Physical Sciences and Mathematics Commons^™

Improved Ships Course-Keeping Robust Control Algorithm Based On Backstepping And Nonlinear Feedback, Sirui Wang

Maritime Safety & Environment Management Dissertations (Dalian)

No abstract provided.

Ciculant Matrix And Fft, Thomas S. Devries

Undergraduate Student Research Internships Conference

The goal was to produce all the eigen values for a BOHEMIAN matrices using coefficient set {0, 1, -1, i, -i} of a size 15 vector. There are 5^15 eigen values so it was attempted to be done in parrallel for parts of the algorithm that permitted.

Leveraging Machine Learning Techniques Towards Intelligent Networking Automation, Cesar A. Gomez

Electronic Thesis and Dissertation Repository

In this thesis, we address some of the challenges that the Intelligent Networking Automation (INA) paradigm poses. Our goal is to design schemes leveraging Machine Learning (ML) techniques to cope with situations that involve hard decision-making actions. The proposed solutions are data-driven and consist of an agent that operates at network elements such as routers, switches, or network servers. The data are gathered from realistic scenarios, either actual network deployments or emulated environments. To evaluate the enhancements that the designed schemes provide, we compare our solutions to non-intelligent ones. Additionally, we assess the trade-off between the obtained improvements and the …

Forensic Artifact Finder (Forensicaf): An Approach & Tool For Leveraging Crowd-Sourced Curated Forensic Artifacts, Tyler Balon, Krikor Herlopian, Ibrahim Baggili, Cinthya Grajeda-Mendez

Electrical & Computer Engineering and Computer Science Faculty Publications

Current methods for artifact analysis and understanding depend on investigator expertise. Experienced and technically savvy examiners spend a lot of time reverse engineering applications while attempting to find crumbs they leave behind on systems. This takes away valuable time from the investigative process, and slows down forensic examination. Furthermore, when specific artifact knowledge is gained, it stays within the respective forensic units. To combat these challenges, we present ForensicAF, an approach for leveraging curated, crowd-sourced artifacts from the Artifact Genome Project (AGP). The approach has the overarching goal of uncovering forensically relevant artifacts from storage media. We explain our approach …

Forensicast: A Non-Intrusive Approach & Tool For Logical Forensic Acquisition & Analysis Of The Google Chromecast Tv, Alex Sitterer, Nicholas Dubois, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

The era of traditional cable Television (TV) is swiftly coming to an end. People today subscribe to a multitude of streaming services. Smart TVs have enabled a new generation of entertainment, not only limited to constant on-demand streaming as they now offer other features such as web browsing, communication, gaming etc. These functions have recently been embedded into a small IoT device that can connect to any TV with High Definition Multimedia Interface (HDMI) input known as Google Chromecast TV. Its wide adoption makes it a treasure trove for potential digital evidence. Our work is the primary source on forensically …

Machine Learning For Analog/Mixed-Signal Integrated Circuit Design Automation, Weidong Cao

McKelvey School of Engineering Theses & Dissertations

Analog/mixed-signal (AMS) integrated circuits (ICs) play an essential role in electronic systems by processing analog signals and performing data conversion to bridge the analog physical world and our digital information world.Their ubiquitousness powers diverse applications ranging from smart devices and autonomous cars to crucial infrastructures. Despite such critical importance, conventional design strategies of AMS circuits still follow an expensive and time-consuming manual process and are unable to meet the exponentially-growing productivity demands from industry and satisfy the rapidly-changing design specifications from many emerging applications. Design automation of AMS IC is thus the key to tackling these challenges and has been …

Continuous-Time And Complex Growth Transforms For Analog Computing And Optimization, Oindrila Chatterjee

McKelvey School of Engineering Theses & Dissertations

Analog computing is a promising and practical candidate for solving complex computational problems involving algebraic and differential equations. At the fundamental level, an analog computing framework can be viewed as a dynamical system that evolves following fundamental physical principles, like energy minimization, to solve a computing task. Additionally, conservation laws, such as conservation of charge, energy, or mass, provide a natural way to couple and constrain spatially separated variables. Taking a cue from these observations, in this dissertation, I have explored a novel dynamical system-based computing framework that exploits naturally occurring analog conservation constraints to solve a variety of optimization …

Algebraic, Computational, And Data-Driven Methods For Control-Theoretic Analysis And Learning Of Ensemble Systems, Wei Miao

McKelvey School of Engineering Theses & Dissertations

In this thesis, we study a class of problems involving a population of dynamical systems under a common control signal, namely, ensemble systems, through both control-theoretic and data-driven perspectives. These problems are stemmed from the growing need to understand and manipulate large collections of dynamical systems in emerging scientific areas such as quantum control, neuroscience, and magnetic resonance imaging. We examine fundamental control-theoretic properties such as ensemble controllability of ensemble systems and ensemble reachability of ensemble states, and propose ensemble control design approaches to devise control signals that steer ensemble systems to desired profiles. We show that these control-theoretic properties …

Duck Hunt: Memory Forensics Of Usb Attack Platforms, Tyler Thomas, Mathew Piscitelli, Bhavik Ashok Nahar, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

To explore the memory forensic artifacts generated by USB-based attack platforms, we analyzed two of the most popular commercially available devices, Hak5's USB Rubber Ducky and Bash Bunny. We present two open source Volatility plugins, usbhunt and dhcphunt, which extract artifacts generated by these USB attacks from Windows 10 system memory images. Such artifacts include driver-related diagnostic events, unique device identifiers, and DHCP client logs. Our tools are capable of extracting metadata-rich Windows diagnostic events generated by any USB device. The device identifiers presented in this work may also be used to definitively detect device usage. Likewise, the DHCP logs …

Another Brick In The Wall: An Exploratory Analysis Of Digital Forensics Programs In The United States, Syria Mccullough, Stella Abudu, Ebere Onwubuariri, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

We present a comprehensive review of digital forensics programs offered by universities across the United States (U.S.). While numerous studies on digital forensics standards and curriculum exist, few, if any, have examined digital forensics courses offered across the nation. Since digital forensics courses vary from university to university, online course catalogs for academic institutions were evaluated to curate a dataset. Universities were selected based on online searches, similar to those that would be made by prospective students. Ninety-seven (n = 97) degree programs in the U.S. were evaluated. Overall, results showed that advanced technical courses are missing from curricula. We …

Quantum Grover's Oracles With Symmetry Boolean Functions, Peng Gao

Dissertations and Theses

Quantum computing has become an important research field of computer science and engineering. Among many quantum algorithms, Grover's algorithm is one of the most famous ones. Designing an effective quantum oracle poses a challenging conundrum in circuit and system-level design for practical application realization of Grover's algorithm.

In this dissertation, we present a new method to build quantum oracles for Grover's algorithm to solve graph theory problems. We explore generalized Boolean symmetric functions with lattice diagrams to develop a low quantum cost and area efficient quantum oracle. We study two graph theory problems: cycle detection of undirected graphs and generalized …

Radiation Effects On Space Solar Cells At Various Earth And Jupiter Orbital Altitudes, Naazneen Rana

Discovery Undergraduate Interdisciplinary Research Internship

Solar cells are used as the primary power source for earth-orbiting satellites and as a primary/secondary power source for various missions within the solar system. However, high energy particles from the sun, planetary magnetospheres, and the galaxy can affect the performance and life expectancy of the space solar cell and associated power systems. As the interests for interplanetary travel and the exploration of planets within our solar system increase, the need to understand a device’s performance within a particular planet’s environment is necessary. Therefore, this study will analyze the performance of space solar cells, particularly the SolAero IMM-α, at various …

Characterization Techniques And Cation Exchange Membrane For Non-Aqueous Redox Flow Battery, Kun Lou

Doctoral Dissertations

The motivation of this work comes from one of the major problems of emerging non-aqueous flow battery (NAFB) that a separator or membrane which facilitates conductivity and blocks redox species crossover does not exist. Although many aspects of principles can be mirrored from mature fuel cell and aqueous flow battery, it is found that some well-defined membrane properties in aqueous systems such as swelling, transport and interactions are different in non-aqueous solvents to some extent. However, the approach of this work does follow the way perfluorosulfonate ion exchange membrane (PFSA) facilitated development of fuel cell and aqueous flow battery in …

Non-Invasive In-Vitro Glucose Monitoring Using Optical Sensor And Machine Learning Techniques For Diabetes Applications, Maryamsadat Shokrekhodaei

Open Access Theses & Dissertations

Diabetes is a major public health challenge affecting more than 451 million people. Physiological and experimental factors influence the accuracy of non-invasive glucose monitoring, and these need to be addressed before replacing the finger prick method with a non-invasive glucose measurement technique. Also, the suitable employment of machine learning techniques on experimental data can significantly improve the accuracy of glucose predictions.

This work includes the design, development, testing and data analysis of an optical based sensor for glucose measurements. The feasibility of non-invasive measurement of glucose within aqueous solutions that assimilate the composition of human blood plasma is investigated. The …

Fast Magnetic Resonance Image Reconstruction With Deep Learning Using An Efficientnet Encoder, Tahsin Rahman

Open Access Theses & Dissertations

This thesis aims to develop an efficient, deep network based method for Magnetic Resonance Imaging (MRI) acceleration through undersampled MR image reconstruction. Deep Neural Networks, particularly Deep Convolutional Networks, have been demonstrated to be highly effective in a wide variety of computer vision tasks, including MRI reconstruction. However, modern highly efficient encoder structures, such as the EfficientNet can potentially reduce reconstruction times further while improving reconstruction quality. To that end, we have developed a multi-channel U-Net MRI reconstruction network which uses an EfficientNet encoder and a custom asymmetric. The network was trained and tested using 5x undersampled multi-channel brain MR …

Transport, Photoluminescence & Photoconduction Characteristics Of Free Standing Two-Dimensional Γ-Alumina & Titanium Superlattice Doped Two-Dimensional Γ-Alumina Grown By Graphene-Assisted Atomic Layer Deposition, Elaheh Kheirandish

Theses and Dissertations

This study presents a facile high-yield bottom-up fabrication, morphology, crystallographic and optoelectronic characterization of free-standing quasi-2D γ-alumina, a non van der Waals 2D material. The synthesis comprises a multi-cycle atomic layer deposition (ALD) of amorphous alumina on a porous interconnected graphene foam as a growth scaffold and removed next by annealing and sintering the alumina/graphene/alumina sandwich at ~ 800 °C in air . The crystallographic and structural characteristics of the formed non-van der Waals quasi 2D γ-alumina were studied by X-ray diffraction (XRD), selected area electron diffraction (SAED), and high-resolution transmission electron microscopy (HRTEM). This analysis revealed the synthesized 2D …

Wavelength And Power Dependence On Multilevel Behavior Of Phase Change Materials, Gary A. Sevison, Joshua A. Burrow, Haiyun Guo, Andrew M. Sarangan, Joshua R. Hendrickson, Imad Agha

Electro-Optics and Photonics Faculty Publications

We experimentally probe the multilevel response of GeTe, Ge2Sb2Te5 (GST), and 4% tungsten-doped GST (W-GST) phase change materials (PCMs) using two wavelengths of light: 1550 nm, which is useful for telecom-applications, and near-infrared 780 nm, which is a standard wavelength for many experiments in atomic and molecular physics. We find that the materials behave differently with the excitation at the different wavelengths and identify useful applications for each material and wavelength. We discuss thickness variation in the thin films used as well and comment on the interaction of the interface between the material and the substrate with regard to the …

Hardware For Quantized Mixed-Precision Deep Neural Networks, Andres Rios

Open Access Theses & Dissertations

Recently, there has been a push to perform deep learning (DL) computations on the edge rather than the cloud due to latency, network connectivity, energy consumption, and privacy issues. However, state-of-the-art deep neural networks (DNNs) require vast amounts of computational power, data, and energyâ??resources that are limited on edge devices. This limitation has brought the need to design domain-specific architectures (DSAs) that implement DL-specific hardware optimizations. Traditionally DNNs have run on 32-bit floating-point numbers; however, a body of research has shown that DNNs are surprisingly robust and do not require all 32 bits. Instead, using quantization, networks can run on …

Forecasting Pedestrian Trajectory Using Deep Learning, Arsal Syed

UNLV Theses, Dissertations, Professional Papers, and Capstones

In this dissertation we develop different methods for forecasting pedestrian trajectories. Complete understanding of pedestrian motion is essential for autonomous agents and social robots to make realistic and safe decisions. Current trajectory prediction methods rely on incorporating historic motion, scene features and social interaction to model pedestrian behaviors. Our focus is to accurately understand scene semantics to better forecast trajectories. In order to do so, we leverage semantic segmentation to encode static scene features such as walkable paths, entry/exits, static obstacles etc. We further evaluate the effectiveness of using semantic maps on different datasets and compare its performance with already …

Knn Based Piezo-Triboelectric Lead-Free Hybrid Energy Films, Abu Masa Abdullah, Muhtasim Ul Karim Sadaf, Farzana Tasnim, Horacio Vasquez, Karen Lozano, M. Jasim Uddin

Chemistry Faculty Publications and Presentations

In recent times, the triboelectric and piezoelectric effects have garnered significant attention towards developing advanced material composites for energy harvesting and sensory applications. In this work, potassium sodium niobate (KNN) based energy films (EF) have been developed to utilize mechanical energy while simultaneously taking advantage of triboelectric and piezoelectric mechanisms. The KNN particles were synthesized using a wet ball milling technique and then incorporated into a polyvinylidene difluoride (PVDF) matrix together with addition of multi wall carbon nanotubes (MWCNT). The film was used to develop a piezoelectric nanogenerator (PENG) fitted with copper electrodes. The piezoelectric output of the film was …

Signal Fingerprinting And Machine Learning Framework For Uav Detection And Identification., Olusiji Oloruntobi Medaiyese

Electronic Theses and Dissertations

Advancement in technology has led to creative and innovative inventions. One such invention includes unmanned aerial vehicles (UAVs). UAVs (also known as drones) are now an intrinsic part of our society because their application is becoming ubiquitous in every industry ranging from transportation and logistics to environmental monitoring among others. With the numerous benign applications of UAVs, their emergence has added a new dimension to privacy and security issues. There are little or no strict regulations on the people that can purchase or own a UAV. For this reason, nefarious actors can take advantage of these aircraft to intrude into …

Industrial Control System Data Resiliency, Daniel A. Bovard

Boise State University Theses and Dissertations

This thesis identifies and fortifies against a critical vulnerability in industrial control system (ICS) security. A properly designed ICS security framework consists of a multi-layered approach starting with heavy fortifications in information technology and ending with control information of operational technology. Currently, ICS security frameworks lack visibility and place blind trust in devices at the lowest level of the control hierarchy. Attaining control data visibility at the lowest level of the control hierarchy is critical to increasing the resiliency of an ICS security posture. This thesis demonstrates how this data can be captured at the lowest level of the control …

Power System Stability Assessment With Supervised Machine Learning, Mirka Mandich

Masters Theses

Power system stability assessment has become an important area of research due to the increased penetration of photovoltaics (PV) in modern power systems. This work explores how supervised machine learning can be used to assess power system stability for the Western Electricity Coordinating Council (WECC) service region as part of the Data-driven Security Assessment for the Multi-Timescale Integrated Dynamics and Scheduling for Solar (MIDAS) project. Data-driven methods offer to improve power flow scheduling through machine learning prediction, enabling better energy resource management and reducing demand on real-time time-domain simulations. Frequency, transient, and small signal stability datasets were created using the …

Experimental Observation Of Topological Z2 Excitonpolaritons In Transition Metal Dichalcogenide Monolayers, Mengyao Li, Ivan Sinev, Fedor Benimetskiy, Tatyana Ivanova, Ekaterina Khestanova, Svetlana Kiriushechkina, Anton Vakulenko, Sriram Guddala, Maurice Skolnick, Vinod M. Menon, Dmitry Krizhanovskii, Andrea Alù, Anton Samusev, Alexander B. Khanikaev

Publications and Research

The rise of quantum science and technologies motivates photonics research to seek new platforms with strong light-matter interactions to facilitate quantum behaviors at moderate light intensities. Topological polaritons (TPs) offer an ideal platform in this context, with unique properties stemming from resilient topological states of light strongly coupled with matter. Here we explore polaritonic metasurfaces based on 2D transition metal dichalcogenides (TMDs) as a promising platform for topological polaritonics. We show that the strong coupling between topological photonic modes of the metasurface and excitons in TMDs yields a topological polaritonic Z₂ phase. We experimentally confirm the emergence of one-way …

On Communication For Distributed Babai Point Computation, Maiara F. Bollauf, Vinay A. Vaishampayan, Sueli I.R. Costa

Publications and Research

We present a communication-efficient distributed protocol for computing the Babai point, an approximate nearest point for a random vector X∈Rn in a given lattice. We show that the protocol is optimal in the sense that it minimizes the sum rate when the components of X are mutually independent. We then investigate the error probability, i.e. the probability that the Babai point does not coincide with the nearest lattice point, motivated by the fact that for some cases, a distributed algorithm for finding the Babai point is sufficient for finding the nearest lattice point itself. Two different probability models for X …

Augmented Human Machine Intelligence For Distributed Inference, Baocheng Geng

Dissertations - ALL

With the advent of the internet of things (IoT) era and the extensive deployment of smart devices and wireless sensor networks (WSNs), interactions of humans and machine data are everywhere. In numerous applications, humans are essential parts in the decision making process, where they may either serve as information sources or act as the final decision makers. For various tasks including detection and classification of targets, detection of outliers, generation of surveillance patterns and interactions between entities, seamless integration of the human and the machine expertise is required where they simultaneously work within the same modeling environment to understand and …

Mathematical Optimization Algorithms For Model Compression And Adversarial Learning In Deep Neural Networks, Tianyun Zhang

Dissertations - ALL

Large-scale deep neural networks (DNNs) have made breakthroughs in a variety of tasks, such as image recognition, speech recognition and self-driving cars. However, their large model size and computational requirements add a significant burden to state-of-the-art computing systems. Weight pruning is an effective approach to reduce the model size and computational requirements of DNNs. However, prior works in this area are mainly heuristic methods. As a result, the performance of a DNN cannot maintain for a high weight pruning ratio. To mitigate this limitation, we propose a systematic weight pruning framework for DNNs based on mathematical optimization. We first formulate …

Efficient, Dual-Particle Directional Detection System Using A Rotating Scatter Mask, Robert Olesen, Bryan V. Egner, Darren E. Holland, Valerie Martin, James E. Bevins, Larry W. Burggraf, Buckley E. O'Day Iii

AFIT Patents

A directional radiation detection system and an omnidirectional radiation detector. The omnidirectional radiation detector detects radiation comprising at least one of: (i) gamma rays; and (ii) neutron particles. A radiation scatter mask (RSM) of the radiation detection system includes a rotating sleeve received over the omnidirectional radiation detector and rotating about a longitudinal axis. The RSM further includes: (i) a fin extending longitudinally from one side of the rotating sleeve; and (ii) a wall extending from the rotating sleeve and spaced apart from the fin having an upper end distally positioned on the rotating sleeve spaced apart or next to …

Electricity Market Operations With Massive Renewable Integration: New Designs, Shengfei Yin

Electrical Engineering Theses and Dissertations

Electricity market has been transitioning from a conventional and deterministic operation to a stochastic operation under the increasing penetration of renewable energy. Industry-level solutions toward the future electricity market operation ask for both accuracy and efficiency while maintaining model interpretability. Hence, reliable stochastic optimization techniques come to the first place for such a complex and dynamic problem.

This work starts at proposing a solution strategy for the uncertainty-based power system planning problem, which acts as a preliminary and instructs the electricity market operation. Considering 100% renewable penetration in the future, it analyzes the cost-effectiveness of renewable energy from a long-term …

Optical Switching Performance Of Thermally Oxidized Vanadium Dioxide With An Integrated Thin Film Heater, Andrew M. Sarangan, Gamini Ariyawansa, Ilya Vitebskiy, Igor Anisimov

Electro-Optics and Photonics Faculty Publications

Optical switching performance of vanadium dioxide produced by thermal oxidation of vanadium is presented in this paper. A 100nm thick vanadium was oxidized under controlled conditions in a quartz tube furnace to produce approximately 200nm thick VO2. The substrate was appropriately coated on the front and back side to reduce reflection in the cold state, and an integrated thin film heater was fabricated to allow in-situ thermal cycling. Electrical measurements show a greater than three orders of magnitude change in resistivity during the phase transition. Optical measurements exhibit 70% transparency at 1500nm and about 15dB extinction across a wide spectral …