Physical Sciences and Mathematics Commons^™

On Vulnerabilities Of Building Automation Systems, Michael Cash

Graduate Thesis and Dissertation 2023-2024

Building automation systems (BAS) have become more commonplace in personal and commercial environments in recent years. They provide many functions for comfort and ease of use, from automating room temperature and shading, to monitoring equipment data and status. Even though their convenience is beneficial, their security has become an increased concerned in recent years. This research shows an extensive study on building automation systems and identifies vulnerabilities in some of the most common building communication protocols, BACnet and KNX. First, we explore the BACnet protocol, exploring its Standard BACnet objects and properties. An automation tool is designed and implemented to …

Dual Frequency Comb Mid-Ir – Thz Spectroscopy, Dmitrii Konnov

Graduate Thesis and Dissertation 2023-2024

The optical frequency comb is a coherent light source whose spectrum consists of hundreds of thousands perfectly equidistant narrow frequency components and precisely expressed in just two radio frequencies. Even though optical frequency combs were developed 25 years ago, that led to the Nobel Prize in Physics 2005, only recently there was a significant progress in generating broadband optical frequency combs in the mid-infrared. These achievements became possible due to the development of new types of robust fiber and solid-state lasers and the efficient downconverting of their frequencies through different techniques based on advanced nonlinear crystals. In this dissertation, I …

Deep Learning One-Class Classification With Support Vector Methods, Hayden D. Hampton

Graduate Thesis and Dissertation 2023-2024

Through the specialized lens of one-class classification, anomalies–irregular observations that uncharacteristically diverge from normative data patterns–are comprehensively studied. This dissertation focuses on advancing boundary-based methods in one-class classification, a critical approach to anomaly detection. These methodologies delineate optimal decision boundaries, thereby facilitating a distinct separation between normal and anomalous observations. Encompassing traditional approaches such as One-Class Support Vector Machine and Support Vector Data Description, recent adaptations in deep learning offer a rich ground for innovation in anomaly detection. This dissertation proposes three novel deep learning methods for one-class classification, aiming to enhance the efficacy and accuracy of anomaly detection in …

Navigating The Noise: Implications Of Increasing Ship Noise For An Arctic Ocean Soundscape, Andrea Lynn

Antioch University Dissertations & Theses

There is no quiet way to churn water. Noise pollution caused by ships is increasing in the Arctic Ocean as sea ice melts, creating more open channels for vessels. This study provides a glimpse into the sources and balance of sounds in a portion of the Arctic Ocean soundscape surrounding the Norwegian archipelago of Svalbard. Characterization of the soundscape provides essential data as the region quickly transforms. This study also considers human perceptions of underwater ocean noise and its impacts in the region, and it reviews current ocean policy, suggesting mitigation strategies and ways forward. Before the rapid development of …

Heed The Warning Signs: The Effectiveness Of Message Popup Warnings For Deterring The Spread Of Misinformation, Hollis Greenberg

CCE Theses and Dissertations

As false news can propagate to others rapidly, social media platforms employ multiple methods to combat misinformation. Debunking techniques are warning features embedded into a platform’s interface that alert readers of misinformation. These warnings have two goals: to “debunk” false information and to prevent the further spread of misinformation. Researchers have evaluated the effectiveness of debunking techniques to understand how users increase their awareness of misinformation, and what users do with the information given in warning messages. Message popup warnings are a newer and understudied type of debunking technique.

The overarching research question of this study was: Are message popup …

Assessing Organizational Investments In Cybersecurity And Financial Performance Before And After Data Breach Incidents Of Cloud Saas Platforms, Munther B. Ghazawneh

CCE Theses and Dissertations

Prior research indicated that providing inappropriate investment in organizations for Information Technology (IT) security makes these organizations suffer from IT security issues that may cause data breach incidents. Data breaches in cloud Software as a Service (SaaS) platforms lead to the disclosure of sensitive information, which causes disruption of services, damage to the organizational image, or financial losses. Massive data breaches still exist in cloud SaaS platforms which result in data leaks and data theft of customers in organizations.

IT security risks and vulnerabilities cost organizations millions of dollars a year as organizations may face an increase in cybersecurity challenges. …

Validating Machine And Human Decision-Making In Forensic Fire Debris Analysis, Frances A. Whitehead

Graduate Thesis and Dissertation 2023-2024

This work presents a background on the chemical complexity of fire debris analysis, including an ever-present matrix of pyrolysis products as the catalyst that led to the creation of the National Center for Forensic Science's Fire Debris Database. A selection of these 1,000+ casework-relevant ground truth samples was used to create two newly proposed analyst workflows to connect the current method of categorical reporting with evaluative reporting practices reflective of the strength of the evidence. Both workflows use linear sequential unmasking to help mitigate bias, a discrete scoring system for quantification of the analysis, and receiver operating characteristic (ROC) curves …

Optical Seed Development For Yb-Fiber Laser, James G. Brutus

Graduate Thesis and Dissertation 2023-2024

Master Oscillator Power Amplifiers (MOPA) are laser systems that utilize a seed and pump amplification system to boost the output power of high-quality lower power seeding signals. MOPAs can generate high gain while avoiding many of the nonlinearities that negatively affect resonance-based lasers that are known to feature higher internal intensities. Additionally, MOPAs provide an easy alternative to the construction of novel laser technologies for higher output power as they can be easily combined with existing laser sources to amplify their output power.

This thesis outlines the design of an ytterbium-doped fiber laser (YDFL), featuring a MOPA architecture. The YDFL …

Collect Spatiotemporally Correlated Data In Iot Networks With An Energy-Constrained Uav, Wenzheng Xu, Heng Shao, Qunli Shen, Jian Peng, Wen Huang, Weifa Liang, Tang Liu, Xin Wei Yao, Tao Lin, Sajal K. Das

Computer Science Faculty Research & Creative Works

UAVs (Unmanned Aerial Vehicles) Are Promising Tools For Efficient Data Collections Of Sensors In IoT Networks. Existing Studies Exploited Both Spatial And Temporal Data Correlations To Reduce The Amount Of Collected Redundant Data, In Which Sensors Are First Partitioned Into Different Clusters, A Master Sensor In Each Cluster Then Collects Raw Data From Other Sensors And Compresses The Received Data. An Energy-Constrained UAV Finally Collects The Maximum Amount Of Compressed Data From Different Master Sensors. We However Notice That The Compressed Data From Only A Portion Of Clusters Are Collected By The UAV In The Existing Studies, While The Data …

Stitching Satellites To The Edge: Pervasive And Efficient Federated Leo Satellite Learning, Mohamed Elmahallawy, Tony Tie Luo

Computer Science Faculty Research & Creative Works

In the Ambitious Realm of Space AI, the Integration of Federated Learning (FL) with Low Earth Orbit (LEO) Satellite Constellations Holds Immense Promise. However, Many Challenges Persist in Terms of Feasibility, Learning Efficiency, and Convergence. These Hurdles Stem from the Bottleneck in Communication, Characterized by Sporadic and Irregular Connectivity between LEO Satellites and Ground Stations, Coupled with the Limited Computation Capability of Satellite Edge Computing (SEC). This Paper Proposes a Novel FL-SEC Framework that Empowers LEO Satellites to Execute Large-Scale Machine Learning (ML) Tasks Onboard Efficiently. its Key Components Include I) Personalized Learning Via Divide-And-Conquer, Which Identifies and Eliminates Redundant …

On A Fully Coupled Nonlocal Multipoint Boundary Value Problem For A Dual Hybrid System Of Nonlinear Q -Fractional Differential Equations, Ahmed Alsaedi, Martin Bohner, Bashir Ahmad, Boshra Alharbi

Mathematics and Statistics Faculty Research & Creative Works

A new class of nonlocal multipoint boundary value problems involving a dual hybrid system of nonlinear Riemann-Liouville-type q-fractional differential equations is studied in this paper. Existence and uniqueness results for the given problem are derived by applying the Leray-Schauder nonlinear alternative and the Banach contraction mapping principle. Examples are presented for illustrating the obtained results. The work established in this paper is a useful contribution to the existing literature on q-fractional differential equations. Some interesting special cases are also discussed.

Critical Point Approaches To Nonlinear Square Root Laplacian Equations, Martin Bohner, Giuseppe Caristi, Shapour Heidarkhani, Amjad Salari

Mathematics and Statistics Faculty Research & Creative Works

This work is devoted to the study of multiplicity results of solutions for a class of nonlinear equations involving the square root of the Laplacian. Indeed, we will use variational methods for smooth functionals, defined on reflexive Banach spaces, in order to achieve the existence of at least three solutions for the equations. Moreover, assuming that the nonlinear terms are nonnegative, we will prove that the solutions are nonnegative. Finally, by presenting an example, we will ensure the applicability of our results.

Identifying The Origins Of Business’ Data Breaches Utilizing Covert Timing Channels, Gayle L. Frisbie

Master's Theses and Doctoral Dissertations

Cybersecurity events and data breaches are on the rise and are very costly to businesses. Businesses rely on connectivity and information systems to conduct business, yet those same information systems can be breached and the organization's data exposed. Today, there is a heavy reliance of organizations upon network connections to connect the entire organization in order to conduct business efficiently and from multiple locations. Covert timing channels are a cybersecurity attack method in which malicious actors embed privileged information into normal network traffic without authorization. Malicious actors, by carefully manipulating timing patterns in covert timing channels, can create a hidden …

Towards A Transparency-Based, Value-Sensitive Design Solution For Bias In Self-Driving Cars: An Ethical Violation Assessment And Risk Analysis Framework On Consumer-Held Values, Nada Ahmad Madkour

Master's Theses and Doctoral Dissertations

Background: The rapid growth of automated systems and artificial intelligence (AI), particularly, self-driving cars (SDCs), has attracted significant investments and can potentially contribute to humanity’s flourishing. However, before widespread adoption, it is important to address ethical violations such as bias in AI, highlighted by many real-world cases of bias in AI leading to unfair outcomes in tools like facial recognition, hiring software, and pedestrian detection. Bias in AI can lead to potentially fatal outcomes in SDCs, emphasizing the need for a thorough examination of bias in SDCs.

Purpose: To enhance AI ethics by providing tools to support transparency and value- …

Automatic Modeling Of Cyber Intrusions Using The Diamond Model Utilizing Security Logs And Events, Mahmoud Al-Maani

Master's Theses and Doctoral Dissertations

Current intrusion analysis models suffer from unreliability and inaccuracy due to their reliance on outdated and inadequate data sources. Numerous models focus on a particular type of data, leading to potential modeling faults in intrusion analysis models' recommendations. The objective of this thesis is to build a modernized model by integrating the diamond model with security information and event management systems. This thesis presents a detailed cyber intrusion analysis model; in which Elasticsearch is being used to collect and analyze logs about cyber attacks and extract major indicators of compromise then finally map them to the diamond model. The results …

Probing The Ising Model’S Thermodynamics Through Restricted Boltzmann Machines, Xiaobei (Emma) Zhang

HMC Senior Theses

This thesis explores the connection between physics and machine learning by using Restricted Boltzmann Machines (RBMs) to study the thermodynamic properties of the Ising model. The Ising model is a simple but realistic model that captures the magnetic behavior of a system, where spins occupy a lattice of sites and different spin configurations correspond to different energies. The model exhibits phase transitions between ferromagnetic and paramagnetic phases as a function of temperature. RBMs are two-layered neural networks that can learn probability distributions over binary spins. The study generates 2D Ising model data at different temperatures using Monte Carlo simulations, including …

Solving Robert Wilson’S 𝑡 ≠ 2 Conjecture On Graham Sequences, Krishna Rajesh

HMC Senior Theses

Ron Graham's sequence is a surprising bijection from the natural numbers to the non-prime integers, which is constructed by looking at sequences whose product is square. In this thesis we will resolve a 22-year-old conjecture about this bijection, by construction of explicit sequences in a modified number theoretic context. Additionally, we will discuss the history of this problem, and give computational techniques for computing this bijection, levering ideas from linear algebra over the finite field of two elements.

The Dual Boundary Complex Of The Moduli Space Of Cyclic Compactifications, Toby Anderson

HMC Senior Theses

Moduli spaces provide a useful method for studying families of mathematical objects. We study certain moduli spaces of algebraic curves, which are generalizations of familiar lines and conics. This thesis focuses on, Δ^(r,n), the dual boundary complex of the moduli space of genus-zero cyclic curves. This complex is itself a moduli space of graphs and can be investigated with combinatorial methods. Remarkably, the combinatorics of this complex provides insight into the geometry and topology of the original moduli space. In this thesis, we investigate two topologically invariant properties of Δ^(r,n). We compute its Euler characteristic and …

Exploring Sigmoidal Bounded Confidence Models With Mean Field Methods, Tian Dong

HMC Senior Theses

Mathematicians use models of opinion dynamics to describe how opinions in a group of people change over time, which can yield insight into mechanisms behind phenomena like polarization and consensus. In these models, mathematicians represent the community as a graph, where nodes represent agents and edges represent possible interactions. Opinion updates are modeled with a system of differential equations (ODEs). Our work focuses on the sigmoidal bounded confidence model (SBCM), where agents update their opinion toward a weighted average of their neighbors' opinions by weighting similar opinions more heavily. Using tools developed in physics (mean-field theory), we derive a continuity …

A Combinatorial Model For Affine Demazure Crystals Of Levels Zero And One, Samuel Spellman

Electronic Theses & Dissertations (2024 - present)

The symmetric and non-symmetric Macdonald polynomials are special families of orthogonal polynomials with parameters q and t. They are indexed by dominant, (resp. arbitrary) weights associated to a root system and generalize several well-known polynomials such as the Schur polynomials, Jack polynomials, Hall-Littlewood polynomials, etc. There are two well-known combinatorial models for computing these polynomials: a tableau model in type A, due to Haglund, Haiman and Loehr, and a type-independent model due to Ram and Yip, based on alcove walks.

Crystals bases are an important construction encoding information about Lie algebra representations. It turns out that there is an interesting …

Examining Downshear Reformation In Tropical Cyclones, Nathalie Rivera Torres

Electronic Theses & Dissertations (2024 - present)

The development of a new low-level circulation center in tropical cyclones, known as downshear reformation, can lead to sudden changes in storm structure and intensity, representing a challenge in forecasting tropical cyclones. This phenomenon is commonly observed in weak tropical cyclones (e.g., tropical depressions, tropical storms) experiencing moderate to strong vertical wind shear (> 5 m s-1), when the vertical wind shear organizes convection in the downshear region of the tropical cyclone, and lower-tropospheric vorticity is generated within the intense convection. Downshear reformation has been proposed as a mechanism for intensification, sometimes rapidly, in tropical cyclones under unfavorable environmental wind …

Polar Stacking Of Dipole Parallel-Aligned Monolayers Of Unsymmetrical 1,4-Diphenyl-1,3-Butadienes Creates Nonlinear Optical Materials: Insights From Experiments Guide Structure Assignments, Harmeet Bhoday, Justin Nulsen, Steven P. Kelley, Rainer Glaser

Chemistry Faculty Research & Creative Works

A series of 1-(4′-halophenyl)-4-(4″-methoxyphenyl) buta-1,3-dienes, (H, MeO, Y)-1,4-diphenylbutadienes with halogens Y = F (1), Cl (2), Br (3), and I (4) are described. Crystal structure analysis establishes that 1-4 present a new class of highly dipole parallel-aligned polar organic molecular materials for nonlinear optics (NLO). Building on previous studies of polar crystals of unsymmetrical acetophenone azines, (R, X, Y)-azines, X-Ph-CR═N-N═CR-Ph-Y (R = Me; X = R'O, PhO, R'OPh; Y = F, Cl, Br, I), we suspected that the all-carbon analogs (R, R'O, Y)-1,4-diphenylbutadienes, R'O-Ph-CR═CH-CH═CR-Ph-Y, could crystallize with polar lattices and feature stronger NLO effects because of their improved conjugation and …

The Effect Of Nanomorphology As Quantified Via The K-Index On The Drug Delivery Properties Of Isocyanate-Derived Aerogels, Stephen Y. Owusu, A. B.M.Shaheen Ud Doulah, Vaibhav A. Edlabadkar, Kamden J. George, Chariklia Sotiriou-Leventis

Chemistry Faculty Research & Creative Works

This study explores the potential to predict the drug-loading and release profiles of aerogels based on their morphologies: a milestone in drug delivery research, which can help save time and cost in formulating new aerogel drug carriers and cut-down evaluation of the drug delivery capabilities of aerogels to a few experimental runs. Polyurea (PUA) and poly(isocyanurate-urethane) (PIR-PUR) aerogels were used as model systems, while 5-fluorouracil (5-FU) and paracetamol (PM) were used as model drugs. These model systems were chosen because they can be synthesized into different morphologies, which can be quantified by the so-called K-index (water contact angle divided by …

Message From Bits 2024 Co-Chairs And Technical Program Co-Chairs; Smartcomp 2024, Sajal K. Das, Hayato Yamana, Keiichi Yasumoto, Shameek Bhattacharjee

Computer Science Faculty Research & Creative Works

No abstract provided.

Landmark-Based Localization Using Stereo Vision And Deep Learning In Gps-Denied Battlefield Environment, Ganesh Sapkota, Sanjay Madria

Computer Science Faculty Research & Creative Works

Localization in a battlefield environment is increasingly challenging as GPS connectivity is often denied or unreliable, and physical deployment of anchor nodes across wireless networks for localization can be difficult in hostile battlefield terrain. This paper proposes a novel framework for the localization of moving objects in non-GPS battlefield environments using stereo vision and a deep learning model by recognizing naturally existing or artificial landmarks as anchors. The proposed method utilizes a custom-calibrated stereo vision camera for distance estimation and the YOLOv8s model, which is trained and fine-tuned with our real-world dataset for landmark anchor recognition. The depth images are …

Posca: Path Optimization For Solar Cover Amelioration In Urban Air Mobility, Debjyoti Sengupta, Anurag Satpathy, Sajal K. Das

Computer Science Faculty Research & Creative Works

Urban Air Mobility (UAM) encompasses both piloted and autonomous aerial vehicles, spanning from small unmanned aerial vehicles (UAVs) like drones to passenger-carrying personal air vehicles (PAVs), to revolutionize smart transportation in congested urban areas. This emerging paradigm is anticipated to offer disruptive solutions to the mobility challenges in congested cities. In this context, a pivotal concern centers on the sustainability of transitioning to this mode of transportation, especially with the focus on incorporating clean technology into developing innovative solutions from the ground up. Recent studies highlight that a significant portion of the total energy consumption in UAM can be attributed …

Tasr: A Novel Trust-Aware Stackelberg Routing Algorithm To Mitigate Traffic Congestion, Doris E.M. Brown, Venkata Sriram Siddhardh Nadendla, Sajal K. Das

Computer Science Faculty Research & Creative Works

A Stackelberg routing platform (SRP) reduces congestion in one-shot traffic networks by proposing optimal route recommendations to the selfish travelers. Traditionally, Stackel-berg routing is cast as a partial control problem where a fraction of the traveler flow complies with route recommendations, while the remaining responds as selfish travelers. In this paper, we formulate a novel Stackelberg routing framework where the agents exhibit probabilistic compliance by accepting SRP's route recommendations with a trust probability. Specifically, we propose a greedy Trust-Aware Stackelberg Routing algorithm (in short, TASR) for SRP to compute unique path recommendations to each traveler flow with a unique demand. …

Minerrouter : Effective Message Routing Using Contact-Graphs And Location Prediction In Underground Mine, Abhay Goyal, Sanjay Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Location-based distributed communication in underground mines has been a hard problem to solve due to unreliable centralized architecture such as leaky feeder systems, high attenuation, and the unavailability of GPS signals. Delay Tolerant Networks (DTN) enable decentralized message routing using the store-carry-forward method that can help in creating situational awareness needed to handle emergency and disaster scenarios. The ability to predict where the DTN nodes (miner) might have been at/are headed to (with respect to the mine regions and pillars) at different times, combined with contact-based routing and intelligent handling of buffer, can be used for better delivery of messages. …

Unsafe Events Detection In Smart Water Meter Infrastructure Via Noise-Resilient Learning, Ayanfeoluwa Oluyomi, Sahar Abedzadeh, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

Residential smart water meters (SWMs) collect real-time water consumption data, enabling automated billing and peak period forecasting. The presence of unsafe events is typically detected via deviations from the benign profile of water usage. However, profiling the benign behavior is non-trivial for large-scale SWM networks because once deployed, the collected data already contain those events, biasing the benign profile. To address this challenge, we propose a real-time data-driven unsafe event detection framework for city-scale SWM networks that automatically learns the profile of benign behavior of water usage. Specifically, we first propose an optimal clustering of SWMs based on the recognition …

Traffic Prediction-Based Vnf Auto-Scaling And Deployment Mechanism For Flexible And Elastic Service Provision, Bo Yi, Jiacheng Wang, Qiang He, Xingwei Wang, Min Huang, Sajal K. Das, Keqin Li

Computer Science Faculty Research & Creative Works

Network Function Virtualization (NFV) provides a flexible way to provision new services by decoupling network functions from hardware and implementing them as Virtual Network Functions (VNFs). However, the rapid development of technologies greatly promotes the explosion of diverse services, which directly results in the exponential increase of heterogeneous traffic. In addition, such a tremendous amount of heterogeneous traffic will generate bursts in a more dynamic and unexpected manner, so it becomes extremely hard to satisfy the customer demands. Aiming at addressing these challenges, this work proposes a positive and elastic VNF deployment mechanism for service provisioning, which introduces three novelties: …