Engineering Commons^™

Arduino Based Game Console With Built In Screen, Mekhi Edwards

Student Research Design & Innovation Symposium

As video games today become more sophisticated, the technological demand they place on gaming consoles increases. Portable consoles like Nintendo’s Game Boy Advance are a thing of the past due to the increase in base requirements needed to run games properly. The Arduino Based Game Console with Built in Screen brings back a retro vibe while running a modern processor that can handle the strain of sophisticated games. The Arduino Based Game Console with Built in Screen has two joysticks, a 7 by 5 inch housing and a display of 4 by 3 inches. The console currently includes one game …

The Bear Paw Wide-Grip, Nathan Couture, Vincent Lin, Manuel Urena, Brandon Daniels

Student Research Design & Innovation Symposium

Spinal cord injuries (SCI) affect millions worldwide every year. Injuries in the low cervical region (C5-C8), significantly limit hand function, grip strength, and range of motion. The Bear Paw addresses these impairments by developing a variable assistive grip device with a personalized fit for those with low cervical SCI. This work adopts a unique approach centered around functionality and autonomy with minimal hand movement requirements.

The Bear Paw includes a Wide-Grip prototype that supports users in performing activities of everyday life, such as dressing, shopping, and preparing food. This device is designed with an extra finger on the lateral side, …

Echo-Location Helmet, Nathan Couture, Manuel Urena, Fernando Ibanez Marcos, Donny Mcinnis, Harrison Ullucci, Mekhi Edwards

Student Research Design & Innovation Symposium

Vision loss affects approximately 50 million Americans annually, ranging from partial impairment to complete blindness. The Echo-Location Helmet is designed to assist individuals with visual impairments by providing real-time spatial awareness. The helmet delivers sufficient information to enable users to navigate unfamiliar environments with greater confidence.

The Echo-Location Helmet was constructed using ultrasonic sensors, visual processing, and speech output, integrated through development software and artificial intelligence (AI). The system employs hardware components such as an Arduino microcontroller, a serial camera, ultrasonic sensors, and an MP3 player to detect and alert the user. This provides users with an easy way to …

Harlem Baking Co., Carm Reed, Julia Tyras, Matteo Lopergolo

Student Research Design & Innovation Symposium

Harlem Baking Co. (HBC) is a business located in New York City that handmakes "Little Jars of Happiness." Their desserts come in a variety of flavors (from New York cheesecake to chocolate lava cake) and are baked inside reusable Jars. The team was tasked with redesigning their logo and packaging in partnership with Dr. Jeff Binczyk's marketing class, and designed a website, fridge wrap, and AD campaign. HBC was also kind enough to send samples of their desserts for a photoshoot, which resulted in some amazing photos that are currently on display in their current ads.

Project Curl Brand Identity & Website Design, Mallary Robert, Danitza Ramos-Perez

Student Research Design & Innovation Symposium

This project was completed in collaboration with a JWU student from the entrepreneurship program as part of the Design Team I class. The objective was to develop a brand identity and fully designed website prototype for ProjectCurl, a startup focused on providing affordable and unique hair care solutions for college students with curly hair using a personalized hair quiz to recommend products.

The project covered the full branding process, including logo development, wireframing, and the creation of a comprehensive website prototype. Throughout both the branding and website process, there was consistent feedback and rounds of revisions, which allowed the design …

Stability And Resonance Of Cylindrical Wave Energy Converters, Gregory M. Ballen

Dissertations, Master's Theses and Master's Reports

With increased focus on wave energy converters that produce power in multiple degrees of motion, more tools are necessary to understand the behavior of these devices and cause them to reach critical resonance points. This thesis uses Floquet theory and a numerical solver to understand how a WEC of given parameters operating in surge, heave and pitch may approach points of instability using feedback control schemes. It finds that a WEC reaches these resonance points when the surge and pitch motions are driven to resonate at either the same frequency as the heave motion, or at half this frequency. This …

Heuristic Approaches For Coordination Of Heterogeneous Robotic Systems In Harvesting Automation With Size Constraints, Hyeseon Lee

Dissertations, Master's Theses and Master's Reports

This thesis presents the development of path planning algorithms for the coordination of heterogeneous robotic systems while considering size constraints. The objective is to generate practical and efficient solutions for real-world applications. The use of heterogeneous collaborative robots is beneficial in many applications, such as transportation operations in warehouses or manufacturing environments, surveillance, and monitoring, and task allocation and path planning are critical techniques that need to be addressed to deploy in real-world applications. This research focuses on automating lavender harvesting, where robots with varying capabilities must collaboratively navigate complex field layouts to efficiently complete harvesting tasks.

The problem considers …

Phase-Field Method Of Fracture In Fiber-Reinforced Polymer Composites: Theory, Modeling, And Validation, Akash Kumar

Dissertations, Master's Theses and Master's Reports

Fiber-reinforced polymer composites (FRPCs) are widely used in aerospace, automotive, and civil engineering due to their high specific strength, stiffness, and durability. However, their anisotropic and heterogeneous nature makes fracture prediction challenging, with damage modes classified as intralaminar failure (matrix cracking, fiber breakage, fiber-matrix debonding) and interlaminar delamination. Delamination, driven by weak through-thickness strength and high interlaminar stresses, is a predominant failure mechanism in FRPCs. While cohesive zone models (CZMs) are commonly used for delamination, they are computationally expensive and inefficient for capturing intralaminar fractures where crack paths are unknown. To address this, we developed a unified phase field-based cohesive …

Knowledge Integrated Deep Learning For Enhanced Fault Diagnosis And Prognosis For Rotating Machinery And Its Applications On Marine Systems, Yunsheng Su

Dissertations, Master's Theses and Master's Reports

This dissertation develops a knowledge-informed deep learning framework for robust fault diagnosis and prognosis across various engineered systems, including bearings, lithium-ion batteries, and mooring systems in wave energy converters (WECs). A deep transfer learning (DTL) method is first developed to improve bearing fault diagnosis by fusing data from multiple sources and extracting key features using convolutional neural networks (CNNs). Building upon this, a knowledge-informed deep network (KIDN) framework is proposed, integrating physics-based features with deep learning to enhance diagnostic accuracy and generalizability. A constrained Gaussian process (CGP) model is further employed to guide adaptive network design and reduce model tuning …

Innovations In Balanced Mix Design: Establishing Gwp Thresholds For Various Nmas And Evaluating Superpave Vs. Bmd In High Content Rubber Asphalt, Kai Xin

Dissertations, Master's Theses and Master's Reports

Traditional volumetric asphalt mix design methods, such as Superpave, frequently struggle to address critical performance parameters, leading many agencies to seek more robust approaches. Balanced Mix Design (BMD) has emerged as a forward-looking alternative by directly measuring rutting and cracking resistance in the laboratory. In this study, BMD principles were applied to asphalt mixtures featuring three distinct NMAS mixes and two high content rubber mixes, introduced through both dry and semi-wet processes. Compared with conventional volumetric constraints, BMD provided greater flexibility in adjusting binder content and gradation to accommodate elevated crumb-rubber levels. The resulting mixtures satisfied both rutting and cracking …

Review And Development Of Centralized Engineering Tool Frameworks To Enhance Productivity And Bridge Gaps In Current Engineering Processes, Elijah M. Sierra

Dissertations, Master's Theses and Master's Reports

This thesis aims to tackle some of the inefficiencies with the current methods of engineering. The issues are specifically the inefficient iteration processes between preliminary design and detail level design, a lack of centralized resources, and poor project organization and management. The thesis provides a case study on the current tools that solve this issue and then presents a new proof of concept framework for an engineering tool. This tool addresses these issues by unifying calculators, databases, external tools, and project management systems under one modular platform.

The tool consists of one central GUI with multiple independent modules such as …

Foreign Object Detection System Development Using Edge Devices Integrated With Quadcopter Autonomous Tracking, Chi-Chia Huang, Ching-Hung Lee

Journal of Marine Science and Technology–Taiwan

This study proposes an all-weather autonomous foreign-object detection system capable of operating in low-light environments. We used a highly mobile quadcopter equipped with an edge-computing device as the inspection platform. On the software side, we integrated a low-light image enhancement model, a path-tracking algorithm, and a foreign object detection model to achieve rapid object detection. A lightweight, low-light image-enhancement model reduces the need for additional lighting equipment, thereby enabling reliable detection under low-light conditions. The path-tracking algorithm integrates a lightweight semantic segmentation model with traditional image-processing techniques to establish flight paths efficiently and reliably. The foreign-object detection component employs state-of-the-art …

Optimization Of Underactuated Ship Sliding Mode Controller Based On The Ddpg Algorithm, Lin Ma, Mingzhe Qi, Shicai Chen, Hongwei Bian, Jian Zhang

Journal of Marine Science and Technology–Taiwan

Due to the development of ship intelligence, the demand for precise controllers is increasing day by day. In traditional design, the trial-and-error method often leads to the use of inaccurate controller parameters, causing oscillations when environmental disturbances are present. To address these issues, this study introduces deep learning to optimize the controller parameters, particularly those used for sliding mode controllers in underactuated ships. We adopt the Deep Deterministic Policy Gradient (DDPG) algorithm based on the Actor Critic framework. By freely setting the reward function to match the control objective, the trained model can output more accurate controller parameters, with the …

Experiments On Aluminum Alloy Additive Layer Waam Lamination Using The Arc Current Reduction Technique, Jukkapun Greebmalai, Eakkachai Warinsiriruk, Chao-Hsiang Hsiao, Yin-Tien Wang

Journal of Marine Science and Technology–Taiwan

Recent advancements in additive manufacturing have been propelled by innovations in processes, materials, and fabrication techniques. This study specifically focuses on the optimizing Wire and Arc Additive Manufacturing (WAAM) within the Gas Metal Arc Welding (GMAW) process. The goal is to build laminated layers using aluminum alloy from the five-thousand series. In this research, a novel technique is introduced that employs a reduced arc-current during the additive layer progress for aluminum alloys, along with a double pulse current in the GMAW process to enhance weldability. The material used for addition is ER5356 aluminum wire, which is applied to an Al-5083 …

An Optimization Model For Supporting Bunkering Decisions In Bulk Shipping, Chung-Cheng Lu, Hui-Chieh Li, Hung-Yu Chen

Journal of Marine Science and Technology–Taiwan

This study addresses a bunkering optimization problem for bulk shipping carriers that explores route deviation to find optimal bunkering locations and amounts. A mixed integer linear programming (MILP) model is developed to determine optimal bunkering decisions that minimize total operating costs, including total daily operating costs, bunkering costs, port charges and surcharges, while observing time window constraints at loading/unloading ports. The proposed model is evaluated using instances generated from real data provided by H company with its operational headquarters in Taipei, Taiwan, which operates a fleet of handysize ships in East and South Asia. The results indicate that optimization models …

Assessment Of Coastal Navigation Safety: A Case Study Of Boka Bay With Stakeholder Perspectives, Ivan Mraković, Rino Bošnjak

Journal of Marine Science and Technology–Taiwan

The navigational safety of Boka Bay, i.e., a critical maritime area along the Adriatic coast, is of great importance owing to its ecological sensitivity, diverse marine traffic, and significant economic activity. This study provides a comprehensive assessment by analyzing the perspectives of various stakeholders, including seafarers, local pilots, skippers, regulatory authorities, and others engaged in maritime activities within the bay. Using a structured questionnaire survey, both quantitative and qualitative data were collected in formats such as descriptive responses, Yes/No choices, and Likert scale ratings, ensuring a well-rounded understanding of navigational practices and challenges. The data were analyzed using descriptive statistics, …

A Hybrid Model To Assess Commercial Port Resilience In Taiwan, Po-Hsing Tseng, James J.H. Liou

Journal of Marine Science and Technology–Taiwan

Since 2021, international commercial ports have faced unprecedented challenges (e.g., COVID-19 pandemic, Suez Canal congestion), impacting daily life, work, human well-being, properties, the environment, and socio-economic activities. The adaptation capability resilience of international commercial ports to respond to external changes has become crucial. This study develops an expert knowledge-based hybrid multi-criteria decision-making model integrating Best-Worst Method (BWM), Rough Dombi Aggregator, and Combined Compromise Solution (CoCoSo) to evaluate port resilience capabilities, using Taiwan's international commercial ports as a case study. We propose four evaluation dimensions encompassing 13 indicators: detection capability, resistance capability, resource integration capability, and recovery capability. The BWM determined …

An Evaluation Of The Possibility And Effectiveness Of Renewable Energy In Container Terminal At Jinhae New Port, Min-Seop Sim, Yul-Seong Kim, Chang-Hee Lee

Journal of Marine Science and Technology–Taiwan

The international goals of combating climate change and reducing greenhouse gas emissions are driving container terminal operators to adopt renewable energy solutions. This study evaluated the feasibility and effectiveness of introducing renewable energy and eco-fuels for energy independence at the Jinhae New Port Container Terminal, South Korea. Using portfolio analysis, eight renewable energy technologies (solar power, solar thermal energy, wind energy, hydropower, marine energy, geothermal energy, bioenergy, and waste-to-energy) and seven eco-friendly fuels (electricity, liquefied natural gas, liquefied petroleum gas, hydrogen fuel cells, ammonia fuel cells, bioenergy, and hybrid systems) were assessed. Survey data from stakeholders identified solar, wind, and …

Nutrient Transport By Overland Sheet Flow On Sites Containing Swine Slurry, John E. Gilley

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Nutrients in agricultural runoff may cause offsite environmental impacts. The objective of this investigation was to examine nutrient transport by overland sheet flow on sites containing swine slurry. Data examined in this study was collected during field rainfall simulation tests conducted on cropland sites in southeast Nebraska, USA. Inflow was added to the top of experimental plots in four successive increments to simulate runoff rates occurring at greater downslope distances. Runoff rates on the experimental sites ranged from 2.3 to 21.2 L min⁻¹ and maximum equivalent downslope distances varied from 5 to 108 m. Phosphorus (P) and nitrogen (N) …

Artificial Intelligence And Seeing The Invisible In Arts - From The Wane Of Axiality And Symmetry To The Wax Of Invisibility And Emptiness, M. Samir Abou El-Seoud

Computer Science

Function has been always an important driver in architectural design. It is the request to generate form directly from purpose and utility. In 1896, Louis Sullivan projected that ‘form follows function’ and that the beauty of a building derives directly from its function and not from references of the built heritage. The assumption was that beauty would naturally derive from design once functional requirements are met. Thus, fitness for purpose equals beauty. In 1908, Adolf Loos even went so far as to ask for the total abandonment of decoration. In 1947, Mies van der Rohe made his paradigm shift, function …

Development Of Bio-Based Coating Materials For Environmentally Friendly Controlled-Release Nitrogen Fertilizer Applications, Anne Carolyne Mendonca Cidreira

Electronic Theses and Dissertations

No abstract provided.

Navigating Big Data In Cyber Archaeology: An Updated Four-Field Approach Applied To San Miceli, Jared Wilson

Dissertations

Problem

This dissertation examines the evolving role of digital technologies in archaeological research, focusing on the management and interpretation of extensive digital datasets generated by modern excavations. Drawing on six years of comprehensive fieldwork at San Miceli, Sicily, this study critically evaluates and updates the established four-field methodology proposed by Levy et al. (2012), encompassing acquisition, analysis, dissemination, and curation. While traditional archaeological documentation methods rely heavily on analog techniques, these approaches increasingly fall short due to the rapid expansion and complexity of digital data.

Method

To address this gap, the dissertation refines and expands the current methodological framework by …

Lunar Payload Logistics: Advancing Autonomous Control For Lunar Surface Transporter Vehicles, Liam Murray

Electronic Theses and Dissertations

No abstract provided.

Global Spectral Clustering Of Temporally Stable Pixels For Epics Identification, Validation, And Hyperspectral Assignment For Satellite Calibration, Juliana Maria Fajardo Rueda

Electronic Theses and Dissertations

No abstract provided.

Refinement Of Trend-To-Trend Cross Calibration Total Uncertainties Utilizing Extended Pseudo Invariant Calibration Sites (Epics) Global Temporally Stable Target, Minura Samaranayake

Electronic Theses and Dissertations

Cross-calibration is an essential technique for calibrating Earth Observation satellite sensors, which involves taking nearly simultaneous images of a ground target to compare uncalibrated sensor to a well-calibrated reference sensor. This study introduces the hyperspectral Trend-to-Trend (T2T) cross-calibration technique utilizing EPICS Cluster 13 Global Temporally Stable (Cluster 13-GTS) as the calibration target, offering better temporal stability than previous targets used in T2T cross-calibration by an absolute difference of 0.4%, between coefficients of variation across all bands excluding CA band. A multispectral sensor-specific normalized hyperspectral profile was developed using the EO-1 Hyperion hyperspectral profile over Cluster 13-GTS to improve Spectral Band …

Exploring Research And Tools In Ai Security: A Systematic Mapping Study, Sidhant Narula, Mohammad Ghasemigol, Javier Carnerero-Cano, Amanda Minnich, Emil Lupu, Daniel Takabi

School of Cybersecurity Faculty Publications

With the pervasive integration of artificial intelligence (AI) in various facets of modern technology, the importance of AI security has been thrust into the spotlight. The field is rapidly evolving, with new challenges and solutions emerging at a swift pace. However, the breadth and depth of AI security research have not been comprehensively mapped in recent times, presenting a crucial need for an extensive review and synthesis of existing literature. Given the increasing reliance on AI in critical domains such as healthcare, finance, and national security, ensuring the resilience and trustworthiness of these systems is imperative. This survey fulfills the …

Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington

School of Cybersecurity Faculty Publications

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue …

Zero Trust Architecture As A Risk Countermeasure In Small-Medium Enterprises And Advanced Technology Systems, Ahmed M. Abdelmagid, Rafael Diaz

Engineering Management & Systems Engineering Faculty Publications

The growing sophistication of cyberattacks exposes small- and medium-sized businesses (SMBs) to a widening range of security risks. As these threats evolve in complexity, the need for advanced security measures becomes increasingly pressing. This necessitates a proactive approach to defending against potential cyber intrusions. Emerging technologies, such as blockchain, artificial intelligence, and Zero Trust security framework, offer crucial tools for strengthening the digital infrastructure of SMBs. The Zero Trust architecture (ZTA) holds significant promise as a critical strategy for protecting SMBs. While existing literature explores the implementation of ZTA in various business settings, discussions specifically addressing the financial, human resource, …

Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Unmanned Aerial Vehicles (UAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These UAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These AAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …