Digital Commons Network^™

Electrophertic Deposition And Characterization Of Molybdenum Disulfide On Silicon Substrates, Alex J. Young

LSU Doctoral Dissertations

The electrical characteristics of 2D materials such as high electron mobility and current density are of great interest to various fields from optoelectronics to renewable energy. Researchers have focused their efforts on transition metal dichalcogenides (TMDCs) due to their direct energy band gap. One such TMDC that has garnered much attention is molybdenum disulfide (MoS2). MoS2 has electrical properties comparable to graphene and is a TMDC with characteristics amenable to applications such as solar cells and sensors. Commonly deposited through time-consuming and complex deposition methods such as chemical vapor deposition (CVD), the viability of MoS2 as an electronic material will …

Cyberinet: Integrated Semi-Modular Sensors For The Computer-Augmented Clarinet, Matthew Bardin

LSU Doctoral Dissertations

The Cyberinet is a new Augmented instrument designed to easily and intuitively provide a method of computer-enhanced performance to the Clarinetist to allow for greater control and expressiveness in a performance. A performer utilizing the Cyberinet is able to seamlessly switch between a traditional performance setting and an augmented one. Towards this, the Cyberinet is a hardware replacement for a portion of a Clarinet containing a variety of sensors embedded within the unit. These sensors collect various real time data motion data of the performer and air fow within the instrument. Additional sensors can be connected to the Cyberinet to …

Learning–Assisted Constraint Filtering To Enhance Power System Optimization Performance, Fouad Hasan

LSU Doctoral Dissertations

Machine learning (ML) is a powerful tool that provides meaningful insights for operators to make fast and efficient decisions by analyzing data from power systems. ML techniques have great potential to assist in solving optimization problems within a shorter time frame and with less computational burden. AC optimal power flow (ACOPF), dynamic economic dispatch (D-ED), and security-constrained unit commitment (SCUC) are the three energy management optimization functions studied in this dissertation. ACOPF is solved every 5~15 minutes. Because of the nonconvex and complex nature of ACOPF, solving this problem for large systems is computationally expensive and time-consuming. Classification and regression …

Stabilizing Control Schemes For Grid-Connected Hybrid Pv-Energy Storage Systems, Indra Narayana Bhogaraju

LSU Doctoral Dissertations

A nonlinear stabilizing control scheme based on Lyapunov theory is proposed for a grid- connected hybrid photovoltaic (PV)/ battery/supercapacitor (SC) system. The system dynamics is developed in the stationary reference frame, and the state-space model of the system is derived and used to formulate the Lyapunov function (LF) candidate. The global asymptotic stability of the LF-based controller is discussed in detail. The real-time implementation feasibility of the proposed control scheme is validated through hardware-in-the-loop (HIL) studies of a grid- connected hybrid system under solar energy generation and grid load variations. To address the issue of digital computational time that leads …

Data-Driven Nonparametric Joint Chance-Constrained Programming For Power Systems Scheduling, Chutian Wu

LSU Doctoral Dissertations

This dissertation is dedicated to implementing data-driven nonparametric joint chance constraints (JCC) to power system optimization problems. Power generated by renewable sources, such as solar farms, is an uncertain parameter. Several approaches solve optimization under uncertainty, including stochastic programming, robust programming, and chance-constrained programming. Uncertain parameters may not belong to any parametric class of probability functions. Thus, methods that consider such uncertainty as a random variable that fits in a known probability density function (PDF) have limitations. This study focuses on chance-constrained programming under nonparametric or data-driven distributionally robust uncertainty settings.

Studies based on chance-constrained programming usually focus on individual …

Application Of Distributed Fiber-Optic Sensing For Pressure Predictions And Multiphase Flow Characterization, Gerald Kelechi Ekechukwu

LSU Doctoral Dissertations

In the oil and gas industry, distributed fiber optics sensing (DFOS) has the potential to revolutionize well and reservoir surveillance applications. Using fiber optic sensors is becoming increasingly common because of its chemically passive and non-magnetic interference properties, the possibility of flexible installations that could be behind the casing, on the tubing, or run on wireline, as well as the potential for densely distributed measurements along the entire length of the fiber. The main objectives of my research are to develop and demonstrate novel signal processing and machine learning computational techniques and workflows on DFOS data for a variety of …

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 …

Spam Detection Using Machine Learning And Deep Learning, Olubodunde Agboola

LSU Doctoral Dissertations

Text messages are essential these days; however, spam texts have contributed negatively to the success of this communication mode. The compromised authenticity of such messages has given rise to several security breaches. Using spam messages, malicious links have been sent to either harm the system or obtain information detrimental to the user. Spam SMS messages as well as emails have been used as media for attacks such as masquerading and smishing ( a phishing attack through text messaging), and this has threatened both the user and service providers. Therefore, given the waves of attacks, the need to identify and remove …

Compilation Optimizations To Enhance Resilience Of Big Data Programs And Quantum Processors, Travis D. Lecompte

LSU Doctoral Dissertations

Modern computers can experience a variety of transient errors due to the surrounding environment, known as soft faults. Although the frequency of these faults is low enough to not be noticeable on personal computers, they become a considerable concern during large-scale distributed computations or systems in more vulnerable environments like satellites. These faults occur as a bit flip of some value in a register, operation, or memory during execution. They surface as either program crashes, hangs, or silent data corruption (SDC), each of which can waste time, money, and resources. Hardware methods, such as shielding or error correcting memory (ECM), …

A Field-Deployable Quartz Crystal Microbalance System For Gas Detection, Jongyoon Park

LSU Doctoral Dissertations

Quartz crystal microbalance (QCM) has been widely studied as a mass sensing technique in laboratory environments and has shown a wide range of industrial applications such as food quality control, various forms of chemical detection, and biomolecular recognition under gas phase as well as liquid phase media. The construction of multi-sensor arrays combined with special sensor coatings enables multiple analyte detections and discrimination of multi-analyte along with statistical analysis. Despite the great sensing capabilities of QCM and growing interest in practical applications beyond the laboratory setup, most QCM studies are still performed in laboratory settings with benchtop QCM instruments. Therefore, …

Novel Texture-Based Probabilistic Object Recognition And Tracking Techniques For Food Intake Analysis And Traffic Monitoring, Robert Jacob Dibiano

LSU Doctoral Dissertations

More complex image understanding algorithms are increasingly practical in a host of emerging applications. Object tracking has value in surveillance and data farming; and object recognition has applications in surveillance, data management, and industrial automation. In this work we introduce an object recognition application in automated nutritional intake analysis and a tracking application intended for surveillance in low quality videos. Automated food recognition is useful for personal health applications as well as nutritional studies used to improve public health or inform lawmakers. We introduce a complete, end-to-end system for automated food intake measurement. Images taken by a digital camera are …

Design Tunneling Transistor And Schottky Junction Solar Cell Using Van Der Waals Semiconductor Heterostructure, Md Azmot Ullah Khan

LSU Doctoral Dissertations

Transition metal di-chalcogenide (TMDC) materials, being semiconductor in nature, offer Two-dimensional (2D) materials such as graphene and molybdenum disulfide (MoS₂) possess unique and unusual properties that are particularly applicable to nanoelectronics and photovoltaic devices. In this dissertation, four different projects have been done that encompass the implementation of these materials to improve the performance of future transistors and Schottky junction solar cells. In chapter 2, an analytical current transport model of a dual gate tunnel field-effect transistor (TFET) is developed by utilizing the principle of band-to-band tunneling (BTBT) and MoS₂ as the channel material. Later, using this …

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 …

Efficient Low Dimensional Representation Of Vector Gaussian Distributions, Md Mahmudul Hasan

LSU Doctoral Dissertations

This dissertation seeks to find optimal graphical tree model for low dimensional representation of vector Gaussian distributions. For a special case we assumed that the population co-variance matrix $\Sigma_x$ has an additional latent graphical constraint, namely, a latent star topology. We have found the Constrained Minimum Determinant Factor Analysis (CMDFA) and Constrained Minimum Trace Factor Analysis (CMTFA) decompositions of this special $\Sigma_x$ in connection with the operational meanings of the respective solutions. Characterizing the CMDFA solution of special $\Sigma_x$, according to the second interpretation of Wyner's common information, is equivalent to solving the source coding problem of finding the minimum …

Practical Considerations And Applications For Autonomous Robot Swarms, Rory Alan Hector

LSU Doctoral Dissertations

In recent years, the study of autonomous entities such as unmanned vehicles has begun to revolutionize both military and civilian devices. One important research focus of autonomous entities has been coordination problems for autonomous robot swarms. Traditionally, robot models are used for algorithms that account for the minimum specifications needed to operate the swarm. However, these theoretical models also gloss over important practical details. Some of these details, such as time, have been considered before (as epochs of execution). In this dissertation, we examine these details in the context of several problems and introduce new performance measures to capture practical …

Novel Platforms For Large-Scale Adherent Culture Of Mammalian Cells, Ashkan Yekrangsafakar

LSU Doctoral Dissertations

With recent advances in biotechnology, there is a strong and urgent need for robust platforms to culture mammalian cells on a large scale to produce biopharmaceuticals. To this end, various bioreactors have been developed over the past decades, but their capacity and efficiency are often limited by insufficient mass transfer rate and excessive shear stress. In this work, multiple novel bioreactors for the large-scale adherent culture of anchorage-dependent cells were developed.

Hollow MicroCarriers (HMC) was developed as an alternative solution for the microcarrier-based culture system in a stirred-tank bioreactor. In the conventional microcarrier technique, cells are exposed to the harmful …

Phase Noise Analyses And Measurements In The Hybrid Memristor-Cmos Phase-Locked Loop Design And Devices Beyond Bulk Cmos, Naheem Olakunle Adesina

LSU Doctoral Dissertations

Phase-locked loop (PLLs) has been widely used in analog or mixed-signal integrated circuits. Since there is an increasing market for low noise and high speed devices, PLLs are being employed in communications. In this dissertation, we investigated phase noise, tuning range, jitter, and power performances in different architectures of PLL designs. More energy efficient devices such as memristor, graphene, transition metal di-chalcogenide (TMDC) materials and their respective transistors are introduced in the design phase-locked loop.

Subsequently, we modeled phase noise of a CMOS phase-locked loop from the superposition of noises from its building blocks which comprises of a voltage-controlled oscillator, …

Design And Fabrication Of A Low-Cost, Portable, Battery-Operated Surface Enhanced Raman Scattering (Sers) Optical Device, Blessing Adewumi

LSU Doctoral Dissertations

Raman Spectroscopy is a time-honored, non-invasive method for analyzing and identifying the molecular composition of materials. However, unenhanced Raman Spectroscopy has extremely low sensitivity which limits its sensing capability. SERS brings rough nano-metallic surfaces in contact with the material molecules to enormously enhance the Raman signals.

The sensitivity of SERS can be exploited in probe applications where the spectrometer needs to be brought near the specimen. For example, a long optical fiber coupled to a SERS device can be used to characterize and identify easy-to-reach cancerous tissues in organisms. Unfortunately, background signals in a long fiber can easily mask any …

An Improved Earned Value Management Method Integrating Quality And Safety, Brian Briggs

LSU Doctoral Dissertations

The construction industry invests significant time and money to improve quality and safety while reducing cost and schedule impacts. The industry has a sincere desire to improve construction project management methods to improve efficiency. Historically, quality and safety underperformances result from undermanaged quality control and safety activities. The cost and schedule impacts associated with poor quality work have always had an impact on construction operations. The unprecedented challenges and uncertainties of COVID-19 highlighted the need to improve the Earned Value Management (EVM) method within construction to reflect these quality and safety activities. The central goal of this dissertation is to …

Advanced Methods For Steady-State And Stability Analyses Of Hybrid Power Systems, Mohammad Mehdi Rezvani

LSU Doctoral Dissertations

The term hybrid power grids refer to the combination of two power systems with different intrinsic characteristic. For instance, ac-dc grids and transmission-distribution systems are kinds of hybrid power grids. Challenges in analyzing the hybrid power grids arise since two sets of equations should be solved either simultaneously or sequentially. In the simultaneous (unified) methods, the ac and dc system of equations are solved simultaneously, while, in the sequential approaches, these equations are solved in an error loop. In this dissertation, a unified method is proposed for steady-state and fault analyses of hybrid ac-dc power grids, while a sequential approach …

Semantics-Guided Human Motion Modeling In Virtual Reality Environment, Matthew Korban

LSU Doctoral Dissertations

Human Motion Modeling is essential in Computer Animation and Human-Computer Interaction. This dissertation studies how to enhance the speed and robustness of Human Motion Modeling in Virtual Reality (VR) environments. Specifically, we aim to design a pipeline to effectively capture and use semantic action information to guide the motion capturing from users in physical worlds and its transfer onto digital avatars in VR environments. To recognize the user's action, we first proposed a new Dynamic Directed Graph Convolutional Network (DDGCN) to model spatial and temporal features from users' skeletal representations. The DDGCN consists of several dynamic feature modeling modules to …

Error Prevention In Sensors And Sensor Systems, Pedro J. Chacon Dominguez

LSU Doctoral Dissertations

Achievements in all fields of engineering and fabrication methods have led towards optimization and integration of multiple sensing devices into a concise system. These advances have caused significant innovation in various commercial, industrial, and research efforts. Integrations of subsystems have important applications for sensor systems in particular. The need for reporting and real time awareness of a device’s condition and surroundings have led to sensor systems being implemented in a wide variety of fields. From environmental sensors for agriculture, to object characterization and biomedical sensing, the application for sensor systems has impacted all modern facets of innovation. With these innovations, …

A Comprehensive Study On Printed Circuit Board Backdoor Coupling In High Intensity Radiated Fields Environments, Ryan Patrick Tortorich

LSU Doctoral Dissertations

Due to the prevalence of unintentional electromagnetic interference (EMI) and the growth of intentional electromagnetic interference (IEMI) or high power microwave (HPM) sources, it is now more important than ever to understand how electronic systems are affected by high intensity radiated fields (HIRF) environments. Both historic events and experimental testing have demonstrated that HIRF environments are capable of disrupting and potentially damaging critical systems including but not limited to civil and military aircraft, industrial control systems (ICS), and internet of things (IoT) devices. However, there is limited understanding on the complex electromagnetic interactions that lead to such effects. This study …

Led-Based Optical Sensing Platforms For Multi-Analyte Detection, Youngho Shin

LSU Doctoral Dissertations

Real-time monitoring of phytoplankton groups provides important information about aquatic ecological states, nutrient abundance, and water pollution. A rapid and accurate method for monitoring phytoplankton in water is commonly performed by detecting fluorescence emission from the plankton; however, commercially available portable fluorescence sensors are still expensive, bulky, and limited in functions, such as lacking the capability of selectively detecting multiple phytoplankton groups. In this regard, a low-cost and portable fluorometer platform for phytoplankton detection was developed in order to address the issues that current portable fluorometers have.

This dissertation has four main goals: (1) perform a study on fluorescence measurement …

Channel Estimation In Multi-User Massive Mimo Systems By Expectation Propagation Based Algorithms, Mohammed Rashid

LSU Doctoral Dissertations

Massive multiple input multiple output (MIMO) technology uses large antenna arrays with tens or hundreds of antennas at the base station (BS) to achieve high spectral efficiency, high diversity, and high capacity. These benefits, however, rely on obtaining accurate channel state information (CSI) at the receiver for both uplink and downlink channels. Traditionally, pilot sequences are transmitted and used at the receiver to estimate the CSI. Since the length of the pilot sequences scale with the number of transmit antennas, for massive MIMO systems downlink channel estimation requires long pilot sequences resulting in reduced spectral efficiency and the so-called pilot …

Intelligent Data-Driven Energy Flow Controllers For Renewable Energy And Electrified Transportation Systems, Juan Rafael Nunez Forestieri

LSU Doctoral Dissertations

In recent years, large scale deployments of electrical energy generation using renewable sources (RES) such as wind, solar and ocean wave power, along with more sustainable means of transformation have emerged in response to different initiatives oriented toward reducing greenhouse gas emissions. Strategies facilitating the integration of renewable generation into the grid and electric propulsion in transportation systems are proposed in this work.

Chapter 2 investigates the grid-connected operation of a wave energy converter (WEC) along with a hybrid supercapacitor/undersea energy storage system (HESS). A combined sizing and energy management strategy (EMS) based on reinforcement learning (RL) is proposed. Comparisons …

Parallel And Asynchronous Distributed Optimization For Power Systems Operation, Ali Mohammadi

LSU Doctoral Dissertations

Distributed optimization approaches are gaining more attention for solving power systems energy management functions, such as optimal power flow (OPF). Preserving information privacy of autonomous control entities and being more scalable than centralized approaches are two primary reasons for developing distributed algorithms. Moreover, distributed/ decentralized algorithms potentially increase power systems reliability against failures of components or communication links.

In this dissertation, we propose multiple distributed optimization algorithms and convergence performance enhancement techniques to solve the OPF problem. We present a multi-level optimization algorithm, based on analytical target cascading, to formulate and solve a collaborative transmission and distribution OPF problem. This …

Fabrication And Application Of Flexible Sensors, Tallis Huther Da Costa

LSU Doctoral Dissertations

A transfer printing method was developed to transfer carbon nanotubes (CNTs) from polyethylene terephthalate (PET) film to poly(dimethyl siloxane) (PDMS) polymer. Carbon nanotubes are composed of carbon atoms arranged in a honeycomb lattice structure, which are electrically conducting. When embedded in a nonconducting polymer, carbon nanotubes impart electrical conductivity to the nanocomposite, thus forming a nanocomposite that has potential applications in highly sensitive strain and pressure sensors. Several printing methods have been studied to deposit carbon nanotubes onto PDMS, including inkjet printing. Inkjet printing is a desirable deposition method since it is low-cost, simple, and allows the processing of aqueous-based …

Temporal Decomposition For Multi-Interval Optimization In Power Systems, Farnaz Safdarian

LSU Doctoral Dissertations

Large optimization problems are frequently solved for power systems operation and analysis of electricity markets. Many of these problems are multi-interval optimization with intertemporal constraints. The size of optimization problems depends on the size of the system and the length of the considered scheduling horizon. Growing the length of the scheduling horizon increases the computational burden significantly and might make solving the problem in a required time span impossible. Many simplifications and approximation techniques are applied to reduce the computational complexity of multi-interval scheduling problems and make them solvable in a reasonable time span. Geographical decomposition is presented in the …

Study Of Fundamental Tradeoff Between Deliverable And Private Information In Statistical Inference, Farhang Bayat

LSU Doctoral Dissertations

My primary objective in this dissertation is to establish a framework under which I launch a systematic study of the fundamental tradeoff between deliverable and private information in statistical inference. My research was partly motivated by arising and prevailing privacy concerns of users in many machine learning problems.

In this dissertation, I begin by introducing examples where I am concerned of privacy leakage versus decision utility in statistical inference problems. I then go into further details about what I have achieved in formulating and solving such problems using information theory related metrics in a variety of settings. Both related works …