Digital Commons Network^™

Machine Learning Demodulator Architectures For Power-Limited Communications, Paul E. Gorday

Electronic Theses and Dissertations 2020 - Present

The success of deep learning has renewed interest in applying neural networks and other machine learning techniques to most fields of data and signal processing, including communications. Advances in architecture and training lead us to consider new modem architectures that allow flexibility in design, continued learning in the field, and improved waveform coding. This dissertation examines neural network architectures and training methods suitable for demodulation in power-limited communication systems, such as those found in wireless sensor networks. Such networks will provide greater connection to the world around us and are expected to contain orders of magnitude more devices than cellular …

Predicting Tropical Cyclone Intensity From Geosynchronous Satellite Images Using Deep Neural Networks, Niranga Mahesh Udumulla

Electronic Theses and Dissertations 2020 - Present

Tropical cyclones are among the most devastating natural disasters for human beings and the natural and manmade assets near to Atlantic basin. Estimating the current and future intensity of these powerful storms is crucial to protect life and property. Many methods and models exist for predicting the evolution of Atlantic basin cyclones, including numerical weather prediction models that simulate the dynamics of the atmosphere which require accurate measurements of the current state of the atmosphere (NHC, 2019). Often these models fail to capture dangerous aspects of storm evolution, such as rapid intensification (RI), in which a storm undergoes a steep …

Online Parameter Learning For Structural Condition Monitoring System, Jaffar Alqazzaz

Electronic Theses and Dissertations 2020 - Present

The purpose of online parameter learning and modeling is to validate and restore the properties of a structure based on legitimate observations. Online parameter learning assists in determining the unidentified characteristics of a structure by offering enhanced predictions of the vibration responses of the system. From the utilization of modeling, the predicted outcomes can be produced with a minimal amount of given measurements, which can be compared to the true response of the system. In this simulation study, the Kalman filter technique is used to produce sets of predictions and to infer the stiffness parameter based on noisy measurement. From …

Statistical Modeling Of Ship Airwakes Including The Feasibility Of Applying Machine Learning, Vaishakh Krishnan

Electronic Theses and Dissertations 2020 - Present

Airwakes are shed behind the ship’s superstructure and represent a highly turbulent and rapidly distorting flow field. This flow field severely affects pilot’s workload and such helicopter shipboard operations. It requires both the one-point statistics of autospectrum and the two-point statistics of coherence (normalized cross-spectrum) for a relatively complete description. Recent advances primarily refer to generating databases of flow velocity points through experimental and computational fluid dynamics (CFD) investigations, numerically computing autospectra along with a few cases of cross-spectra and coherences, and developing a framework for extracting interpretive models of autospectra in closed form from a database along with an …

The Johannine Community, The Synoptic Gospels, And The Traditions Behind John’S Resurrection Narrative, J. D. Reiner

Electronic Theses and Dissertations 2020 - Present

What sources informed the resurrection narrative of Jesus in the Gospel of John? Some scholars argue that the author of John used the Synoptic Gospels along with oral traditions as sources, but others maintain that John used only independent traditions to write his resurrection story. This paper argues that John did not use the Synoptics for this narrative because the reconstructed history of the Johannine community provides an adequate basis for postulating independent traditions which succeed at explaining both the similarities and differences between John and the Synoptics. While it does not claim to prove that the author was unaware …

The Change Point Problem For Two Classes Of Stochastic Processes, Cory Ball

Electronic Theses and Dissertations 2020 - Present

The change point problem is a problem where a process changes regimes because a parameter changes at a point in time called the change point. The objective of this problem is to estimate the change point and each of the parameters of the stochastic process. In this thesis, we examine the change point problem for two classes of stochastic processes. First, we consider the volatility change point problem for stochastic diffusion processes driven by Brownian motions. Then, we consider the drift change point problem for Ornstein-Uhlenbeck processes driven by _-stable Levy motions. In each problem, we establish the consistency of …

Digital Holography Experiments With Degraded Temporal Coherence, Douglas E. Thornton, Davin Mao, Mark F. Spencer, Christopher A. Rice, Glen P. Perram

Faculty Publications

To simulate the effects of multiple-longitudinal modes and rapid fluctuations in center frequency, we use sinusoidal phase modulation and linewidth broadening, respectively. These effects allow us to degrade the temporal coherence of our master-oscillator laser, which we then use to conduct digital holography experiments. In turn, our results show that the coherence efficiency decreases quadratically with fringe visibility and that our measurements agree with our models to within 1.8% for sinusoidal phase modulation and 6.9% for linewidth broadening.

Bow Lake (Station 1 - Ledges) 2020 Water Quality Highlight Report, Robert Craycraft

UNH Cooperative Extension

A Water Quality Highlight Report by UNH CE Lakes Lay Monitoring Program for Bow Lake, Station - 1 Ledges (Barrington and Northwood, NH) for 2020.

Climate Change Instruction In Higher Education: Pre-Service Teachers’ Engagement In An Interdisciplinary Pop-Up Learning Community, Mary Charlotte Bautistia

Master's Theses and Doctoral Dissertations

An interdisciplinary pop-up learning community (PLC) allowed students from various disciplines with different levels of content knowledge to discuss their perspectives and beliefs on climate change. The impact of this event on students was gauged by a survey from the Yale Project on Climate Change Communication. Furthermore, this project focuses on pre-service teachers who participated in the PLC. In order to investigate how pre-service teachers understand climate change and how they may or may not integrate these issues into their own instruction, individual interviews were conducted prior to and after the PLC to determine if the event had an impact …

Robustness Metric For Robust Design Optimization Under Time-And Space-Dependent Uncertainty Through Metamodeling, Xinpeng Wei, Xiaoping Du

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Product performance varies with respect to time and space in many engineering applications. This paper discusses how to measure and evaluate the robustness of a product or component when its quality characteristics (QCs) are functions of random variables, random fields, temporal variables, and spatial variables. At first, the existing time-dependent robustness metric is extended to the present time-and space-dependent problem. The robustness metric is derived using the extreme value of the quality characteristics with respect to temporal and spatial variables for the nominal-the-better type quality characteristics. Then, a metamodel-based numerical procedure is developed to evaluate the new robustness metric. The …

Primal-Dual 2-Approximation Algorithm For The Monotonic Multiple Depot Heterogeneous Traveling Salesman Problem, S. Rathinam, R. Ravi, J. Bae, K. Sundar

Michigan Tech Publications, Part 1

We study a Multiple Depot Heterogeneous Traveling Salesman Problem (MDHTSP) where the cost of the traveling between any two targets depends on the type of the vehicle. The travel costs are assumed to be symmetric, satisfy the triangle inequality, and are monotonic, i.e., the travel costs between any two targets monotonically increases with the index of the vehicles. Exploiting the monotonic structure of the travel costs, we present a 2-approximation algorithm based on the primal-dual method.

Evaluating The Safety And Mobility Of The Cumulative-Anticipative Car-Following Model For Connected Autonomous Vehicles, Hafiz Usman Ahmed

Civil and Environmental Engineering

The advancements of vehicle automation are progressively improving resulting in safer driving environments in addition to more efficient mobility and fuel cost savings. However, autonomous and connected autonomous vehicles (AVs, CAVs) require decades to achieve complete market penetration. It is important to investigate the coexistence of conventional and autonomous cars during such a transition period. Traditionally, adaptive cruise control (ACC) and cooperative ACC (CACC) models were used for the AVs to guide their car-following. Recently, the cumulative-anticipative car-following (CACF) model was developed with consideration of the cumulative influences from surrounding vehicles through vehicle-to-everything (V2X) communication. This study further evaluates the …

Improved Hydrologic Modeling For Characterizing Variable Contributing Areas And Threshold-Controlled Overland Flow In Depression-Dominated Areas, Lan Zeng

Civil and Environmental Engineering

Surface depressions are important topographic features, which affect overland flow, infiltration, and other hydrologic processes. Specifically, depressions undergo filling-spilling-merging-splitting processes under natural rainfall conditions, featuring discontinuity in hydrologic connectivity and variability in contributing area. However, a constant and time-invariant contributing area is often assumed in traditional hydrologic modeling, and consequently, the real threshold-controlled overland flow dynamics cannot be captured. The overall goal of this dissertation research is to improve hydrologic modeling, especially for depression-dominated areas, by quantifying the hydrologic effects of depressions. The specific objectives are to analyze the hydrotopographic characteristics of depressions and identify the intrinsic relationships of hydrologic …

Investigation Of Novel Approaches For Improved Amphiphilic Fouling-Release Coatings, Alireza Rahimi

Coatings and Polymeric Materials

Marine biofouling has troubled mankind, both environmentally and economically, since they set sail, resulting in many undesired consequences such as increased drag, reduced maneuverability, increased fuel consumption and greenhouse gas emissions, and heightened maintenance costs. This problem is highly complex as it involves more than 4000 marine organisms with varying modes of adhesion and surface preferences as well as many aquatic environments. The common state-of-the-art approaches to contend with marine biofouling on the submerged surfaces of ships in seawater has antifouling (AF) and fouling-release (FR) surfaces. As AF coating systems utilize biocides which are often toxic to the environment to …

Brain Cancer Detection Using Mri Scans, Shanthanreddy Thotapally

Computer Science

An estimate of about 700,000 Americans today live with a brain tumor. Of these, 70% are benign and 30% are malicious. The average survival rate of all the malicious brain tumor patients is 35%. Diagnosing these tumors early on gives the best chance for survival. The Doctors use MRI scans to identify the presence of a tumor and it’s characteristics like the type and size. In this paper, I implemented a Deep learning convolutional neural network model that classifies the brain tumors using MRI scans. We shall use VGG-16 deep-learning approach to implement the machine learning algorithm. The proposed system …

Extracting Useful Information And Building Predictive Models From Medical And Health-Care Data Using Machine Learning Techniques, Md Faisal Kabir

Computer Science

In healthcare, a large number of medical data has emerged. To effectively use these data to improve healthcare outcomes, clinicians need to identify the relevant measures and apply the correct analysis methods for the type of data at hand. In this dissertation, we present various machine learning (ML) and data mining (DM) methods that could be applied to the type of data sets that are available in the healthcare area. The first part of the dissertation investigates DM methods on healthcare or medical data to find significant information in the form of rules. Class association rule mining, a variant of …

Prediction Accuracy Of Financial Data - Applying Several Resampling Techniques, Mohammad Reza Ali

Computer Science

With the help of Data Mining and Machine Learning, prediction has been a very popular and demanding instrument to plan and accomplish a future goal. The financial sector is one of the crucial sectors of present human society. Predicting the correct outcome is a pivotal matter in this sector. In this work, an assessment was done to the prediction efficiency by applying several Machine Learning Classification Algorithms and resampling methods. These techniques were applied to financial data, more specifically to Bank Marketing in order to predict the tendency of clients to subscribe to a bank term deposit. For the correct …

A Comparative Study On Different Big Data Tools, Sifat Ibtisum

Computer Science

Big data has long been the topic of fascination for computer science enthusiasts around the world, and has gained even more prominence in recent times with the continuous explosion of data resulting from the likes of social media and the quest for tech giants to gain access to deeper analysis. This paper discusses various tools in big data technology and conducts a comparison among them. Different tools namely Sqoop, Apache Flume, Apache Kafka, Hive, Spark and many more are included. Various datasets are used for the experiment and a comparative study is made to figure out which tool works faster …

Photometric Study Of Two Near-Earth Asteroids In The Sloan Digital Sky Survey Moving Objects Catalog, Christopher James Miko

Theses and Dissertations

Classifying asteroids by color and spectral features is important for understanding their compositions, linkages to meteorite types, and formation and evolution of the Solar System in general. Large data collection efforts such as the Sloan Digital Sky Survey (SDSS) have allowed for more comprehensive studies of the asteroid population. However, the repeatability and therefore reliability of such observations comes into question, especially as Szabo et al. (2004) and Carvano et al. (2010) reported sizable fractions of asteroids observed by SDSS exhibiting taxonomic variability.

This research studied two near-Earth asteroids (NEAs) with just one observation each by SDSS: 2059 Baboquivari and …

The Value Of Existential Worlds: Creation And Validation Of A Measure To Explore The Four Existential Worlds, Matthew Robert Rozzi

Theses and Dissertations

The field of counseling psychology is increasingly focused on understanding clients in holistic and non-pathologizing ways. Existential theory provides the four-worlds model for exploring human existence in line with this modern psychological zeitgeist. In this model, humans are understood to exist simultaneously in four “worlds” or “dimensions”: physical (umwelt), social (mitwelt), psychological (eigenwelt), and spiritual/philosophical (uberwelt). This measure construction project is the first known study to examine the personal importance (value) of existing in each world. The Value of Existential Worlds (VEW) was created and psychometrically examined. From the initial 63-items, a shorter (20-item) version was created which demonstrated a …

Modeling Of Gas-Solid Flows In Industry With Computational Fluid Dynamics Tools: An Assessment Of Modeling Methodologies And Computational Challenges, Kaylee Smith

Theses and Dissertations

The modeling work and simulation results contained within this thesis come from two different applications that together emphasize multiple of the challenges currently faced by researchers in the field of numerical modeling of gas-solids flows with computational fluid dynamics (CFD) tools, and highlight avenues of potential resolutions to these challenges.

In the first body of work, the MFiX CFD suite, developed by the Department of Energy’s National Energy Technology Laboratory (DOE NETL), was utilized to model and simulate several experimental conditions of a fluidized bed (in 2D), and a hopper (in 3D), where solid-solid collision effects play a dominant role. …

Case Judgments And Juror Perceptions Of The Stereotypical Defendant, Mariah Sorby

Theses and Dissertations

Stereotypes and prejudice have been shown to bias information processing and decision making. There are physical traits that are stereotypically associated with criminals, i.e. tattoos, dark skin-tone, facial untrustworthiness, Afrocentric facial features. These features have been shown to influence verdict decisions and sentencing outcomes. However, there is a paucity of research investigating the additive effects of these features while also considering individual levels of prejudice and egalitarian views. The current research aimed to investigate the effects of several combinations of features on defendant verdict, sentencing, and criminal appearance ratings, rather than assessing the features in isolation, while controlling for prejudice …

Automation And Visualization Of Program Correctness For Automatically Generating Code, Jason Michael Hicks

Theses and Dissertations

Program synthesis systems can be highly advantageous in that users can automatically generate code to fit a wide variety of applications from high-level specifications without needing any low-level programming skills or knowledge of which type of data structures and algorithms should be used. NASA has developed and uses two of these systems, AUTOFILTER and AUTOBAYES. Though much is gained in terms of time and cost efficiency in the use of these systems, they suffer from an issue that is inherent in all code generator systems, the verifiability of the correctness of the generated code against the input specifications. Many times, …

Student Athlete Perceptions On Coaches' Relationships Impacting Continued Participation In High School Athletics, Mark Allen Rerick

Theses and Dissertations

The researcher’s purpose for this study was to review and analyze student-athlete perceptions of their high school athletic experience based on one school district’s collected data. This research provides athletic directors knowledge and direction to better define the philosophy, goals, and organizational structure of their athletic departments. Using quantitative and qualitative data, the researcher examined secondary data collected to identify reasons student-athletes participate in athletics as well as understand why some student-athletes may not continue their participation in high school athletics. The researcher also explored the contrasting goals of privately run sports clubs and high school sponsored athletic programs and …

Design Of Spouted Fluidized Bed Computational Models For Advanced Energy Applications, Ryder Shallbetter

Theses and Dissertations

Fluidization process technology has been widely studied since its first commercial uses during the 1920s. Initially, developmental progress was slow as fluidized beds are complex in design and often hard to scale up in size. However, with the emergence of computational fluid dynamics (CFD), researchers were able to simulate wide ranges of fluidization and create new fluidization designs to help solve complex problems. This study aims to determine if CFD can be effective in designing spout-fluid beds with draft tubes for advanced energy applications. CFD has been employed for studying the fluidization characteristics, design, and optimization of spouted beds. However, …

Modelling Of A Microgrid Using Z Notation, Binisha Shrestha

Theses and Dissertations

A Microgrid is a group of electrical sources and connected loads that operate energy grids in grid-connected or islanded mode. Microgrid usage has increased recently due to improved technology andthe effectiveness of renewable energy sources. To produce a balanced and stable power supply frommicrogrids and meet the load demand is a challenging research area in both the electrical engineering and software engineering fields. This work presents a formal model for representing the microgrid system to prevent failure or inconsistencies in the power generation and usage. A methodology for creating a formal model for a microgrid is a critical approach to …

Uncertainty Quantification And Economic Dispatch Models For The Power Grid, Arun Sukumaran Nair

Theses and Dissertations

The modern power grid is constrained by several challenges, such as increased penetration of Distributed Energy Resources (DER), rising demand for Electric Vehicle (EV) integration, and the need to schedule resources in real-time accurately. To address the above challenges, this dissertation offers solutions through data-driven forecasting models, topology-aware economic dispatch models, and efficient optional power flow calculations for large scale grids. Particularly, in chapter 2, a novel microgrid decomposition scheme is proposed to divide the large scale power grids into smaller microgrids. Here, a two-stage Nearest-Generator Girvan-Newman (NGGN) algorithm, a graphicalclustering-based approach, followed by a distributed economic dispatch model, is …

Aqueous Imbibition With Enhanced Contact Area Approaches To Extract Oil From The Bakken Formation, Jin Zhang

Theses and Dissertations

The techniques of horizontal wells and hydraulic fracturing enable oil production from low-permeable shale, sandstone and carbonate rock formations. However, the extremely low permeability and porosity properties lead a sharp declination of oil production and a low oil recovery factor (typically, 5-15% of original oil in place) as reservoir pressures dropping. Surfactant EOR (enhanced oil recovery) has been considered as one of the best options for geological challenging formations. In our previous studies, we developed a method using surfactant formulation spontaneous imbibition to stimulate the oil recovery from tight formations through wettability alteration and the interfacial tension (IFT) reduction. However, …

Enhanced Hydrocarbon Recovery In Tight And Shale Reservoirs Using Surfactants And Supercritical Co2, Shaojie Zhang

Theses and Dissertations

The tight and shale oil reservoirs have been becoming increasingly important energy resources. However, the flow and storage mechanisms of oil in tight and shale reservoirs are still ambiguous. The inorganic pores, organic pores, and kerogen skeleton in shale oil reservoirs can store hydrocarbons. Besides, the oil recovery of tight and shale oil reservoirs is extremely low even with the assistance of hydraulic fracturing and horizontal drilling. The surfactants and CO2 are commonly used to enhance the oil recovery in tight and shale reservoirs. A comprehensive review of CO2 and surfactants EOR is shown in Chapter Ⅱ.

The spontaneous imbibition …

Removal Of Blocking Artifacts From Jpeg-Compressed Images Using Neural Network, Md Nurul Amin

Theses and Dissertations

The goal of this research was to develop a neural network that will produce considerable improvement in the quality of JPEG compressed images, irrespective of compression level present in the images. In order to develop a computationally efficient algorithm for reducing blocky and Gibbs oscillation artifacts from JPEG compressed images, we integrated artificial intelligence to remove blocky and Gibbs oscillation artifacts. In this approach, alpha blend filter [7] was used to post process JPEG compressed images to reduce noise and artifacts without losing image details. Here alpha blending was controlled by a limit factor that considers the amount of compression …