projects

When using third-party packages or libraries, it is crucial to understand their behavior. Typically, this requires developers to either conduct code reviews or set up sandbox environments for testing or write unit tests with mocked values for every function used in their code. However, these approaches are often inefficient and time-consuming. A more effective solution would provide developers with a broad understanding of the functionality required by the code they plan to import. This can be done using object capabilities, where a particular functionality is the capability that an object must possess, in order to be able to perform the …

Disease Diagnosis Using Rag Llm With Smart Prompt Engineering, Qadeerullah Syed

Master's Projects

Although recent trends indicate that LLMs outperform traditional methods in solving complex problems with enhanced reasoning, there has been barely any progress in replicating the quality of diagnoses like those of actual human doctors. The identification of an accurate diagnosis with thorough reasoning is still a significant challenge, even with advanced AI models. The process of performing accurate diagnosis remains challenging due to a lack of transparency in state-of-the-art models existing today, a lack of explanation in the diagnosis process, an emphasis on results rather than reasoning, and a lack of foundational knowledge in models, along with limited exploration of …

Malware Generation And Classification Using Pixelcnn, Mounika Krishna Teja Karumudi

Master's Projects

Malware poses a serious threat to both data privacy and system security. With the wide variety of malware families and the surge in cyber-attacks, the accurate classification of malware is crucial for building effective detection and prevention systems. In recent years, deep learning (DL) methods in computer vision have shown promise in classifying malware by converting malware files into visual representations and applying DL algorithms to classify the resulting images. Among the different approaches to malware family classification, image-based methods have gained significant interest. This research focuses on leveraging DL techniques for image-based classification of malware. The success of identifying …

Mastering Enterprise Networks (2nd Ed), Mathew J. Heath Van Horn

OER Main

Mastering Enterprise Networks, is a comprehensive guide to building, defending, and attacking enterprise networks. It covers a wide range of topics, from network fundamentals to advanced security concepts. The book is well-organized and easy to follow, making it a valuable resource for both beginners and experienced network professionals.

One of the strengths of this book is its focus on hands-on learning. The book includes 50 chapters of labs that allow readers to practice the concepts they have learned. These labs are a great way to reinforce your understanding of enterprise networks while developing practical skills.

This book covers security from …

Smart: Semantic Mapping And Analysis For Regional Terrain Using Multi-Scale U-Net And Topsis, Rashmi Sonth

Master's Projects

Accurate land use classification is the backbone for urban planning. But with poor quality satellite images, varied landscapes and structures which are changing faster than ever, it becomes a challenge to define clear boundaries and hence to urban planning. This research explores the application of deep-learning model for land use classification and asses the suitability of the land. The proposed model combines a multi-scale U-Net architecture with Transformer blocks applied on a multi-spectral satellite images that improves the semantic segmentation greatly across the urban and rural regions. Additionally, a patch-wise segmentation is applied to overcome the common problem of feature …

St-Hybrid: Dynamic Graph Learning With Multi-Scale Spatio-Temporal Attention For Traffic Forecasting, Dhe Yeong Ewaza Tchalla

Electronic Theses and Dissertations

Accurate short-term traffic forecasting is central to modern Intelligent Transportation Systems, supporting route guidance, adaptive signal control, and incident response. Yet producing reliable predictions remains difficult because traffic is highly non-stationary. The relationships among roadway sensors shift during congestion, incidents, weather changes, or fluctuations in demand, and the temporal structure of traffic spans several scales from abrupt minute-level variations to broader daily and weekly rhythms. Models that rely on fixed spatial graphs or a single temporal scale tend to miss these evolving and layered dependencies. This thesis addresses these challenges by developing a graph-learning framework that adapts to changing traffic …

Smartphones On Wheels In Southeast Asia: A Crossroads For Data Governance, Attamongkol Tantratian, Gunn Jiravuttipong

Journal of Law and Mobility

While the transformation of automobiles into data-generating “smartphones on wheels” has revolutionized mobility, it has also raised critical concerns over data privacy and sovereignty. Equipped with sensors and connected technologies, smart vehicles collect vast amounts of data, including personal information, driving patterns, and biometric identifiers. While auto-exporting jurisdictions such as the United States, the European Union, and China have introduced regulatory measures to address these challenges, countries importing smart vehicles remain vulnerable due to their limited influence over the auto companies’ integrated technology and data policies.

This Article examines the regulatory and economic challenges faced by developing nations integrating foreign-designed …

A Comprehensive Review And Bibliometric Analysis On Collaborative Robotics For Industry: Safety Emerging As A Core Focus, Aida Haghighi, Morteza Cheraghi, Jérôme Pocachard, Valérie Botta-Genoulaz, Sabrina Jocelyn, Hamidreza Pourzarei

Revues de littérature, synthèses de connaissances

Research organizations and academics often seek to map the development of scientific fields, identify research gaps, and guide the direction of future research. In cobot-related research, the scientific literature consulted does not propose any comprehensive research agenda. Moreover, cobots, industrial robots inherently designed to collaborate with humans, bring with them emerging issues. To solve them, interdisciplinary research is often essential (e.g., combination of engineering, ergonomics and biomechanics expertise to handle safety challenges). This paper proposes an exhaustive study that employs a scoping review and bibliometric analysis to provide a structured macro perspective on the developments, key topics, and trends in …

Multi-Objective Deep Reinforcement Learning For Dynamic Algorithm Selection In Open Ran, Saish Urumkar, Byrav Ramamurthy, Sachin Sharma

Conference papers

Open Radio Access Networks (Open RAN) provide flexible, modular multi-vendor interoperability. Growing mobile data demand requires balancing network performance with power efficiency. Mobile operators need intelligent resource management to achieve Key Performance Indicator (KPI) targets while maintaining operational efficiency. This paper proposes a solution using a multi-objective deep reinforcement learning (MODRL) model deployed on the Open RAN Intelligent Controller (RIC). Three customizable operator profiles (Power Saving, Balanced, and Performance) are used which define specific priority ratios between performance and power saving objectives.

To evaluate, individual algorithms (CPU scheduling and UE connection state switching) are implemented in Open RAN, achieving 5–20%CPU …

Performance Evaluation Of Managed Switch Configurations For Secure And Efficient Plc-Based Industrial Automation Networks, Ahmed Salama

All Graduate Theses, Dissertations, and Other Capstone Projects

This thesis explores how managed switches can improve network performance and security in PLC-based industrial systems. Using simulations in GNS3 and Cisco Packet Tracer, and packet analysis via Wireshark, the study compares unmanaged and managed switch configurations. Redundancy protocols, particularly STP and RSTP, are evaluated under failure scenarios. Results show that RSTP offers faster recovery times, making it more suitable for time-sensitive environments. Additionally, managed switch features like VLANs, port security, and MAC filtering significantly reduce vulnerabilities and improve network segmentation. The findings highlight the importance of incorporating both cybersecurity and redundancy in industrial network design as systems move toward …

‘Waves Of Imagination’ Unconditional Spectogram Diffusion Using Diffusion Architecture., Rahul Vanukuri

Computer Science and Engineering Theses - Archive

The swift evolution of wireless communication technologies,particularly in the field of rf signals or in CBRS bands,demands increasingly sophisticated signal processing techniques to ensure efficient transmission, reception, and spectrum management.Traditional approaches to signal generation and reconstruction, although effective in controlled environments, often struggle to cope with the challenges presented by real-world noisy conditions, hardware constraints, and limited access to large-scale datasets. In response to these limitations, this thesis explores the application of diffusion models—a class of generative models known for their ability to produce high-fidelity samples—to the domain of spectrogram generation for communication signals.

Different from conventional strategies to simulate …

Collaborative Online Interactive Laboratory On Software Defined Radio Fundamentals, Otilia Popescu, Dimitrie C. Popescu, Emanuel Puschita

Engineering Technology Faculty Publications

Teaching of fundamentals of communication systems varies widely across programs in US and abroad, mainly due to the type of undergraduate engineering programs and the depth of the communications field within the curricula. The variety is spread across electrical engineering and electrical engineering technology programs, and programs with focus on telecommunications or which only offer core or elective courses in communications. Adding to the variety, some programs include hands-on laboratory courses, others include simulation-based laboratories most of the time using Matlab, while others may only include lecture courses with no labs. The accessibility of the new software defined radio (SDR) …

Multimodal Feature Fusion And Machine Learning For Adhd Detection Using Neuroimaging Data, Isabel Pham

Master's Projects

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopment disorder that can significantly affect a person’s attention, impulse control, and executive function. Currently, the traditional diagnosis method often relies on clinical assessments and observations. However, these methods can be subjective and lead to inconsistencies in diagnosis between individuals. To address this challenge, neuroimaging and machine learning (ML) are promising tools for providing a more objective diagnosis of ADHD. The goal of this project is to apply a multimodal approach in which structural and functional features of specific regions of the brain are used to develop a more accurate and objective …

Simple Vs. Complex Human Activity Classification Via Hybrid Machine Learning Models, Anusha Kukreja

Master's Projects

In-depth understanding of the complexity of daily human activities is crucial for building responsive health monitoring and assistive technologies. However, limited research has focused on distinguishing activities based on their involvement level, as most existing work classifies only the type of activity performed. In this thesis, we address this gap by proposing a method to classify human activities as either simple or complex using sensor data from the Opportunity dataset. We define complex activities as those involving object interactions or multiple coordinated movements (e.g., drinking from a cup, cleaning a table), and simple activities as static or low-effort postures (e.g., …

Visionmate: Ai-Powered Image Captioning Web Application, Sai Anoushka Kokku

Master's Projects

VisionMate is a web application that generates captions for camera-captured images. It is designed
to assist users with visual impairments by converting visual input into spoken and written text. The
application uses the GIT-base model from Hugging Face, which processes the image and returns a
descriptive caption. Users can take a picture using the device camera—either via webcam on
desktop or the native camera interface on mobile. The app provides audio output using the
SpeechSynthesis API and uses full-screen tap interaction to simplify accessibility.

The frontend is implemented in React.js, and the backend is built with FastAPI. The backend calls …

Phishing Detection Using Continual Learning And Large Language Models, Gopi Prajeev Battula

Master's Projects

Adaptive phishing detection remains crucial as the nature of cyber-attacks changes over time, which renders static models obsolete. This project extends phishing detection through the implementation of continual learning approaches, namely Elastic Weight Consolidation (EWC) and Learning Without Forgetting (LWF) with RoBERTa, a Large Language Model (LLM) and compares the results of these approaches against GPT-4o-mini, another LLM. Our approach begins with fine-tuning RoBERTa on multiple phishing datasets to establish an effective baseline. EWC is then implemented to preserve vital model parameters based on their importance measured by the Fisher Information Matrix, while LWF uses knowledge distillation to retain prior …

On The Adversarial Robustness Of Quantized Neural Networks Against Common Adversaries In Time-Series Forecasting, Maanak Arora

Master's Projects

Real-world edge applications now use modern machine learning models which require both resource efficiency and robustness against adversarial threats. Deep neural networks which include time series forecasting models still face risks from adversarial perturbations while quantization techniques used for memory and compute efficiency create unpredictable robustness challenges. This project investigates the adversarial resistance of Long Short-Term Memory (LSTM) models after applying post-training quantization at three different precision levels: 16-bit floating point (FP16), 8-bit integer (INT8) and custom 4-bit quantization. The Jena Climate dataset serves as our main benchmark for training a fullprecision LSTM model followed by multiple quantization strategies which …

Physiotrack: A Gamified Physiotherapy System, Pranavi Chaturvedula

Master's Projects

Traditional physiotherapy methods tend to be non-interactive and provide little to no personalized instruction, even though physiotherapy is critical to stroke recovery. This thesis explores a fully adaptive, sensor-based, feedback architecture intended for stroke patients which remotely supervises movement and personalizes exercises enabled by multimodal sensors. The system uses filtering and windowed segmentation of accelerometer and skeletal data to compute features like jerk, speed, and joint movement angular range. A game engine applies accelerometer and skeletal features together with optimized, lightweight ML models to drive adaptive feedback, scoring, and difficulty adjustment. The architecture supports responsive continuous sensor streaming within the …

Secured Data Storage Management With Deduplication In Cloud Computing And Local Gpt Integration, Pavan Myana

Master's Projects

Exponential growth in cloud computing has brought enormous changes in data storage and processing, but also raised several questions on the security, privacy, and efficient storage of data. This report provides a dual-focused approach toward solving these challenges. First, we try to build an application securely and efficiently using data deduplication and Proxy Re-Encryption for optimization of storage and enabling secure data sharing. Deduplication ensures that redundant data is removed before encryption for maximum efficiency in storage, while PRE enables the safe sharing of encrypted data by re-encrypting the keys for specified recipients without the leakage of sensitive information. We …

Performance Comparison Of Machine Learning Across Metal, Cuda, And Neuromorphic Frameworks, Ryan Saini

Master's Projects

Machine learning’s computational demands necessitate optimal performance and utilization. This research compares Apple Silicon M3 Pro with MPS, NVIDIA RTX 3070 GPU with CUDA, and neuromorphic computing for machine learning methods. We provide a cross-platform and cross-architecture performance analysis of machine learning methods to identify optimal configurations for training and inference scenarios. On traditional neural networks, Apple Silicon with MPS delivers superior energy efficiency at the cost of longer processing times for training and inference. NVIDIA with CUDA offers faster computation in training and inference at higher energy costs. Convolutional spiking neural networks perform competitively on event-based data, particularly on …

Malware Opcode Embedding And Quality Assessment Of Generative Sample Embeddings, Atishay Jain

Master's Projects

Malware is software used to damage and disrupt computer systems with the intent to cause damage to the victim. Malware detection and classification into malware families is a crucial problem for cybersecurity researchers. One of the major bottlenecks in improving these systems is the shortage of good quality labeled malware data, especially for malware families with scarce samples. Researchers have utilized generative models to generate malware data to address this issue. Malware embeddings encode patterns within a malware file, which can be used to detect and classify malware. Recently, encouraging results have been obtained in generating malware embeddings using generative …

Gen Ai For Malicious Network Data, Aneesh Maturu

Master's Projects

Though botnet attacks are on the rise, they also have become sophisticated and difficult to detect. Such a rising threat demands more and more sophisticated cybersecurity that leverages machine learning technology. Nevertheless, one of the biggest bottlenecks remains the unavailability of large and well-balanced datasets, particularly for malicious traffic, which hampers the efficacy of detection models. In an attempt to address this issue, our research utilizes Generative Adversarial Networks (GANs) to produce synthetic samples of botnet traffic from the CTU-13 dataset. While the majority of generative models have been targeting image data, we use GANs for a new application: generating …

Human Vs. Llm: Interpreting Emotion From Neutral Faces In Contextually Charged Backgrounds, Sai Mounika Peteti

Master's Projects

In this study, we investigate the intersection of cultural context, visual perception, and implicit bias in interpreting neutral human expressions. Specifically, we explore how emotionally charged backgrounds can shape viewers interpretations of neutral facial expressions. Using an experimental setup, participants are shown neutral human portraits paired with varying background types ranging from emotionally neutral or pleasant to evocative scenes. Participants are tasked with selecting which background best matches the emotional state of the person depicted in the portraits. Eye-tracking data is collected to analyze visual attention patterns and cognitive processing. In parallel, we also evaluate how large language models (LLMs) …

An Iterative Shifting Disaggregation Algorithm For Multi-Source, Irregularly Sampled, And Overlapped Time Series, Colin O. Quinn, Ronald H. Brown, George F. Corliss, Richard J. Povinelli

Electrical and Computer Engineering Faculty Research and Publications

Accurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only single, uniformly sampled time series, have limited applicability in real-world, multi-source scenarios. This paper introduces the Iterative Shifting Disaggregation (ISD) algorithm, designed to process and disaggregate time series derived from sensor-sourced low-frequency measurements, transforming multiple, nonuniformly sampled sensor data streams into a single, coherent high-frequency signal. ISD operates in an iterative, two-phase process: a prediction phase that uses multiple linear regression to generate high-frequency series from low-frequency …

Retrofitting Of A Chevy-Bolt Ipm Motor With A Pm-Assisted Synchronous Reluctance Rotor Enabled With A Blend Of Heavy-Rare-Earth-Free Neodymium And Ferrite Magnets, Praveen Kumar, Ali Hussain Al-Qarni, Robin Wilson, Ayman M. El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

In this paper, the rotor of an existing Chevy-Bolt Interior Permanent Magnet (IPM) motor, consisting of rare-earth permanent magnet based rotor, is replaced and retrofitted with a PM-Assisted Synchronous Reluctance Machine (PMASynRM) rotor enabled with a combination of Heavy-Rare-Earth (Dy-free) Neodymium and Ferrite magnets. For this, a multi-objective design optimization of the PMASynRM rotor, aimed at minimizing rare-earth magnet volume and permanent demagnetization risk of magnets, while achieving performance comparable to the Chevy-Bolt motor across its operating speed range, is performed. This is followed by mechanical stress optimization of rotor lamination at maximum speed, to ensure its structural integrity. The …

Uninterruptable Non-Isolated Integrated Power Electronics Converter (Unipec) For Commercial Truck Auxiliary Power Unit, Pouya Zolfi, Ahmad Alzahrani, Ayman M. El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

A low component count high efficiency and high-power density three-port DC-DC converter named UNIPEC is introduced for commercial trucks auxiliary power unit (APU) application. The proposed UNIPEC is a single magnetic core topology which is achieved by integration and modification of switched capacitor technology and bidirectional multi-level DC-DC converter. With peak simulation efficiency of 97.3% and power density of 12.6 kW/L, proposed unit is an attractive candidate for conventional and refrigerated freight transportation applications. Comparison study and low power experimental validation results are presented. Reliability analysis of UNIPEC is also conducted and mean time to failure (MTTF) reported. A 1 …