Computer Engineering Commons^™

Multi-Modal Sensor Fusion Of Radar And Lidar For Enhanced Navigation In Obstacle-Occluded Environments, Kyler Ashton Farrar

Theses and Dissertations

Multi-sensor fusion is a practical and well-researched methodology to combine a variety of incoming sensory data into an enhanced digital representation of a real-world environment. A typical use-case for multi-sensor fusion is the combination of LiDAR and RADAR data to obtain simultaneous 3D positioning and velocity measurements for a particular RoI (Region of Interest). This study investigates LiDAR/RADAR sensor fusion for enhanced navigation information when placed in obstacle-occluded environments such as highly vegetated areas. Specifically, a novel fusion-map approach is designed and evaluated for use with a LiDAR/RADAR sensor suite to produce a fused cost map to determine optimal and …

Frontlines Of Influence: State Vs. Nonstate Disinformation Campaigns, Lily Wershbale

Cybersecurity Undergraduate Research Showcase

This paper examines the evolution of disinformation campaigns conducted by state and nonstate actors, focusing specifically on Russia and the Islamic State as representative case studies. Through historical examples and qualitative comparative analysis, this research identifies the similarities and differences in disinformation’s role in actors’ core missions, resource allocation, and targeting decisions. This paper further explores the implications of artificial intelligence on disinformation campaigns, investigating how emerging technology will impact the influence operations of state governments and nonstate organizations alike. The findings reveal that while the ultimate intent of both state and non-state actors is to destabilize societies, their approaches …

Terraincraft: Automated Land-Cover–Driven Terrain Generation For Marine Robot Simulations, Xinyue Liang

Dartmouth College Master’s Theses

From self-driving cars navigating city streets to all-terrain vehicles tackling rugged landscapes, recent leaps in robotic autonomy due to fast pace development in deep learning are reshaping how machines interact with the real world. However, autonomy in the aquatic environment is still limited, due to difficulty in testing and unavailability of realistic simulation environments.

In this project, we aim to create an automated system that simplifies the processes of creating synthetic datasets for marine robots navigation training tasks. We achieved this through a land cover map controlled terrain generation. Our goal is to provide an automatic terrain generation system that …

Developments On Abbreviations Towards Machine Reading Comprehension, Sing Choi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Machine reading comprehension is a critical step in development of applications that require the semantic understanding of human speech-to-text driven work. Many devices such as smart home appliances like the Amazon Echo Dot, Google Home, or smart assistants like Apple Siri or Microsoft Cortana are examples of these applications. The comprehension task involves a deeper understanding and recognition of named entities such as person names, locations, medicals codes, quantities, abbreviations, and acronyms in speech or text data. In this dissertation, we explore and extend the different approaches and techniques in modern research that tackles the problem of recognition and definition …

Mfgat: Map-Free Trajectory Prediction With Graph Attention Networks For Autonomous Vehicles, Zehra Gunindi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurate trajectory prediction is a key component for ensuring safe and efficient navigation of autonomous vehicles in complex traffic scenarios. While traditional methods rely heavily on high-definition (HD) maps, these approaches face significant challenges, including high costs, limited availability, and susceptibility to rapid obsolescence. This thesis proposes an end-to-end, map-free trajectory prediction model that leverages Graph Attention Networks (GAT) to dynamically capture spatial-temporal interactions among road agents, eliminating the need for HD maps.The research introduces UNLVTraj, a novel LiDAR-based dataset collected around the University of Nevada, Las Vegas campus, specifically along Cottage Grove Street, Harmon Avenue, and Maryland Parkway. This …

Elevating Education: Leveling Up Individual Learning Plans, Maximum Mgrdich-Ararat Sirabian

UNLV Theses, Dissertations, Professional Papers, and Capstones

This three-article dissertation investigated the effectiveness, implementation quality, and automation of Individual Learning Plans (ILPs) in promoting college and career readiness. Article 1 analyzed High School Longitudinal Study of 2009 data and found that ILPs did not significantly guide course alignment. Article 2 examined ILP implementation across Nevada high schools, revealing inconsistent quality, limited standardization, and few culturally responsive practices. These findings informed the creation of a new high-quality ILP framework. Article 3 employed a convergent parallel mixed methods design to assess an automated ILP prototype based on this framework. Participants in the automated group reported significantly higher scores in …

Transfer Learning For Temporal Logic Objectives, Lucas M. Santana Rovira

McKelvey School of Engineering Graduate Student Theses & Dissertations

Reinforcement learning algorithms can enable autonomous systems to learn the control skills needed to accomplish a task specified by a linear temporal logic formula. However, they cannot be transferred to a new task, even when the two are very similar. For each new task, the policy must be redesigned from scratch, which is a common limitation of existing reinforcement learning methods for temporal logic tasks. A proposed solution to this problem leverages the similarity between past and new tasks to reuse already learned control skills to accomplish the new task, with minimal or no retraining.

Rather than learning a single …

Algorithms & Design Behind Autonomous Uavs And Ugvs Coordinated System, Aashish Dhakal

Honors Theses

Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs), when coordinated effectively, offer substantial potential for automating large-scale tasks—from search and rescue operations to precision agriculture. However, synchronizing these autonomous systems remains challenging, especially in time-sensitive missions requiring precision. This thesis investigates the design and algorithmic coordination of autonomous UAVs and UGVs, examining both single-vehicle scenarios and multi-agent (swarming) approaches. Using the Robot Operating System (ROS) as a communication backbone, I integrate GPS positioning with computer vision techniques through OpenCV, enabling accurate localization and object detection. During the development phase, I validate my methods using ArduPilot Software-in-the-Loop (SITL) simulations within …

The Effects Of Internet Service Criteria On Institutional Performance, Mohammed Naji, Sivadass Thiruchelvam, Mohammed Khudari

Iraqi Journal for Computer Science and Mathematics

Internet service today greatly affects individuals, companies, and organizations around the world, as the Internet contributes to many things such as facilitating procedures, reducing effort, and saving time. The instability of the Internet system is a major obstacle to the successful implementation of institutional plans, leading to many workflow problems, including delays in providing services to citizens and insufficient communication between the components of the institution. It also leads to a lack of information needed for decision-making, which negatively affects customer satisfaction and operational efficiency. There is a gap in the literature regarding the evaluation of the relationships between Internet …

Advancing Lymphoma Diagnosis In Histopathology Image Classification Using Multi Deep Learning Models, Ahmed Elaraby, Andrey Nechaevskiy, Aymen Saad

Iraqi Journal for Computer Science and Mathematics

Deep learning's rapid development is generating significant interest in its potential to improve medical imaging. It has shown promising results in detecting malignant lymphoma in histopathology medical images. Image classification methods are widely used to aid in making diagnoses from medical images. In recent years, deep learning methods have achieved high performance in detecting malignant lymphoma in histopathology images. This study proposes a novel approach to improving lymphoma diagnosis in histopathology images called the Lightweight Convolutional Neural Network (LWCNN). The proposed LWCNN model comprises multiple deep learning architectures, including a convolutional neural network (CNN) that has been trained to classify …

Improved Harmony Search Algorithm For Sdn Controller Placement, Yousra Abdul Alsahib S. Aldeen, Ahmed T. Sadiq, Abeer E. Abed, Omar F. Hussain, Syed Hamid Hussain Madni

Iraqi Journal for Computer Science and Mathematics

The Software-Defined Networking (SDN) paradigm decouples the control and the data plane. One of the most significant challenges in this paradigm is SDN controller placement optimization, since improper placement may dramatically influence latency, load balancing, and network resilience. The paper proposes the Improved Harmony Search Algorithm (IHSA) as a new approach for the placement of SDN controllers. To overcome the disadvantages of conventional optimization methods like HSA, GA, and PSO, adaptive parameter tuning, dynamic harmony memory management, and updating rules for enhanced memory are incorporated into the IHSA. The complete simulations of IHSA over small, medium, and large-scale SDN topologies …

Designing And Mcat Study App That Updates Automatically And Includes A Novel Goal Of Optomizing A User's Mental Health, Richard Vasquez

Graduate Theses & Non-Theses

This project investigates the multifaceted role of heart rate variability (HRV) as both a physiological and cognitive marker of human performance, with particular emphasis on its implications for academic preparation and decision- making. Drawing from recent literature, I examine the influence of exercise, sleep quality, stress management, and slow-paced breathing on HRV, highlighting its predictive value for executive function, emotional regulation, and adaptive decision-making. Additionally, the paper introduces a conceptual framework for an educational application that leverages HRV monitoring and breathing interventions such as, resonance breathing to enhance study effectiveness. Additionally, the application will feature a supplemental website to reinforce …

Benefits And Applications Of Learning With Virtual Reality, Michael W. Timm

Honors Program: Senior Projects (Public)

Education is a fundamental pillar of society. It equips students for employment and interpersonal relations. Virtual reality (VR) has emerged as a transformative technology in the field of education. The aim of this paper is to synthesize existing research in order to determine the benefits of utilizing virtual reality in a variety of education settings, such as K-12 classrooms, universities, and workplace training. This paper observes significant benefits of virtual reality in constructivist and experiential learning, gamified learning, and tailored practice. This analysis also finds that virtual reality is advantageous for educational accessibility, particularly for absentee students and impoverished students. …

Ecodrone: Autonomous Environmental Monitoring, Belsen Lee

Student Scholar Symposium Abstracts and Posters

This project presents EcoDrone, an autonomous aerial drone designed for continuous and automated environmental monitoring. Current environmental monitoring methods rely on stationary sensors or manual data collection, limiting real-time response capabilities. This reliance leads to delayed, incomplete, and spatially limited data and restricts the ability to capture real-time changes. Another challenge includes the difficulty of environmental monitoring in challenging terrain, whether it be wildfire areas, dense forestry, or mountainous terrain. EcoDrone overcomes these challenges by autonomously navigating difficult terrain to collect real-time data, offering more flexible and timely monitoring than stationary or manual methods. The central research question investigates integrating …

Adaptive Noise Estimation And Denoising With Deep Learning For Nmr Spectroscopy, Naveen Asokan

McKelvey School of Engineering Graduate Student Theses & Dissertations

Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical technique widely used for molecular structure elucidation in chemistry, biology, and medicine. However, spectral accuracy is often degraded by noise—particularly in low acquisition time settings—resulting in reduced resolution and obscured chemical features. While traditional noise reduction techniques such as signal averaging can improve spectral quality, they require longer acquisition times, limiting their utility in real-time and high-throughput applications.

This thesis presents a deep learning-based denoising framework designed to enhance the quality of complex-valued NMR spectra. The proposed model, built upon a U-Net architecture, incorporates both real and imaginary components of the …

Generic Fpga Preprocessing For Astrophysics Instruments In Hls, Qinzhou Song

McKelvey School of Engineering Graduate Student Theses & Dissertations

FPGAs are widely deployed on high-energy astroparticle physics instruments to preprocess large volumes of streaming data from various sensors. Increasingly, these deployments are finding their way to space-borne instruments, where constraints on size, weight, and power (SWaP) require careful balancing of speed and resource utilization. Although telescope designs vary widely, they often share common preprocessing elements, including channel-level readout, pedestal subtraction, waveform integration, and zero suppression from front-end ADCs, as well as identification and centroiding of signal islands across groups of multiple channels. High-Level Synthesis (HLS) tools allow these designs to be expressed at a conceptual level, which automates a …

Development Of Interactive Games On An Affordable Braille Display, Daniel Tsivkovski, Dylan Ravel, Maryam Etezad

Student Scholar Symposium Abstracts and Posters

Developing an affordable and STEM learning-focused Braille display addresses a significant disparity in the market for Braille displays, where most fail to provide a cost-effective, accessible, and education-oriented solution. This research aims to bridge this gap through innovative hardware and software development, offering a comprehensive learning experience to elementary school children (K-6) who are blind/visually impaired. The hardware features a piezo-electric tactile display that displays up to six Braille characters at once or a shape in an 8x8 pin array configuration. The educational software includes a user-friendly website packed with engaging STEM activities specifically designed for blind/visually impaired children. The …

Strategic Reactor Allocation For Deadlock-Free Execution, Jeevan Sivamohan

McKelvey School of Engineering Graduate Student Theses & Dissertations

As it becomes harder to increase the computation power of a single machine, we are turning towards parallel and distributed systems to extract additional performance by breaking down the problem into pieces and solving it simultaneously. While this provides a great opportunity for increased performance,e it comes with additional problems not present in the sequential approach. One such problem is deadlock. Deadlock is defined as the state in which program execution stalls because the system has run out of resources to manage and execute the program properly, or there exists some circular dependency in the data between parallel or distributed …

Soft Modular Robots: From Modular Tensegrity Structures To Bioinspired Sea Robots, Luyang Zhao

Dartmouth College Ph.D Dissertations

The rapid advancement of robotics necessitates systems capable of adapting to complex, unstructured environments. Soft robots, with their flexibility and compliance, excel in delicate interactions, making them ideal for medical applications and search-and-rescue missions. Modular robots, on the other hand, offer reconfigurability, enabling diverse task-specific adaptations in dynamic settings. Despite their individual advantages, the integration of soft and modular robotics remains underexplored. This proposal aims to develop soft modular robots that combine the adaptability of soft robotics with the versatility of modularity. These systems will be capable of autonomously transitioning between locomotion, manipulation, and infrastructure assembly across land, water, and …

Unified Evaluation Of Real-World Iot-Based Federated Learning, Yi Gu

Master's Theses

Federated learning (FL) is a novel paradigm that enables the training of a global machine learning (ML) model across distributed devices by exchanging model parameters instead of raw data in the training process. Internet of Things (IoT) devices typically operate with limited resources, have weaker security protections, and are more vulnerable to potential thermal stress (TS). Current evaluations of FL are mostly conducted through simulations of multiple clients on a single device. However, there remains a gap in understanding how FL performs under TS in real-world, low-power IoT environments. Conformal prediction (CP) is an effective method for quantifying uncertainty in …

Breaking New Ground: Division Directly In Memory, M. Hassan Najafi, Mehran Shoushtari Moghadam

Faculty Scholarship

In-memory computing (IMC) has emerged as a promising paradigm for overcoming the limitations of traditional von Neumann architectures by reducing data movement and enhancing computational efficiency. Despite significant advancements in this area, implementing complex arithmetic operations, such as division, directly within memory has remained an elusive challenge. This paper introduces a pioneering technique for performing division operations directly in memory, representing the first successful integration of such functionality into the IMC framework. Our approach leverages an innovative circuit based on an unconventional model of computing–stochastic computing (SC). Our technique extends the computational capabilities of IMC systems and paves the way …

Retracted: Data Envelopment Analysis Using Stochastic Frontier Analysis And Bootstrap Confidence Intervals, Fahad F. Alqahtani

Iraqi Journal for Computer Science and Mathematics

Evaluating company growth potential has moved away from traditional financial focused ratios and ratios analysis that has origins in the early twentieth-century economics. However, these conventional methods might not be accurate in measuring such efficient factors as this combined proposed framework of Data Envelopment Analysis (DEA) and improved mathematical models do. The present research focuses on the prospect of growth of companies through evaluating the performance of 40 DMUs in terms of efficiency DEA and MMTs. DEA is used to determine the efficient DMUs while SFA underline the factors such as investment on research and development, effective marketing strategies and …

A Hybrid Technique For Approximating The Solution Of Fractional Order Partial Integro-Differential Equations, Ahmed K. Mohsin, Fajir A. Abdulkhaleq, Osama H. Mohammed

Iraqi Journal for Computer Science and Mathematics

In this paper, we discuss the numerical solution of fractional order partial integro-differential equations. The type of fractional derivative used is a Caputo derivative. The method proposed in this paper known as transform optimal perturbation iteration method. This method combines the optimal perturbation iteration method and the Laplace transform in order to converge to the exact solution. The proposed method is highly efficient and provides the means of controlling the approximate solutions convergence. Illustrative examples prove that the suggested approach is very accurate when compared with the exact solution and the results of the existing methods.

Dna Storage Of Images: From Pixels To Molecules, Cihan Ruan

Engineering Ph.D. Theses

DNA storage is rapidly emerging as a transformative solution for long-term, ultra-dense, and energy-efficient archival systems. This dissertation builds upon a deep understanding of image compression principles to design encoding frameworks that are uniquely attuned to the biochemical constraints of DNA storage. By fusing insights from traditional coding with molecular-level design, we develop a series of technically advanced solutions tailored for this emerging medium.

Our first contribution introduces Dynamic DNA-Fountain, a constrained codec that maps H.266 /VVC-compressed bitstreams to quaternary DNA sequences, optimizing for both information density and biochemical synthesis constraints. Building upon this foundation, we shift focus toward robustness—leveraging …

Adaptive Oversight In Action: Proposing Context-Aware Governance For Ai In Cybersecurity, Russell S. Cunningham

Boise State Graduate Student Projects

Artificial intelligence is increasingly woven into cybersecurity operations, shaping everything from threat detection to automated incident response. While these technologies improve speed and scalability, they also raise urgent questions about governance, ethical use, and operational risk. Although frameworks such as the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act, IEEE's Ethically Aligned Design, and OECD AI Principles each offer structure, they tend to address only parts of the problem. Most focus on compliance or ethics but rarely both, and few are tailored to the high-pressure, risk-sensitive environments found in cybersecurity.

To bridge these gaps, this research proposes …

Gpu-Based Visual Effects System, Matthew Jaffe

Programming Theses and Dissertations

The objective of my thesis is to create a robust and efficient VFX system that can be used to edit and add particle effects to games. This system utilizes a compute shading pipeline to simulate millions of particles in real time. The behavior of particles is widely customizable through many different properties which can be manipulated changed over the lifetime of particles and introduce procedural randomness. There are many ways to customize the motion of the particles with various forces and collision. Additionally, particles can be rendered as billboarded quads, full meshes or partial meshes with different settings to further …

Design And Implementation Of A Notification System For The Purpose Of Improving Communication Between Nurses And Patients, Aindrila Bhattacharya

2025 Spring Honors Capstone Projects - Archive

Communication is an important part of how nurses administer treatment to their patients. Without proper communication, it is difficult for the nurses to connect with their patients and vice versa. This paper will be going into some previous literature about communication between patients and nurses in general, communication with respect to pediatric patients and how notification systems have been implemented in the past. It will then discuss the methodology and implementation of a notification system for a medical app. The medical app is meant to facilitate communication from the side of the patient to the nurse, while the notification system …

Controlling A Mobile Inverted Pendulum And Optimizing Leaning Angle To Apply Force Using Reinforcement Learning, Aryan Mediratta

2025 Spring Honors Capstone Projects - Archive

Reinforcement Learning is a Machine Learning paradigm that involves simulating learning through rewards and penalties in intelligent systems. This technique is often employed in robotics when traditional control methods are insufficient or when human intuition does not provide a good solution on how to control robot systems, This project involves training a Segway-style Mobile Inverted Pendulum (MIP) robot to balance and push a box forward. The BeagleBone Blue board is used that includes a built-in Inertial Measurement Unit (IMU) and encoder ports. These sensors enable the system to measure its current state. The goal is to find the optimal leaning …

Integration Of Notes Section With Access Management, Inshaad Merchant

2025 Spring Honors Capstone Projects - Archive

This research explores the development of a notes section with access management, specifically designed to assist Computer Science and Engineering students to revisit and revise all the notes and key points highlighted in their tutoring sessions. While the senior design project focuses on the CSE Student Success Center application that allows students to schedule tutoring sessions, manage appointments, and manage their profiles within this application, the Honors capstone project adds on a specific section for students to save all their notes and important video links and attachments to continue their learning outside of the tutoring sessions. A centralized platform for …

Development Of A Cost-Effective Daq For Measuring Brake Performance In Race Cars, Adrin Alias

2025 Spring Honors Capstone Projects - Archive

This project explores the feasibility of creating a cost-effective data acquisition (DAQ) system for high-speed, real-time brake performance testing of Formula SAE racecars. The research addresses the limitations of the current MoTeC DAQ system currently employed by the team, which is costly and time-consuming to set up for on-car testing. The team will use a brake dynamometer for steady-state comparisons of different brake pad compounds (senior design project), but evaluating real-world performance requires on-car testing. By systematically comparing various hardware platforms, sensors, communication protocols, and storage solutions, this project aims to balance cost-efficiency with reliability and performance. The research evaluates …