Digital Commons Network^™

Improving The Operator-Swarm Dynamic Under Mental Fatigue Constraints In Search And Rescue Operations, Jordan Morrow

Masters Theses

"Human-robot applications that allow for work to be done remotely are largely dependent on the lassitude of the operators. The exhaustion of these operators is a result of work completion and duration. Previous research attempts to evaluate the impact on reaction by quantiying human weariness. This paper examines how human weariness affects the human-robot dynamic in UAV-assisted search and rescue missions. An explanation of the connection between mental exhaustion and operator responsiveness over prolonged durations is provided by the search and rescue operations using UAV swarms (SAROUS) model. Through the use of artificial intelligence, SAROUS is modernized. This allows the …

Advanced 3d Lidar-Based Systems For Urban Traffic And Pedestrian Monitoring: Integrating Elevated Lidar, Data Collection, And Deep Learning For Precise Detection And Activity Classification, Nawfal Guefrachi

Masters Theses

"Accurate and real-time monitoring of urban traffic and pedestrian activities is crucial for intelligent transportation systems (ITS) and smart cities. Traditional camera-based methods struggle with issues like lighting and privacy. This research leverages advanced three-dimension light detection and ranging (3D LiDAR) technology and computational frameworks to address these challenges, providing transformative solutions for urban traffic management and pedestrian safety. By strategically deploying elevated LiDAR sensors, detailed 3D point cloud data is captured, enabling precise monitoring of urban environments. Enhancements to LiDAR-based frameworks, such as fine-tuning the Point Voxel Region-Based Convolutional Neural Network (PV-RCNN), improve the detection of vehicles and pedestrians …

On Optimizing Sensor Data Collection, Processing, And Storage For Industrial Additive Manufacturing, Steven Thompson

Masters Theses

The widespread adoption of digital data management methods for transformative technologies, such as additive manufacturing (AM), within the aerospace industry is impeded by poor interoperability between AM component manufacturing processes. Moreover, data quality may be compromised due to sensor failures or other corruptions. Additionally, massive amounts of data are collected during these processes, often needing to remain accessible for decades. These storage costs can place a significant financial burden on smaller suppliers. This work aims to make digital data management methods more affordable and, therefore, approachable for smaller suppliers.

First, the design and initial implementation of an affordable and adaptable …

Development Of A Method To Measure Blast Overpressure Exposure Using Orthogonal Sensor Orientations, Nicholas Kuehl

Masters Theses

Measuring and recording blast exposure to military personnel from shoulder-fired weaponry or improvised explosive devices to correlate health outcomes like mild traumatic brain injury has been a goal for many years. As such, many wearable sensors have been developed and tested for accuracy and reliability. Despite this, knowing the sensor orientation in relation to the blast source is important to fully correlate the actual personnel exposure to clinical outcomes. This research investigates whether pressures recorded at three independent orientations in the x, y and z planes can be combined to a representative pressure that is independent of orientation. Important metrics …

Development Of Stalling Detection Algorithms And Operator Behavior Models For Evaluating Hydraulic Excavator Performance, Mateo Fernando Montenegro Defaz

Masters Theses

This study aims to develop a reliable algorithm for stalling detection and investigate how operator behaviors impact the truck-loading process, focusing on swing and bucket rotations. While the study of excavator digging conditions and equipment performance is well understood, describing stalling from telemetry data and the influence of operators’ behavior on overall system efficiency remain underexplored. This study seeks to: (i) develop a reliable algorithm for stall detection by analyzing key operational variables such as velocity and angles; (ii) implement statistical and machine learning techniques, specifically a Support Vector Machine (SVM) classification algorithm, to differentiate between ideal and non-ideal digging …

Enabling Drone-Integrated Active Microwave Thermography Via A Slot Antenna Design, Alec P. Fitzmaurice

Masters Theses

Civil infrastructure inspection quality and inspector safety may be enhanced from the advancement in the capabilities of nondestructive testing and evaluation of remote or otherwise hard-to-reach areas such as nuclear power plants, wind turbines, bridges, or other civil infrastructure using drone-based Active Microwave Thermography (AMT). AMT is a nondestructive testing technique that utilizes high frequency energy (often radiated from an antenna) to induce heating in a specimen. Following this thermal excitation, an infrared camera is used to measure the resulting surface thermal profile. From this, defect indications may be detected. To enable drone-based deployment of AMT, where the antenna size …

Extraction And Separation Of Metal Cations From Synthetic Acidic Solutions Using Emulsion Liquid Membrane, Nouhaila Filali

Masters Theses

The work is organized into two main papers supported by experimental and comparative studies using Emulsion Liquid Membrane (ELM) systems. The first paper explores the individual extraction of heavy metals (Cd, Cr, As, Ni) from synthetic phosphoric acid using Alamine 336 and D2EHPA as carriers. Alamine 336 achieved the highest extraction efficiency (>90%) and stability, offering a promising route to reduce toxic impurities in fertilizer-grade acid. The second paper examines the selective separation of Ni2+ and Co2+ from various acidic solutions using the ELM configuration. Systematic variation of carriers, stripping agent and feed acidity, clarified transport mechanisms and stability, …

Integrated Sensing And Covert Communications With Ris Adaptive And Non-Adaptive Modes, Jia Zhang, Dengfeng Zhang, Ke Liu, Jiande Sun, Min Li, Shihao Yan

Research outputs 2022 to 2026

In this work, we consider an integrated sensing and covert communication (ISACC) system with a finite blocklength L aided by a reconfigurable intelligent surface (RIS) with N elements. Specifically, with the aid of RIS, a transmitter Alice is to sense the potential existence of a target, and she is also probabilistically trying to send information to a receiver Bob covertly (i.e., trying to hide her transmissions from a warden Willie). Meanwhile, Willie is to detect whether Alice is sensing the target only or conducting ISACC. We consider two RIS operation modes, i.e., a non-adaptive mode, where RIS employs a common …

Development And Characterization Of Sustainable Coal–Biomass Briquettes Of Sub-Bituminous Coal And Levistona Chinensis Biomass, Amad Ullah Khan, Borhen Louhichi, Muhammad Abas, Muhammad Saleem, Nashmi H. Alrasheedi, Farida Anjum, Aamer Sharif

Research outputs 2022 to 2026

The increasing demand for sustainable energy solutions has driven the exploration of alternative fuels, such as coal–biomass briquettes, which combine the benefits of both coal and renewable biomass sources. This study focuses on the development and characterization of sustainable coal–biomass briquettes made from sub-bituminous coal (SubC) and Levistona chinensis seed biomass, with the aim of reducing environmental impact and enhancing combustion properties. Briquettes were produced by varying the coal-to-biomass ratios (90:0–0:90), with 10% corn starch used as a binder. Proximate analysis revealed that increasing biomass content raised moisture (5.85%–14.25%) and volatile matter (VM; 24.75%–32.5%), while reducing ash (15.75%–3.39%) and fixed …

Perspectives Of Corporate Instructional Design Experts On Effectiveness, Personal Traits And Attributes, And Technology, Thomas A. Gant

Theses and Dissertations

This qualitative phenomenological descriptive study explored corporate instructional design experts' perspectives on effectiveness, personal traits and attributes, and technology that enhance their expertise. Three central themes emerged: "Mastering Instructional Design: The Journey of Continuous Learning and Skill Development," highlighting the importance of mentorship, lifelong learning, and professional development; "Accessing Instructional Design Expertise Through Individual Strengths," emphasizing adaptability, problem-solving, and empathy; and "Navigating Technological Integration and Innovation in Instructional Design," showcasing the transformative role of tools such as virtual reality, learning management systems, and authoring software. Semi-structured interviews conducted via Zoom with 10 corporate instructional designers with at least 10 years …

Nanotube Spectral Fingerprinting And Machine Learning For Optimized Bioimaging/Sensing And Disease Detection Applications In Als, Rodrigo Monroy Lopez

Open Access Master's Theses

Single-walled carbon nanotubes (SWCNTs) possess unique physicochemical and optical properties that make them ideal candidates for biomedical imaging, biosensing, and disease diagnostics. This thesis explores the potential of SWCNT-based spectral fingerprinting combined with ML (Machine Learning) algorithms to optimize bioimaging, disease detection and prediction, with a specific focus on differentiating between healthy and amyotrophic lateral sclerosis (ALS) lymphoblastic patient samples. By functionalizing SWCNTs with single-stranded DNA, we enhance their stability and target specificity, enabling their application in serum patient samples.

A comprehensive spectral analysis of DNA-SWCNTs was conducted using near-infrared fluorescence spectroscopy and other characterization techniques, including UV-Vis-absorption spectroscopy. The …

The Impact Of Urbanization On Water Scarcity And Waterborne Diseases In Eastern Africa: A Case Study Of Nairobi, Lucy N. Kamau

Open Access Master's Theses

Urbanization in Eastern African cities has rapidly accelerated, placing immense strain on existing water infrastructure and sanitation systems. In Nairobi, this has resulted in spatial disparities in access to clean water and heightened vulnerability to waterborne disease risks. The study aimed to (1) assess the impact of urbanization on water scarcity and waterborne diseases in Nairobi using geospatial analysis and remote sensing (2) quantify urbanization trends 1999–2024 (3) identify spatio-temporal water scarcity hotspots for the years 2019 and 2024 (4) model waterborne disease risk maps for 2019 and 2024 (5) Overlay disease risk map with hospitals. To quantify urbanization for …

Psychosocial Determinants Of Public Transportation Use Among Brazilian And American Users: An Integrated Modeling Approach, Ingrid Luiza Neto, Hartmut Günther, Bryan E. Porter, Taciano L. Milfont, Pastor Willy Gonzales Taco, Caroline Cardoso Machado

Psychology Faculty Publications

Overreliance on cars can promote individual, environmental, economic and social problems, requiring the development of measures to reduce car use and encourage the use of more sustainable transport options. Contributing to this call, here we report a cross-cultural study conducted in Brazil (n = 312) and the United States (n = 518) investigating the applicability of the model of Bamberg and Möser in predicting the use of public transport. Results indicated the model is equivalent across samples, regarding both the measures and the relations between the variables of the model. Intention strongly predicted self-reported public transport behaviour, explaining 70% of …

Using Electro-Peroxone Process To Remediate Soil Contaminated With Phenol, Elaheh Faghih Nasiri, Farhad Qaderi, S. Mustapha Rahmaninezhad

Civil Engineering Faculty Publications

Industrial processes are among human activities that cause production of a large volume of wastewater containing organic pollutants such as phenol and its derivatives. Soil remediation is crucial for enhancing environmental quality for both humans and other living organisms. This study investigate the use of an electro-peroxone system to remove environmental pollutants from soil. In conjunction with ozonation, the study employed electrochemically generated hydrogen peroxide using a carbon electrode, addressing concerns about transportation and storage. Experiments were structured using response surface methodology (RSM) with three variables: ozone dosages ranging from 4 to 8 l/h, initial pollutant concentrations from 20 to …

Ai-Based Predictive Analytics For Network Operations, Timur Nikisin, David White

Academic Poster Collection

AI-Based Predictive Analytics for Network Operations

Cost Optimization In Open Telemetry, Niksa Jadric, Cormac Keogh

Academic Poster Collection

Cost Optimization in Open Telemetry

The Digital Loophole: Evaluating The Effectiveness Of Child Age Verification Methods On Social Media, Fatmaelzahraa Eltaher Ph.D, Rahul Gajula, Luis Miralles-Pechuán, Christina Thorpe, Susan Mckeever

Conference papers

Social media platforms are an integral part of daily life for nearly five billion people worldwide. However, the growing presence of underage users on these platforms raises significant concerns regarding children's exposure to harmful content and its impact on their mental health. This paper examines the effectiveness of age verification measures implemented on leading platforms Facebook, YouTube, Instagram, TikTok, Snapchat, and X. We evaluate the age verification processes required for account creation by simulating the registration steps for minors on these platforms. We also compare these methods to best practices in online age assurance in finance, betting and public transportation …

Sift Feature-Based Relative Altitude Estimation Enhanced With Siamese Network, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In GPS-denied environments or when GPS signals are unreliable or unavailable, alternative methods of accurate localization with coordinate generation become critical. To address localization, the scale-invariant feature transform (SIFT) algorithm, along with its numerous adaptations, is extensively utilized in computer vision and remote sensing for matching image features to identify objects and perform localization. This article presents a novel approach for estimating the relative altitude of unmanned aerial vehicles (UAVs) using SIFT features' scale (size), omitting the need for additional data like camera intrinsic parameters, as well as extensive image datasets are also required for training. Furthermore, the approach enhances …

Microwave Photonics-Assisted Interrogation Of Fiber-Optic Interferometric Sensors With Joint Frequency-Time Domain Analysis, Ruimin Jie, Jie Huang, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

Fiber-optic interferometers are widely used in localized sensing applications due to their compact size, high sensitivity, and immunity to electromagnetic interference. In this paper, we propose and experimentally demonstrate a novel interrogation scheme for fiber-optic interferometric sensors, utilizing microwave photonics (MWP) and joint frequency-time domain analysis. As a proof of concept, a miniature fiber in-line Fabry-Perot interferometer (FPI) is integrated with a microwave photonic single-passband filter, enhanced by a dispersion compensation module to improve sensing performance. By applying an inverse Fourier transform to the system's complex frequency response, the time-domain representation of the signal is obtained, translating spectral shifts of …

Discrimination Of Temperature And Strain By Characterizing Two Femtosecond Laser-Written Coincident Sapphire Fiber Bragg Gratings For Harsh Environment Applications, Farhan Mumtaz, Bohong Zhang, Koustav Dey, Jeffrey D. Smith, Ronald J. O'Malley, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

In this study, two co-incident sapphire fiber Bragg gratings (SFBGs) were successfully inscribed utilizing a femtosecond (fs) laser to achieve a high fringe contrast interferogram. These two SFBGs employ a unique configuration, one parallel to the center axis, called p-SFBG, and the other forming an angle from the center-axis, called a-SFBG, allowing for simultaneous strain and temperature measurements with low crosstalk. As a proof of concept, p-SFBG and a-SFBG using line-by-line method are characterized, which are shorter in length (i.e., 1.5 mm), producing reflectivity of ~3dB. This effort demonstrates the use of two coincident SFBGs forming an angle of 2.29° …

Simultaneous Measurement Of Early-Stage Corrosion And Strain Levels In Steel Rebar Based On Graphene Oxide-Coated Ncf-Fbg Fiber Optic Sensor, Fujian Tang, Baihe Qu, Hong Nan Li, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Rebar corrosion significantly reduces the lifespan of reinforced concrete structures. The value of rebar strain, especially for some key structural components, indicates the safety margin of structures. In this study, a graphene oxide (GO) coated no-core fiber-fiber Bragg grating (NCF-FBG) fiber optic sensor is proposed for simultaneously measuring strain values and early-stage corrosion of steel rebar for the first time. The impact of GO coating thickness on the monitoring sensitivity is considered. A setup was manufactured to simultaneously perform tension, optical, and corrosion tests. The strain was applied up to 1200 μϵ. The rebar corrosion was assessed using electrochemical method …

Explainable And Safety Aware Deep Reinforcement Learning-Based Control Of Nonlinear Discrete-Time Systems Using Neural Network Gradient Decomposition, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents an explainable deep-reinforcement learning (DRL)-based safety-aware optimal adaptive tracking (SOAT) scheme for a class of nonlinear discrete-time (DT) affine systems subject to state inequality constraints. The DRL-based SOAT utilizes a multilayer neural network (MNN)-based actor-critic to estimate the cost function and optimal policy while the MNN update laws are tuned both using the singular value decomposition (SVD) of activation function gradient in order to mitigate the vanishing gradient issue and safety-aware Bellman error at each layer. An approximate safety-aware optimal policy is developed using Karush–Kuhn–Tucker (KKT) conditions by incorporating the higher-order control barrier function (HOCBF) into the …

Safe Optimal Control Of Quadrotor Formations Using Multilayer Neural Networks And Continual Learning, Ehsan Soleimani, Irfan Ahmad Ganie, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents an integral reinforcement learning-based optimal formation tracking scheme for multiple quadrotors unmanned aerial vehicles (QUAVs) experiencing nonlinear coupled dynamics and subject to constraints. We use multilayer neural networks (MNN) within an actor-critic framework where the MNN weights are tuned using singular value decomposition (SVD) of the activation function gradient to approximate optimal control policy via backstepping. Additionally, barrier Lyapunov functions (BLF) are introduced to ensure set invariance, thereby maintaining the quadrotors within a defined safety space due to constraints. A novel weight update law for each layer is derived using the HJB approximation error and control input …

Large-Range And High-Sensitivity Displacement Sensing Based On Extrinsic Fabry-Perot Interferometer Assisted Microwave Photonic Filter, Shiyu Li, Ruimin Jie, Osamah Alsalman, Jie Huang, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

Displacement is a pivotal physical parameter, and advancements in displacement sensor technology have enabled the creation of a diverse array of physical and mechanical sensors through seamless integration with mechanical transducers. In this study, we introduce a displacement sensing technique leveraging an extrinsic Fabry-Perot interferometer (EFPI) assisted microwave photonic filter. By translating displacement-induced variations in the EFPI's optical reflection into peak frequency shifts within its frequency response, we achieve large-dynamic-range displacement measurements with outstanding signal quality and demodulation ease. Proof-of-concept demonstrations showcase a substantial 5 mm range with a remarkable sensitivity of 1.148 GHz/mm, achieved using a basic single-mode fiber-based …

Microwave Photonic Fiber Ring Resonator For Optical Sensing Based On In-Ring And Out-Of-Ring Modulation, Shiyu Li, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

Intensity-modulated optical fiber sensors (IM-OFSs) have garnered significant research interest due to their advantageous characteristics, including simplified fabrication procedures, cost-efficient systems, and straightforward signal demodulation, leading to their widespread application across diverse fields. Nevertheless, the multiplexing technique for IM-OFSs remains underexplored, primarily because isolating the contributions of individual sensors within the system using traditional power measurements poses a significant challenge. In this study, we introduce and experimentally validate a novel approach leveraging a simple microwave-photonic fiber ring resonator (MWP-FRR). This approach enables the concurrent interrogation of two IM-OFSs based on an in-and-out-of-ring-modulation (IORM) strategy. The transmission losses of both IM-OFSs …

Cognitive Fatigue Detection Using Photoplethysmography (Ppg) And Reaction Time Data, Anhar Sami Mohammed, Prajoy Podder, Maciej Jan Zawodniok, Cihan Dagli

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a framework for real-time cognitive fatigue detection among shift workers using an integrated approach that combines photoplethysmography (PPG) data and reaction time analysis with advanced deep learning models, including Long Short-Term Memory (LSTM) networks and Feedforward Neural Networks (FNNs). The system leverages heart rate variability (HRV) and reaction time data to identify fatigue indicators. The results demonstrate significant performance, with the first FNN model achieving a test accuracy of 98.94% and a loss of 0.2928, while the second FNN model achieved the same accuracy with a slightly higher loss of 0.3089. The LSTM model, designed for sequential …

Online Learning-Driven Human Intent Estimation And Control For Human-Robot Interaction, Irfan Ganie, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a novel Stackelberg-game theoretic multilayer-online learning framework for cooperative control of nonlinear Physical Human-Robot Interaction (pHRI), where the human is modeled as the leader guiding a robot follower. This hierarchical interaction is captured as a dynamic Stackelberg game, with the human's intention estimated in real-time through online multilayer neural networks (MNNs). We introduce SVD-based weight update laws for actor-critic MNNs, which approximate value functions and control inputs for both human and robot, eliminating the need for predefined basis functions. In this framework, the human objective is first inferred and used to guide the robot actions by shaping …

Multi-Model Safe Neuro-Optimal Output Tracking Control Of Autonomous Surface Vessels With Explainable Ai, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a safety-aware deep reinforcement learning (DRL)-based trajectory tracking control of autonomous surface vessels (ASVs). A multilayer neural network (MNN) observer estimates the ASV's state and uncertain dynamics. By utilizing the estimate state vector from the observer, a safety-aware DRL-based optimal policy is formulated using control barrier function (CBF) and Karush-Kuhn-Tucker (KKT) conditions. An actor-critic MNN with singular value decomposition (SVD)-based update mitigates vanishing gradients. To enhance adaptability, an online safe lifelong learning (SLL) scheme counters catastrophic forgetting across varying ASV dynamics. The Shapley Additive Explanations (SHAP) method identifies key features influencing the control policy. Simulations on an …

Online Adaptive Optimal Tracking Control Of Uncertain Strict Feedback Discrete-Time Systems With Hardware Verification Using A Quadrotor Uav, Maxwell Geiger, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article considers the infinite time horizon optimal tracking control problem for discrete time (DT) partially uncertain strict feedback systems with application to quadrotor UAVs. First, the strict feedback DT system is transformed into an equivalent affine nonlinear DT system in terms of tracking error dynamics. The optimal tracking control problem is solved using an augmented system approach, where a horizon of future reference trajectory points are used in the augmented state, as compared to using a single point. The internal dynamics of the original nonlinear strict feedback system and the transformed affine system in terms of error dynamics are …

Enhancing Measurement Accuracy In Industrial Applications: The Impact Of Sensor Data Imputation On Model Parameter Estimation, Steven Thompson, Michkath Omanda Bouraima, Maciej Jan Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

Digital twins are meant to revolutionize the manufacturing industry by enabling advanced condition monitoring and predictive maintenance processes. However, disruptions within the manufacturing process, such as sensor malfunctions or connectivity issues are inevitable and will cripple these advanced analysis methods if not properly addressed. Therefore, efficient data management and analysis practices are key to advancing this technology. This work examines the impact of missing data imputation on model parameter estimation, a crucial task in developing models for digital twins. We theoretically derive the Cramer-Rao Lower Bound (CRLB) for a DC signal with an unknown scalar parameter in the presence of …