Physical Sciences and Mathematics Commons^™

Theoretical Analysis Of Charge Conduction And Rectification In Self-Assembled-Monolayers In Molecular Junctions, Francis Adoah

Electronic Theses and Dissertations, 2020-2023

As electrical devices shrink to the atomic scale, it is expected that Moore's law will soon be obsolete for semiconductor devices. In 1974, Avriam and Ratner predicted that organic devices could replace semiconductor technology, leading to extensive research on molecular-based organic devices. This dissertation delves into the theoretical frameworks used to examine the transport in molecular junctions and aims to enhance our comprehension of charge transport and conduction properties. The studies presented in this thesis illustrates that a molecule's alteration by just a single atom can change it from an insulator to a conductor, and also that, by fine-tuning the …

Vertical Federated Learning Using Autoencoders With Applications In Electrocardiograms, Wesley William Chorney

Theses and Dissertations

Federated learning is a framework in machine learning that allows for training a model while maintaining data privacy. Moreover, it allows clients with their own data to collaborate in order to build a stronger, shared model. Federated learning is of particular interest to healthcare data, since it is of the utmost importance to respect patient privacy while still building useful diagnostic tools. However, healthcare data can be complicated — data format might differ across providers, leading to unexpected inputs and incompatibility between different providers. For example, electrocardiograms might differ in sampling rate or number of leads used, meaning that a …

The Synthesis And Optimization Of Sulfide And Halide Solid Electrolytes For All Solid-State Batteries, Teerth Brahmbhatt

Doctoral Dissertations

Countries and organizations around the world have established ambitious targets to transition away from fossil fuel-based energy sources and devices. The transition is focused on cleaning up power generation by converting coal, natural gas, and oil-based power generation to renewables and nuclear energy. Decarbonizing other sectors of energy use, transportation for example, will require broader electrification. To drive this move away from fossil fuel powered transportation will require portable energy storage devices. Conventional lithium-ion batteries are a popular candidate to lead this shift. However, these batteries often rely on flammable liquid electrolytes and carbon anodes that suffer from low energy …

Inkjet-Printed Electrochemical Sensors For Lead Detection, Annatoma Arif

Open Access Theses & Dissertations

This PhD dissertation research has developed a simple, miniaturized, sensitive, selective, reproducible, and disposable 3D (inkjet printed – additive manufacturing technology) gold (Au) plated electrochemical sensor (ECS) on shape memory polymer (SMP) for aqueous lead detection. This technology has shown promising performance in the application of electrochemical sensing (lead (II) detection) due to increased effective electrode surface area (7.25 mm^2 ± 0.15 mm^2) despite miniaturizing lateral surface area (4.19 mm^2). The design, fabrication processes, optimization including bismuth functionalization, evaluation, uncertainty analysis, and cost analysis of the novel SMP based inkjet printed Au plated sensor have been delineated in this manuscript …

Characteristics Of Refractivity And Sea State In The Marine Atmospheric Surface Layer And Their Influence On X-Band Propagation, Douglas Matthew Pastore

Electronic Theses and Dissertations

Predictions of environmental conditions within the marine atmospheric surface layer (MASL) are important to X-band radar system performance. Anomalous propagation occurs in conditions of non-standard atmospheric refractivity, driven by the virtually permanent presence of evaporation ducts (ED) in marine environments. Evaporation ducts are commonly characterized by the evaporation duct height (EDH), evaporation duct strength, and the gradients below the EDH, known as the evaporation duct curvature. Refractivity, and subsequent features, are estimated in the MASL primarily using four methods: in-situ measurements, numerical weather and surface layer modeling, boundary layer theory, and inversion methods.

The existing refractivity estimation techniques often assume …

Integrating Sensor Development, Risk Assessment, And Community Engagement To Support Environmental Justice In The Rural Community Of El Tiple, Colombia, David Bahamon Pinzon

All Dissertations

In Colombia, ethnic communities have traditionally been responsible stewards of natural resources. They recognize the importance of these resources for their livelihood, as well as their ancestral and cultural heritage. El Tiple, a rural Afro-Colombian community, has been affected by the incursion of private corporations that promoted the expansion of sugarcane monocrops in their territory. Since the introduction of the monoculture industry, local freshwater sources have been depleted due to intensive water use for irrigation of the sugarcane crops. Additionally, the intensive usage of agrochemicals has been linked with loss of native flora, damages to family farms, and pollution of …

Spectral Broadening Effects On Pulsed-Source Digital Holography, Steven A. Owens, Mark F. Spencer, Glen P. Perram

Faculty Publications

Using a pulsed configuration, a digital-holographic system is setup in the off-axis image plane recording geometry, and spectral broadening via pseudo-random bit sequence is used to degrade the temporal coherence of the master-oscillator laser. The associated effects on the signal-to-noise ratio are then measured in terms of the ambiguity and coherence efficiencies. It is found that the ambiguity efficiency, which is a function of signal-reference pulse overlap, is not affected by the effects of spectral broadening. The coherence efficiency, on the other hand, is affected. As a result, the coherence efficiency, which is a function of effective fringe visibility, is …

Generalizable Deep-Learning-Based Wireless Indoor Localization, Ali Owfi

All Theses

The growing interest in indoor localization has been driven by its wide range of applications in areas such as smart homes, industrial automation, and healthcare. With the increasing reliance on wireless devices for location-based services, accurate estimation of device positions within indoor environments has become crucial. Deep learning approaches have shown promise in leveraging wireless parameters like Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) to achieve precise localization. However, despite their success in achieving high accuracy, these deep learning models suffer from limited generalizability, making them unsuitable for deployment in new or dynamic environments without retraining. To …

Novel Non-Invasive Detection Of Thin Film Biofilm And Classification Of Deposits Using Machine Learning, Sachin Davis

Theses and Dissertations

Clean, safe, readily available water is vital for public health, irrespective of whether it is used for drinking, domestic use, food production, or recreational purposes. Globally, around two billion people use feces-contaminated water sources, which poses a high risk to the safety of drinking water due to the high probability of water contamination. Microbial-influenced corrosion is a significant problem in several industries, including but not limited to wastewater treatment, drinking water distribution systems, food industries, power plants, paper industries, and chemical manufacturing facilities. The presence of microorganisms causes around 70% of the corrosion in gas transmission pipelines, and corrosion accounts …

Towards A Robust Defense: A Multifaceted Approach To The Detection And Mitigation Of Neural Backdoor Attacks Through Feature Space Exploration And Analysis, Liuwan Zhu

Electrical & Computer Engineering Theses & Dissertations

From voice assistants to self-driving vehicles, machine learning(ML), especially deep learning, revolutionizes the way we work and live, through the wide adoption in a broad range of applications. Unfortunately, this widespread use makes deep learning-based systems a desirable target for cyberattacks, such as generating adversarial examples to fool a deep learning system to make wrong decisions. In particular, many recent studies have revealed that attackers can corrupt the training of a deep learning model, e.g., through data poisoning, or distribute a deep learning model they created with “backdoors” planted, e.g., distributed as part of a software library, so that the …

Study Of Radiation Effects In Gan-Based Devices, Han Gao

Electrical Engineering Theses and Dissertations

Radiation tolerance of wide-bandgap Gallium Nitride (GaN) high-electron-mobility transistors (HEMT) has been studied, including X-ray-induced TID effects, heavy-ion-induced single event effects, and neutron-induced single event effects. Threshold voltage shift is observed in X-ray irradiation experiments, which recovers over time, indicating no permanent damage formed inside the device. Heavy-ion radiation effects in GaN HEMTs have been studied as a function of bias voltage, ion LET, radiation flux, and total fluence. A statistically significant amount of heavy-ion-induced gate dielectric degradation was observed, which consisted of hard breakdown and soft breakdown. Specific critical injection level experiments were designed and carried out to explore …

Empowering 5g Mmwave: Leveraging Kml Placemarks For Enhanced Rf Design And Deployment Efficiency, Gustavo A. Fernandez

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

This publication explores the significance of Keyhole Markup Language (KML) in telecommunications, particularly in the context of 5G mmWave RF design and planning. With the advent of 5G mmWave technology, the demand for seamless and efficient network deployments has never been greater. The deployment of small cells and repeaters for 5G mmWave necessitates utmost precision in location accuracy and rapid information exchange during site surveys and evaluations. The challenges of mmWave frequencies, including their limited range and susceptibility to attenuation, intensify the complexity and criticality of this process. Network operators must ensure that the chosen location is devoid of obstacles …

Characterization And Estimation Of Musculoskeletal Pain Using Machine Learning, Boluwatife Faremi

Master's Theses

Traditional scales utilized for recording pain are known to be highly subjective and biased due to inaccuracies in recollecting actual pain intensities. As a result, machine learning (ML) models that are trained using these scores as ground truth are reported to have low performance for objective pain classification because of the huge disparity between what was felt in moments of pain and the scores recorded afterward.

In the present study, two devices were designed for gathering real-time, continuous in-session subjective pain scores and the recording of the autonomic nervous system (ANS) altered endodermal (EDA) activity. 24 participants were recruited to …

Recent Progress Of Bifunctional Electrocatalysts For Oxygen Electrodes In Unitized Regenerative Fuel Cells, Tian-Long Zheng, Ming-Yu Ou, Song Xu, Xin-Biao Mao, Shi-Yi Wang, Qing-Gang He

Journal of Electrochemistry

Unitized regenerative fuel cells (URFCs), which oxidize hydrogen to water to generate electrical power under thefuel cells (FCs) mode and electrolyze water to hydrogen under the water electrolysis (WE) mode for recycling, areknown as clean and sustainable energy conversion devices. In contrast to the hydrogen oxidation reaction (HOR) andhydrogen evolution reaction (HER) on the hydrogen electrode side, the sluggish kinetics of oxygen reduction reaction(ORR) and oxygen evolution reaction (OER) on the oxygen electrode side requires highly efficient bifunctional oxygencatalysts. Conventional precious metal oxygen catalysts combine Pt and IrO₂ with excellent ORR and OER activities toachieve bifunctional electrocatalysis performance, but …

Band Alignments Of Metal/Oxides-Water Interfaces Using Ab Initio Molecular Dynamics, Yong-Bin Zhuang, Jun Cheng

Journal of Electrochemistry

Band alignments of electrode-water interfaces are of crucial importance for understanding electrochemical interfaces. In the scenario of electrocatalysis, applied potentials are equivalent to the Fermi levels of metals in the electrochemical cells; in the scenario of photo(electro)catalysis, semiconducting oxides under illumination have chemical reactivities toward redox reactions if the redox potentials of the reactions straddle the conduction band minimums (CBMs) or valence band maximums (VBMs) of the oxides. Computational band alignments allow us to obtain the Fermi level of metals, as well as the CBM and VBM of semiconducting oxides with respect to reference electrodes. In this tutorial, we describe …

Well-Conditioned T-Matrix Formulation For Scattering By A Dielectric Obstacle, Murat Enes Hati̇poğlu, Fati̇h Di̇kmen

Turkish Journal of Electrical Engineering and Computer Sciences

The classic formulation of the extended boundary condition method is revisited to inject the regularization operators for the unknown coefficients of the eigen-function expansions for the travelling and standing waves throughout the dielectric scatterer. It is shown that, using the new definitions, the existing algorithm of the scattering field calculation can be kept the same for its well-conditioned version. This is exemplified for scalar 2D problems for both TM and TE polarization under illumination of a line source. The condition numbers of the matrix operators in the new version of the algorithm are drastically reduced when the regularization interfaces are …

Lightweight Deep Neural Network Models For Electromyography Signal Recognition For Prosthetic Control, Ahmet Mert

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, lightweight deep learning methods are proposed to recognize multichannel electromyography (EMG) signals against varying contraction levels. The classical machine learning, and signal processing methods namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), root mean square (RMS), and waveform length (WL) are adopted to convolutional neural network (CNN), and long short-term memory neural network (LSTM). Eight-channel recordings of nine amputees from a publicly available dataset are used for training and testing the proposed models considering prosthetic control strategies. Six class hand movements with three contraction levels are applied to WL and RMS-based feature extraction. After that, they …

Improving Unet Segmentation Performance Using An Ensemble Model In Images Containing Railway Lines, Mehmet Sevi̇, İlhan Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

This study aims to make sense of the autonomous system and the railway environment for railway vehicles. For this purpose, by determining the railway line, information about the general condition of the line can be obtained along the way. In addition, objects such as pedestrian crossings, people, cars, and traffic signs on the line will be extracted. The rails and the rail environment in the images will be segmented with a semantic segmentation network. In order to ensure the safety of rail transport, computer vision, and deep learning-based methods are increasingly used to inspect railway tracks and surrounding objects. In …

A Practical Framework For Early Detection Of Diabetes Using Ensemble Machine Learning Models, Qusay Saihood, Emrullah Sonuç

Turkish Journal of Electrical Engineering and Computer Sciences

The diagnosis of diabetes, a prevalent global health condition, is crucial for preventing severe complications. In recent years, there has been a growing effort to develop intelligent diagnostic systems for diabetes utilizing machine learning (ML) algorithms. Despite these efforts, achieving high accuracy rates using such systems remains a significant challenge. Recent advancements in ensemble ML methods offer promising opportunities for early detection of diabetes, as they are known to be faster and more cost-effective than traditional approaches. Therefore, this study proposes a practical framework for diagnosing diabetes that involves three stages. The data preprocessing stage encompasses several crucial tasks, including …

Adaptive Plasmonic Metasurfaces For Radiative Cooling And Passive Thermoregulation, Azadeh Didari-Bader, Nooshin M. Estakhri, Nasim Mohammadi Estrakhri

Engineering Faculty Articles and Research

In this work, we investigate a class of planar photonic structures operating as passive thermoregulators. The radiative cooling process is adjusted through the incorporation of a phase change material (Vanadium Dioxide, VO₂) in conjunction with a layer of transparent conductive oxide (Aluminum-doped Zinc Oxide, AZO). VO₂ is known to undergo a phase transition from the “dielectric” phase to the “plasmonic” or “metallic” phase at a critical temperature close to 68°C. In addition, AZO shows plasmonic properties at the long-wave infrared spectrum, which, combined with VO₂, provides a rich platform to achieve low reflections across the …

Scanning Photoelectrochemical Microscopic Study In Photoinduced Electron Transfer Of Supramolecular Sensitizers-Tio2 Thin Films Systems, Sheng-Ya Zhang, Min Yao, Ze Wang, Tian-Jiao Liu, Rong-Fang Zhan, Hui-Qin Ye, Yan-Jun Feng, Xiao-Quan Lu

Journal of Electrochemistry

Crafting charge transfer channels at titanium dioxide (TiO₂) based photoanodes remain a pressing bottleneck in solar-to-chemical conversion technology. Despite the tremendous attempts, TiO₂ as the promising photoanode material still suffers from sluggish charge transport kinetics. Herein, we propose an assembly strategy that involves the axial coordination grafting metalloporphyrin-based photosensitizer molecules (MP) onto the surface-modified TiO₂ nanorods (NRs) photoanode, forming the composite MP/TiO₂ NRs photoelectrode. As expected, the resulted unique MP_B/TiO₂ NRs photoelectrode displays significantly improved photocurrent density as compared to TiO₂ NRs alone and MP_A/TiO₂ NRs photoelectrode. Scanning …

Enabling Third Layer Bitcoin Applications Using Lightning Network, Ahmet Kurt

FIU Electronic Theses and Dissertations

When Bitcoin was introduced in 2009, it created a big sensation in the world as it was first of its kind. Since then, a lot of different cryptocurrencies were proposed. Today, cryptocurrencies can be used to pay for goods and services similar to using cash or credit cards. However, none of them could replace or supersede Bitcoin in usage or market capitalization. Current market conditions still imply that it will stay the same way. However, Bitcoin suffers from very low transaction per second (TPS) which limits its usability on large scale. There have been numerous proposals to increase its scalability …

Deep Learning Enhancement And Privacy-Preserving Deep Learning: A Data-Centric Approach, Hung S. Nguyen

USF Tampa Graduate Theses and Dissertations

Deep Learning and its applications have become attractive to a lot of research recentlybecause of its capability to capture important information from large amounts of data. While most of the work focuses on finding the best model parameters, improving machine learning performance from data perspective still needs more attention. In this work, we propose techniques to enhance the robustness of deep learning classification by tackling data issue. Specifically, our data processing proposals aim to alleviate the impacts of class-imbalanced data and non- IID data in deep learning classification and federated learning scenarios. In addition, data pre-processing strategies such that dimensionality …

Optimal Estimation Inversion Of Ionospheric Electron Density From Gnss-Pod Limb Measurements: Part I-Algorithm And Morphology, Dong L. Wu, Nimalan Swarnalingam, Cornelius Csar Jude H. Salina, Daniel J. Emmons, Tyler C. Summers, Robert Gardiner-Garden

Faculty Publications

GNSS-LEO radio links from Precise Orbital Determination (POD) and Radio Occultation (RO) antennas have been used increasingly in characterizing the global 3D distribution and variability of ionospheric electron density (Ne). In this study, we developed an optimal estimation (OE) method to retrieve Ne profiles from the slant total electron content (hTEC) measurements acquired by the GNSS-POD links at negative elevation angles (ε < 0°). Although both OE and onion-peeling (OP) methods use the Abel weighting function in the Ne inversion, they are significantly different in terms of performance in the lower ionosphere. The new OE results can overcome the large Ne oscillations, sometimes negative values, seen in the OP retrievals in the E-region ionosphere. In the companion paper in this Special Issue, the HmF2 and NmF2 from the OE retrieval are validated against ground-based ionosondes and radar observations, showing generally good agreements in NmF2 from all sites. Nighttime hmF2 measurements tend to agree better than the daytime when the ionosonde heights tend to be slightly lower. The OE algorithm has been applied to all GNSS-POD data acquired from the COSMIC-1 (2006–2019), COSMIC-2 (2019–present), and Spire (2019–present) constellations, showing a consistent ionospheric Ne morphology. The unprecedented spatiotemporal sampling of the ionosphere from these constellations now allows a detailed analysis of the frequency–wavenumber spectra for the Ne variability at different heights. In the lower ionosphere (~150 km), we found significant spectral power in DE1, DW6, DW4, SW5, and SE4 wave components, in addition to well-known DW1, SW2, and DE3 waves. In the upper ionosphere (~450 km), additional wave components are still present, including DE4, DW4, DW6, SE4, and SW4. The co-existence of eastward- and westward-propagating wave4 components implies the presence of a stationary wave4 (SPW4), as suggested by other earlier studies. Further improvements to the OE method are proposed, including a tomographic inversion technique that leverages the asymmetric sampling about the tangent point associated with GNSS-LEO links.

Adversary Aware Continual Learning, Muhammad Umer

Theses and Dissertations

Continual learning approaches are useful as they help the model to learn new information (classes) sequentially, while also retaining the previously acquired information (classes). However, these approaches are adversary agnostic, i.e., they do not consider the possibility of malicious attacks. In this dissertation, we have demonstrated that continual learning approaches are extremely vulnerable to the adversarial backdoor attacks, where an intelligent adversary can introduce small amount of misinformation to the model in the form of imperceptible backdoor pattern during training to cause deliberate forgetting of a specific class at test time. We then propose a novel defensive framework to counter …

Exploring The Potential Of Pavegen’S Kinetic Energy Generating Floor For Sustainable Energy Solutions: A Proposal For Cal Poly Slo, Brandon J. Cuneo

Construction Management

This paper proposes the installation of Pavegen's kinetic energy generating floors at Cal Poly’s campus as a sustainable energy solution. Pavegen has developed a pioneering technology that converts footsteps into clean and renewable energy. The versatility of these floors is demonstrated through successful implementations in various settings, such as transportation hubs and public spaces, generating power from foot traffic. Collaborations with Schneider Electric, installation at Dupont Circle, and integration at Heathrow Airport showcase the potential for sustainable urban infrastructure. This paper outlines research conducted on Pavegen and similar solutions, including communication with company representatives and examining proposed installation locations at …

Novel Approach For Non-Invasive Prediction Of Body Shape And Habitus, Emma Young

Electronic Theses and Dissertations

While marker-based motion capture remains the gold standard in measuring human movement, accuracy is influenced by soft-tissue artifacts, particularly for subjects with high body mass index (BMI) where markers are not placed close to the underlying bone. Obesity influences joint loads and motion patterns, and BMI may not be sufficient to capture the distribution of a subject’s weight or to differentiate differences between subjects. Subjects in need of a joint replacement are more likely to have mobility issues or pain, which prevents exercise. Obesity also increases the likelihood of needing a total joint replacement. Accurate movement data for subjects with …

Neuroevolution Application To Collaborative And Heuristics-Based Connected And Autonomous Vehicle Cohort Simulation At Uncontrolled Intersection, Frederic Jacquelin, Jungyun Bae, Bo Chen, Darrell Robinette

Michigan Tech Publications, Part 2

Artificial intelligence is gaining tremendous attractiveness and showing great success in solving various problems, such as simplifying optimal control derivation. This work focuses on the application of Neuroevolution to the control of Connected and Autonomous Vehicle (CAV) cohorts operating at uncontrolled intersections. The proposed method implementation’s simplicity, thanks to the inclusion of heuristics and effective real-time performance are demonstrated. The resulting architecture achieves nearly ideal operating conditions in keeping the average speeds close to the speed limit. It achieves twice as high mean speed throughput as a controlled intersection, hence enabling lower travel time and mitigating energy inefficiencies from stop-and-go …

P-Doped Ru-Pt Alloy Catalyst Toward High Performance Alkaline Hydrogen Evolution Reaction, Rong-Qin Huang, Wei-Ping Liao, Meng-Xuan Yan, Shi Liu, Yuan-Ming Li, Xiong-Wu Kang

Journal of Electrochemistry

Electrocatalytic water splitting represents grand promise for hydrogen fuel in modern energy equipment, and the design and fabrication of higher performance catalysts are at the central. Herein, we report the sequential phosphorus (P)-doping into ruthenium (Ru) nanoparticles (Ru-P/C) by thermal annealing of Ru nanoparticles in phosphine (PH₃) atmosphere and deposition of extremely low concentration of platinum (Pt) to obtain P-doped Ru-Pt alloy catalyst supported on carbon nanotubes (CNTs), which is denoted as (Ru-P)#Pt/C. The data by X-ray diffraction spectroscopy and transmission electron microscopy show that the Ru nanoparticles existed in the form of hexagonal close-packed (hcp) phase with …

Deep Euteceic Solvents-Assisted Synthesis Of Novel Network Nanostructures For Accelerating Formic Acid Electrooxidation, Jun-Ming Zhang, Xiao-Jie Zhang, Yao Chen, Ying-Jian Fan, You-Jun Fan, Jian-Feng Jia

Journal of Electrochemistry

Deep eutectic solvents (DESs) have been reported as a type of solvent for the controllable synthesis of metal nanostructures. Interestingly, flower-like palladium (Pd) nanoparticles composed of staggered nanosheets and nanospheres are spontaneously transformed into three-dimensional (3D) network nanostructures in choline chloride-urea DESs using ascorbic acid as a reducing agent. Systematic studies have been carried out to explore the formation mechanism, in which DESs itself acts as a solvent and soft template for the formation of 3D flower-like network nanostructures (FNNs). The amounts of hexadecyl trimethyl ammonium bromide and sodium hydroxide also play a crucial role in the anisotropic growth and …