Computer Engineering Commons^™

Method For Dynamic Coalition Formation Of Wargame Agent For Force Cooperation, Changhua Yao, Shanning Bi, Rufei Ma, Xiaohan Yu, Jiaqiang Li, Jinli Chen

Journal of System Simulation

Abstract: Regarding the issue of cooperative task alliance formation and adjustment in multi-agent dynamic confrontation scenarios at the tactical level, this method comprehensively considers factors such as target value, task allocation, and operator characteristics, as well as the benefits and costs of executing different types of tasks. we propose a targeted force coordination adjustment for dynamic task alliance formation based on behavioral constraints. The “MiaoSuan-Wise Winning Instant Strategy Human-Computer Confrontation Platform” of Chinese Academy of Sciences (CAS) is used as an experimental platform to conduct confrontation experiments. The experiment demonstrates that the proposed method improves the dynamic coordination ability of …

Research On Decision-Making Of Autonomous Driving In Highway Environment Based On Knowledge And Large Language Model, Xiang Wang, Guozhen Tan

Journal of System Simulation

Abstract: Aiming at the lack of continuous learning and interpretability of current autonomous driving system, a decision model with cognition, generalization and learning ability is proposed. The model utilizes large language model (LLM) and attention mechanisms to understand and explain driving scenes. the system can accumulate and learn from driving experiences, continuously improving its decisionmaking ability. In a simulation environment, the closed-loop test decision model is applied in high-speed scenarios.The simulation results show that the success rate of the knowledge-driven model is 7% and 4% higher than those of the rule-based and data-driven methods. Additionally, the model exhibits generalization and …

Exploring The Limits Of Multimodal Foundation Models For Visual Temporal Reasoning And Gesture Recognition Tasks, Ziyao Shangguan

Computer Science Theses

Multimodal foundation models (MFMs) have demonstrated impressive capabilities in static vision-language tasks such as image captioning, video summarization, and cross modal retrieval. However, their ability to reason over time—especially in gesture-rich video inputs—remains limited. This thesis investigates the temporal reasoning capabilities of MFMs in the context of gesture understanding, a critical component for enabling more expressive human-robot interaction. Through a preliminary study, we show that prompting-based strategies offer only marginal improvements in temporal reasoning, despite producing accurate frame-by-frame descriptions.

To more rigorously evaluate these limitations, we introduce TOMATO, a benchmark designed to assess visual …

Multiagent Copilot In Industrial Ai Applications, Chathurangi Shyalika, Renjith Prasad, Utkarshani Jaimini, Cory Henson, Fadi El Kalach, Amit Sheth

Publications

In the era of smart automation and digital transformation, achieving efficiency, precision, and adaptability is essential for industries to remain competitive. Sectors, including manufacturing, supply chain and logistics, healthcare, finance, and retail, face significant challenges in deploying Artificial Intelligence (AI) solutions tailored to their unique needs, particularly in critical, resource-constrained applications. According to Gartner’s 2024 Hype Cycle for Artificial Intelligence, composite AI, which integrates techniques like machine learning, knowledge graphs, and rule-based systems, is becoming foundational for industries, enhancing predictions, decisions, and scalability across complex environments.

The complexity of real-world systems requires Industrial AI solutions to be customizable to business …

Development And Evaluation Of A Simple Human Body Link Model Considering The Degrees-Of-Freedom Of Shoulders, Mizuki Takeda, Yuki Saito, Kaiji Sato

Progress in Scale Modeling, an International Journal

Modeling of the human body is widely utilized in the field of human–robot interaction, warranting the development of a simple model to measure and recognize the human body movements. To this end, a two-dimensional (2D) human body link model in the sagittal plane has been used to represent the human body with rotating joints and links connecting these joints. The joint positions can be determined by using the coordinates of a limited number of points on the links and estimated using a few sensor outputs when the links are of constant length. However, models with constant link lengths may result …

Meshlet Rendering Using D3d12 Mesh Shading Pipeline, Anishva Bardhan

Programming Theses and Dissertations

Modern video games must render scenes with increasingly complex geometry. Technologies like Nanite in Unreal Engine 5 enable the handling of scenes with significantly higher object and triangle counts than ever before. This project draws inspiration from Nanite by operating on triangle clusters, allowing artists to focus solely on creating high-poly meshes. The primary objective is to implement fine-grained culling techniques on meshlets, combined with efficient meshlet instancing, to reduce render time and memory usage.

Meshlet instancing plays a crucial role in optimizing rendering performance by allowing multiple objects sharing the same geometry to be rendered efficiently. Instead of duplicating …

3d Multi-Threaded Ai Navigation With Pathfinding And Obstacle Avoidance, Jabari Belgrave

Programming Theses and Dissertations

In this thesis, I developed a 3D multi-threaded AI navigation system using my own custom-built C++ game engine. The system combines triangle-based A* pathfinding with real-time obstacle avoidance using a set of velocity-obstacle algorithms. It is designed to support large numbers of agents navigating complex environments while avoiding collisions. I created two main simulation modes: Navigation Mode, which integrates A* with ORCA to handle large-scale pathfinding and movement, and Obstacle Avoidance Mode, which allows direct comparison between VO, RVO, HRVO, and ORCA in a controlled test setting.

The terrain is procedurally generated using Perlin noise, and this terrain data is …

An Overview Of Global Navigation Satellite System Reflectometry In Coastal Wetlands, Luke Andrew Redwine

Theses and Dissertations

With rising global temperatures, increasing sea levels, and the accelerated erosion of coastal wetlands, efficient methods for monitoring this vulnerable ecosystem are crucial. Traditional approaches, such as manual surveys, are labor-intensive, hazardous, and invasive to the environment they are attempting to protect, while current remote sensing methods are cost prohibitive and rely on irregular data collection techniques. To address these challenges, a scalable solution is needed for reliable and frequent data collection. This study explores the use of GNSS Reflectometry (GNSS-R) combined with unmanned aerial vehicles (UAVs) to monitor the shifting topology in wetlands with minimal human invasion. By leveraging …

Adaptive Multi-Sensor Fusion For Robust Autonomous Perception In Unstructured Environments, Samantha S. Carley

Theses and Dissertations

Autonomous vehicles commonly employ multiple sensors to perceive their surroundings. Coupling these sensors would ideally improve perception compared to using a single sensor. An autonomous system can be equipped with object localization and classification, often performed using a visual camera to understand a scene intelligently. Object detection and classification can also be applied to LiDAR and infrared (IR) sensors to further enhance scene awareness of the autonomous system. Herein, sensor-level, decision-level, and feature-level fusion are explored to assess their impact on perception and mitigate sensor disagreements. Specifically, the fusing of RGB, LiDAR, and IR sensor data to improve object classification …

A Fine-Tined Bert Model For Improved Querying Of The Unmanned Aerial System Integration Safety And Security Technology Ontology, Minh Hong To

Theses and Dissertations

The use of unmanned aerial vehicles (UAS) in all industries is steadily increasing every year. To govern the use of UAS, the Federal Aviation Administration (FAA) seeks to provide a foundation of rules and regulations for UAS operation in the National Airspace System (NAS). The UAS Integration Safety and Security Technology Ontology (ISSTO) was developed using the Web Ontology Language (OWL) in 2023. In 2024, a query application was developed to search ISSTO for information about the safety and security of UAS operations. While the application is functional, the search results can be further fine-tuned to match what the user …

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 …

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 …

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 …

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 …

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 …

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 …