Physical Sciences and Mathematics Commons^™

Region Detection & Segmentation Of Nissl-Stained Rat Brain Tissue, Alexandro Arnal

Open Access Theses & Dissertations

People who analyze images of biological tissue rely on the segmentation of structures as a preliminary step. In particular, laboratories studying the rat brain delineate brain regions to position scientific findings on a brain atlas to propose hypotheses about the rat brain and, ultimately, the human brain. Our work intersects with the preliminary step of delineating regions in images of brain tissue via computational methods.

We investigate pixel-wise classification or segmentation of brain regions using ten histological images of brain tissue sections stained for Nissl substance. We present a deep learning approach that uses the fully convolutional neural network, U-Net, …

Glacier Segmentation From Remote Sensing Imagery Using Deep Learning, Bibek Aryal

Open Access Theses & Dissertations

Large-scale study of glaciers improves our understanding of global glacier change and is imperative for monitoring the ecological environment, preventing disasters, and studying the effects of global climate change. In recent years, remote sensing imagery has been preferred over riskier and resource-intensive field visits for tracking landscape level changes like glaciers. However, periodic manual labeling of glaciers over a large area is not feasible due to the considerable amount of time it requires while automatic segmentation of glaciers has its own set of challenges. Our work aims to study the challenges associated with segmentation of glaciers from remote sensing imagery …

Online/Incremental Learning To Mitigate Concept Drift In Network Traffic Classification, Alberto R. De La Rosa

Open Access Theses & Dissertations

Communication networks play a large role in our everyday lives. COVID19 pandemic in 2020 highlighted their importance as most jobs had to be moved to remote work environments. It is possible that the spread of the virus, the death toll, and the economic consequences would have been much worse without communication networks. To remove sole dependence on one equipment vendor, networks are heterogeneous by design. Due to this, as well as their increasing size, network management has become overwhelming for network managers. For this reason, automating network management will have a significant positive impact. Machine learning and software defined networking …

Synthetic Data Generation For Intelligent Inspection Of Structural Environments, Noshin Habib

Open Access Theses & Dissertations

Automated detection of cracks and corrosion in pavements and industrial settings is essential to a cost-effective approach to maintenance. Deep learning has paved the path for vast levels of improvement in the area. Such models require a plethora of data with accurate ground truth and enough variation for the model to generalize to the data, which is notwidely available. There has been recent progress in computer graphics being used for the creation of synthetic data to address the issue of deficient data availability, but it is limited to specific objects, such as cars and human beings. Textures and deformities within …

Analyzing And Quantifying The Impact Of Software Diversification On Return-Oriented Programming (Rop) Based Exploits, David Reyes

Open Access Theses & Dissertations

With the implementation of modern software mitigation techniques such: as Address Space Layout Randomization (ASLR), stack canaries, and the No-Execute bit (N.X.), attackers can no longer achieve arbitrary code execution simply by injecting shellcode into a vulnerable buffer and redirecting execution to this vulnerable buffer. Instead, attackers have pivoted to Return Oriented Programming (ROP) to achieve the same arbitrary code execution. Using this attack method, attackers string together ROP gadgets, assembly code snippets found in the target binary, to form what are known as ROP Chains. Using these ROP Chains, attackers can achieve the same malicious behavior as previous code …

Radio Frequency Fingerprinting And Its Application To Scada Environments, Evan White

Open Access Theses & Dissertations

With the introduction of IoT into ICS and smartgrid environments there has been a mod-ernization of communication protocols through the internet. This has led to the use of features such as TCP/IP but with it comes modernized attack vectors against these sys- tems. These attacks can be Man In the Middle (MITM), rogue device communication and device cloning. To prevent these attacks, this thesis deploys Radio Frequency Fingerprint- ing (RFF) techniques to verify the uniqueness and legitimacy of known devices. It is crucial to employ security measures within ICS that do not add to the network complexity as this effects …

Decision Making Under Uncertainty With A Special Emphasis On Geosciences And Education, Laxman Bokati

Open Access Theses & Dissertations

In many practical situations, we need to make a decision. In engineering, we need to decideon the best design of a system, and, for existing systems - on the best control strategy. In financial applications, we need to decide what is the best way to invest money. In geosciences, we need to decide whether we should explore a possible mineral deposit - or whether we should perform more experiments and measurements (and what exactly). In some cases, we can compute the exact consequences of each decision - e.g., if we are controlling a satellite. However, in many other cases, we …

Metrological Challenges Of Practical Computer-Enhanced Measurements, Hector Alejandro Reyes

Open Access Theses & Dissertations

As technology progresses, sensors and computers become cheaper, so we can afford to perform more measurements and process the data faster. However, this also brings challenges.The goal of this thesis is to enumerate these challenges and to provide possible solutions. The first challenge is related to the fact that the existing metrological recommendations are mostly based on the previous practice, when we could only afford to have a small number of measurements. In this regard, our objective is to describe the related problem and to propose a solution to this problem. These description (on the example of the design of …

Oil Particle Analysis Using Machine Learning And Holography Imaging, Daniel Cruz

Open Access Theses & Dissertations

Holographic cameras show potential as a sensor to monitor oil spills. Holographic cameras record the light interference from particles in a volume of space, producing an image called a hologram. Processing these holograms is known as hologram reconstruction. It produces a representation of particles located in three-dimensional space. These cameras can record precise shapes and sizes of particles in a volume of water. However, it is very time-consuming and resource-intensive to process the images. Most algorithms that perform particle analysis require the hologram reconstruction step. The well-documented hybrid method is one such algorithm. Machine learning is one possible technique that …

Continuous Field Sensor Authentication And Process Integrity Assurance Mechanisms In Critical National Infrastructures, Abel Osvaldo Gomez Rivera

Open Access Theses & Dissertations

The growing modernization of traditional Industrial Cyber-Physical Systems (ICPSs) has increased the probability and effectiveness of cyber attacks by integrating modern communication technologies that expose security vulnerabilities like lack of access control policies to adversaries. The exponential growth of cyber attacks has caught the attention of stakeholders that have proposed cybersecurity initiatives to protect ICPSs. ICPSs are part of the Critical National Infrastructures (CNIs) supporting the society's sustainability and national security. The cybersecurity initiatives aiming to address cyber attacks proposed by stakeholders must continuously authenticate constrained devices. Operational thresholds and process integrity assurance must also be maintained. A centralized ICPS …

Investigating The Effects Of Decoupling Cache And Core Speed On Power, Throughput, And Energy, David Daniel Pruitt

Open Access Theses & Dissertations

A variety of computer systems from HPC to mobile systems are power limited and performance sensitive. These systems use very similar components at different scales. Dynamic Voltage and Frequency Scaling (DVFS) features enable modulation of CPU performance and efficiency characteristics to power, energy and timing requirements.Programs have a variety of computational characteristics. If a CPU subsystem substantially limits a particular programâ??s execution progress, that programâ??s throughput will vary proportionally with the subsystemâ??s clock frequency. In contrast, if a CPU subsystem does not substantially limit throughput, the impact of a change in its clock frequency will result in a diminimus change …

Material Synthesis And Machine Learning For Additive Manufacturing, Jaime Eduardo Regis

Open Access Theses & Dissertations

The goal of this research was to address three key challenges in additive manufacturing (AM), the need for feedstock material, minimal end-use fabrication from lack of functionality in commercially available materials, and the need for qualification and property prediction in printed structures. The near ultraviolet-light assisted green reduction of graphene oxide through L-ascorbic acid was studied with to address the issue of low part strength in additively manufactured parts by providing a functional filler that can strengthen the polymer matrix. The synthesis of self-healing epoxy vitrimers was done to adapt high strength materials with recyclable properties for compatibility with AM …

Covid Synergy: A Machine Learning Approach Uncovering Potential Treatment Combinations For Sars-Cov-2, Jason Eden Sanchez

Open Access Theses & Dissertations

For more than two years, the COVID-19 pandemic has upended the lives of billions of individualsworldwide leading to disruptions in healthcare, the economy and society at large. As the pandemic enters its third year, the human impact cannot be overstated and the need to develop effective pharmaceuticals remains. Though there currently exits FDA-approved medications for COVID-19, the emergence of novel variants, such as Omicron, highlights the importance of discovering new therapies which will continue to be effective regardless of the pandemicâ??s progression. Because discovering new medications is a costly and timeintensive endeavor, my approach entails drug repurposing to test medications …

A Machine Learning Approach To Stochastic Optimal Control, Pablo Ever Avalos

Open Access Theses & Dissertations

Merton's portfolio optimization problem is a well-renowned problem in financial mathematics which seeks to optimize the investment decision for an investor. In the simplest situation, the market consists of a risk-less asset (i.e. a bond) that pays back a relatively low interest rate, and a risky asset (i.e. a stock) that follows a geometric Brownian motion. The optimal allocation strategy of the investor's wealth is found by optimizing the expected utility along the stochastic evolution of the market. This thesis focuses on several different applications of this optimization problem. We look at pre-constructed analytical solutions and showcase the results. We …

Game-Theoretic Deception Modeling For Distracting Network Adversarie, Mohammad Sujan Miah

Open Access Theses & Dissertations

In this day and age, adversaries in the cybersecurity space have become alarmingly capable of identifying network vulnerabilities and work out various targets to attack where deception is becoming an increasingly crucial technique for the defenders to delay these attacks. For securing computer networks, the defenders use various deceptive decoy objects to detect, confuse, and distract attackers. By trapping the attackers, these decoys gather information, waste their time and resources, and potentially prevent future attacks. However, we have to consider that an attacker with the help of smart techniques may detect the decoys and avoid them. One of the well-known …