Computer Engineering Commons^™

Railroad Condition Monitoring Using Distributed Acoustic Sensing And Deep Learning Techniques, Md Arifur Rahman

Electronic Theses and Dissertations

Proper condition monitoring has been a major issue among railroad administrations since it might cause catastrophic dilemmas that lead to fatalities or damage to the infrastructure. Although various aspects of train safety have been conducted by scholars, in-motion monitoring detection of defect occurrence, cause, and severity is still a big concern. Hence extensive studies are still required to enhance the accuracy of inspection methods for railroad condition monitoring (CM). Distributed acoustic sensing (DAS) has been recognized as a promising method because of its sensing capabilities over long distances and for massive structures. As DAS produces large datasets, algorithms for precise …

Assessing The Performance Of A Particle Swarm Optimization Mobility Algorithm In A Hybrid Wi-Fi/Lora Flying Ad Hoc Network, William David Paredes

UNF Graduate Theses and Dissertations

Research on Flying Ad-Hoc Networks (FANETs) has increased due to the availability of Unmanned Aerial Vehicles (UAVs) and the electronic components that control and connect them. Many applications, such as 3D mapping, construction inspection, or emergency response operations could benefit from an application and adaptation of swarm intelligence-based deployments of multiple UAVs. Such groups of cooperating UAVs, through the use of local rules, could be seen as network nodes establishing an ad-hoc network for communication purposes.

One FANET application is to provide communication coverage over an area where communication infrastructure is unavailable. A crucial part of a FANET implementation is …

Ocean Wave Prediction And Characterization For Intelligent Maritime Transportation, Pujan Pokhrel

University of New Orleans Theses and Dissertations

The national Earth System Prediction (ESPC) initiative aims to develop the predictions
for the next generation predictions of atmosphere, ocean, and sea-ice interactions in the scale of days to decades. This dissertation seeks to demonstrate the methods we can use to improve the ESPC models, especially the ocean prediction model. In the application side of the weather forecasts, this dissertation explores imitation learning with constraints to solve combinatorial optimization problems, focusing on the weather routing of surface vessels. Prediction of ocean waves is essential for various purposes, including vessel routing, ocean energy harvesting, agriculture, etc. Since the machine learning approaches …

A Versatile Python Package For Simulating Dna Nanostructures With Oxdna, Kira Threlfall

Computer Science and Computer Engineering Undergraduate Honors Theses

The ability to synthesize custom DNA molecules has led to the feasibility of DNA nanotechnology. Synthesis is time-consuming and expensive, so simulations of proposed DNA designs are necessary. Open-source simulators, such as oxDNA, are available but often difficult to configure and interface with. Packages such as oxdna-tile-binding pro- vide an interface for oxDNA which allows for the ability to create scripts that automate the configuration process. This project works to improve the scripts in oxdna-tile-binding to improve integration with job scheduling systems commonly used in high-performance computing environments, improve ease-of-use and consistency within the scripts compos- ing oxdna-tile-binding, and move …

Using Bluetooth Low Energy And E-Ink Displays For Inventory Tracking, David Whelan

Computer Science and Computer Engineering Undergraduate Honors Theses

The combination of Bluetooth Low energy and E-Ink displays allow for a low energy wire-less display. The application of this technology is far reaching especially given how the Bluetooth Low Energy specification can be extended. This paper proposes an extension to this specification specifically for inventory tracking. This extension combined with the low energy E-Ink display results in a smart label that can keep track of additional meta data and inventory counts for physical inventory. This label helps track the physical inventory and can help mitigate any errors in the logical organization of inventory.

Side-Channel Analysis On Post-Quantum Cryptography Algorithms, Tristen Teague

Computer Science and Computer Engineering Undergraduate Honors Theses

The advancements of quantum computers brings us closer to the threat of our current asymmetric cryptography algorithms being broken by Shor's Algorithm. NIST proposed a standardization effort in creating a new class of asymmetric cryptography named Post-Quantum Cryptography (PQC). These new algorithms will be resistant against both classical computers and sufficiently powerful quantum computers. Although the new algorithms seem mathematically secure, they can possibly be broken by a class of attacks known as side-channels attacks (SCA). Side-channel attacks involve exploiting the hardware that the algorithm runs on to figure out secret values that could break the security of the system. …

A Practical Approach To Automated Software Correctness Enhancement, Aleksandr Zakharchenko

Dissertations

To repair an incorrect program does not mean to make it correct; it only means to make it more-correct, in some sense, than it is. In the absence of a concept of relative correctness, i.e. the property of a program to be more-correct than another with respect to a specification, the discipline of program repair has resorted to various approximations of absolute (traditional) correctness, with varying degrees of success. This shortcoming is concealed by the fact that most program repair tools are tested on basic cases, whence making them absolutely correct is not clearly distinguishable from making them relatively more-correct. …

A Framework To Detect The Susceptibility Of Employees To Social Engineering Attacks, Hashim H. Alneami

Doctoral Dissertations and Master's Theses

Social engineering attacks (SE-attacks) in enterprises are hastily growing and are becoming increasingly sophisticated. Generally, SE-attacks involve the psychological manipulation of employees into revealing confidential and valuable company data to cybercriminals. The ramifications could bring devastating financial and irreparable reputation loss to the companies. Because SE-attacks involve a human element, preventing these attacks can be tricky and challenging and has become a topic of interest for many researchers and security experts. While methods exist for detecting SE-attacks, our literature review of existing methods identified many crucial factors such as the national cultural, organizational, and personality traits of employees that enable …

The U-Net-Based Active Learning Framework For Enhancing Cancer Immunotherapy, Vishwanshi Joshi

Theses, Dissertations and Capstones

Breast cancer is the most common cancer in the world. According to the U.S. Breast Cancer Statistics, about 281,000 new cases of invasive breast cancer are expected to be diagnosed in 2021 (Smith et al., 2019). The death rate of breast cancer is higher than any other cancer type. Early detection and treatment of breast cancer have been challenging over the last few decades. Meanwhile, deep learning algorithms using Convolutional Neural Networks to segment images have achieved considerable success in recent years. These algorithms have continued to assist in exploring the quantitative measurement of cancer cells in the tumor microenvironment. …

Design Project: Smart Headband, John Michel, Jack Durkin, Noah Lewis

Williams Honors College, Honors Research Projects

Concussion in sports is a prevalent medical issue. It can be difficult for medical professionals to diagnose concussions. With the fast pace nature of many sports, and the damaging effects of concussions, it is important that any concussion risks are assessed immediately. There is a growing trend of wearable technology that collects data such as steps and provides the wearer with in-depth information regarding their performance. The Smart Headband project created a wearable that can record impact data and provide the wearer with a detailed analysis on their risk of sustaining a concussion. The Smart Headband uses accelerometers and gyroscopes …

Investigating Single Precision Floating General Matrix Multiply In Heterogeneous Hardware, Steven Harris

McKelvey School of Engineering Theses & Dissertations

The fundamental operation of matrix multiplication is ubiquitous across a myriad of disciplines. Yet, the identification of new optimizations for matrix multiplication remains relevant for emerging hardware architectures and heterogeneous systems. Frameworks such as OpenCL enable computation orchestration on existing systems, and its availability using the Intel High Level Synthesis compiler allows users to architect new designs for reconfigurable hardware using C/C++. Using the HARPv2 as a vehicle for exploration, we investigate the utility of several of the most notable matrix multiplication optimizations to better understand the performance portability of OpenCL and the implications for such optimizations on this and …

Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh

Electronic Thesis and Dissertation Repository

Rapid growth in numbers of connected devices, including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

An Fpga-Based Hardware Accelerator For The Digital Image Correlation Engine, Keaten Stokke

Graduate Theses and Dissertations

The work presented in this thesis was aimed at the development of a hardware accelerator for the Digital Image Correlation engine (DICe) and compare two methods of data access, USB and Ethernet. The original DICe software package was created by Sandia National Laboratories and is written in C++. The software runs on any typical workstation PC and performs image correlation on available frame data produced by a camera. When DICe is introduced to a high volume of frames, the correlation time is on the order of days. The time to process and analyze data with DICe becomes a concern when …

Nonlinear Least Squares 3-D Geolocation Solutions Using Time Differences Of Arrival, Michael V. Bredemann

Mathematics & Statistics ETDs

This thesis uses a geometric approach to derive and solve nonlinear least squares minimization problems to geolocate a signal source in three dimensions using time differences of arrival at multiple sensor locations. There is no restriction on the maximum number of sensors used. Residual errors reach the numerical limits of machine precision. Symmetric sensor orientations are found that prevent closed form solutions of source locations lying within the null space. Maximum uncertainties in relative sensor positions and time difference of arrivals, required to locate a source within a maximum specified error, are found from these results. Examples illustrate potential requirements …

A Visual Analytics System For Making Sense Of Real-Time Twitter Streams, Amir Haghighatimaleki

Electronic Thesis and Dissertation Repository

Through social media platforms, massive amounts of data are being produced. Twitter, as one such platform, enables users to post “tweets” on an unprecedented scale. Once analyzed by machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types of biases and provide uncertain outputs. This thesis describes a visual analytics system (i.e., a tool that combines data visualization, human-data interaction, and …

Empathetic Computing For Inclusive Application Design, Kenny Choo Tsu Wei

Dissertations and Theses Collection (Open Access)

The explosive growth of the ecosystem of personal and ambient computing de- vices coupled with the proliferation of high-speed connectivity has enabled ex- tremely powerful and varied mobile computing applications that are used every- where. While such applications have tremendous potential to improve the lives of impaired users, most mobile applications have impoverished designs to be inclusive– lacking support for users with specific disabilities. Mobile app designers today haveinadequate support to design existing classes of apps to support users with specific disabilities, and more so, lack the support to design apps that specifically target these users. One way to resolve …

Automatic Identification Of Animals In The Wild: A Comparative Study Between C-Capsule Networks And Deep Convolutional Neural Networks., Joel Kamdem Teto, Ying Xie

Master of Science in Computer Science Theses

The evolution of machine learning and computer vision in technology has driven a lot of

improvements and innovation into several domains. We see it being applied for credit decisions, insurance quotes, malware detection, fraud detection, email composition, and any other area having enough information to allow the machine to learn patterns. Over the years the number of sensors, cameras, and cognitive pieces of equipment placed in the wilderness has been growing exponentially. However, the resources (human) to leverage these data into something meaningful are not improving at the same rate. For instance, a team of scientist volunteers took 8.4 years, …

Analyzing And Modeling Users In Multiple Online Social Platforms, Roy Lee Ka Wei

Dissertations and Theses Collection (Open Access)

This dissertation addresses the empirical analysis on user-generated data from multiple online social platforms (OSPs) and modeling of latent user factors in multiple OSPs setting.

In the first part of this dissertation, we conducted cross-platform empirical studies to better understand user's social and work activities in multiple OSPs. In particular, we proposed new methodologies to analyze users' friendship maintenance and collaborative activities in multiple OSPs. We also apply the proposed methodologies on real-world OSP datasets, and the findings from our empirical studies have provided us with a better understanding on users' social and work activities which are previously not uncovered …

Enhancing 3d Visual Odometry With Single-Camera Stereo Omnidirectional Systems, Carlos A. Jaramillo

Dissertations, Theses, and Capstone Projects

We explore low-cost solutions for efficiently improving the 3D pose estimation problem of a single camera moving in an unfamiliar environment. The visual odometry (VO) task -- as it is called when using computer vision to estimate egomotion -- is of particular interest to mobile robots as well as humans with visual impairments. The payload capacity of small robots like micro-aerial vehicles (drones) requires the use of portable perception equipment, which is constrained by size, weight, energy consumption, and processing power. Using a single camera as the passive sensor for the VO task satisfies these requirements, and it motivates the …

Distributed Knowledge Discovery For Diverse Data, Hossein Hamooni

Computer Science ETDs

In the era of new technologies, computer scientists deal with massive data of size hundreds of terabytes. Smart cities, social networks, health care systems, large sensor networks, etc. are constantly generating new data. It is non-trivial to extract knowledge from big datasets because traditional data mining algorithms run impractically on such big datasets. However, distributed systems have come to aid this problem while introducing new challenges in designing scalable algorithms. The transition from traditional algorithms to the ones that can be run on a distributed platform should be done carefully. Researchers should design the modern distributed algorithms based on the …

A Robust Framework For Mining Youtube Data, Zifeng Tian

Theses, Dissertations and Capstones

YouTube is currently the most popular and successful video sharing website. As YouTube has broad and profound social impact, YouTube analytics has become a hot research area. The videos on YouTube have become a treasure of data. However, getting access to the immense and massive YouTube data is a challenge. Previous research, studies, and analysis so far, are only conducted on very small volumes of YouTube video data. To date, there exists no mechanism to systematically and continuously collect, process and store the rich set of YouTube data. This thesis presents a methodology to systematically and continuously mine and store …

Representation And Analysis Of Multi-Modal, Nonuniform Time Series Data: An Application To Survival Prognosis Of Oncology Patients In An Outpatient Setting, Jennifer Winikus

Dissertations, Master's Theses and Master's Reports

The representation of nonuniform, multi-modal, time-limited time series data is complex and explored through the use of discrete representation, dimensionality reduction with segmentation based techniques, and with behavioral representation approaches. These explorations are done with a focus on an outpatient oncology setting with the classification and regression analysis being used for length of survival prognosis. Each decision of representation and analysis is not independent, with implications of each decision in method for how the data is represented and then which analysis technique is used. One unique aspect of the work is the use of outpatient clinical data for patients, which …

Computational Framework For Small Animal Spect Imaging: Simulation And Reconstruction, Sang Hyeb Lee

Doctoral Dissertations

Small animal Single Photon Emission Computed Tomography (SPECT) has been an invaluable asset in biomedical science since this non-invasive imaging technique allows the longitudinal studies of animal models of human diseases. However, the image degradation caused by non-stationary collimator-detector response and single photon emitting nature of SPECT makes it difficult to provide a quantitative measure of 3D radio-pharmaceutical distribution inside the patient. Moreover, this problem exacerbates when an intra-peritoneal X-ray contrast agent is injected into a mouse for low-energy radiotracers.

In this dissertation, we design and develop a complete computational framework for the entire SPECT scan procedure from the radio-pharmaceutical …

Secure And Reliable Routing Protocol For Transmission Data In Wireless Sensor Mesh Networks, Nooh Adel Bany Muhammad

Dissertations

Abstract

Sensor nodes collect data from the physical world then exchange it until it reaches the intended destination. This information can be sensitive, such as battlefield surveillance. Therefore, providing secure and continuous data transmissions among sensor nodes in wireless network environments is crucial. Wireless sensor networks (WSN) have limited resources, limited computation capabilities, and the exchange of data through the air and deployment in accessible areas makes the energy, security, and routing major concerns in WSN. In this research we are looking at security issues for the above reasons. WSN is susceptible to malicious activities such as hacking and physical …

Automated Image Interpretation For Science Autonomy In Robotic Planetary Exploration, Raymond Francis

Electronic Thesis and Dissertation Repository

Advances in the capabilities of robotic planetary exploration missions have increased the wealth of scientific data they produce, presenting challenges for mission science and operations imposed by the limits of interplanetary radio communications. These data budget pressures can be relieved by increased robotic autonomy, both for onboard operations tasks and for decision- making in response to science data.

This thesis presents new techniques in automated image interpretation for natural scenes of relevance to planetary science and exploration, and elaborates autonomy scenarios under which they could be used to extend the reach and performance of exploration missions on planetary surfaces.

Two …

Software Porting Of A 3d Reconstruction Algorithm To Razorcam Embedded System On Chip, Kevin Curtis Gunn

Graduate Theses and Dissertations

A method is presented to calculate depth information for a UAV navigation system from Keypoints in two consecutive image frames using a monocular camera sensor as input and the OpenCV library. This method was first implemented in software and run on a general-purpose Intel CPU, then ported to the RazorCam Embedded Smart-Camera System and run on an ARM CPU onboard the Xilinx Zynq-7000. The results of performance and accuracy testing of the software implementation are then shown and analyzed, demonstrating a successful port of the software to the RazorCam embedded system on chip that could potentially be used onboard a …

Programming Dense Linear Algebra Kernels On Vectorized Architectures, Jonathan Lawrence Peyton

Masters Theses

The high performance computing (HPC) community is obsessed over the general matrix-matrix multiply (GEMM) routine. This obsession is not without reason. Most, if not all, Level 3 Basic Linear Algebra Subroutines (BLAS) can be written in terms of GEMM, and many of the higher level linear algebra solvers' (i.e., LU, Cholesky) performance depend on GEMM's performance. Getting high performance on GEMM is highly architecture dependent, and so for each new architecture that comes out, GEMM has to be programmed and tested to achieve maximal performance. Also, with emergent computer architectures featuring more vector-based and multi to many-core processors, GEMM performance …

Exploring Computational Chemistry On Emerging Architectures, David Dewayne Jenkins

Doctoral Dissertations

Emerging architectures, such as next generation microprocessors, graphics processing units, and Intel MIC cards, are being used with increased popularity in high performance computing. Each of these architectures has advantages over previous generations of architectures including performance, programmability, and power efficiency. With the ever-increasing performance of these architectures, scientific computing applications are able to attack larger, more complicated problems. However, since applications perform differently on each of the architectures, it is difficult to determine the best tool for the job. This dissertation makes the following contributions to computer engineering and computational science. First, this work implements the computational chemistry variational …

Parallel For Loops On Heterogeneous Resources, Frederick Edward Weber

Doctoral Dissertations

In recent years, Graphics Processing Units (GPUs) have piqued the interest of researchers in scientific computing. Their immense floating point throughput and massive parallelism make them ideal for not just graphical applications, but many general algorithms as well. Load balancing applications and taking advantage of all computational resources in a machine is a difficult challenge, especially when the resources are heterogeneous. This dissertation presents the clUtil library, which vastly simplifies developing OpenCL applications for heterogeneous systems. The core focus of this dissertation lies in clUtil's ParallelFor construct and our novel PINA scheduler which can efficiently load balance work onto multiple …

Dynamic Task Execution On Shared And Distributed Memory Architectures, Asim Yarkhan

Doctoral Dissertations

Multicore architectures with high core counts have come to dominate the world of high performance computing, from shared memory machines to the largest distributed memory clusters. The multicore route to increased performance has a simpler design and better power efficiency than the traditional approach of increasing processor frequencies. But, standard programming techniques are not well adapted to this change in computer architecture design.

In this work, we study the use of dynamic runtime environments executing data driven applications as a solution to programming multicore architectures. The goals of our runtime environments are productivity, scalability and performance. We demonstrate productivity by …