Physical Sciences and Mathematics Commons^™

Electrical Modeling For Dynamic Performance Prediction And Optimization Of Mcpms Layout, Quang Minh Le

Graduate Theses and Dissertations

In recent years, the fast development of Multichip Power Modules (MCPM) packaging and Wide Bandgap (WBG) technology has enabled higher voltage and current ratings, better thermal performance, lower parasitic parameters, and higher mechanical reliability. However, the design of the MCPM layout is a multidisciplinary problem leading to many time-consuming analyses and tedious design processes. Because of these challenges, the design automation tool for MCPM layout has become an emerging research area and gained much attention from the power electronics community. The two critical objectives of a design automation tool for MCPM layout are fast and accurate models for design insights …

A Patient-Specific Algorithm For Lung Segmentation In Chest Radiographs, Manawaduge Supun De Silva, Barath Narayanan Narayanan, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

Lung segmentation plays an important role in computer-aided detection and diagnosis using chest radiographs (CRs). Currently, the U-Net and DeepLabv3+ convolutional neural network architectures are widely used to perform CR lung segmentation. To boost performance, ensemble methods are often used, whereby probability map outputs from several networks operating on the same input image are averaged. However, not all networks perform adequately for any specific patient image, even if the average network performance is good. To address this, we present a novel multi-network ensemble method that employs a selector network. The selector network evaluates the segmentation outputs from several networks; on …

Sers Platform For Single Fiber Endoscopic Probes, Debsmita Biswas

LSU Doctoral Dissertations

Molecular detection techniques have huge potential in clinical environments. In addition to many other molecular detection techniques, endoscopic Raman spectroscopy has great ability in terms of minimal invasiveness and real-time spectra acquisition. However, Raman Effect is low in sensitivity, limiting the application. Surface-Enhanced Raman Scattering (SERS), addresses this limitation. SERS brings rough nano-metallic surfaces in contact with specimen molecules which enormously enhances Raman signals. This provides Raman spectroscopy with immense capabilities for diverse fields of applications.

Generally, in clinical probe applications, the spectrometer is brought near the target molecules for detection. Typically, optical fibers are used to couple spectrometers to …

Frontiers In The Self-Assembly Of Charged Macromolecules, Khatcher O. Margossian

Doctoral Dissertations

The self-assembly of charged macromolecules forms the basis of all life on earth. From the synthesis and replication of nucleic acids, to the association of DNA to chromatin, to the targeting of RNA to various cellular compartments, to the astonishingly consistent folding of proteins, all life depends on the physics of the organization and dynamics of charged polymers. In this dissertation, I address several of the newest challenges in the assembly of these types of materials. First, I describe the exciting new physics of the complexation between polyzwitterions and polyelectrolytes. These materials open new questions and possibilities within the context …

An Inexpensive Maximum Power Point Tracking System For An Insulated Solar Electric Cooker Using An Off-The-Shelf Buck Converter, Andrew A. Perez

Physics

A specialized control circuit using an off-the-shelf buck converter is built for an Insulated Solar Electric Cooker (ISEC). Cost and efficient power delivery are the focus. An ISEC is synonymous to a direct load heat resistor, allowing a specific maximum power point tracking (MPPT) algorithm and fewer components. Only a microcontroller, voltage sensor, and digital-to-analog converter are used with the buck converter to maximize the power delivered by a 100W solar panel for the 3.3Ω load.

Developing Positive Thermal Coefficient (Ptc) Heaters For Solar Electric Cooking, Katarina Ivana Brekalo, Andrew Shepherd

Physics

Positive Thermal Coefficients, PTCs, are materials that abruptly change in resistance in response to changes in temperature. The purpose of this experiment is to explore the viability of using the switching type ceramic PTC thermistor as a replacement for current resistive heaters. These types of PTCs have a nonlinear change in resistance with increases in temperature. This device will be used as a temperature-controlling heating element intended to power an Insulated Solar Electric Cooker (ISEC). The ISEC is designed to cook meals throughout the day for impacted communities as an alternative cooking method that doesn’t require biofuel as an energy …

Performance Analysis Of The Dominant Mode Rejection Beamformer, Enlong Hu

Dissertations

In array signal processing over challenging environments, due to the non-stationarity nature of data, it is difficult to obtain enough number of data snapshots to construct an adaptive beamformer (ABF) for detecting weak signal embedded in strong interferences. One type of adaptive method targeting for such applications is the dominant mode rejection (DMR) method, which uses a reshaped eigen-decomposition of sample covariance matrix (SCM) to define a subspace containing the dominant interferers to be rejected, thereby allowing it to detect weak signal in the presence of strong interferences. The DMR weight vector takes a form similar to the adaptive minimum …

Model-Based Deep Learning For Computational Imaging, Xiaojian Xu

McKelvey School of Engineering Theses & Dissertations

This dissertation addresses model-based deep learning for computational imaging. The motivation of our work is driven by the increasing interests in the combination of imaging model, which provides data-consistency guarantees to the observed measurements, and deep learning, which provides advanced prior modeling driven by data. Following this idea, we develop multiple algorithms by integrating the classical model-based optimization and modern deep learning to enable efficient and reliable imaging. We demonstrate the performance of our algorithms by validating their performance on various imaging applications and providing rigorous theoretical analysis.

The dissertation evaluates and extends three general frameworks, plug-and-play priors (PnP), regularized …

An Unmanned Surface Vehicle: Autonomous Sensor Integration System For Bathymetric Surveys, Fernando Sotelo Torres

Open Access Theses & Dissertations

Unmanned Surface Vehicles (USVs) have been applied to earth sciences, with only a few studies conducted in water environments, as these systems provide autonomous measurement capabilities and transferability to other environmental settings. In this thesis, a reliable, yet economical, USV has been developed for bathymetric surveying of lakes. The system combines an autonomous navigation framework, environmental sensors and a multibeam echosounder to collect submerged topography, temperature, windspeed and monitor the vehicle status during prescribed path planning missions.

The main objective of this study is to provide a methodological framework to build a USV, with independent decision-making, efficient control, and long-range …

Reduced Fuel Emissions Through Connected Vehicles And Truck Platooning, Paul D. Brummitt

Electronic Theses and Dissertations

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag …

Glaciernet2: A Hybrid Multi-Model Learning Architecture For Alpine Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Michael P. Bishop, Jeffrey S. Kargel, Theus Aspiras

Electrical and Computer Engineering Faculty Publications

In recent decades, climate change has significantly affected glacier dynamics, resulting in mass loss and an increased risk of glacier-related hazards including supraglacial and proglacial lake development, as well as catastrophic outburst flooding. Rapidly changing conditions dictate the need for continuous and detailed ob-servations and analysis of climate-glacier dynamics. Thematic and quantitative information regarding glacier geometry is fundamental for understanding climate forcing and the sensitivity of glaciers to climate change, however, accurately mapping debris-cover glaciers (DCGs) is notoriously difficult based upon the use of spectral information and conventional machine-learning techniques. The objective of this research is to improve upon an …

Towards A Low-Cost Solution For Gait Analysis Using Millimeter Wave Sensor And Machine Learning, Mubarak A. Alanazi, Abdullah K. Alhazmi, Osama Alsattam, Kara Gnau, Meghan Brown, Shannon Thiel, Kurt Jackson, Vamsy P. Chodavarapu

Electrical and Computer Engineering Faculty Publications

Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram …

Diverse Effects Of Ev Charging Infrastructure On Electric Power Distribution Systems, Travis Michael Moore Newbolt

Open Access Theses & Dissertations

The advanced technology of today has allowed for an avenue into cleaner forms of energy that will not only protect our environment but also continue to advance our society. Among the many forms of clean energy, electric vehicles (EV) have the potential to mitigate our consumption of fossil fuels in vehicle transportation industries. In the U.S. for 2021, EVs account for approximately 700,000 registrations. That number is projected to increase to 2 million by 2030. Although EVs do reduce the number of emissions when compared to an internal combustion engine, they do however shift the responsibility to utility companies to …

Characterization Of Electrophoretic Deposited Zinc Oxide Nanopartices For The Fabrication Of Next-Generation Nanoscale Electronic Applications, Fawwaz Abduh A. Hazzazi

LSU Doctoral Dissertations

Several reports state that it is crucial to analyze nanoscale semiconductor materials and devices with potential benefits to meet the need for next-generation nanoelectronics, bio, and nanosensors. The progress in the electronics field is as significant now, with modern technology constantly evolving and a greater focus on more efficient robust optoelectronic applications. This dissertation focuses on the study and examination of the practicality of Electrophoretic Deposition (EPD) of zinc oxide (ZnO) nanoparticles (NPs) for use in semiconductor applications.

The feasibility of several synthesized electrolytes, with and without surfactants and APTES surface functionalization, is discussed. The primary objective of this study …

Design And Commissioning Of An E-Beam Irradiation Beamline At The Upgraded Injector Test Facility At Jefferson Lab, Xi Li, Helmut Baumgart, Charles Bott, Gianluigi Ciovati, Shaun Gregory, Fay Hannon, Mike Mccaughan, Robert Pearce, Matthew Poelker, Hannes Vennekate, Shaoheng Wang

Electrical & Computer Engineering Faculty Publications

The Upgraded Injector Test Facility (UITF) at Jefferson Lab is a continuous-wave superconducting linear accelerator capable of providing an electron beam with energy up to 10 MeV. A beamline for electron-beam irradiation has been designed, installed and successfully commissioned at this facility, aimed at the degradation study of 1,4-dioxane and per- and polyfluoroalkyl substances (PFAS) in wastewater treatment. A solenoid with a peak axial magnetic field of up to 0.28 T and a set of raster coils were used to obtain a Gaussian beam profile with a transverse standard deviation of ∼15.0 mm at the target location. Monte-Carlo simulations using …

Noncontact Liquid Crystalline Broadband Optoacoustic Sensors, Hengky Chandrahalim, Michael T. Dela Cruz

AFIT Patents

An optoacoustic sensor includes a liquid crystal (LC) cell formed between top and bottom plates of transparent material. A transverse grating formed across the LC cell that forms an optical transmission bandgap. A CL is aligned to form a spring-like, tunable Bragg grating that is naturally responsive to external agitations providing a spectral transition regime, or edge, in the optical transmission bandgap of the transverse grating that respond to broadband acoustic waves. The optoacoustic sensor includes a narrowband light source that is oriented to transmit light through the top plate, the LC cell, and the bottom plate. The optoacoustic sensor …

Imnets: Deep Learning Using An Incremental Modular Network Synthesis Approach For Medical Imaging Applications, Redha A. Ali, Russell C. Hardie, Barath Narayanan Narayanan, Temesguen Messay

Electrical and Computer Engineering Faculty Publications

Deep learning approaches play a crucial role in computer-aided diagnosis systems to support clinical decision-making. However, developing such automated solutions is challenging due to the limited availability of annotated medical data. In this study, we proposed a novel and computationally efficient deep learning approach to leverage small data for learning generalizable and domain invariant representations in different medical imaging applications such as malaria, diabetic retinopathy, and tuberculosis. We refer to our approach as Incremental Modular Network Synthesis (IMNS), and the resulting CNNs as Incremental Modular Networks (IMNets). Our IMNS approach is to use small network modules that we call SubNets …

One-Stage Blind Source Separation Via A Sparse Autoencoder Framework, Jason Anthony Dabin

Dissertations

Blind source separation (BSS) is the process of recovering individual source transmissions from a received mixture of co-channel signals without a priori knowledge of the channel mixing matrix or transmitted source signals. The received co-channel composite signal is considered to be captured across an antenna array or sensor network and is assumed to contain sparse transmissions, as users are active and inactive aperiodically over time. An unsupervised machine learning approach using an artificial feedforward neural network sparse autoencoder with one hidden layer is formulated for blindly recovering the channel matrix and source activity of co-channel transmissions. The BSS sparse autoencoder …

X-Band Phased-Array Weather-Radar Polarimetry Testbed, William Heberling Iv

Doctoral Dissertations

Phased-array weather radar have potential to replace reflector dish radar in major weather radar networks such as NEXRAD, providing faster update times and greater scan flexibility. However, the use of electronic scanning introduces polarization errors on weather radar measurables, requiring polarimetric bias calibration. The sources of polarimetric bias have been described theoretically, but experimental verification is still limited. Additionally, no standard method of calibration for polarimetric bias exists for phased-arrays. Therefore, the University of Massachusetts Amherst (UMass) presents a fully operational X-Band phased-array weather radar polarimetric testbed. The testbed evaluates the calibration of a planar dual-polarization X-band phased-array radar through …

Hinged Temperature-Immune Self-Referencing Fabry–Pérot Cavity Sensors, Jeremiah C. Williams, Hengky Chandrahalim

AFIT Patents

A passive microscopic Fabry-Pérot Interferometer (FPI) sensor includes a three-dimensional microscopic optical structure formed on a cleaved tip of the optical fighter using a two-photon polymerization process on a photosensitive polymer by a three-dimensional micromachining device. The three-dimensional microscopic optical structure having a hinged optical layer pivotally connected to a distal portion of a suspended structure. A reflective layer is deposited on a mirror surface of the hinged optical layer while in an open position. The hinged optical layer is subsequently positioned in the closed position to align the mirror surface to at least partially reflect a light signal back …

Grid-Connected Renewable Energy Systems For Residential Hvac Load Management, Oscar Samuel Acosta

Open Access Theses & Dissertations

With an ongoing mission of utility operators to maintain a resilient and reliable power grid in the face of continuously increasing load demand, it is essential that advancements be made in developing both technology and methodology to help account for the increasing energy requirements. According to the U.S. Department of Energy (DOE) and Energy Information Administration (EIA), the residential end-use sector alone counted for 22% of all electricity used in the U.S. in 2020. Of this, approximately 32% of household electricity load is the direct result of air conditioning and space heating units (HVAC). One way to account for this …

Co-Planar Waveguides For Microwave Atom Chips, Morgan Logsdon

Undergraduate Honors Theses

This thesis describes research to develop co-planar waveguides (CPW) for coupling microwaves from mm-scale coaxial cables into 50 μm-scale microstrip transmission lines of a microwave atom chip. This new atom chip confines and manipulates atoms using spin-specific microwave AC Zeeman potentials and is particularly well suited for trapped atom interferometry. The coaxial-to-microstrip coupler scheme uses a focused CPW (FCPW) that shrinks the microwave field mode while maintaining a constant 50 Ω impedance for optimal power coupling. The FCPW development includes the simulation, design, fabrication, and testing of multiple CPW and microstrip prototypes using aluminum nitride substrates. Notably, the FCPW approach …

The Bracelet: An American Sign Language (Asl) Interpreting Wearable Device, Samuel Aba, Ahmadre Darrisaw, Pei Lin, Thomas Leonard

Chancellor’s Honors Program Projects

No abstract provided.

Deep Learning For Load Forecasting With Smart Meter Data: Online And Federated Learning, Mohammad Navid Fekri

Electronic Thesis and Dissertation Repository

Electricity load forecasting has been attracting increasing attention because of its importance for energy management, infrastructure planning, and budgeting. In recent years, the proliferation of smart meters has created new opportunities for forecasting on the building and even individual household levels. Machine learning (ML) has achieved great successes in this domain; however, conventional ML techniques require data transfer to a centralized location for model training, therefore, increasing network traffic and exposing data to privacy and security risks. Also, traditional approaches employ offline learning, which means that they are only trained once and miss out on the possibility to learn from …

Microscopic Nuclei Classification, Segmentation, And Detection With Improved Deep Convolutional Neural Networks (Dcnn), Md Zahangir Alom, Vijayan K. Asari, Anil Parwani, Tarek M. Taha

Electrical and Computer Engineering Faculty Publications

Background Nuclei classification, segmentation, and detection from pathological images are challenging tasks due to cellular heterogeneity in the Whole Slide Images (WSI). Methods In this work, we propose advanced DCNN models for nuclei classification, segmentation, and detection tasks. The Densely Connected Neural Network (DCNN) and Densely Connected Recurrent Convolutional Network (DCRN) models are applied for the nuclei classification tasks. The Recurrent Residual U-Net (R2U-Net) and the R2UNet-based regression model named the University of Dayton Net (UD-Net) are applied for nuclei segmentation and detection tasks respectively. The experiments are conducted on publicly available datasets, including Routine Colon Cancer (RCC) classification and …

Towards Improved Inertial Navigation By Reducing Errors Using Deep Learning Methodology, Hua Chen, Tarek M. Taha, Vamsy P. Chodavarapu

Electrical and Computer Engineering Faculty Publications

Autonomous vehicles make use of an Inertial Navigation System (INS) as part of vehicular sensor fusion in many situations including GPS-denied environments such as dense urban places, multi-level parking structures, and areas with thick tree-coverage. The INS unit incorporates an Inertial Measurement Unit (IMU) to process the linear acceleration and angular velocity data to obtain orientation, position, and velocity information using mechanization equations. In this work, we describe a novel deep-learning-based methodology, using Convolutional Neural Networks (CNN), to reduce errors from MEMS IMU sensors. We develop a CNN-based approach that can learn from the responses of a particular inertial sensor …

Volitional Control Of Lower-Limb Prosthesis With Vision-Assisted Environmental Awareness, S M Shafiul Hasan

FIU Electronic Theses and Dissertations

Early and reliable prediction of user’s intention to change locomotion mode or speed is critical for a smooth and natural lower limb prosthesis. Meanwhile, incorporation of explicit environmental feedback can facilitate context aware intelligent prosthesis which allows seamless operation in a variety of gait demands. This dissertation introduces environmental awareness through computer vision and enables early and accurate prediction of intention to start, stop or change speeds while walking. Electromyography (EMG), Electroencephalography (EEG), Inertial Measurement Unit (IMU), and Ground Reaction Force (GRF) sensors were used to predict intention to start, stop or increase walking speed. Furthermore, it was investigated whether …

Considerations For Radio Frequency Fingerprinting Across Multiple Frequency Channels, Jose A. Gutierrez Del Arroyo, Brett J. Borghetti, Michael A. Temple

Faculty Publications

Radio Frequency Fingerprinting (RFF) is often proposed as an authentication mechanism for wireless device security, but application of existing techniques in multi-channel scenarios is limited because prior models were created and evaluated using bursts from a single frequency channel without considering the effects of multi-channel operation. Our research evaluated the multi-channel performance of four single-channel models with increasing complexity, to include a simple discriminant analysis model and three neural networks. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models can lead to a deterioration in performance from …

The Surface Conditions Of Spacecraft Panels May Significantly Affect Spacecraft Survivability, Trace Taylor

Research on Capitol Hill

USU junior Trace grew up in Brigham City and studies physics and electrical engineering. The majority of spacecraft failure is caused by electron charging on the outer surfaces of the craft. Additionally, contaminants on the craft can cause a film over surface panels, increasing the problem. Trace is studying how roughness on panels can mitigate this contamination as it affects the charging that can lead to craft failure. This research will help determine what optimal panel materials should be used in future spacecraft construction. Trace started research almost as soon as he came to campus in his freshman year, and …

A Comparison Of Sporadic-E Occurrence Rates Using Gps Radio Occultation And Ionosonde Measurements, Rodney Carmona, Omar A. Nava, Eugene V. Dao, Daniel J. Emmons

Faculty Publications

Sporadic-E (E_s) occurrence rates from Global Position Satellite radio occultation (GPS-RO) measurements have shown to vary by a factor of five between studies, motivating the need for a comparison with ground-based measurements. In an attempt to find accurate GPS-RO techniques for detecting E_s formation, occurrence rates derived using five previously developed GPS-RO techniques are compared to ionosonde measurements over an eight-year period from 2010–2017. GPS-RO measurements within 170 km of a ionosonde site are used to calculate E_s occurrence rates and compared to the ground-truth ionosonde measurements. The techniques are compared individually for each ionosonde site …