Digital Commons Network^™

Impact Of Ai On The Hri Dynamic In Search And Rescue Operations Using Uav Swarms, Jordan Morrow, Maciej Jan Zawodniok, Anhar Sami Mohammed

Electrical and Computer Engineering Faculty Research & Creative Works

Artificial intelligence (AI) offers significant benefits in search and rescue applications by enhancing the efficiency and effectiveness of the search. However, an over-reliance on AI can hinder the operation due to biases embedded in the underlying algorithms. This partiality, if left un-monitored, can pose a risk to the safety of those in need of disaster relief. Typically manifests into inaccuracies in the decision-making processes and if not carefully monitored can cause larger issues. This paper extends the knowledge presented in a previous work, which presents the design and modeling of search and rescue operations using unmanned aerial vehicle (UAV) swarms. …

A Wavegan Approach For Mmwave-Based Fanet Topology Optimization, Enas Odat, Hakim Ghazzai, Ahmad Alsharoa

Electrical and Computer Engineering Faculty Research & Creative Works

The integration of dynamic Flying Ad hoc Networks (FANETs) and millimeter Wave (mmWave) technology can offer a promising solution for numerous data-intensive applications, as it enables the establishment of a robust flying infrastructure with significant data transmission capabilities. However, to enable effective mmWave communication within this dynamic network, it is essential to precisely align the steerable antennas mounted on Unmanned Aerial Vehicles (UAVs) with their corresponding peer units. Therefore, it is important to design a novel approach that can quickly determine an optimized alignment and network topology. In this paper, we propose a Generative Adversarial Network (GAN)-based approach, called WaveGAN, …

Controller Area Network With Flexible Data Rate (Can Fd) Eye Diagram Prediction, Junyong Park, Manho Lee, Shinyoung Park, Jonghoon Kim, Joungho Kim, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

A method for predicting the eye diagram for a controller area network with a flexible data rate (CAN FD) is proposed in this article. A CAN FD changes a data rate according to the status to overcome the limitation of latency. In other words, when data to be transmitted are accumulated, the CAN FD increases the data rate up to 5 Mb/s. The CAN FD has a bus topology consisting of multiple electronic control units, which results in a significant amount of signal reflection. Thus, the above causes the signal integrity analysis uncertain. To avoid this, this article proposes a …

Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha Petrenko, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Understanding the performance and validity of clustering algorithms is both challenging and crucial, particularly when clustering must be done online. Until recently, most validation methods have relied on batch calculation and have required considerable human expertise in their interpretation. Improving real-time performance and interpretability of cluster validation, therefore, continues to be an important theme in unsupervised learning. Building upon previous work on incremental cluster validity indices (iCVIs), this paper introduces the Meta- iCVI as a tool for explainable and concise labeling of partition quality in online clustering. Leveraging a time-series classifier and data-fusion techniques, the Meta- iCVI combines the outputs …

A Compact And High-Efficiency Antenna Design For Tire Pressure Monitoring System Applications, Lei Deng, Lidong Chi, Francesco De Paulis, Yihong Qi

Electrical and Computer Engineering Faculty Research & Creative Works

This article proposes a novel design for a compact antenna that exhibits high efficiency and low interference and is suitable for tire pressure monitoring systems (TPMSs) based on Bluetooth communication interface at 2.4 GHz. Tires constitute a dissipative environment, where the efficiency of the TPMS is affected by both antenna losses and losses from the tire parts, both conductive and dielectric. This article conducts an analysis and simulation of the electromagnetic (EM) environment of the tire and identifies the optimal polarization and placement position for the TPMS antenna to minimize resonance losses of EM waves within the tire, enhance lateral …

Prompt, Accurate, And Noncontact Material Identification Using A Single Microwave Sensor With Machine Learning Analysis, Chen Zhu, Osamah Alsalman, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

In This Article, a Novel Approach to Microwave Sensors is Proposed and Demonstrated that Allows for Prompt, Accurate, and Noncontact Material Identification Even with Arbitrary Lift-Off Distances between the Sensor and the Material under Test (MUT). a Multilayer Perceptron (MLP) is Trained to Directly Learn the Relation of the Measured Reflection Spectra of a Homemade Open-Ended Coaxial Cable Resonator Probe with Respect to Different MUTs and Then Achieve One-To-One Mapping between a Measured Spectrum and the MUT. as a Proof-Of-Concept Demonstration, the Performance of the MLP Model is Tested using Easily Accessible Materials, Including Wood, Glass, Water, and Metal. Reflection …

A Structure From Motion Photogrammetric Method For Continuously Measuring And Tracking The Deformations Of Soils During Triaxial Testing, Xiaolong Xia

Doctoral Dissertations

"Measuring soil deformation characteristics during triaxial testing is an important part of characterizing the stress-strain behavior of both saturated and unsaturated soils. The total and local deformations and volume changes of a soil specimen are indispensable parameters that need to be measured and are of great significance for understanding volume change and shear strength characteristics of the soil. Over the past several decades, numerous efforts have been made to measure the soil deformations during triaxial testing. However, it remains a great challenge for researchers to measure the volume-changes of unsaturated soils due to the existence of the air phase.

This …

Synthesis And Characterization Of Quantum Materials, Yunsheng Qiu

Doctoral Dissertations

"In this study, attempts were made to grow quantum materials that have recently undergone a profound change of perspective. These materials are involved in intricate macroscopic properties rooted in the subtle nature of quantum physics. To explore our understanding of quantum materials, this study includes three projects: Magnetic Topological Insulators, Topological Superconductors, and high-temperature superconductors.

A Cr-doped Sb₂Te₃ is added to the category for the magnetic topological insulators project. Their transport properties are studied, and the origin of ferromagnetism is studied. Anomalous Hall effect is observed in the Hall measurements, and serval factors (cooling rate, dopant deficiency) …

Re-Evaluating Missouri’S Strategic Element Potential: A Geochemical Study Of The Mesoproterozoic Fe-Cu-Co-Ree Deposits In Southeast Missouri, Usa, Brandon James Sullivan

Doctoral Dissertations

"Iron-oxide-copper-gold (IOCG) deposits are poorly understood mineral systems. For example, we do not know why Cu- and Co-rich IOCG deposits typically occur proximal to Fe ore deposits that are notably Cu and Co-poor, such as Iron Oxide Apatite (IOA) deposits. To better understand the formation of IOA and IOCG deposits in Missouri, USA, this PhD thesis examines the genesis of the Kratz Spring IOA and the Boss Central Dome IOCG deposits. This study presents the first constraints on formation conditions and fluid sources in the studied deposits using integrated petrographic, mineral composition, and Fe isotope analyses of oxide minerals. Observations …

Characterization Of The Acquisition Parameters Of A Submersible Gamma-Ray Computed Tomography System, Zhongmin Jin

Doctoral Dissertations

"The purpose of this work is to study a component of a submersible gamma-ray computed tomography (CT) system used in the non-destructive testing of irradiated nuclear fuels. The first section of this study proposes two acceleration approaches for rapidly modeling a transmission-type gamma-ray tomography system. The first relies on Monte Carlo simulations with a monodirectionally biased source sampled from a sub-volume of the whole source volume. This method estimates the real count rate using analytical correction factors. The second way of acceleration is based on deterministic calculations that use the Beer-Lambert law and detector response characteristics. It shows that both …

Novel Uses Of Distributed Optical Fiber Sensing For Geo-Mechanical Monitoring, Samuel Vincent Nowak

Doctoral Dissertations

"Rock mass monitoring in underground excavations is crucial for ensuring workplace safety and efficiency. As mines become deeper and more complex, the need to monitor larger volumes of rock during mining becomes impractical with existing instruments due to high costs. Distributed Optical Fiber Sensing (DOFS) is a powerful technology with advantages like long sensor length, real-time measurement capabilities, ease of installation, low cost, and distributed measurement. However, its application in rock mass monitoring is limited, especially in the mining industry. This study addresses a major gap in DOFS technology, specifically the inability of long-range distributed optical fiber strain sensors to …

Modeling And Analysis Methods For Esd And Emi Problems, Xin Yan

Doctoral Dissertations

"Electrostatic discharge (ESD) failures and Electromagnetic interference (EMI) problems are becoming more critical in electronic devices and large systems. In this work, four studies are presented to model and analyze ESD and EMI problems.

First, a simplified physical-based model for deep-snapback transient voltage suppressors (TVS) is developed. While based on physics, the number of parameters and components is minimized. Results show that the proposed model captures the most important behaviors of the TVS response using a limited number of parameters, allowing the model to be tuned relatively easily using data obtained only from package-level transient and quasi-static measurements. Second, a …

Melt Pool Feature Extraction, A State-Space Model And Spatial Layer-To-Layer Control For Powder Bed Fusion, Xin Wang

Doctoral Dissertations

"Powder Bed Fusion (PBF) is an Additive Manufacturing (AM) technique which can reduce material waste and is a fast-prototyping technique for complex geometries. However, the lack of consistent quality is a major problem in industrial applications. An InfraRed (IR) camera is a common tool to monitor a surface temperature field and melt pool morphology in PBF. With melt pool features from camera videos, manufacturing researchers use spatial feature maps to visualize defect locations and control researchers use layer-to-layer control to achieve more uniform melt pools and less defects.

Paper I presents a simulation method to characterize the effect of down-sampling …

The Science Of Gravitational-Wave Sources And Beyond Compact Binary Coalescences, Yanyan Zheng

Doctoral Dissertations

"This work focuses on the field of gravitational-wave astronomy by extending the scope of detectable sources beyond compact binary coalescences, All the gravitational-wave detections so far come from compact binary coalescences. Focusing on core-collapse supernovae as promising sources for short gravitational-wave transients, this work reports optically targeted searches for gravitational-wave emitted by core-collapse supernovae during the third observing run of the LIGO and Virgo detectors. It also predicts the search sensitivity for the ongoing fourth and forthcoming fifth observing runs. Moreover, the work introduces a novel computational framework for testing the spatial distribution of binary black hole sources, allowing for …

Material Models For Simulation Of Steel Hot Rolling Process Using Experimentally Determined Properties At Elevated Temperatures And Strain Rates, Siva Sai Krishna Dasari

Doctoral Dissertations

"Hot rolling is one of the most important and complex process that includes large material deformation, different contact conditions, complex geometries and different temperature gradients along with micro structural changes such as viscoplasticity, dynamic recrystallization, and static softening. In the present work, the viscoplasticity, damage evolution, static softening and void closure of alloyed metal during hot rolling process was numerically investigated. Gleeble^TM hot compression and double hit tests were performed to provide experimental stress-strain curves at recrystallization temperatures and at different strain rates. Johnson-Cook viscoplastic model was calibrated using experimental stress-strain curves. Static softening model was developed at varying …

Advanced Mass Spectrometry Method Development And Applications For Assessment Of Traumatic Brain Injury And Legionella Pneumophila Dissinfection With Copper, Austin Chase Sigler

Doctoral Dissertations

"Pathological processes often involve complex biochemical changes which can be assessed using advanced mass spectrometry. In this present dissertation, two fields were studied: traumatic brain injury (TBI), and water contamination by L. pneumophilia. TBI is a pressing public health concern for which current clinical tools remain inadequate. We present newly developed mass spectrometric methods to access metabolites associated with blast induced TBI. We applied these methods to the biofluids of soldiers conducting explosives training. Significant changes in several metabolites were observed between pre- and post-blast specimens, including changes that increased with repeated exposure. These changes point to the possibility of …

Deep Learning Techniques For Image Segmentation In Dermoscopic Skin Cancer Images, Norsang Lama

Doctoral Dissertations

"Melanoma is recognized as the most lethal type of skin cancer, responsible for a significant proportion of skin cancer-related deaths. However, early detection of melanoma is essential for successful treatment outcomes. Computer-aided skin cancer diagnosis tools can save lives by enabling earlier detection of skin cancer. Image segmentation is a crucial step in computer-aided diagnosis as it allows the detection of critical features or regions in an image. Thus, an accurate image segmentation method is necessary to create a more precise computer-aided diagnostic tool for skin cancer diagnosis. This dissertation includes investigating and developing deep learning techniques to improve image …

Improving Decision-Making Processes In Construction And Infrastructure Bidding: Qualitative And Quantitative Approaches Including Graph Theory, Game Theory, And Machine Learning, Muaz Osman Elzubeir Ahmed

Doctoral Dissertations

"Construction and infrastructure bidding is a highly competitive process that entails various uncertainties faced by contractors. Contractors weigh various factors to determine the expected benefits of a construction project and decide their bid value. However, the situation is more complex in multi-stage bidding (MSG), where general contractors must account for the bids of their subcontractors and face a greater threat of the winner’s curse (i.e., situation where the winning contractor underestimates the actual cost of the project). As such, bidding-related complexities, risks, and uncertainties, if uncontrolled, can lead to the rise of claims and disputes between stakeholders. Existing research falls …

Semiconductor Physics Based Signal Integrity Analysis For 3d Ic, Ze Sun

Doctoral Dissertations

"Three-dimensional integrated circuits (3DICs) are becoming increasingly popular in high-speed electrical systems. 3DICs are made by stacking multiple dies vertically and connecting them with through-silicon vias (TSVs). In this dissertation, three studies were carried out to model the performance of 3DICs in extreme working conditions and the signal integrity (SI) performance of 3DICs.

First, an in-house Monte Carlo particle simulator was developed to characterize extreme working conditions (ESD/EMP) for ICs at the microscopic level instead of the macroscopic level. The ability of the simulator was demonstrated by modeling a voltage regulator diode under forward and reverse bias. The diode simulation …

Non-Linearity Modeling And Quantifications For Practical Rf Interference Control, Shengxuan Xia

Doctoral Dissertations

"Radio frequency (RF) interference can degrade the receiving sensitivity of antennas (desense problem). It is essential to model the nonlinearity as it is the root-cause of the unwanted frequency components. Understanding the electromagnetic (EM) coupling or radiated emissions is also important.

Nonlinearity causes modulation-involved desense problems, and it consists of two categories: upconvertion of the baseband noise by the transmitting (TX) signal, and the passive intermodulation (PIM) of the transmitting signal itself. The upconvertion caused desense can be modeled and analyzed with the dipole-moment based coupling framework. PIM has been identified as another nonlinear distortion mechanism, specifically in the metallic …

Methodology For Parameter Determination For Simulation Software, Aaron Flood

Doctoral Dissertations

"This research aims to present a methodology for optimizing input datasets to increase the accuracy of mathematical models and accelerate their application to engineering problems. To accomplish this goal, this work focused on the application of mathematical models to metal additive manufacturing (AM), specifically the thermal history of laser directed energy deposition (DED) of aluminum alloys. The initial steps of this body of work were to develop a mathematical model that is capable of simulating the metal AM process and applying it to the laser DED of aluminum. It was validated using the well characterized material Ti-64 and shown to …

A Lightweight Machine-Learning Framework For Enhancing Security In Iot Blockchain Networks, Charles Connor Rawlins

Doctoral Dissertations

"Blockchain is one of the fastest technologies that rivals the Internet in terms of adoption speed. This security method is applicable to data-centric environments for validating data in the presence of faults. However, traditional blockchain implementation introduces bottlenecks with computationally intense security measures to prevent malicious spam and resolve conflicts. This dissertation explores a new direction for blockchain technology that allows limited nodes, like IoT devices, to make independent decisions with compressed knowledge of past blockchain history through the use of machine-learning for active decisions (or the first machine-intelligent blockchain protocol). Proposing to introduce machine-intelligence into the rapidly evolving paradigm …

Development And Testing Of High Strength Scalable High Entropy Alloys, Hans Jakob Pommerenke

Doctoral Dissertations

"High entropy alloys have shown promise through their ability to be tailored to specific scenarios. From highly corrosive environments to precipitation strengthened alloys to radiation environments high entropy alloys allow tailor made alloys for every solution. New compositions are developed and tested but little work focuses on the processing effect choosing rather than using processing as a steppingstone to achieve properties without investigating the processing effects. This work focuses on single phase face-centered cubic alloys strengthened by processing and precipitation strengthening. In conjunction with the development of these alloys, the ability to scale production will be investigated and characterized to …

Communication Modality And Activity Recognition In Smart Manufacturing, Haodong Chen

Doctoral Dissertations

"Advancements in sensors, computational intelligence, and big data have been rapidly transforming and revolutionizing the manufacturing sector, leading to robot-rich and digitally connected factories. To facilitate such a transformative process, workforce training and coordination between the human workforce and robots have remained a major challenge. This study aims to create intelligent workforce training systems for human-computer interaction and human-robot collaboration. It proposes the use of dynamic gestures and speech commands for seamless communication between a worker and a robot, leveraging Convolutional Neural Networks (CNN) and multiple threading for recognition and integration. Additionally, a fine-grained activity recognition model has been crafted …

Modeling And Control For Precision Robotic Machining, Patrick Bazzoli

Doctoral Dissertations

"Robots are used in a wide variety of manufacturing applications, but machining applications in which robots can excel are limited by their lower accuracy and stiffness relative to traditional CNC machines. This work is composed of two parts: one to evaluate a robot’s accuracy and one to compensate for the vibrations of the robot due to its lower stiffness.

In order to evaluate whether a robot has the necessary accuracy to perform a given machining task, Paper 1 discusses a novel Model Invalidation method. This methodology provides a statistical framework as well as a measurement strategy for determining if a …

Applications Of Computational Intelligence And Data Fusion Techniques For Biomedical Images, Anand Krishnadas Nambisan

Doctoral Dissertations

"The realm of melanoma diagnosis has been significantly advanced by deep learning (DL) techniques, yet the current approaches are not without limitations, including missed diagnoses and the challenge of interpreting these "black box" models. The research is comprised of three studies, each contributing uniquely towards advancing melanoma detection accuracy and interpretability. The first study focuses on improving the detection of specific dermoscopic structures through a deep learning-based segmentation approach, while the second study builds upon this by employing a fusion technique that combines traditional image features with advanced deep learning models. This method significantly improves melanoma detection, particularly in recall …

Mission-Driven Inverse-Design Of Conventional And Unconventional Airplane Configurations Using Machine Learning, Rohan Srinivas Sharma

Doctoral Dissertations

"A key challenge in airplane design has been the long-desired ability to quickly create an airplane design with specific performance properties. To tackle this problem, researchers have traditionally leveraged design space exploration methodologies to iteratively explore a design space via surrogate models. On the other hand, inverse design is a process wherein performance objectives are fixed while a suitable design for satisfying these requirements is calculated. In recent years, artificial neural networks (ANNs) have been explored as viable surrogate models because traditional surrogate models struggle with the large number of output to input mapping needed for inverse design problems. In …

Lithospheric Drip And Mantle Thermal Upwelling Beneath Central Asia And The Southern Great Plains Of North America: Evidence From Receiver Function Imaging, Yangyang Liao

Doctoral Dissertations

"Investigating the depth of mantle transition zone (MTZ) discontinuities provides insights into mantle dynamics, including regional-scale downwelling of mantle lithosphere (i.e., lithospheric drip) and upwelling of thermal materials (i.e., mantle plume). In our study, we employ receiver functions to image the 410 km (d410) and 660 km (d660) mantle discontinuities and to ascertain thickness of the MTZ beneath Central Asia and the Great Plains in United States. The findings unveil a significantly thickened transition zone, exhibiting an average thickness of 284 km beneath East Sayan. When considered alongside geochemical observations, our results indicate that the descent of the lithosphere is …

Exploring Seismic Anisotropy In The Crust And Mantle Of The Tibetan Plateau And Analyzing Seismic Velocity Variations In Yellowstone, Cong Shen

Doctoral Dissertations

"Seismology is a scientific discipline that utilizes seismic waves to investigate the deep structures within the Earth's interior, which are otherwise inaccessible to direct observation by humans. These seismic waves can be categorized into two types: body waves and surface waves, each with distinct propagation modes and behaviors, carrying valuable information about their travel paths. The first paper concentrates on the analysis and inversion of body waves produced by large seismic events. It employs shear wave splitting and receiver function methods to explore the geodynamic structure of the south-central Tibetan Plateau, near the plate boundary. The investigation covers the depth …

Efficient Modeling And Simulation For The Aerothermal Analysis Of Particle-Laden Hypersonic Flows, Andrew Hinkle

Doctoral Dissertations

"Standard approaches for simulating hypersonic flows in particle-laden environments have key limitations in practice, such as high cost and numerical noise. In this work, efficient solution strategies for particle-laden hypersonic flows were developed using both Lagrangian and Eulerian models for the particle phase with the focus on investigating spacecraft surface erosion, as well as convective and radiative heat flux augmentation due to atmospheric particulate encountered during planetary entry.

First, an efficient integration strategy for Lagrangian methods, the Trajectory Control Volume (TCV) method, was developed for steady analysis of dilute particle phase problems on general geometries. The TCV method was shown …