Engineering Commons^™

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 …

Optimized And Automated Machine Learning Techniques Towards Iot Data Analytics And Cybersecurity, Li Yang

Electronic Thesis and Dissertation Repository

The Internet-of-Things (IoT) systems have emerged as a prevalent technology in our daily lives. With the wide spread of sensors and smart devices in recent years, the data generation volume and speed of IoT systems have increased dramatically. In most IoT systems, massive volumes of data must be processed, transformed, and analyzed on a frequent basis to enable various IoT services and functionalities. Machine Learning (ML) approaches have shown their capacity for IoT data analytics. However, applying ML models to IoT data analytics tasks still faces many difficulties and challenges. The first challenge is to process large amounts of dynamic …

Algorithm Optimization And Hardware Acceleration For Machine Learning Applications On Low-Energy Systems, Jianchi Sun

All Dissertations

Machine learning (ML) has been extensively employed for strategy optimization, decision making, data classification, etc. While ML shows great triumph in its application field, the increasing complexity of the learning models introduces neoteric challenges to the ML system designs. On the one hand, the applications of ML on resource-restricted terminals, like mobile computing and IoT devices, are prevented by the high computational complexity and memory requirement. On the other hand, the massive parameter quantity for the modern ML models appends extra demands on the system's I/O speed and memory size. This dissertation investigates feasible solutions for those challenges with software-hardware …

Machine Learning Applications In Plant Identification, Wireless Channel Estimation, And Gain Estimation For Multi-User Software-Defined Radio, Viraj K. Gajjar

Doctoral Dissertations

"This work applies machine learning (ML) techniques to selected computer vision and digital communication problems. Machine learning algorithms can be trained to perform a specific task without explicit programming. This research applies ML to the problems of: plant identification from images of leaves, channel state information (CSI) estimation for wireless multiple-input-multiple-output (MIMO) systems, and gain estimation for a multi-user software-defined radio (SDR) application.

In the first task, two methods for plant species identification from leaf images are developed. One of the methods uses hand-crafted features extracted from leaf images to train a support vector machine classifier. The other method combines …

Detection Of Rotorcraft Landing Sites: An Ai-Based Approach, Abdullah Nasir

Theses and Dissertations

The updated information about the location and type of rotorcraft landing sites is an essential asset for the Federal Aviation Administration (FAA) and the Department of Transportation (DOT). However, acquiring, verifying, and regularly updating information about landing sites is not straightforward. The lack of current and correct information about landing sites is a risk factor in several rotorcraft accidents and incidents. The current FAA database of rotorcraft landing sites contains inaccurate and missing entries due to the manual updating process. There is a need for an accurate and automated validation tool to identify landing sites from satellite imagery. This thesis …

Machine Learning With Big Data For Electrical Load Forecasting, Alexandra L'Heureux

Electronic Thesis and Dissertation Repository

Today, the amount of data collected is exploding at an unprecedented rate due to developments in Web technologies, social media, mobile and sensing devices and the internet of things (IoT). Data is gathered in every aspect of our lives: from financial information to smart home devices and everything in between. The driving force behind these extensive data collections is the promise of increased knowledge. Therefore, the potential of Big Data relies on our ability to extract value from these massive data sets. Machine learning is central to this quest because of its ability to learn from data and provide data-driven …

Total Sky Imager Project, Ryan D. Maier, Benjamin Jack Forest, Kyle X. Mcgrath

Mechanical Engineering

Solar farms like the Gold Tree Solar Farm at Cal Poly San Luis Obispo have difficulty delivering a consistent level of power output. Cloudy days can trigger a significant drop in the utility of a farm’s solar panels, and an unexpected loss of power from the farm could potentially unbalance the electrical grid. Being able to predict these power output drops in advance could provide valuable time to prepare a grid and keep it stable. Furthermore, with modern data analysis methods such as machine learning, these predictions are becoming more and more accurate – given a sufficient data set. The …

Application Of Machine Learning For Predicting Iemi Upset In Multi-Architecture Microcontrollers, Daniel S. Guillette

Electrical and Computer Engineering ETDs

Four microcontrollers were programmed to execute a simple counting program. Pulsed RF signals – also known as Intentional ElectroMagnetic Interference (IEMI) – were injected into the clock input of the microcontrollers. At the same time, the output lines were monitored to determine whether the IEMI signal altered the output of the counting program – referred to as an upset. A state-of-the-art automated testing apparatus was used to collect and process 120,960 samples of IEMI upset data. The data was used to perform a traditional upset trends study and train a series of machine learning (ML) techniques – k-Nearest Neighbors, Support …

Predicting The Progression Of Diabetes Mellitus Using Dynamic Plantar Pressure Parameters, Mathew Sunil Varre

UNLV Theses, Dissertations, Professional Papers, and Capstones

Introduction: Diabetic peripheral neuropathy is one of the common complications of type-2 diabetes mellitus (DM). Changes in the intrinsic plantar tissue coupled with repetitive mechanical loads and loss of sensation may lead to foot related complications (skin break down, ulcerations, and amputations) in persons with neuropathy if left untreated. The purpose of this dissertation was to stratify individuals with pre-diabetes, diabetes with and without neuropathy using dynamic plantar pressure parameters during walking, using machine learning algorithms.Methods: Plantar pressure data was collected from one hundred participants during walking with pressure measuring insoles fixed between the feet and thin socks. Simultaneously high-definition …

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.

Automated Segmentation Of The Inner Ear And Round Window In Computed Tomography Scans Using Convolutional Neural Networks, Kyle A. Rioux

Electronic Thesis and Dissertation Repository

Computed tomography (CT) scans are acquired prior to cochlear implant (CI) surgery. Three-dimensional segmentations of the inner ear (IE) and round window (RW) based on clinical CTs can improve the CI procedure. Software pipelines are presented here which employ convolutional neural networks to automatically segment the IE and RW. The first pipeline produces high resolution segmentations of the IE and RW in tightly cropped CTs. Mean IE Dice score and RW centroid error were 0.88, 0.57mm and 0.93, 0.18mm in implanted and non-implanted samples, respectively. The second pipeline automatically segments the IE in large field of view CTs of any …

An Analytical Methodology To Security Constraints Management In Power System Operation, Shubo Zhang

ETD Archive

In a deregulated electricity market, Independent System Operators (ISOs) are responsible for dispatching power to the load securely, efficiently, and economically. ISO performs Security Constrained Unit Commitment (SCUC) to guarantee sufficient generation commitment, maximized social welfare and facilitating market-driven economics. A large number of security constraints would render the model impossible to solve under time requirements. Developing a method to identify the minimum set of security constraints without overcommitting is necessary to reduce Mixed Integer Linear Programming (MILP) solution time. To overcome this challenge, we developed a powerful tool called security constraint screening. The proposed approach effectively filters out non-dominating …

Remote Crop Disease Detection Using Deep Learning With Iot, Ivy Chung, Anoushka Gupta

Electrical and Computer Engineering Senior Theses

Agriculture is such a vital part of our society, and according to the United Nations’ Food and Agricultural Organization (FAO), plant diseases are considered one of the two main causes of decreasing food availability. This paper explores not only the methods and findings of building a CNN-based disease detection model, but that of building a deployable remote crop disease detection system incorporating IoT technology. By using transfer learning with AlexNet, we were able to predict with 89.8% accuracy tomato plant images into one of the ten pre-defined disease classes. Our proposed system tracks plant health throughout the day by using …

The Role Of Transient Vibration Of The Skull On Concussion, Rodrigo Dalvit Carvalho Da Silva

Electronic Thesis and Dissertation Repository

Concussion is a traumatic brain injury usually caused by a direct or indirect blow to the head that affects brain function. The maximum mechanical impedance of the brain tissue occurs at 450±50 Hz and may be affected by the skull resonant frequencies. After an impact to the head, vibration resonance of the skull damages the underlying cortex. The skull deforms and vibrates, like a bell for 3 to 5 milliseconds, bruising the cortex. Furthermore, the deceleration forces the frontal and temporal cortex against the skull, eliminating a layer of cerebrospinal fluid. When the skull vibrates, the force spreads directly to …

A Machine Learning Approach To Intended Motion Prediction For Upper Extremity Exoskeletons, Justin Berdell

Graduate Research Theses & Dissertations

A fully solid-state, software-defined, one-handed, handle-type control device built around a machine-learning (ML) model that provides intuitive and simultaneous control in position and orientation each in a full three degrees-of-freedom (DOF) is proposed in this paper. The device, referred to as the “Smart Handle”, and it is compact, lightweight, and only reliant on low-cost and readily available sensors and materials for construction. Mobility chairs for persons with motor difficulties could make use of a control device that can learn to recognize arbitrary inputs as control commands. Upper-extremity exoskeletons used in occupational settings and rehabilitation require a natural control device like …

Learning Robot Motion From Creative Human Demonstration, Charles C. Dietzel

Theses and Dissertations

This thesis presents a learning from demonstration framework that enables a robot to learn and perform creative motions from human demonstrations in real-time. In order to satisfy all of the functional requirements for the framework, the developed technique is comprised of two modular components, which integrate together to provide the desired functionality. The first component, called Dancing from Demonstration (DfD), is a kinesthetic learning from demonstration technique. This technique is capable of playing back newly learned motions in real-time, as well as combining multiple learned motions together in a configurable way, either to reduce trajectory error or to generate entirely …

Smart City Management Using Machine Learning Techniques, Mostafa Zaman

Theses and Dissertations

In response to the growing urban population, "smart cities" are designed to improve people's quality of life by implementing cutting-edge technologies. The concept of a "smart city" refers to an effort to enhance a city's residents' economic and environmental well-being via implementing a centralized management system. With the use of sensors and actuators, smart cities can collect massive amounts of data, which can improve people's quality of life and design cities' services. Although smart cities contain vast amounts of data, only a percentage is used due to the noise and variety of the data sources. Information and communication technology (ICT) …