Engineering Commons^™

Advanced Analytics In Smart Manufacturing: Anomaly Detection Using Machine Learning Algorithms And Parallel Machine Scheduling Using A Genetic Algorithm, Meiling He

Theses and Dissertations

Industry 4.0 offers great opportunities to utilize advanced data processing tools by generating Big Data from a more connected and efficient data collection system. Making good use of data processing technologies, such as machine learning and optimization algorithms, will significantly contribute to better quality control, automation, and job scheduling in Smart Manufacturing. This research aims to develop a new machine learning algorithm for solving highly imbalanced data processing problems, implement both supervised and unsupervised machine learning auto-selection frameworks for detecting anomalies in smart manufacturing, and develop a genetic algorithm for optimizing job schedules on unrelated parallel machines. This research also …

Cell Bioprinting: A Novel Approach For Alpha Cell To Beta Cell Transdifferentiation, Atzimba Casas

Open Access Theses & Dissertations

Diabetes is a chronic disease that occurs in the body when the pancreas fails to either produce insulin (TID) or does not effectively use the insulin produced (TIID) and poses further health complications as well as an insurmountable economic impact.[1] Type I diabetes is an autoimmune disease characterized by a deficient amount of insulin production on account of the body’s immune system destroying its own β-cells.[2] Current diabetes treatment methods include the administration of insulin via injections or islet transplantation therapy. However, although both are viable options, they come with limitations that make the managing of this disease difficult. It …

Development Of Metaheuristic Algorithms For The Efficient Allocation Of Power Flow Control Devices, Eduardo Jose Castillo Fatule

Open Access Theses & Dissertations

Modern energy grids have become extremely complex systems, requiring more variable and active flow control. As a remedy to this, Distributed Flexible AC Transmission Systems (D-FACTS) are cost-efficient devices used to mitigate power flow congestion and integrate renewable energies. The objective of this research is then to propose an efficient multiple objective evolutionary algorithm to solve a stochastic model for D-FACTS allocation, which aims to optimize various objectives related to cost, grid health, and environmental impacts. The model was implemented on a modified RTS-96 test system, and the results show that optimally allocating D-FACTS modules using the proposed model can …

Adopting Accessibility Guidelines For Videogames To Collectible Card Games, Cooper Biancur

Electrical Engineering Master’s Theses

The development of accessible video games has been discussed through multiple publications in the 21st century; however, little to no attention has been given to non-electronic gaming. Like video games, Collectible Card Games (CCG) have also gained massive popularity, but no accessible guidelines have been created to help the disabled better play them. The need for inclusion in gaming is critical because it can act as a medium for social interaction and a learning tool for teaching. Today offers numerous technologies that can help those with disabilities, such as microcomputers, Artificial Intelligence (AI), Optical Character Recognition (OCR), and Text to …

Spatiotemporal Pattern Detection With Neuromorphic Circuits, Robert C. Ivans

Boise State University Theses and Dissertations

In this dissertation, neuromorphic circuits are used to implement spiking neural networks in order to detect spatiotemporal patterns. Unsupervised training and detection-by-design techniques were used to attain the appropriate connectomes and perform pattern detection.

Unsupervised training was performed by feeding random digital spikes with a repeating embedded spatiotemporal pattern to a spiking neural network composed of leaky integrate-and-fire neurons and memristor-R(t) element circuits which implement spike-timing-dependent plasticity learning rules.

Detection-by-design was achieved using neuromporphic circuits and digital logic gates. When detection-by-design was achieved using both neuromorphic circuits and digital logic gates, a network was created of spatiotemporal pattern detector circuits, …

A Wireless, Multi-Channel Printed Capacitive Strain Gauge System For Structural Health Monitoring, Kshama Lakshmi Ranganatha

Boise State University Theses and Dissertations

Structural health monitoring of soft structural textiles plays a key role within the space industry to ensure the safety and integrity of space habitats, parachutes, and decelerator systems. Strain monitoring could be an effective means to evaluate structural integrity, but conventional monitoring systems are not suitable because they are intended for large, rigid structures. To overcome the limitations of rigid sensors, we recently proposed using printed capacitive strain gauges (CSGs) on flexible substrates to monitor the structural health of soft structure materials. Here, we present a strategy and implementation of a wireless, multi-channel readout system for distributed monitoring of soft …

Network Management, Optimization And Security With Machine Learning Applications In Wireless Networks, Mariam Nabil

Theses and Dissertations

Wireless communication networks are emerging fast with a lot of challenges and ambitions. Requirements that are expected to be delivered by modern wireless networks are complex, multi-dimensional, and sometimes contradicting. In this thesis, we investigate several types of emerging wireless networks and tackle some challenges of these various networks. We focus on three main challenges. Those are Resource Optimization, Network Management, and Cyber Security. We present multiple views of these three aspects and propose solutions to probable scenarios. The first challenge (Resource Optimization) is studied in Wireless Powered Communication Networks (WPCNs). WPCNs are considered a very promising approach towards sustainable, …

Design, Control, And Development Of A Multilevel Converter Medium Voltage Ac To Low Voltage Dc For Fleet Electric Vehicle Charge Station, Garry Jean-Pierre

Theses and Dissertations

There is a shift in the technology of vehicles from gas and diesel engines to electric vehicles(EVs). Approximately ten million EVs were available globally in 2020 and it is projected that number will reach 145 million by 2030. To power the increasing number of EVs, the number of EV charging stations is growing at a significant rate. In order to provide flexibility and longer driving ranges to customers, the trend is to install DC fast charging stations. These chargers demand high power at low voltage, which our existing electrical distribution system cannot accommodate without major upgrades. Currently, bulky transformers are …

Common-Mode Modeling Of Neutral Point Clamped Converter Based Dual Active Bridge, Ryan James Olson

Theses and Dissertations

Modern power converters designed with wide-bandgap semiconductors are known to generate substantial conducted electromagnetic interference as a side effect of high edge rate and high frequency switching. With the advancement in power electronic converters, the significant EMI challenges need to be addressed for distribution level power systems. The goal is to provide a computationally efficient method of EMI characterization for conducted emissions for this future generation of power distribution systems. The first step in making this possible is through creating an accurate EMI characterization platform for the neutral point clamped dual active bridge. In this thesis, a formalized common-mode modeling …

Distributed And Optimal State Estimation For Heterogeneous Dynamic Systems Operating Within A Strongly Connected Network, Matthew Howard

Electronic Theses and Dissertations, 2020-2023

This dissertation is aimed at developing optimal and distributed state estimation algorithms for a team of cooperating nodes with the goal of improving accuracy through local sharing of relevant information. The nodes are assumed to be individually equipped with heterogeneous sensors for measuring a common target which can be dynamic and time-varying. Additionally, the nodes are assumed to be connected through a dynamically changing communication network modeled as a sequence of strongly connected digraphs allowing for local communication and distributed interactions. Using the data sharing afforded by the communication network, a weighted average state estimate consensus can be found across …

Acoustoelectric Amplification In Piezoelectric-Silicon Micromachined Lamb Wave Devices, Hakhamanesh Mansoorzare

Electronic Theses and Dissertations, 2020-2023

In this dissertation, heterostructured micro-acoustic devices are explored that leverage the interactions between acoustic phonons and electrons to enable radio frequency (RF) signal amplification or attenuation. Thin films of piezoelectric and semiconductor material are tailored into a heterostructure that allows for a strong acoustoelectric (AE) effect due to the combination of high electromechanical coupling and high electron drift velocity in said films respectively. In such devices, the relative electron drift and acoustic velocities could determine whether the RF signal undergoes AE gain or loss, rendering the device non-reciprocal. This is a highly sought-after property for building isolators and circulators which …

Directional Spectral Solar Energy For Building Performance: From Simulation To Cyber-Physical Prototype, Joseph Del Rocco

Electronic Theses and Dissertations, 2020-2023

The original research and development in this dissertation contributes to the field of building performance by actively harnessing a wider spectrum of directional solar radiation for use in buildings. Solar radiation (energy) is often grouped by wavelength measurement into the spectra ultraviolet (UV), visible (light), and short and long-wave infrared (heat) on the electromagnetic spectrum. While some of this energy is directly absorbed or deflected by our atmosphere, most of it passes through, scatters about, and collides with our planet. Modern building performance simulations, tools, and control systems often oversimplify this energy into scalar values for light and heat, when …

Performance Loss Rate And Temperature Modeling In Predictive Energy Yield Programs For Utility-Scale Solar Power Plants, Katelynn M. Dinius

Master's Theses

The Gold Tree Solar Farm, designed by REC Solar, has a rated output power of 4.5 MW and began operation in 2018 to provide electricity to Cal Poly’s campus. Gold Tree Solar Farm site terrain consists of rolling hills and uneven slopes. The uneven typography results in interrow shading, requiring a modified tracking control algorithm to maximize power production. Predicting a utility solar field’s lifetime energy yield is a critical step in assessing project feasibility and calculating project revenue. The MATLAB-based predictive power model developed for this field overpredicted power in the middle of the day. The purpose of this …

Augmented Communications : A Solution For Overcoming High Spatial Correlation Of The Massive-Miso Vlc Channel, Monette Khadr

Legacy Theses & Dissertations (2009 - 2024)

A key challenge for future wireless networks is to come upon a riveting compromise between spectral efficiency, complexity, and energy efficiency. The challenge is also intensified due to the pace at which the Internet-of-Things (IoT) technology is arriving, causing an upheaval to pre-existing network infrastructures in terms of elevating spectrum scarcity. To keep pace with the exploding data demand forecasts, a circumvention is required. One realization is by utilizing the high-band spectrum and the rich body of knowledge on multiple-input multiple-output (MIMO) technologies. One of the prominent high frequency technologies is visible light communications (VLC). VLC provide a large unregulated …

Low Power Reconfigurable Antenna With Continuous Beam Steering Capability, Glendyn Darryn King

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Each year, the number of wireless devices increases, and the size of the devices’ data is increases, 8k video streaming, for example. More and more bandwidth is needed for wireless networks to meet these growing demands. Higher frequencies allow for more bandwidth; however, using higher frequencies comes with some trade-offs. The higher the wireless signal frequency, the shorter the distance it can travel before the signal strength becomes too weak for the receiver to pick it up. One solution might be to increase the power of the signal provider, but that would waste a lot of energy. Most antennas radiate …

Small Satellite Development And Mission Design For Low Earth Orbit And Geostationary Transfer Orbit Missions, Ashiqur Rahman

Open Access Theses & Dissertations

Aerospace Center formerly known as cSETR at UTEP is a NASA funded center of excellence in aerospace and exploration, focusing on strategic capability development in propulsion and robotic lander, lunar surface exploration, and small spacecraft technologies. To support the center’s mission for space exploration and to demonstrate advancements in small satellite technologies, the Aerospace Center at The University of Texas at El Paso is on track to develop and launch several CubeSats in next few years. A CubeSat is a small satellite that has become popular in recent years due to their low cost, low complexity and short development timeline. …

Airspace Integration Of New Entrants And Safety Risk Management Models, Fadjimata Issoufou Anaroua

Doctoral Dissertations and Master's Theses

In recent years, the demand for airspace access of Unmanned Aerial Systems (UAS) increased significantly and is continuously increasing for different altitude-types UAS. A similar evolution is expected from Commercial Space Operations (CSO) in the next years. These aviation/aerospace systems will need to be seamlessly integrated into the National Airspace System (NAS), at their operational altitude levels, and accounted for from all perspectives, including proactively addressing their safety hazards. This thesis captures the requirements for the new entrants’ integration, and then identifies and analyzes the safety risks added to the NAS operations by its new entrants, the future omnipresent UAS …

Nevr: Learning Continuous Neural Video Representation With Local Feature Codes For Video Interpolation, Wentao Shangguan

McKelvey School of Engineering Theses & Dissertations

Video frame interpolation aims to synthesis a non-exists intermediate frame guided by two successive frames. Recently, some work shows excellent results in learning continuous representation of temporally-varying 3D objects with neural field (NF), which could be used for interpolating the original video. However, these methods require several videos from different viewing angles, the information of camera poses, learning for each specific scene, and achieving sub-optimal results for video frame interpolation. To this end, we propose a new learning neural field representation-based model, Neural Video Representation (NeVR) to learn a continuous representation of videos for high-quality video interpolation. Unlike the traditional …

A Deep Recurrent Neural Network With Iterative Optimization For Inverse Image Processing Applications, Masaki Ikuta

Theses and Dissertations

Many algorithms and methods have been proposed for inverse image processing applications, such as super-resolution, image de-noising, and image reconstruction, particularly with the recent surge of interest in machine learning and deep learning methods.

As for Computed Tomography (CT) image reconstruction, the most recently proposed methods are limited to image domain processing, where deep learning is used to learn the mapping between a true image data set and a noisy image data set in the image domain. While deep learning-based methods can produce higher quality images than conventional model-based algorithms, these methods have a limitation. Deep learning-based methods used in …

Deep Learning-Guided Prediction Of Material’S Microstructures And Applications To Advanced Manufacturing, Jianan Tang

All Dissertations

Material microstructure prediction based on processing conditions is very useful in advanced manufacturing. Trial-and-error experiments are very time-consuming to exhaust numerous combinations of processing parameters and characterize the resulting microstructures. To accelerate process development and optimization, researchers have explored microstructure prediction methods, including physical-based modeling and feature-based machine learning. Nevertheless, they both have limitations. Physical-based modeling consumes too much computational power. And in feature-based machine learning, low-dimensional microstructural features are manually extracted to represent high-dimensional microstructures, which leads to information loss.

In this dissertation, a deep learning-guided microstructure prediction framework is established. It uses a conditional generative adversarial network (CGAN) …

Analysis Of Deep Learning Methods For Wired Ethernet Physical Layer Security Of Operational Technology, Lucas Torlay

All Theses

The cybersecurity of power systems is jeopardized by the threat of spoofing and man-in-the-middle style attacks due to a lack of physical layer device authentication techniques for operational technology (OT) communication networks. OT networks cannot support the active probing cybersecurity methods that are popular in information technology (IT) networks. Furthermore, both active and passive scanning techniques are susceptible to medium access control (MAC) address spoofing when operating at Layer 2 of the Open Systems Interconnection (OSI) model. This thesis aims to analyze the role of deep learning in passively authenticating Ethernet devices by their communication signals. This method operates at …

Split-Horizon Dual-Stage Dispatch Scheme For A Standalone Microgrid, Aslam Amir

Theses

The advent of microgrids has prompted plenty of studies into its design, control, protection, and implementation, with several operational as well as pilot systems being commissioned worldwide. This necessitates the development of hardware and software for the Energy Management System, the supervisory controller in a microgrid. Hence, this thesis provides a novel dual-stage dispatch scheme for the Energy Management System of a Standalone microgrid by “splitting” the dispatch time horizon into four equal quarters to facilitate better usage of power forecast accuracies. The two stages include Unit Commitment/Scheduling and Economic Dispatch for the dispatchable Distributed Energy Resources based on renewable …

Ac/Dc Led Light Bulb Adaptor With Internal Rechargeable Batteries, Esteban A. Rubio, Matthew R. Delaby

Electrical Engineering

To make a sustainable product and provide a temporary source of lights on during emergencies, this project aims to create a small unit that can be attached to a light bulb of any variety and connect it to a socket. Within the device is a rechargeable battery that, when the device is not connected to an 120V AC, 240V AC, or 48V DC power source, will power the light for a short period of time. The battery can be charged during the standard operation of the device and can be powered by the US and EU standard outlet voltages as …

Deep Learning Based Speech Enhancement And Its Application To Speech Recognition, Ju Lin

All Dissertations

Speech enhancement is the task that aims to improve the quality and the intelligibility of a speech signal that is degraded by ambient noise and room reverberation. Speech enhancement algorithms are used extensively in many audio- and communication systems, including mobile handsets, speech recognition, speaker verification systems and hearing aids. Recently, deep learning has achieved great success in many applications, such as computer vision, nature language processing and speech recognition. Speech enhancement methods have been introduced that use deep-learning techniques, as these techniques are capable of learning complex hierarchical functions using large-scale training data. This dissertation investigates the deep learning …

Study Of Mos2/High-K Interface And Implementation Of Mos2 Based Memristor For Neuromorphic Computing Applications, Durjoy Dev

Electronic Theses and Dissertations, 2020-2023

The scientific world is witnessing an unprecedented triumph of artificial neural network (ANN)- a computing system inspired by the biological neural network. With the enthralling quest for Internet of Everything (IoE), it is expected to have an unparalleled dominance of ANN in our day-to-day life. In recent times, memristor has come as an emerging candidate to realize ANN through emulating biological synapse and neuron behavior. Molybdenum disulfide (MoS2), one well-known two-dimensional (2D) transition metal dichalcogenides (TMDCs), has drawn interest for high speed, flexible, low power electronic devices since it has a tunable bandgap, reasonable carrier mobility, high Young's modulus, and …

Spectral Dependence Of Deep Subwavelength Metallic Apertures In The Mid-Wave Infrared, Heath Gemar

Electronic Theses and Dissertations, 2020-2023

For two decades, extraordinary optical transmission (EOT) has amplified exploration into subwavelength systems. Researchers have previously suggested exploiting the spectrally selective electromagnetic field confinement of subwavelength cavities for multispectral detectors. Utilizing the finite-difference frequency domain (FDFD) method, we examine electromagnetic field confinement in both 2-dimensional and 3-dimensional scenarios from 2.5 to 6 microns (i.e., mid-wave infrared or MWIR). We explore the trade space of deep subwavelength cavities and its impact on resonant enhancement of the electromagnetic field. The studies provide fundamental understanding of the coupling mechanisms allowing for prediction of resonant spectral behavior based on cavity geometry and material properties. …

Tinyml For Gait Stride Classification, Priyanka Rajendra

UNLV Theses, Dissertations, Professional Papers, and Capstones

Human gait classification and analysis become very important when a person has been diagnosed with a neurological disorder or has suffered an injury which has affected their ability to walk correctly. Gait strides are an important parameter to be studied as it helps the doctor to diagnose any underlying gait condition and evaluate what type of treatment suits the best for the patient’s recovery. Studying gait strides also helps athletes to improve their performance.In today’s world, machine learning has emerged as one of the most widely used technology for classification and analysis of gait characteristics. TinyML is a field of …

Designs And Outcomes Of Transcranial Magnetic Stimulation (Tms) And Repetitive Transcranial Magnetic Stimulation (Rtms) Circuits, Daniel Senda

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis reports the design and outcomes of several circuits intended for transcranial magnetic stimulation (TMS) and repetitive transcranial magnetic stimulation (rTMS) research. In simple terms, TMS circuits are composed of four main blocks: high voltage power source, energy storage bank, control switch, and coil. Each one of these blocks has characteristics that influence how well the circuit will perform for TMS procedures. A successful TMS research circuit must have the ability to emit controlled electromagnetic pulses through a coil connected to it. For the first block, voltages ranging from 50 V to 2 kV were used. In the second …

Simulation And Fabrication Of All Oxide-Based Ito/Tio2/Cuo/Au Heterostructure For Solar Cell Applications, Sajal Islam

MSU Graduate Theses

Oxide heterostructures have drawn great attention lately, due to their environment-friendly properties and potential applications in optoelectronic devices. In this work, a simulation study of a heterojunction solar cell was performed with SCAPS (a solar cell simulator) using TiO2 as an n-type and CuO as a p-type layer. The thickness and the dopant-dependent simulations have shown that the solar cell operates at a maximum efficiency of 19.2% when the thickness of the TiO2/CuO layers is chosen 1.4µm/1.2µm compared to the 11.5% efficiency when FTO is replaced with ITO. An indium-doped tin oxide (ITO) vs fluorine-doped tin oxide (FTO) comparison study …

Distributed Estimation And Inverse Reinforcement Learning For Multi-Agent Systems, Bosen Lian

Electrical Engineering Dissertations

Consensus-based distributed Kalman filters for estimation with multiple targets have attracted considerable attention. Most of the existing Kalman filters use the average consensus approach, which tends to have a low convergence speed. They also rarely consider the impacts of limited sensing range and target mobility on the information flow topology. The robustness properties, i.e., gain margins and phase margins of distributed Kalman filtering algorithms are still open problems. In the interactions of controlled dynamical agents, it is often assumed that the agents are "rational" in the sense of attempting to act in such a way as to optimize some prescribed …