Physical Sciences and Mathematics Commons^™

Mobility Management In Industrial Iot Environments, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

The Internet Engineering Task Force (IETF) has defined the 6TiSCH architecture to enable the Industrial Inter-net of Things (IIoT). Unfortunately, 6TiSCH does not provide mechanisms to manage node mobility, while many industrial applications involve mobile devices (e.g., mobile robots or wearable devices carried by workers). In this paper, we consider the Synchronized Single-hop Multiple Gateway framework to manage mobility in 6TiSCH networks. For this framework, we address the problem of positioning Border Routers in a deployment area, which is similar to the "Art Gallery"problem, proposing an efficient deployment policy for Border Routers based on geometrical rules. Moreover, we define a …

Mdz: An Efficient Error-Bounded Lossy Compressor For Molecular Dynamics, Kai Zhao, Sheng Di, Danny Perez, Xin Liang, Zizhong Chen, Franck Cappello

Computer Science Faculty Research & Creative Works

Molecular dynamics (MD) has been widely used in today's scientific research across multiple domains including materials science, biochemistry, biophysics, and structural biology. MD simulations can produce extremely large amounts of data in that each simulation could involve a large number of atoms (up to trillions) for a large number of timesteps (up to hundreds of millions). In this paper, we perform an in-depth analysis of a number of MD simulation datasets and then develop an efficient error-bounded lossy compressor that can significantly improve the compression ratios. The contributions are fourfold. (1) We characterize a number of MD datasets and summarize …

Spade: Multi-Stage Spam Account Detection For Online Social Networks, Federico Concone, Giuseppe Lo Re, Marco Morana, Sajal K. Das

Computer Science Faculty Research & Creative Works

In recent years, Online Social Networks (OSNs) have radically changed the way people communicate. The most widely used platforms, such as Facebook, Youtube, and Instagram, claim more than one billion monthly active users each. Beyond these, news-oriented micro-blogging services, e.g., Twitter, are daily accessed by more than 120 million users sharing contents from all over the world. Unfortunately, legitimate users of the OSNs are mixed with malicious ones, which are interested in spreading unwanted, misleading, harmful, or discriminatory content. Spam detection in OSNs is generally approached by considering the characteristics of the account under analysis, its connection with the rest …

Look-Up Table Based Fhe System For Privacy Preserving Anomaly Detection In Smart Grids, Ruixiao Li, Shameek Bhattacharjee, Sajal K. Das, Hayato Yamana

Computer Science Faculty Research & Creative Works

In advanced metering infrastructure (AMI), the customers' power consumption data is considered private but needs to be revealed to data-driven attack detection frameworks. In this paper, we present a system for privacy-preserving anomaly-based data falsification attack detection over fully homomorphic encrypted (FHE) data, which enables computations required for the attack detection over encrypted individual customer smart meter's data. Specifically, we propose a homomorphic look-up table (LUT) based FHE approach that supports privacy preserving anomaly detection between the utility, customer, and multiple partied providing security services. In the LUTs, the data pairs of input and output values for each function required …

Message From The Bits 2022 Workshop Co-Chairs, Sajal K. Das, Hayato Yamana, Keiichi Yasumoto, Shameek Bhattacharjee

Computer Science Faculty Research & Creative Works

No abstract provided.

Sum-Rate Optimization For Visible-Light-Band Uav Networks Based On Particle Swarm Optimization, Yuwei Long, Nan Cen

Computer Science Faculty Research & Creative Works

The mobility nature of unmanned aerial vehicles (UAVs) takes them into high consideration in military, public, and civilian applications in recent years. However, scaling out millions of UAVs in the air will inevitably lead to a more crowded radio frequency (RF) spectrum. Therefore, researchers have been focused on new technologies such as millimeter-wave, Terahertz, and visible light communications (VLCs) to alleviate the spectrum crunch problem. VLC has shown its great potential for UAV networking because of its high data rate, interference-free to legacy RF spectrum, and low-complex frontends. While the physical layer design of the VLC system has been extensively …

Theoretical And Experimental Application Of Neural Networks In Spaceflight Control Systems, Pavel Galchenko

Doctoral Dissertations

“Spaceflight systems can enable advanced mission concepts that can help expand our understanding of the universe. To achieve the objectives of these missions, spaceflight systems typically leverage guidance and control systems to maintain some desired path and/or orientation of their scientific instrumentation. A deep understanding of the natural dynamics of the environment in which these spaceflight systems operate is required to design control systems capable of achieving the desired scientific objectives. However, mitigating strategies are critically important when these dynamics are unknown or poorly understood and/or modelled. This research introduces two neural network methodologies to control the translation and rotation …

Targeted Content-Sharing In A Multi-Group Dtn Application Using Attribute-Based Encryption, Xiaofei Cao, Shudip Datta, Ram Charan Bolla, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

In a battlefield, multiple groups operate with different missions, but their missions and groups can dynamically change based on the evolving situation. Due to the unavailability of network infrastructure after deployment, group members form a Delay Tolerant Network (DTN) which is prone to security attacks. Hence, based on the mission attributes, group memberships, nodes' interests, and data tags determination, targeted contents need to be distributed in a secure fashion to different users. Though existing Attributes Based Encryption (ABE) can provide security of information, revoking a member from a group is always an issue in DTN as the Attribute Authority (AA) …

Leveraging Spanning Tree To Detect Colluding Attackers In Federated Learning, Priyesh Ranjan, Federico Coro, Ashish Gupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated learning distributes model training among multiple clients who, driven by privacy concerns, perform training using their local data and only share model weights for iterative aggregation on the server. In this work, we explore the threat of collusion attacks from multiple malicious clients who pose targeted attacks (e.g., label flipping) in a federated learning configuration. By leveraging client weights and the correlation among them, we develop a graph-based algorithm to detect malicious clients. Finally, we validate the effectiveness of our algorithm in presence of varying number of attackers on a classification task using a well-known Fashion-MNIST dataset.

Asymptotic Properties Of Kneser Solutions To Third-Order Delay Differential Equations, Martin Bohner, John R. Graef, Irena Jadlovská

Mathematics and Statistics Faculty Research & Creative Works

The aim of this paper is to extend and complete the recent work by Graef et al. (J. Appl. Anal. Comput., 2021) analyzing the asymptotic properties of solutions to third-order linear delay differential equations. Most importantly, the authors tackle a particularly challenging problem of obtaining lower estimates for Kneser-type solutions. This allows improvement of existing conditions for the nonexistence of such solutions. As a result, a new criterion for oscillation of all solutions of the equation studied is established.

Establishing Pteridine Metabolism In A Breast Cancer Cell Model And Quantification Of Silver Nanoparticle Interactions With Yeast Cells Using Mass Spectrometry, Lindsey Katherine Rasmussen

Doctoral Dissertations

"Recent advances in analytical methods have furthered the quantitative insights that can be gleaned from cellular analyses, with applications in cancer and nanoparticle research. In the first part of the research presented, a recently developed HPLC-MS/MS method was advanced to elucidate pteridine metabolism in an isogenic progressive MCF10A breast cancer cell model. The folate-derived pteridine pathway in breast cells was established by individually dosing cell cultures with folic acid and 15 pteridines. Eight potential pteridine biomarkers in breast cancer cells were identified including pterin and isoxanthopterin, which yielded the first in vitro evidence for the cellular metabolisms behind previously reported …

Data-Driven Modeling And Simulations Of Seismic Waves, Yixuan Wu

Doctoral Dissertations

"In recent decades, nonlocal models have been proved to be very effective in the study of complex processes and multiscale phenomena arising in many fields, such as quantum mechanics, geophysics, and cardiac electrophysiology. The fractional Laplacian(−Δ)^𝛼/2 can be reviewed as nonlocal generalization of the classical Laplacian which has been widely used for the description of memory and hereditary properties of various material and process. However, the nonlocality property of fractional Laplacian introduces challenges in mathematical analysis and computation. Compared to the classical Laplacian, existing numerical methods for the fractional Laplacian still remain limited. The objectives of this research are …

Secure And Efficient Information Management In Delay(Disruption) Tolerant Network, Shudip Datta

Doctoral Dissertations

"In environments like international military coalitions on the battlefield or multi-party relief work in a disaster zone, multiple teams are deployed to serve different mission goals by the command-and-control center (CC). They may need to survey damages and send information to the CC for situational awareness and also transfer messages to each other for mission purposes. However, due to the damaged network infrastructure in the emergency, nodes need to relay messages using the store and forward paradigm, also called Delay-tolerant Networks (DTNs). In DTN, the limited bandwidth, energy, and contacts among the nodes, and their interdependency impose several challenges such …

Design And Synthesis Of Purine Based Neuroprotectors And Novel Synthetic Methods For The Trifluoromethylation Of Aldehyde Hydrazones, Puspa Aryal

Doctoral Dissertations

"Purine-derived compounds are widely investigated as cyclin-dependent kinase inhibitors that have broad applications in the design of pharmaceuticals for treating diseases, such as diabetes, atherosclerosis, and cancers. Towards the goal of effective AGE-inhibitors, and neuroprotector compounds we have synthesized a series of novel purine-based triazoles and investigated their neuroprotective effects, using SHSY-3Y human neuroblastoma cell line. Through these studies, we have identified purine-based neuroprotector compounds that favorably modulate oxidative stress induced by the Fenton reaction-generated reactive oxygen species (ROS).

The C(sp²−H)-trifluoromethylation of hydrazones would give access to the αtrifluoromethylated hydrazones that can serve as intermediates in the synthesis …

Mantle Flow And Transition Zone Discontinuities Beneath The Carribean Plate: Constraints From Shear Wave Splitting And Receiver Function Analyses, Tu Xue

Doctoral Dissertations

"Azimuthal anisotropy quantified by teleseismic SKS, SKKS, PKS (“XKS”) and local S wave splitting parameters is used to investigate lithospheric deformation and asthenospheric flow beneath the boundary zone of the North American and Caribbean plates and adjacent areas. A total of 4915 XKS and 1202 pairs of local S wave splitting parameters were obtained at 24 broad band seismic stations. The XKS observations can be divided into two groups based on the spatial distribution of the resulting fast polarization orientations. Those observed on the Caribbean Plate are mostly WNW-ESE which are roughly trench-parallel. In contrast, the fast orientations observed on …

Application Of Machine Learning In Geophysics: Ranking Teleseismic Shear Wave Splitting Measurements And Classifying Different Types Of Earthquakes, Yanwei Zhang

Doctoral Dissertations

"During the past decades, applications of Machine Learning have been explosively developed to solve various academic and industrial problems, and over-human performance has been shown in diverse areas. In geophysical research, Machine Learning, especially Convolutional Neural Network (CNN), has been applied in numerous studies and demonstrated considerable potential. In this study, we applied CNN to solve two geophysical problems, ranking teleseismic shear splitting (SWS) measurements and classifying different types of earthquakes.

For ranking teleseismic SWS measurements, we utilized a CNN-based method to automatically select reliable SWS measurements. The CNN was trained by human-verified teleseismic SWS measurements and tested using synthetic …

Electrocatalytic Processes For Energy Storage & Conversion, Apurv Saxena

Doctoral Dissertations

"The continuous excessive usage of fossil fuels has resulted in its fast depletion leading to an escalating energy crisis as well as several environmental issues leading to increased research towards sustainable energy conversion. Electrocatalysts play crucial role in the development of numerous novel energy conversion devices including fuel cells and solar fuel generators.

High-efficiency and cost-effective catalysts are required for large-scale implementation of these new devices. Over the last few years transition metal chalcogenides have emerged as highly efficient electrocatalysts for several electrochemical devices such as water splitting, carbon dioxide electroreduction and, solar energy converters. These transition metal chalcogenides exhibit …

Laterally Heterogeneous Seismic Anisotropy Investigated By Shear Wave Splitting Analyses, Yan Jia

Doctoral Dissertations

"Numerous geophysical studies suggest that seismic anisotropy is a nearly ubiquitous property of the Earth’s crust and upper mantle. In this study, we utilize the shear wave splitting technique to investigate the piercing-point-dependent azimuthal anisotropy beneath the northeastern edge of the Sichuan Basin in central China, and the spatial and temporal variations of anisotropy near the 2019 M7.1 Ridgecrest earthquake in California, respectively. A clear back azimuthal dependence of the splitting parameters and the lack of a 90° or 180° periodicity of azimuthal variation in the observed fast orientations provide strong evidence for the existence of piercing-point-dependent anisotropy beneath the …

Disorder Effects In Frustrated Magnets And Absorbing State Transitions, Xuecheng Ye

Doctoral Dissertations

"Correlation, topology, and disorder can fundamentally affect the properties of interacting many-particle systems. After a short introduction which covers the basic concepts of phase transitions and scaling as well as the physics of Josephson junctions, the dissertation focuses on three separate projects.

The first project is motivated by the stripe and nematic phases observed e.g. in cuprate superconductors and iron pnictides. To understand the effects of disorder on such phases, we have investigated the behavior of the diluted J₁-J₂ Ising model. Spinless impurities generate a random-field disorder for the spin-density (stripe) order parameter, which destroys the stripe …

Deep Learning-Based Surrogate Models For Post-Earthquake Damage Assessment, Xinzhe Yuan

Doctoral Dissertations

"Seismic damage assessment is a critical step to enhance community resilience in the wake of an earthquake. This study aims to develop deep learning-based surrogate models for widely used fragility curves to achieve more accurate and rapid assessment in practice. These surrogate models are based on artificial neural networks trained from the labelled ground motions whose resulting damage classes on targeted structures are determined by nonlinear time history analyses. The development of various surrogate models is progressed in four phases. In Phase I, the multilayer perceptron (MLP) is used to develop multivariate seismic classifiers with up to 50 hand-crafted intensity …

Representation Learning On Heterogeneous Spatiotemporal Networks, Dakshak Keerthi Chandra

Doctoral Dissertations

“The problem of learning latent representations of heterogeneous networks with spatial and temporal attributes has been gaining traction in recent years, given its myriad of real-world applications. Most systems with applications in the field of transportation, urban economics, medical information, online e-commerce, etc., handle big data that can be structured into Spatiotemporal Heterogeneous Networks (SHNs), thereby making efficient analysis of these networks extremely vital. In recent years, representation learning models have proven to be quite efficient in capturing effective lower-dimensional representations of data. But, capturing efficient representations of SHNs continues to pose a challenge for the following reasons: (i) Spatiotemporal …

Variational Data Assimilation For Two Interface Problems, Xuejian Li

Doctoral Dissertations

“Variational data assimilation (VDA) is a process that uses optimization techniques to determine an initial condition of a dynamical system such that its evolution best fits the observed data. In this dissertation, we develop and analyze the variational data assimilation method with finite element discretization for two interface problems, including the Parabolic Interface equation and the Stokes-Darcy equation with the Beavers-Joseph interface condition. By using Tikhonov regularization and formulating the VDA into an optimization problem, we establish the existence, uniqueness and stability of the optimal solution for each concerned case. Based on weak formulations of the Parabolic Interface equation and …

Effects Of Vacancies And Electron Temperature On The Electron Phonon Coupling In Cubic Silicon Carbide And Their Connection To The Inelastic Thermal Spike, Salah Al-Smairat

Doctoral Dissertations

“The electron-phonon interaction is an important interaction in many solids as it influences transport phenomena and related quantities such as the electrical and thermal conductivities, especially in nuclear and space applications. The importance of the electron-phonon interaction in primary damage production in 3C-SiC is the subject of this research.

The electron-phonon coupling factor was calculated using a hybrid Density Functional Perturbation Theory - Classical Electron Gas model. The coupling factor was calculated as a function of electron temperature in pristine and defective 3C-SiC, and relaxed defective cells. The electron-phonon coupling is found to depend strongly on the electronic temperature and …

Numerical Investigations Of 2-D Magnetic Nozzles On Pulsed Plasma Plumes, Joshua Daniel Burch

Masters Theses

"This research presents studies of a novel type of magnetic nozzle that allows for three-dimensional (3-D) steering of a plasma plume. Numerical simulations were performed using Tech-X's USim® software to quantify the nozzle's capabilities. A2-D planar magnetic nozzle was applied to plumes of a nominal pulsed inductive plasma (PIP) source with discharge parameters similar to those of Missouri S&T's Missouri Plasmoid Experiment (MPX). Argon and xenon plumes were considered. Simulations were verified and validated through a mesh convergence study as well as comparison with available experimental data. Periodicity was achieved over the simulation run time and phase angle samples were …

Maintenance Optimization In A Digital Twin For Industry 4.0, Abhijit Gosavi, Vy Khoi Le

Engineering Management and Systems Engineering Faculty Research & Creative Works

The advent of Internet of Things and artificial intelligence in the era of Industry 4.0 has transformed decision-making within production systems. In particular, many decisions that previously required significant human activity are now made automatically with minimal human intervention via so-called digital twins (DTs). In the context of maintenance and reliability modeling, this naturally calls for new paradigms that can be seamlessly integrated within DTs for decision-making. The input data for time to failure needed in reliability computations are directly collected from the work center in a digital setting and often do not satisfy a known distribution. A neural network …

Active Learning Augmented Folded Gaussian Model For Anomaly Detection In Smart Transportation, Venkata Praveen Kumar Madhavarapu, Prithwiraj Roy, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

Smart transportation networks have become instrumental in smart city applications with the potential to enhance road safety, improve the traffic management system and driving experience. A Traffic Message Channel (TMC) is an IoT device that records the data collected from the vehicles and forwards it to the Roadside Units (RSUs). This data is further processed and shared with the vehicles to inquire the fastest route and incidents that can cause significant delays. The failure of the TMC sensors can have adverse effects on the transportation network. In this paper, we propose a Gaussian distribution-based trust scoring model to identify anomalous …

The Kanarra Fold-Thrust System -- The Leading Edge Of The Sevier Fold-Thrust Belt, Southwest Utah, William Joseph Michael Chandonia

Doctoral Dissertations

“The Jurassic to Eocene Sevier fold-thrust belt is the subject of continued scientific curiosity in tectonics, stratigraphy, and industry. Understanding its development in southwest Utah is hindered in part due to the multiple origins proposed for the Kanarra anticline, a major leading edge structure -- a drag fold along the Hurricane fault, Laramide monocline, Sevier fault propagation fold, or a combination of these -- which have confused its tectonic significance and regional context. This confusion results from the structural complexity of its exposed eastern limb, as well as displacement and burial of its crest and western limb beneath Neogene sediments …

Advances And Applications In High-Dimensional Heuristic Optimization, Samuel Alexander Vanfossan

Doctoral Dissertations

“Applicable to most real-world decision scenarios, multiobjective optimization is an area of multicriteria decision-making that seeks to simultaneously optimize two or more conflicting objectives. In contrast to single-objective scenarios, nontrivial multiobjective optimization problems are characterized by a set of Pareto optimal solutions wherein no solution unanimously optimizes all objectives. Evolutionary algorithms have emerged as a standard approach to determine a set of these Pareto optimal solutions, from which a decision-maker can select a vetted alternative. While easy to implement and having demonstrated great efficacy, these evolutionary approaches have been criticized for their runtime complexity when dealing with many alternatives or …

Synthesis, Characterization And Chemistry Of Two-Dimensional Transition Metal Carbides And Nitrides (Mxenes), Shuohan Huang

Doctoral Dissertations

"MXenes represent a relatively new and quickly growing family of two-dimensional (2D) early transition-metal carbides and nitrides first synthesized in 2011 from bulk layered crystalline MAX phases. Because of their 2D structure and unique combination of high conductivity and hydrophilicity, MXenes have raised a significant interest for various applications. However, it has been found that in some cases colloidal MXene flakes are not stable and can spontaneously degrade on a time scale from hours to days. In this work, we investigate the crucial factors for MXene degradation and demonstrate gas analysis as a powerful method to gain further insights into …

Synthesis And Process Optimization Of Colloidal Unimolecular Polymer, Cup, Particle Formation, And Its Interfacial Surface Tension Behavior, Ashish Zore

Doctoral Dissertations

"Colloidal Unimolecular Polymer (CUP) particles are 3-9 nm size single-chain polymer nanoparticles that are made from amphiphilic acrylic co-polymers using the process of water reduction. The formation of CUP particles was driven by the polymer-polymer interactions being greater than polymer-solvent interactions as well as the charge-charge repulsion due to the increasing dielectric of the medium. CUPs provide a surfactant or additive-free nanoparticle system that was useful for studying the interfacial behavior of pure aqueous nanoparticles using a maximum bubble pressure tensiometer. The equilibrium surface tension shows a dependence on concentration and the charge density of the CUP particle. The equilibrium …