Engineering Commons^™

Ai-Enabled Anticipatory Handover Predictions In 5g Networks, Ojas Ankush Naik

Master's Projects

As users move across network cells in 5G, it is critical to maintain seamless connectivity through efficient and fast handovers. However, as 5G networks have a very dense deployment of cells and higher carrier frequencies, handovers are often more frequent and challenging, leading to failures or the ping-pong effect. In this research, we are going to use Artificial Intelligence (AI) techniques to enable predicting handover(HO) events proactively as opposed to reactively, aiming to reduce HO failures and unnecessary handovers. We develop Long-Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models to forecast future signal measurements, predict handover trigger points, and compare …

Shifting Students’ Perceptions About Homelessness: Quantitative Assessment Of A Project-Based Approach, Diana A. Chen, Mark A. Chapman, Joel Alejandro Mejia

School of Engineering: Faculty Scholarship

Although engineering institutional bodies uphold public welfare and the impact of engineering on people and society, engineering curricula rarely scaffold students to connect their technical learning with sociotechnical perspectives. This paper describes a project-based learning approach where engineering students engaged with issues faced by people experiencing homelessness to better understand the sociotechnical nature of effective, user-centered, engineering design. We conducted a quantitative assessment to de- termine how well and in what ways the project-based learning curriculum shifted students’ perceptions about homelessness. We collected pre-/post-survey data from students on 21 statements about their perceptions and attitudes about homelessness prior to and …

Bio-Inspired Sutures: Localizing Damage By Isolating Strain Energy, Diana A. Chen, Melissa M. Gibbons

School of Engineering: Faculty Scholarship

This study draws upon bio-inspiration from anatomical sutures found in hard structures, such as turtle shells, to explore if impact energy can be dissipated through geometric parameterization rather than relying on energy-absorbing materials. While previous finite element analysis studies identified optimal dovetail suture geometries for maximizing the global stiffness and toughness of archway structures, this paper explores how different suture geometries might optimize localization effects through segmentation to isolate damage caused by the propagation of strain energy. We compare the global toughness of each suture geometry to its scaling factor, defined as the ratio of strain energy in the center …

Extended-Duration Satellite Testing Via Altitude-Controlled High-Altitude Balloon, Drew Early, Evan Champney, Nikki Allen, Riley Galatis, Lukas Hezel, Joseph Kloess, Andrew Donnell, Sam Whitlock, Kaden Queener

Von Braun Symposium Student Posters

Undergraduate runner-up, 2025 Von Braun Symposium Student Poster Contest

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

OER Main

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

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

This book covers security from …

Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework For Enhanced Nuclear Reactor Power Prediction, James Daniell, Kazuma Kobayashi, Ayodeji Alajo, Syed Bahauddin Alam

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

The accurate and efficient modeling of nuclear reactor transients is crucial for ensuring safe and optimal reactor operation. Traditional physics-based models, while valuable, can be computationally intensive and may not fully capture the complexities of real-world reactor behavior. This paper introduces a novel hybrid digital twin-focused multi-stage deep learning framework that addresses these limitations, offering a faster and more robust solution for predicting the final steady-state power of reactor transients. By leveraging a combination of feed-forward neural networks with both classification and regression stages, and training on a unique dataset that integrates real-world measurements of reactor power and controls state …

Impact Of Surface And Physical Property On Multiphase Flow In Sealed Vessel: Liquid Dropdown Performance, Mehedi Hasan Tusar, Palash K. Bhowmik, Kazuma Kobayashi, Syed Bahauddin Alam, Shoaib Usman

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

This study explores multi-phase (i.e., liquid-gas) and multi-fluid (i.e., air-water, and water-silicone oil) flow-pattern and flow-blockage physics phenomena for wall wettability conditions ranging from superhydrophilic to superhydrophobic cases in sealed vessels utilizing computational fluid dynamics (CFD) simulation tools and volume-of-fluid (VOF) method with sharp interface modeling. Detailed modeling and simulation (M&S) of such physics phenomena—in which liquid (e.g., water) stands over top of gas (e.g., air or steam) in a closed channel and exhibited flow blockage, flow reversal related challenges—are pivotal for design, analysis, and qualification of component-level (e.g., heat pipes, heat exchangers) to system-level (e.g., emergency core cooling systems …

Dynamic Stability Test Technique For Blunt Bodies In A Magnetic Suspension And Balance System, Caleb Hull, Colin Britcher, Dave E. Cox, Mark Schoenenberger, Hisham M. Shehata

Mechanical & Aerospace Engineering Faculty Publications

Techniques for the measurement of subsonic dynamic stability derivatives of blunt-body re-entry capsules are being developed using a Magnetic Suspension and Balance System at NASA Langley Research Center. The measured aerodynamics of a 45-degree sphere-cone similar to configurations being considered for Mars Sample Return Earth Entry Vehicle are reported. A novel test method has been developed where forced oscillatory translation of a test article is used to excite yaw attitude oscillations. The forced motion is then halted to observe free-to-oscillate behavior of the test article. Parameter identification methods are used to extract static and dynamic yawing moment stability characteristics from …

Fault Tree Analysis For Robust Design, Jonathan Degroff, Gene Jean-Win Hou

Mechanical & Aerospace Engineering Faculty Publications

The objective of this research is to incorporate system failure into a robust design formation and solution process. The system failure referred to here will be built using fault tree analysis (FTA), which will take all lower-level failure events into consideration. Two examples are investigated here. One will directly treat the probabilities of the basis events as design variables, The other will be formulated in five different models: deterministic design optimization, the reliability index-based, the “and” gate-based, the “or” gate-based and the “inhibit” gate-based robust design. Their corresponding optimization solutions will be compared with each other. The post-optimality analysis of …

Polarized Positrons At Ce⁺Baf, A. Ushakov, J. Benesch, M. Bruker, L. Cardman, J. Conway, S. Covrig Dusa, P. Degtiarenko, Y. Enomoto, S. Gessner, P. Ghoshal, S. Gopinath, J. Grames, C. Gulliford, S. Habet, G. Hays, C. Hernández-García, A. Hofler, R. Kazimi, M. Kostin, V. O. Kostroun, F. Lin, V. Lizárraga-Rubio, K. Mahler, Y. Morikawa, S. Nagaitsev, S. Ogur, G. Palacios-Serrano, N. Raut, B. Rimmer, Y. Roblin, A. Seryi, K. Smolenski, M. Stutzman, R. Suleiman, A. Sy, N. Taylor, D. Turner, A. Ushakov, C. Valerio-Lizarraga, E. Voutier, S. Wang, Y. Zhang

Mechanical & Aerospace Engineering Faculty Publications

A baseline concept for a continuous wave (CW) polarized positron injector was developed for the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab [1]. This concept is based on positron beam generation by a high current polarized electron beam (1 mA, 120 MeV, >85% polarization) irradiating a rotating water-cooled tungsten target or liquid metal target. The update on the development of the Ce⁺BAF injector concept including the polarized electron source, the development of high power targets, simulations of positron capture, the design of the transport line from the positron injector to the CEBAF North Linac and the planned experiment …

Revealing Spatiotemporal Neural Activation Patterns In Electrocorticography Recordings Of Human Speech Production By Mutual Information, Julio Kovacs, Dean Krusienski, Minu Maninder, Willy Wriggers

Mechanical & Aerospace Engineering Faculty Publications

Background

Spatiotemporal mapping of neural activity during continuous speech production has been traditionally approached using correlation coefficient (CC) analysis between cortical signals and speech recordings. A prior study employed this approach using electrocorticography (ECoG) data from participants who underwent invasive intracranial monitoring for epilepsy. However, CC cannot detect nonlinear relationships and is dominated by the correspondence between periods of silence and of non-silence.

New Method

We introduce the mutual information (MI) measure, which can capture both linear and nonlinear dependencies. We validated CC and MI on the sub-second spatiotemporal brain activity recorded during continuous speech tasks. To refine the results, …

Traction Force Microscopy Using An Epifluorescence Microscope: Experimental Considerations And Caveats, Zaria Booth, Mazen Mezher, Rudra Patel, Venkat Maruthamuthu

Mechanical & Aerospace Engineering Faculty Publications

Forces exerted by cells due to their internal contractility play fundamental roles in a host of processes, including adhesion, migration, survival and differentiation. Traction force microscopy (TFM) enables the determination of forces exerted by cells or cell collectives on their environment, which is typically taken to be an extra-cellular matrix (ECM)-coated substrate. Sample preparation for TFM involves the plating of cells onto an environment embedded with fiducial markers. The imaging of these fiducial markers in the presence and absence of the cells then enables calculation of the displacement of localized regions of the environment, and, consequently, the spatial distribution of …

Scaffolds With Spatiotemporally Controlled Growth Factor Delivery And Cyclodextrin-Enabled Antagonism Of Growth Factor Receptor Sequestration Promote Cutaneous Wound Healing, Jelena Marjanovic, Veronica Jurczuk, Lilian Valadares Tose, Yarixa Cintron Diaz, Francisco Fernandez Lima, Beatriz Abdo Abujamra, Sara Danker, Sinan Jabori, Devinder Singh, Jamie L. Burgess, Joshua Tam, Mohamadmahdi Samandari, Rivka C. Stone, Stephen C. Davis, Robert S. Kirsner, Marjana Tomic-Canic, Fotis M. Andreopoulos, Ivan Jozic

Mechanical & Aerospace Engineering Faculty Publications

Chronic wounds present a major burden to patients, health care professionals, and health care systems worldwide, yet treatment options remain limited and often ineffective. Although initially promising, growth factor-based therapies displayed limited and underwhelming effectiveness largely due to poor bioavailabilbioity and impaired receptor function within the chronic wound microenvironment. Here we demonstrate that chronic wounds exhibit elevated cholesterol synthesis, which disrupts growth factor signaling by sequestering receptors within lipid rafts. To address this, we developed a novel therapy combining growth factors with cyclodextrin in an ECM-mimetic scaffold, enabling localized cholesterol modulation and improved receptor accessibility. We demonstrate that this approach …

Mass-Adaptive Admittance Control For Robotic Manipulators, Hossein Gholampour, Jonathon E. Slightam, Logan E. Beaver

Mechanical & Aerospace Engineering Faculty Publications

Handling objects with unknown or changing masses is a common challenge in robotics, often leading to errors or instability if the control system cannot adapt in realtime. In this paper, we present a novel approach that enables a six-degrees-of-freedom robotic manipulator to reliably follow waypoints while automatically estimating and compensating for unknown payload weight. Our method integrates an admittance control framework with a mass estimator, allowing the robot to dynamically update an excitation force to compensate for the payload mass. This strategy mitigates end-effector sagging and preserves stability when handling objects of unknown weights. We experimentally validated our approach in …

Optimal Manipulation Motion Action Planner Enabled By Physics Informed Neural Networks, Jonathon E. Slightam, Logan E. Beaver

Mechanical & Aerospace Engineering Faculty Publications

Autonomous robotic manipulation in unstructured environments faces many challenges and is hindered by capabilities that bridge the gap between perception and acting on the world. Action plans that are centric to object motion rather than end-of-arm tooling behavior may aid this. This paper presents an autonomous action planner for a feedback linearizeable system comprised of three base motions that can be leveraged on their own or in combination to give custom motion plans. The optimization routine for the three different types of motion are presented, which are integrated into physics informed neural networks. A component of this is the autonomy …

A Large Electroencephalogram Database Of Freewill Reaching And Grasping Tasks For Brain Machine Interfaces, Bhoj Raj Thapa, John Boggess, Jihye Bae

Electrical and Computer Engineering Faculty Publications

Brain machine interfaces (BMIs) offer great potential to improve the quality of life for individuals with neurological disorders or severe motor impairments. Among various neural recording modalities, electroencephalogram (EEG) is particularly favorable for BMIs due to its noninvasive nature, portability, and high temporal resolution. Existing EEG datasets for BMIs are often limited to experimental settings that fail to address subjects’ freewill in decision making. We present a large EEG dataset, containing a total of 6808 trials, recorded from 23 healthy young adults (eight females and 15 males with an age range from 18 to 24 years) while performing reaching and …

Burnout At University: Cross-Sectional Study Among Dental Students, Manal Elhijazi, Hajar Nafia, Ihsane Benyahya

Health Sciences

Context: The university constitutes a demanding and competitive learning environment which can impact the well-being of students. The objective of this survey is to assess the burnout of students at the faculty of dentistry.

Materials and methods: A cross-sectional study was conducted via an anonymous questionnaire with 3 sections: socio-demographic data, lifestyle and habit, and burnout through the Maslach burnout inventory-student survey (MBI-SS) . Continuous variables were presented as means and standard deviations,categorical variables as numbers and percentages. The variables were tested in univariate analysis to assess predictive factors related to burnout using the Khi-2 test. The significance level was …

Effective Management Of Disused Sealed Radioactive Sources In Uganda, Susan Nalumansi

International Journal of Nuclear Security

Sealed radioactive sources are used in a wide range of applications in Uganda, such as medicine, agriculture, geology, industry, and other fields. After a given useful period, the sealed sources can no longer perform their intended purpose and are thus regarded as disused. However, depending on the activity of the source, the disused sources are highly active and can cause significant damage to human health and the environment when mismanaged. A regulatory body was established with the mandate to regulate peaceful applications of ionizing radiation in Uganda. Given the vulnerability of sealed sources at the end of their life cycle …

Applying Machine Learning Methods To Generate Understandings Of Differential Item Functioning In A Flu Knowledge Assessment, William L. Romine, Tanvi Banerjee, Derrick Cox

Computer Science and Engineering Faculty Publications

Current influenza trends, including the severity of the 2025 flu season and the prevalence of H5 bird flu in livestock, necessitate efforts to better understand how to educate students about its transmission. Although validated assessments of influenza knowledge exist, these have not been evaluated for affective and demographic biases. We explore differential item functioning (DIF) effects in four items focused on specific aspects of flu transmission derived from a validated influenza knowledge assessment. In doing so, we introduce and utilize a machine learning framework for exploration of DIF which offers greater flexibility than traditional statistical approaches in terms of studying …

Axial Flux Permanent Magnet Generators For Direct-Drive Wind Turbines - Review And Optimal Design Studies, Matin Vatani, Ali Mohammadi, Donovin D. Lewis, John F. Eastham, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents a comprehensive analysis of the feasibility, cost, and electromagnetic performance of five distinct types of axial flux permanent magnet (AFPM) generators designed for direct-drive wind turbines. The generator configurations investigated include a single-sided AFPM generator with a surface-mounted PM rotor (AFPMG), a double-sided AFPM generator featuring PMs on the stator and a reluctance rotor (AFPMG-RR), a coreless stator AFPM generator with surface PMs (CAFPMG-SPM), and a coreless stator AFPM generator with a Halbach PM array rotor (CAFPMG-Hal). Each generator’s operating principles and configurations are thoroughly explained and compared. Large-scale multi-objective design optimizations were conducted on each type, …

Review Of Coreless Axial Flux Permanent Magnet Machines With Applications Including Electric Aircraft Propulsion, Matin Vatani, Donovin D. Lewis, John F. Eastham, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Axial flux permanent magnet (AFPM) machines with entirely or stator coreless configurations offer significant advantages, including low rotational losses, the absence of cogging torque, minimal torque ripple, and the potential for high specific power density. Advances in permanent magnet (PM) technology and cooling techniques have enabled fully coreless AFPM machines to achieve high magnetic and current loadings without core saturation, making them suitable for high performance applications like electric aircraft propulsion. This paper reviews recent advancements in coreless AFPM machines, tracing their historical and chronological development. Various topologies, including single- and double-sided and multi-disk designs, are examined to identify the …

Speed Variation Based Power Regulation Concept For Dynamic Wireless Charging, Donovin D. Lewis, Sudarshan T. Harave, Omer Onar, Mostak Mohammad, Veda Prakash Galigekere, Gui-Jia Su, Dan Ionel

Electrical and Computer Engineering Graduate Research

On-road wireless charging of electric vehicles (EVs) in-motion could potentially reduce range anxiety or battery size with wide-spread deployment. The planning and implementation of such systems are greatly complicated due to their susceptibility to load variation inherent to traffic flow. This paper proposes a method for derisking the potential for traffic slowdowns by compensating for reduced vehicle speed and investigates how implementation may affect system performance. A load modeling case study is presented at 200kW for a mile of high-speed roadway employing speed-based power regulation with results indicating average power usage and maximum car hosting capability can be reduced by …

Fault-Tolerant High-Power Two-Phase Coreless Afpm Motor-Drive System Concept, Yaser Chulaee, Ali Mohammadi, Matin Vatani, Aaron M. Cramer, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper proposes a fault-tolerant, high-power two-phase coreless axial flux permanent magnet (AFPM) motor drive system concept. Initially, the performance of a two-phase variant of the coreless AFPM machine is compared with that of its three-phase counterpart using both 3D finite element analysis (FEA) and experiments. Thanks to the intrinsic unique features of coreless AFPM machines, the analysis of the results demonstrates that the two-phase configuration has comparable power density (kW/kg) and efficiency. In terms of fault tolerance, the two-phase configuration exhibits superior performance, as the mutual inductance between phases is zero, resulting in an electric machine with magnetically decoupled …

Two-Level Multi-Objective Design Optimization Including Torque Ripple Minimization For Stator Excited Synchronous And Flux Switching Machines, Ali Mohammadi, Oluwaseun A. Badewa, Yaser Chulaee, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Electric machine performance can be enhanced through torque ripple mitigation, which reduces mechanical vibrations and improves system stability and operation. This paper proposes a novel two-level optimization method for synchronous flux-switching and hybrid excitation machines, featuring an innovative multi-point spline shaping approach for torque ripple minimization. The study employs experimentally validated models based on a prototype with a similar topology. The optimization process is implemented on two distinct designs: a 20-pole inner rotor PM-excited stator machine and a 28-pole outer rotor DC-excited stator machine. Analysis results demonstrate that the proposed innovative method significantly reduces torque ripple in both configurations. Force …

Electromagnetic Design Characterization Of Synchronous Machines With Flux Switching Effect Employing Reluctance Rotors And Stators With Pms And Ac Concentrated Coils, Oluwaseun A. Badewa, Ali Mohammadi, Donovin D. Lewis, Somasundaram Essakiappan, Madhav Manjrekar, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper introduces an innovative design for a synchronous electric motor that has phase coils and permanent magnets (PM) embedded in the stator. The electric machine contains both concentrated winded phase coils in dedicated slots and spoke-type permanent magnets solely in the stator. The spoke-type magnet arrangement achieves a high flux intensity even with low remanence non-rare earth permanent magnets. As the rotor has no active electromagnetic components, the risk for demagnetization is reduced and advanced cooling applied to the stator can enable high-speed operation. The principle of operation and polarity of the motor are investigated with a unique approach …

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

Master's Projects

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

Deep Learning Approaches For Predicting Strain Energy In Heterogeneous Materials, Junesh Gautam

Electronic Theses and Dissertations

Finite Element Analysis (FEA) faces computational challenges when analyzing nonlinear and heterogeneous materials. Utilizing the Mechanical MNIST dataset, comprising 60,000 simulated samples of 28x28 pixel domains under large deformation, the study evaluates classical regression methods (Linear Regression, Random Forest, Gradient Boosting) and advanced deep learning architectures (Convolutional Neural Networks (CNN) and Residual Networks (ResNet)). CNN models achieved superior performance, with a Mean Squared Error (MSE) of 4.21 and an R2 value of approximately 0.982, outperforming classical regression models and slightly surpassing ResNet architectures. These deep learning methods automatically learn spatial relationships from pixel-based representations, eliminating the need for manual feature …

Leveraging Deep Learning For The Automated Detection Of Hemophilic Arthropathy In Knee Radiographs, En-Wei Lin, Edzer Wu, Ming-Ching Shen, Shao-Li Han

Rehabilitation Practice and Science

Background/Purpose:

Hemophilic arthropathy (HA) is a progressive joint disease caused by recurrent hemarthroses in patients with hemophilia. Early radiographic identification of HA is critical for timely intervention and long-term joint preservation. This study aimed to develop and evaluate deep learning models for automated detection of HA in knee radiographs.

Methods:

A retrospective dataset of 849 knee radiographs (AP and lateral views) was used to train three convolutional neural networks: ResNet-34, DenseNet-121, and EfficientNet-B0. Model performance was assessed using standard classification metrics, including accuracy, precision, recall, F1-score, and AUC. Interpretability was evaluated using Grad-CAM heatmaps.

Results:

DenseNet-121 achieved balanced performance on …

Representation In Engineering-Focused Children's Literature: A Critical Content Analysis, Angela Eckhoff

Teaching & Learning Faculty Publications

Children’s picturebooks have long been understood as a powerful medium for exploring aspects of children’s lives, experiences, and identities serving as mirrors which allow children to see themselves reflected, as windows through which they can gain insight into the experiences of others, and as sliding glass doors when children are connected to the story and characters in meaningful ways. Utilizing a critical content analytic approach, this research examined the identity markers and engineering practices found in contemporary children’s picturebooks with an engineering focus. The analysis yielded key themes that highlight the ways children can interact and connect to the characters …

Florida Statewide Coastal Restoration Guide, Melinda J. Donnelly, Matthew Martin, Christen Fleming, Kelly Kibler, Linda Walters, Patrick Shearer, Lauren Jennison, Erika Lin, George Zaragoza

Faculty Scholarship and Creative Works

The Statewide Coastal Restoration Guide (SCRG) is a comprehensive planning resource developed through a collaboration between Florida Department of Environmental Protection (FDEP), University of Central Florida (UCF), and Resource Environmental Solutions (RES) to strengthen and unify shoreline restoration efforts across Florida. With over 4,500 miles of estuarine shoreline, Florida’s coasts are vital to the state’s ecological health, economic prosperity, and community resilience. However, they face increasing threats from sea level rise, stronger storms, erosion, habitat degradation, and development. The SCRG responds to these challenges by providing science-based, non-regulatory guidance for implementing resilient, nature-based restoration strategies that protect both ecosystems and …