Engineering Commons^™

Underwater Acoustic Signal Analysis Toolkit, Kirk Bienvenu Jr

University of New Orleans Theses and Dissertations

This project started early in the summer of 2016 when it became evident there was a need for an effective and efficient signal analysis toolkit for the Littoral Acoustic Demonstration Center Gulf Ecological Monitoring and Modeling (LADC-GEMM) Research Consortium. LADC-GEMM collected underwater acoustic data in the northern Gulf of Mexico during the summer of 2015 using Environmental Acoustic Recording Systems (EARS) buoys. Much of the visualization of data was handled through short scripts and executed through terminal commands, each time requiring the data to be loaded into memory and parameters to be fed through arguments. The vision was to develop …

Breadcrumbs: Privacy As A Privilege, Prachi Bhardwaj

Capstones

Breadcrumbs: Privacy as a Privilege Abstract

By: Prachi Bhardwaj

In 2017, the world saw more data breaches than in any year prior. The count was more than the all-time high record in 2016, which was 40 percent more than the year before that.

That’s because consumer data is incredibly valuable today. In the last three decades, data storage has gone from being stored physically to being stored almost entirely digitally, which means consumer data is more accessible and applicable to business strategies. As a result, companies are gathering data in ways previously unknown to the average consumer, and hackers are …

Proactive Sequential Resource (Re)Distribution For Improving Efficiency In Urban Environments, Supriyo Ghosh

Dissertations and Theses Collection (Open Access)

Due to the increasing population and lack of coordination, there is a mismatch in supply and demand of common resources (e.g., shared bikes, ambulances, taxis) in urban environments, which has deteriorated a wide variety of quality of life metrics such as success rate in issuing shared bikes, response times for emergency needs, waiting times in queues etc. Thus, in my thesis, I propose efficient algorithms that optimise the quality of life metrics by proactively redistributing the resources using intelligent operational (day-to-day) and strategic (long-term) decisions in the context of urban transportation and health & safety. For urban transportation, Bike Sharing …

Asynchronous 3d (Async3d): Design Methodology And Analysis Of 3d Asynchronous Circuits, Francis Corpuz Sabado

Graduate Theses and Dissertations

This dissertation focuses on the application of 3D integrated circuit (IC) technology on asynchronous logic paradigms, mainly NULL Convention Logic (NCL) and Multi-Threshold NCL (MTNCL). It presents the Async3D tool flow and library for NCL and MTNCL 3D ICs. It also analyzes NCL and MTNCL circuits in 3D IC. Several FIR filter designs were implement in NCL, MTNCL, and synchronous architecture to compare synchronous and asynchronous circuits in 2D and 3D ICs. The designs were normalized based on performance and several metrics were measured for comparison. Area, interconnect length, power consumption, and power density were compared among NCL, MTNCL, and …

Enhanced Version Control For Unconventional Applications, Ahmed Saleh Shatnawi

Theses and Dissertations

The Extensible Markup Language (XML) is widely used to store, retrieve, and share digital documents. Recently, a form of Version Control System has been applied to the language, resulting in Version-Aware XML allowing for enhanced portability and scalability. While Version Control Systems are able to keep track of changes made to documents, we think that there is untapped potential in the technology. In this dissertation, we present novel ways of using Version Control System to enhance the security and performance of existing applications. We present a framework to maintain integrity in offline XML documents and provide non-repudiation security features that …

Threshold Free Detection Of Elliptical Landmarks Using Machine Learning, Lifan Zhang

Theses and Dissertations

Elliptical shape detection is widely used in practical applications. Nearly all classical ellipse detection algorithms require some form of threshold, which can be a major cause of detection failure, especially in the challenging case of Moire Phase Tracking (MPT) target images. To meet the challenge, a threshold free detection algorithm for elliptical landmarks is proposed in this thesis. The proposed Aligned Gradient and Unaligned Gradient (AGUG) algorithm is a Support Vector Machine (SVM)-based classification algorithm, original features are extracted from the gradient information corresponding to the sampled pixels. with proper selection of features, the proposed algorithm has a high accuracy …

A Framework For Clustering And Adaptive Topic Tracking On Evolving Text And Social Media Data Streams., Gopi Chand Nutakki

Electronic Theses and Dissertations

Recent advances and widespread usage of online web services and social media platforms, coupled with ubiquitous low cost devices, mobile technologies, and increasing capacity of lower cost storage, has led to a proliferation of Big data, ranging from, news, e-commerce clickstreams, and online business transactions to continuous event logs and social media expressions. These large amounts of online data, often referred to as data streams, because they get generated at extremely high throughputs or velocity, can make conventional and classical data analytics methodologies obsolete. For these reasons, the issues of management and analysis of data streams have been researched extensively …

Developing Leading And Lagging Indicators To Enhance Equipment Reliability In A Lean System, Dhanush Agara Mallesh

Masters Theses

With increasing complexity in equipment, the failure rates are becoming a critical metric due to the unplanned maintenance in a production environment. Unplanned maintenance in manufacturing process is created issues with downtimes and decreasing the reliability of equipment. Failures in equipment have resulted in the loss of revenue to organizations encouraging maintenance practitioners to analyze ways to change unplanned to planned maintenance. Efficient failure prediction models are being developed to learn about the failures in advance. With this information, failures predicted can reduce the downtimes in the system and improve the throughput.

The goal of this thesis is to predict …

Automated Program Profiling And Analysis For Managing Heterogeneous Memory Systems, Adam Palmer Howard

Masters Theses

Many promising memory technologies, such as non-volatile, storage-class memories and high-bandwidth, on-chip RAMs, are beginning to emerge. Since each of these new technologies present tradeoffs distinct from conventional DRAMs, next-generation systems are likely to include multiple tiers of memory storage, each with their own type of devices. To efficiently utilize the available hardware, such systems will need to alter their data management strategies to consider the performance and capabilities provided by each tier.

This work explores a variety of cross-layer strategies for managing application data in heterogeneous memory systems. We propose different program profiling-based techniques to automatically partition program allocation …

Graph-Based Latent Embedding, Annotation And Representation Learning In Neural Networks For Semi-Supervised And Unsupervised Settings, Ismail Ozsel Kilinc

USF Tampa Graduate Theses and Dissertations

Machine learning has been immensely successful in supervised learning with outstanding examples in major industrial applications such as voice and image recognition. Following these developments, the most recent research has now begun to focus primarily on algorithms which can exploit very large sets of unlabeled examples to reduce the amount of manually labeled data required for existing models to perform well. In this dissertation, we propose graph-based latent embedding/annotation/representation learning techniques in neural networks tailored for semi-supervised and unsupervised learning problems. Specifically, we propose a novel regularization technique called Graph-based Activity Regularization (GAR) and a novel output layer modification called …

Strong-Dism: A First Attempt To A Dynamically Typed Assembly Language (D-Tal), Ivory Hernandez

USF Tampa Graduate Theses and Dissertations

Dynamically Typed Assembly Language (D-TAL) is not only a lightweight and effective solution to the gap generated by the drop in security produced by the translation of high-level language instructions to low-level language instructions, but it considerably eases up the burden generated by the level of complexity required to implement typed assembly languages statically. Although there are tradeoffs between the static and dynamic approaches, focusing on a dynamic approach leads to simpler, easier to reason about, and more feasible ways to understand deployment of types over monomorphically-typed or untyped intermediate languages. On this occasion, DISM, a simple but powerful and …

Adaft: A Resource-Efficient Framework For Adaptive Fault-Tolerance In Cyber-Physical Systems, Ye Xu

Doctoral Dissertations

Cyber-physical systems frequently have to use massive redundancy to meet application requirements for high reliability. While such redundancy is required, it can be activated adaptively, based on the current state of the controlled plant. Most of the time the physical plant is in a state that allows for a lower level of fault-tolerance. Avoiding the continuous deployment of massive fault-tolerance will greatly reduce the workload of CPSs. In this dissertation, we demonstrate a software simulation framework (AdaFT) that can automatically generate the sub-spaces within which our adaptive fault-tolerance can be applied. We also show the theoretical benefits of AdaFT, and …

A High Quality, Eulerian 3d Fluid Solver In C++, Lejon Anthony Mcgowan

Computer Science and Software Engineering

Fluids are a part of everyday life, yet are one of the hardest elements to properly render in computer graphics. Water is the most obvious entity when thinking of what a fluid simulation can achieve (and it is indeed the focus of this project), but many other aspects of nature, like fog, clouds, and particle effects. Real-time graphics like video games employ many heuristics to approximate these effects, but large-scale renderers aim to simulate these effects as closely as possible.

In this project, I wish to achieve effects of the latter nature. Using the Eulerian technique of discrete grids, I …

Improving Hpc Communication Library Performance On Modern Architectures, Matthew G. F. Dosanjh

Computer Science ETDs

As high-performance computing (HPC) systems advance towards exascale (10^18 operations per second), they must leverage increasing levels of parallelism to achieve their performance goals. In addition to increased parallelism, machines of that scale will have strict power limitations placed on them. One direction currently being explored to alleviate those issues are many-core processors such as Intel’s Xeon Phi line. Many-core processors sacrifice clock speed and core complexity, such as out of order pipelining, to increase the number of cores on a die. While this increases floating point throughput, it can reduce the performance of serialized, synchronized, and latency sensitive code …

Hierarchical Fusion Based Deep Learning Framework For Lung Nodule Classification, Kazim Sekeroglu

LSU Doctoral Dissertations

Lung cancer is the leading cancer type that causes the mortality in both men and women. Computer aided detection (CAD) and diagnosis systems can play a very important role for helping the physicians in cancer treatments. This dissertation proposes a CAD framework that utilizes a hierarchical fusion based deep learning model for detection of nodules from the stacks of 2D images. In the proposed hierarchical approach, a decision is made at each level individually employing the decisions from the previous level. Further, individual decisions are computed for several perspectives of a volume of interest (VOI). This study explores three different …

Methodology To Perform Cyber Lethality Assessment, Matthew W. Zurasky

Engineering Management & Systems Engineering Theses & Dissertations

The Naval Surface Warfare Center, Dahlgren Division (NSWCDD) Lethality and Effectiveness Branch is the Navy’s subject matter experts (SME) on target vulnerability, weapon lethality, and weapon effectiveness. Branch personnel currently exercise expertise in the kinetic and directed energy weapon domains. When the Navy develops weapons in the kinetic and directed energy domains, there are clear and well established procedures and methodologies for performing target characterization that support weapon-target pairing. Algorithms exist to describe the likelihood of damage effects. It is natural that in the paradigm shift to cyberspace warfare that the Branch provide these same services to the warfighter in …

Modeling Energy Consumption Of High-Performance Applications On Heterogeneous Computing Platforms, Gary D. Lawson Jr.

Computational Modeling & Simulation Engineering Theses & Dissertations

Achieving Exascale computing is one of the current leading challenges in High Performance Computing (HPC). Obtaining this next level of performance will allow more complex simulations to be run on larger datasets and offer researchers better tools for data processing and analysis. In the dawn of Big Data, the need for supercomputers will only increase. However, these systems are costly to maintain because power is expensive. Thus, a better understanding of power and energy consumption is required such that future hardware can benefit.

Available power models accurately capture the relationship to the number of cores and clock-rate, however the relationship …

Computational Modeling For Abnormal Brain Tissue Segmentation, Brain Tumor Tracking, And Grading, Syed Mohammad Shamin Reza

Electrical & Computer Engineering Theses & Dissertations

This dissertation proposes novel texture feature-based computational models for quantitative analysis of abnormal tissues in two neurological disorders: brain tumor and stroke. Brain tumors are the cells with uncontrolled growth in the brain tissues and one of the major causes of death due to cancer. On the other hand, brain strokes occur due to the sudden interruption of the blood supply which damages the normal brain tissues and frequently causes death or persistent disability. Clinical management of these brain tumors and stroke lesions critically depends on robust quantitative analysis using different imaging modalities including Magnetic Resonance (MR) and Digital Pathology …

Comparing And Improving Facial Recognition Method, Brandon Luis Sierra

Electronic Theses, Projects, and Dissertations

Facial recognition is the process in which a sample face can be correctly identified by a machine amongst a group of different faces. With the never-ending need for improvement in the fields of security, surveillance, and identification, facial recognition is becoming increasingly important. Considering this importance, it is imperative that the correct faces are recognized and the error rate is as minimal as possible. Despite the wide variety of current methods for facial recognition, there is no clear cut best method. This project reviews and examines three different methods for facial recognition: Eigenfaces, Fisherfaces, and Local Binary Patterns to determine …

The Future Is Coming : Research On Maritime Communication Technology For Realization Of Intelligent Ship And Its Impacts On Future Maritime Management, Jiacheng Ke

Maritime Safety & Environment Management Dissertations (Dalian)

No abstract provided.

Research On Improving Navigation Safety Based On Big Data And Cloud Computing Technology For Qiongzhou Strait, Rui Wang

Maritime Safety & Environment Management Dissertations (Dalian)

No abstract provided.

Secure Integer Comparisons Using The Homomorphic Properties Of Prime Power Subgroups, Rhys A. Carlton

Electronic Thesis and Dissertation Repository

Secure multi party computation allows two or more parties to jointly compute a function under encryption without leaking information about their private inputs. These secure computations are vital in many fields including law enforcement, secure voting and bioinformatics because the privacy of the information is of paramount importance.

One common reference problem for secure multi party computation is the Millionaires' problem which was first introduced by Turing Award winner Yao in his paper "Protocols for secure computation". The Millionaires' problem considers two millionaires who want to know who is richer without disclosing their actual worth.

There are public-key cryptosystems that …

Improving Pure-Tone Audiometry Using Probabilistic Machine Learning Classification, Xinyu Song

McKelvey School of Engineering Theses & Dissertations

Hearing loss is a critical public health concern, affecting hundreds millions of people worldwide and dramatically impacting quality of life for affected individuals. While treatment techniques have evolved in recent years, methods for assessing hearing ability have remained relatively unchanged for decades. The standard clinical procedure is the modified Hughson-Westlake procedure, an adaptive pure-tone detection task that is typically performed manually by audiologists, costing millions of collective hours annually among healthcare professionals. In addition to the high burden of labor, the technique provides limited detail about an individual’s hearing ability, estimating only detection thresholds at a handful of pre-defined pure-tone …

Information Theoretic Study Of Gaussian Graphical Models And Their Applications, Ali Moharrer

LSU Doctoral Dissertations

In many problems we are dealing with characterizing a behavior of a complex stochastic system or its response to a set of particular inputs. Such problems span over several topics such as machine learning, complex networks, e.g., social or communication networks; biology, etc. Probabilistic graphical models (PGMs) are powerful tools that offer a compact modeling of complex systems. They are designed to capture the random behavior, i.e., the joint distribution of the system to the best possible accuracy. Our goal is to study certain algebraic and topological properties of a special class of graphical models, known as Gaussian graphs. First, …

Visualization And 3d Printing Of A 3d Solar Tracker Model Using Mayavi And Pov-Ray, Aditya Mehra

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

In this work, we have created a realistic model of a solar tracker using Mayavi: 3D scientific data visualization and plotting in Python, Enthought Canopy:a comprehensive Python analysis environment and Persistence of Vision Ray Tracer, or POV-Ray, a ray tracing program which generates photo-realistic images from a text-based scene description, a model of the solar tracker was also 3D printed.

Operating System Identification By Ipv6 Communication Using Machine Learning Ensembles, Adrian Ordorica

Graduate Theses and Dissertations

Operating system (OS) identification tools, sometimes called fingerprinting tools, are essential for the reconnaissance phase of penetration testing. While OS identification is traditionally performed by passive or active tools that use fingerprint databases, very little work has focused on using machine learning techniques. Moreover, significantly more work has focused on IPv4 than IPv6. We introduce a collaborative neural network ensemble that uses a unique voting system and a random forest ensemble to deliver accurate predictions. This approach uses IPv6 features as well as packet metadata features for OS identification. Our experiment shows that our approach is valid and we achieve …

Bayesian Methods And Machine Learning For Processing Text And Image Data, Yingying Gu

Theses and Dissertations

Classification/clustering is an important class of unstructured data processing problems. The classification (supervised, semi-supervised and unsupervised) aims to discover the clusters and group the similar data into categories for information organization and knowledge discovery. My work focuses on using the Bayesian methods and machine learning techniques to classify the free-text and image data, and address how to overcome the limitations of the traditional methods. The Bayesian approach provides a way to allow using more variations(numerical or categorical), and estimate the probabilities instead of explicit rules, which will benefit in the ambiguous cases. The MAP(maximum a posterior) estimation is used to …

Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi

Electronic Theses and Dissertations

While understanding of machine learning and data mining is still in its budding stages, the engineering applications of the same has found immense acceptance and success. Cybersecurity applications such as intrusion detection systems, spam filtering, and CAPTCHA authentication, have all begun adopting machine learning as a viable technique to deal with large scale adversarial activity. However, the naive usage of machine learning in an adversarial setting is prone to reverse engineering and evasion attacks, as most of these techniques were designed primarily for a static setting. The security domain is a dynamic landscape, with an ongoing never ending arms race …

A Data Science Pipeline For Educational Data : A Case Study Using Learning Catalytics In The Active Learning Classroom., Asuman Cagla Acun Sener

Electronic Theses and Dissertations

This thesis presents an applied data science methodology on a set of University of Louisville, Speed School of Engineering student data. We used data mining and classic statistical techniques to help educational researchers quickly see the data trends and peculiarities. Our data includes scores and information about two Engineering Fundamental Class. The format of these classes is called an inverted classroom model or flipped class. The purpose of this study is to analyze the data in order to uncover potentially hidden information, tell interesting stories about the data, examine student learning behavior and learning performance in an active learning environment, …

Improving Pattern Recognition And Neural Network Algorithms With Applications To Solar Panel Energy Optimization, Ernesto Zamora Ramos

UNLV Theses, Dissertations, Professional Papers, and Capstones

Artificial Intelligence is a big part of automation and with today's technological advances, artificial intelligence has taken great strides towards positioning itself as the technology of the future to control, enhance and perfect automation. Computer vision includes pattern recognition and classification and machine learning. Computer vision is at the core of decision making and it is a vast and fruitful branch of artificial intelligence. In this work, we expose novel algorithms and techniques built upon existing technologies to improve pattern recognition and neural network training, initially motivated by a multidisciplinary effort to build a robot that helps maintain and optimize …