Electrical and Computer Engineering Commons^™

Adaptive Gps Antenna Array Beam Nulling Effectiveness Under Varying Antenna Element Positioning, Aadesh Neel

Electrical and Computer Engineering ETDs

Global Positioning System (GPS) is an essential part of modern life but is susceptible to same frequency jamming. GPS jamming can add excessive noise to a received low power signal and have the capability to change or completely distort information being sent through the GPS signal. Adaptive antenna arrays have long since been a solution to mitigating GPS jamming via beamnulling algorithms. However, there is little research on the effectiveness of these beamnulling algorithms under varying element positioning. In this work, an adaptive antenna array, consisting of Right-Hand Circularly Polarized (RHCP) nearly square GPS antenna elements, was constructed and tested …

Optimizing High-Performance Computing Design: The Impacts Of Bandwidth And Topology Across Workloads For Distributed Shared Memory Systems, Jonathan A. Milton

Electrical and Computer Engineering ETDs

With the complexity of high-performance computing designs continuously increasing, the importance of evaluating with simulation also grows. One of the key design aspects is the network architecture; topology and bandwidth greatly influence the overall performance and should be optimized. This work uses simulations written to run in the Structural Simulation Toolkit software framework to evaluate a variety of architecture configurations, identify the optimal design point based on expected workload, and evaluate the changes with increased scale. The results show that advanced topologies outperform legacy architectures justifying the additional design complexity; and that after a certain point increasing the bandwidth provides …

Data-Driven Porosity Prediction For Directed Energy Deposition, Georgia E. Kaufman

Electrical and Computer Engineering ETDs

Stochastic flaw formation leading to poor print quality is a major obstacle to the utility of directed energy deposition (DED), a laser and metal powder-based additive manufacturing technology for construction and repair of custom metal parts. While melt pool temperature variability is known to be a major factor in flaw formation, control schemes to decrease flaw formation are limited by a lack of physics-based models that fully and accurately describe DED. In this work, a stochastic reachability analysis with a data-driven model based on thermal images of the melt pool was conducted to determine the likelihood of violating melt pool …

Progression Of Surface Flashover In Vacuum With Polymer Insulators, Kimberly M. Faris

Electrical and Computer Engineering ETDs

In pulsed power devices, an insulator is needed to isolate the transmission line from vacuum chambers. Vacuum is used as the insulator because it contains no atoms. Since it is being used as an insulator in these pulsed power insulation systems, surface flashover in vacuum is one of the most extensively studied areas since it being the greatest constraint in providing power due to dielectrics that are not able to sustain the voltage the system is operating under. These dielectrics are used in between high voltage electrodes in various pulsed power applications as electrical insulators to limit the electric current …

Reinforcement Learning-Based Resilience And Decision Making In Cyber-Physical Systems, Fisayo Sangoleye

Electrical and Computer Engineering ETDs

Cyber-physical systems (CPS) transform how humans interact with technology by integrating sensing, computation, networking, and control with physical processes to facilitate smart services and innovative applications in our environments. Recent advances in CPS have led to rapid growth in the amount of information constantly generated by people, systems, and processes. Most of this information, however, is underutilized due to the lack of efficient information utilization and decision-making techniques. Also, the increasing interconnectivity of CPSs presents security risks that, if left unaddressed, could be highly disruptive to systems, processes, and economies. In this dissertation, we present a study and proposal of …

Modeling And Characterization Of Cmos Logic Gates Under Large Signal Rf Injection, Zahra Abedi

Electrical and Computer Engineering ETDs

This research addresses the issue of electromagnetic interference (EMI) in digital electronics, which can cause undesired behavior in active electronic components and systems. As technology advances and transistor size decreases, devices become more susceptible to EMI. In addition, as voltage is scaled down to save energy, silicon chips become more susceptible to soft errors. While there are several ways to mitigate the impact of severe EMI on digital systems, minimal electromagnetic coupling can still cause significant problems. We develop analytical models to predict device upset due to EMI. The study focuses on identifying vulnerable parameters of operation related to device …

Incentives To Learn: A Location-Based Federated Learning Model, Ryan Kilpatrick-Morrison Brown

Electrical and Computer Engineering ETDs

Federated Learning (FL) effectiveness depends, among others, on the quality and quantity of the training data and process realized at the end computing nodes. In this paper, we introduce a novel location-based federated learning model, enabled by a low-cost and fast deployable Reconfigurable Intelligent Surfaces (RIS) - based approach that allows to accurately determine the distributed computing nodes’ positions. Furthermore, in order to train a global model to support different types of smart city applications, while considering two types of servers, offering a prime and common service, respectively, under different costs, the proposed location-based FL model is complemented by an …

Machine Learning Based Prediction Models For Silicon Heterojunction Solar Cell Optimization, Rahul Jaiswal

Electrical and Computer Engineering ETDs

Silicon heterojunction solar cell of Heterojunction with Thin Intrinsic Layer (HIT) structure is a commercially available technology, and its market share will significantly increase by the next decade. With such a significant market share, any minor improvement in the device’s overall efficiency can be beneficial three folds - customer return on investment, industry revenue, and the overall carbon footprint (from manufacturing to recycling/ disposing of the device). Conventionally, device optimization for solar cells has been achieved using a hit & trial approach where multiple experiments are done to evaluate the best process conditions and device parameters. This approach has some …

Energy-Efficient Operation And Performance Optimization In Computing Systems, Nafis Irtija

Electrical and Computer Engineering ETDs

In the modern world, an expansive range of computing systems are being used, and in all of these systems, energy efficiency and performance optimization are of utmost significance. But performance optimization means different things for different computing systems. Because of their difference in nature, computing systems have different requirements for efficiency and optimization. Distributed systems such as smart-grid systems, edge-computing environments, and blockchain systems focus on the behavior of the agents. Thus, the application of network economics principles, such as Game Theory and Contract Theory, can improve the operation of these systems in a multitude of ways. On the other …

Vi Energy-Efficient Memristor-Based Neuromorphic Computing Circuits And Systems For Radiation Detection Applications, Jorge Iván Canales Verdial

Electrical and Computer Engineering ETDs

Radionuclide spectroscopic sensor data is analyzed with minimal power consumption through the use of neuromorphic computing architectures. Memristor crossbars are harnessed as the computational substrate in this non-conventional computing platform and integrated with CMOS-based neurons to mimic the computational dynamics observed in the mammalian brain’s visual cortex. Functional prototypes using spiking sparse locally competitive approximations are presented. The architectures are evaluated for classification accuracy and energy efficiency. The proposed systems achieve a 90% true positive accuracy with a high-resolution detector and 86% with a low-resolution detector.

Long-Term Human Video Activity Quantification In Collaborative Learning Environments, Venkatesh Jatla

Electrical and Computer Engineering ETDs

Research on video activity detection has mainly focused on identifying well-defined human activities in short video segments, often requiring large-parameter systems and extensive training datasets. This dissertation introduces a low-parameter, modular system with rapid inference capabilities, capable of being trained on limited datasets without transfer learning from large-parameter systems. The system accurately detects specific activities and associates them with students in real-life classroom videos. Additionally, an interactive web-based application is developed to visualize human activity maps over long classroom videos.

Long-term video activity detection in classrooms presents challenges, such as multiple simultaneous activities, rapid transitions, long-term occlusions, duration exceeding 15 …

Theory, Simulation, And Experiments On A Magnetically Insulated Transmission Line Terminated By A Bremsstrahlung Diode, Troy Clay Powell

Electrical and Computer Engineering ETDs

Foundational concepts necessary for power flow analysis of a self-magnetically insulated transmission line (MITL) are introduced in theoretical form and several developments to the theory are described. These include cold-cathode electron emission physics, self-magnetic insulation physics, self-limited MITL current, and relativistic secondary ion production from anode surfaces. Modeling these physics is performed using EMPIRE, an electromagnetic particle-in-cell code.

Self-limited MITL current theory described numerically by Pointon is developed here in analytic form and is then used to drive simulations to compare to experiments that were performed in EMPIRE. Carefully calibrated current sensors from HERMES-III experiments show good agreement with EMPIRE …

A Reconfigurable Architecture For Matrix Multiplication For Low Power Applications, Jeffrey Love

Electrical and Computer Engineering ETDs

This thesis presents a hardware architecture for performing matrix multiplication via a systolic array to reduce time complexity and power consumption. The proposed architecture, the Neural Network Accelerator (NNA), was designed in Verilog HDL to perform 8-bit multiplication to reduce the resources required to implement the NNA on low-power FPGAs. The NNA’s open architecture is designed to support radiation test for fault tolerant designs targeting space applications. Commercial hardware architecture information is not public knowledge, which led us to build our own matrix multiplication architecture so that we could later study its feasibility for space applications.

The NNA was compared …

3d Speaker Geometry Inference From Digital Video Using 2d Projective Geometry, Sebastian Alonso Janampa Rojas

Electrical and Computer Engineering ETDs

The thesis discusses the need for a 3D world model reconstruction from raw video frames using 2D projective geometry. We propose a computer-aided approach to reconstructing a 3D speaker geometry from classroom videos of students learning Python.

The proposed method uses a transformer model to detect line candidates. Once the users identify lines corresponding to three orthogonal directions, the method computes the three vanishing points and the camera matrix. The method identifies the student’s mouths based on face landmark detection. After the estimates of the projections of the students’ mouths on the table are verified by the users, the proposed …

Data-Driven Stochastic Optimal Control Using Hilbert Space Embeddings Of Distributions, Adam J. Thorpe

Electrical and Computer Engineering ETDs

Autonomous systems are increasingly being deployed in complex environments subject to real-world uncertainty. For such systems, it may be exceptionally difficult or even impossible to compute a simple mathematical model of the system--for instance due to the presence of human elements, complex mechanics or system dynamics, or learning-enabled components. Data-driven control has recently gained significant attention in this area, where observations taken from the system evolution are used to compute an implicit representation of the system that is amenable to analysis and control. However, data-driven algorithms for control present new challenges, and require new insights to enable their use. The …

Chance Constrained Stochastic Optimal Control Of Discrete Time Linear Stochastic Systems With Applications In Multi-Satellite Operations, Shawn Priore

Electrical and Computer Engineering ETDs

Stochastic disturbances arise in a variety of engineering applications. For tractability, Gaussian disturbances are often assumed. However, this may not always be valid, such as when a disturbance exhibits heavy-tailed or skewed phenomena. As autonomous systems become more ubiquitous, non-Gaussian disturbances will become more common due to the compounding effects of sensing, actuation, and external forces. Despite this, little has been done to develop formal methods that are both computationally efficient and allow for analytical assurances with non-Gaussian disturbances. Addressing convex polytopic set acquisition and non-convex collision avoidance chance constraints with quantile and moment-based reformulations, this dissertation proposes novel stochastic …

Long-Term Human Participation Detection Using A Dynamic Scene Analysis Model, Wenjing Shi

Electrical and Computer Engineering ETDs

The dissertation develops new methods for assessing student participation in long (>1 hour) classroom videos. First, the dissertation introduces the use of multiple image representations based on raw RGB images and AM-FM components to detect specific student groups. Second, a dynamic scene analysis model is developed for tracking under occlusion and variable camera angles. Third, a motion vector projection system identifies instances of students talking.

The proposed methods are validated using digital videos from the Advancing Out-of-school Learning in Mathematics and Engineering (AOLME) project. The proposed methods are shown to provide better group detection, and better talking detection at …

Evaluation Of The Dynamic Vision Sensor’S Photoreceptor Circuit For Infrared Event-Based Sensing, Zinah M. Alsaad

Electrical and Computer Engineering ETDs

For space surveillance applications, neuromorphic imaging is being studied as it may perform sensing and tracking tasks with less power and downstream datalink demand. The read-out of the event-based camera is made to only be sensitive to changes in the signals it receives from the photodetector, which results in a datastream of events indicating where and when changes in illumination occur. This is in contrast to the conventional framing camera, which produces images by essentially counting the electrons produced by light incident on each pixel’s photodetector. These cameras are commercially available with siliconbased detectors for applications involving visible wavelengths. However, …

Network Economics-Based Crowdsourcing In Online Social Networks, Natasha S. Kubiak

Electrical and Computer Engineering ETDs

This thesis addresses the challenge of user recruitment by various competing marketing agencies (MAs) in Online Social Networks. A labor economics approach, following the principles of contract theory, is devised to enable MAs to reveal the potential of each participating user to contribute a personalized level of quality and quantity of information to the crowdsourcing process. The MAs objective is to maximize their personal benefit, i.e., total utility obtained, given its budget. The latter optimization problem is formulated as a Generalized Colonel Blotto (GCB) game among the MAs, where each MA aims at incentivizing each user to report its information. …

Improved Experimental Validation Of An Electromagnetic Subcell Model For Narrow Slots With Depth, Michael Anthony Illescas

Electrical and Computer Engineering ETDs

The coupling of electromagnetic (EM) energy into a system can disrupt operation of essential electronics present within it. Metal enclosures are used to shield these systems from potentially harmful electromagnetic interference (EMI). Seams and gaps in such metal enclosures are minimized but unavoidable for reasons such as maintenance and repair. These seams and gaps create an entry point for EM energy to couple into the system. Entry points are often modeled by EM analysts as narrow slots defined by their length, width, and depth. The depth of these slots can become significant compared to the wavelength, introducing resonances associated with …

Effective Utilization Of Battery-Supercapacitor Hybrid Energy Storage Systems In Dc Nano-Grids, Seyyed Ali Ghorashi Khalil Abadi

Electrical and Computer Engineering ETDs

This PhD dissertation proposes novel control strategies to improve the flexibility and reliability of DC nanogrids by utilizing battery-supercapacitor hybrid energy storage systems (HESSs). The dissertation is divided into five sections, each addressing a specific challenge in DC nanogrids and proposing unique control strategies to overcome them. The proposed strategies include an adaptive control system for multiple nanogrids, a distributed power management strategy for a grid-connected DC nano-grid, a model predictive control strategy to improve voltage quality and stability in islanded DC nanogrids, a cloud HESS technology to improve voltage stability in clustered DC nanogrids, and a method of electric …

Measuring Energy Deposition From A Laser Induced Plasma In Air Which Generates Broadband Microwave Radiation, Anna M. Janicek

Electrical and Computer Engineering ETDs

The absorbed energy from a short pulse laser produced plasma is proportional to the magnitude of the acoustic wave the plasma launches; however, methods to resolve absolute energy from the acoustic signal are still being developed. This is the first report of quantitatively estimating the energy deposited by a femtosecond laser-induced plasma using a shock wave approximation from acoustic measurements. To further understand energy deposition mechanisms, two diagnostics, a single microphone which measures the acoustic signal propagation and an array of microphones which measure the changing acoustic signal longitudinally along the plasma were developed and implemented to measure the energy …

Autonomous Decision-Making In Interdependent Computing Systems Based On Artificial Intelligence, Georgios Fragkos

Electrical and Computer Engineering ETDs

With the advent of Artificial Intelligence (AI), the notion of autonomy, in terms of acting and thinking based on personal experience and judgment, has paved the way towards an autonomous decision-making future. This future can address the complex domain of the interdependent computing systems, whose main challenge is that they interact with each other with unpredictable and often unstable outcomes. It is crucial to envision and design this AI-driven autonomy for the reciprocal computing systems which cover a variety of use-cases ranging from the Internet of Things (IoT) to cybersecurity. This can be achieved by cloning the human decision-making process, …

Towards Robust Channel Dynamics: Photoionization In Nitrogen And Air, Justin K. Smith

Electrical and Computer Engineering ETDs

In present photoionization models used in computational studies, there is a lack of experimental data and several assumptions pertaining to photo-electron production in the gas. It is widely accepted that wavelengths between 98 nm and 102.5 nm are the primary contributors to photoionization of oxygen in an air plasma. In air, the collisional kinetics of nitrogen provide the photons, which results in the ionization of oxygen. Photoionization rate measurements from a low current corona discharge in synthetic air mixtures are reported to provide new empirical data. In addition, this provides a method to estimate the scaling parameter w/a. Concurrently, vacuum …

A Horn Microstrip Transmission Line For The Characterization And Investigation Of Multipactor At Various Pressure And Power, Shaho Hamadamin

Electrical and Computer Engineering ETDs

The University of New Mexico is designing, building, and testing a multipactor testbed based on a horn-microstrip transmission line to characterize and investigate the multipactor effect for a range of vacuum pressures and power. A horn-shaped, 50 Ω Al microstrip line is positioned inside a six-way cross stainless-steel chamber that is vacuumed down to low ~10−7 torr. A 100 𝑀𝑀𝑀𝑀𝑀𝑀 sine signal is fed to a rf amplifier with a bandwidth of 250 − 105 𝑀𝑀𝑀𝑀𝑀𝑀, that can reach 120𝑊𝑊 average (CW) power. Two multipacting detection methods, i.e., global, and local methods, are implemented for accurate multipaction detection. The former …

Protection And Control Of Electric Power Grid Under High Penetration Of Ders, Binod Prasad Poudel

Electrical and Computer Engineering ETDs

Modernizing power grids with communication-based technologies has introduced new challenges to the operation of the grid, especially when the communication network experiences failure or data is corrupted during the transfer. This issue is studied in this dissertation from the control and protection perspective. First, the cyber attack detection and mitigation of distributed control of microgrids is addressed when the distributed energy resources (DER) are exposed to false data injection attacks. A cyber-threat detection technique is proposed based on Kullback-Liebler divergence-based criterion. This criterion with a threshold can detect the misbehavior of a compromised DER control unit and, consequently, calculates the …

Reinforcement Learning-Based Demand Response Management In Smart Grid Systems With Prosumers, Jenilee Jao

Electrical and Computer Engineering ETDs

In this thesis, we introduce a reinforcement learning-based price-driven Demand Response Management (DRM) mechanism in smart grid systems consisting of prosumers. Our proposed approach accounts for the prosumers behavioral characteristics and models the emerging interactions among all the involved actors in the smart grid system, i.e., prosumers, Energy Management System (EMS) and utility companies. In particular, an off-policy reinforcement learning is introduced enabling the EMS to determine the optimal price that should be announced to the prosumers on an hourly-basis towards minimizing the overall systems cost. In this process, the utility companies hourly-based wholesale price and the prosumers energy generation …

Exploratory Analysis Of Machine Learning For Images: Methods And Applications, Meenu Ajith

Electrical and Computer Engineering ETDs

This research focuses on implementing four different applications of machine learning on images. The various categories of digital images considered for these applications are grayscale, RGB, and infra-red images. The first framework uses an unsupervised learning strategy for detecting fire and smoke from an infra-red image dataset. This problem was solved using a classical machine learning algorithm since the dataset was small and unlabeled. Next, a semi-supervised deep learning model was used for facial expression recognition. Here we detect emotions from a moderately large dataset containing labeled and unlabeled grayscale images. The third application focused on single image superresolution, which …

Gp-K: A Probabilistic Method For Hourly Day-Ahead Power Load Forecasting, Miguel A. Hombrados-Herrera

Electrical and Computer Engineering ETDs

The elevated costs that incur power grid stakeholders due to forecasting errors in power load demand have created the need for forecasting methods that provide accurate predictions and allow for assessing the reliability of their predictions. This thesis proposes a probabilistic forecasting method for multi-step ahead forecasting.

In particular, it presents a probabilistic method to perform a 24-hours-ahead power load forecasting that arises as the combination of Gaussian Process regressors with NMF (nonnegative matrix factorization) and integrates the advantages of both methods. Instead of training 24 independent processes for each hour of the predicted day, this work proposes to factorize …

Combinatorial Cnn Meta-Structures In Deep Learning Applications, Aswathy Rajendra Kurup

Electrical and Computer Engineering ETDs

The study aims in applying combinatorial structures motivated from the basic CNN in various applications. Convolutional neural networks (CNNs) have grown to be very popular in the field of deep learning. The ability of such networks to learn both Spatial and temporal characteristics in the data have helped in deploying them in various fields. Through this research we explore different kinds of CNN-derived architectures and how these structures can be trained and setup in combinatorial environment to solve problems in deep learning applications. First, we introduce the basic idea of a deep learning meta-structures which is used in the application …