Physical Sciences and Mathematics Commons^™

Exploring Topological Phonons In Different Length Scales: Microtubules And Acoustic Metamaterials, Ssu-Ying Chen

Dissertations

The topological concepts of electronic states have been extended to phononic systems, leading to the prediction of topological phonons in a variety of materials. These phonons play a crucial role in determining material properties such as thermal conductivity, thermoelectricity, superconductivity, and specific heat. The objective of this dissertation is to investigate the role of topological phonons at different length scales.

Firstly, the acoustic resonator properties of tubulin proteins, which form microtubules, will be explored The microtubule has been proposed as an analog of a topological phononic insulator due to its unique properties. One key characteristic of topological materials is the …

Boundary Integral Equation Methods For Superhydrophobic Flow And Integrated Photonics, Kosuke Sugita

Dissertations

This dissertation presents fast integral equation methods (FIEMs) for solving two important problems encountered in practical engineering applications.

The first problem involves the mixed boundary value problem in two-dimensional Stokes flow, which appears commonly in computational fluid mechanics. This problem is particularly relevant to the design of microfluidic devices, especially those involving superhydrophobic (SH) flows over surfaces made of composite solid materials with alternating solid portions, grooves, or air pockets, leading to enhanced slip.

The second problem addresses waveguide devices in two dimensions, governed by the Helmholtz equation with Dirichlet conditions imposed on the boundary. This problem serves as a …

Carrier Transport Engineering In Wide Bandgap Semiconductors For Photonic And Memory Device Applications, Ravi Teja Velpula

Dissertations

Wide bandgap (WBG) semiconductors play a crucial role in the current solid-state lighting technology. The AlGaN compound semiconductor is widely used for ultraviolet (UV) light-emitting diodes (LEDs), however, the efficiency of these LEDs is largely in a single-digit percentage range due to several factors. Until recently, AlInN alloy has been relatively unexplored, though it holds potential for light-emitters operating in the visible and UV regions. In this dissertation, the first axial AlInN core-shell nanowire UV LEDs operating in the UV-A and UV-B regions with an internal quantum efficiency (IQE) of 52% are demonstrated. Moreover, the light extraction efficiency of this …

Software Protection And Secure Authentication For Autonomous Vehicular Cloud Computing, Muhammad Hataba

Dissertations

Artificial Intelligence (AI) is changing every technology we deal with. Autonomy has been a sought-after goal in vehicles, and now more than ever we are very close to that goal. Vehicles before were dumb mechanical devices, now they are becoming smart, computerized, and connected coined as Autonomous Vehicles (AVs). Moreover, researchers found a way to make more use of these enormous capabilities and introduced Autonomous Vehicles Cloud Computing (AVCC). In these platforms, vehicles can lend their unused resources and sensory data to join AVCC.

In this dissertation, we investigate security and privacy issues in AVCC. As background, we built our …

Performance Analysis Of The Dominant Mode Rejection Beamformer, Enlong Hu

Dissertations

In array signal processing over challenging environments, due to the non-stationarity nature of data, it is difficult to obtain enough number of data snapshots to construct an adaptive beamformer (ABF) for detecting weak signal embedded in strong interferences. One type of adaptive method targeting for such applications is the dominant mode rejection (DMR) method, which uses a reshaped eigen-decomposition of sample covariance matrix (SCM) to define a subspace containing the dominant interferers to be rejected, thereby allowing it to detect weak signal in the presence of strong interferences. The DMR weight vector takes a form similar to the adaptive minimum …

One-Stage Blind Source Separation Via A Sparse Autoencoder Framework, Jason Anthony Dabin

Dissertations

Blind source separation (BSS) is the process of recovering individual source transmissions from a received mixture of co-channel signals without a priori knowledge of the channel mixing matrix or transmitted source signals. The received co-channel composite signal is considered to be captured across an antenna array or sensor network and is assumed to contain sparse transmissions, as users are active and inactive aperiodically over time. An unsupervised machine learning approach using an artificial feedforward neural network sparse autoencoder with one hidden layer is formulated for blindly recovering the channel matrix and source activity of co-channel transmissions. The BSS sparse autoencoder …

Nystrom Methods For High-Order Cq Solutions Of The Wave Equation In Two Dimensions, Erli Wind-Andersen

Dissertations

An investigation of high order Convolution Quadratures (CQ) methods for the solution of the wave equation in unbounded domains in two dimensions is presented. These rely on Nystrom discretizations for the solution of the ensemble of associated Laplace domain modified Helmholtz problems. Two classes of CQ discretizations are considered: one based on linear multistep methods and the other based on Runge-Kutta methods. Both are used in conjunction with Nystrom discretizations based on Alpert and QBX quadratures of Boundary Integral Equation (BIE) formulations of the Laplace domain Helmholtz problems with complex wavenumbers. CQ in conjunction with BIE is an excellent candidate …

Private Information Retrieval And Function Computation For Noncolluding Coded Databases, Sarah A. Obead

Dissertations

The rapid development of information and communication technologies has motivated many data-centric paradigms such as big data and cloud computing. The resulting paradigmatic shift to cloud/network-centric applications and the accessibility of information over public networking platforms has brought information privacy to the focal point of current research challenges. Motivated by the emerging privacy concerns, the problem of private information retrieval (PIR), a standard problem of information privacy that originated in theoretical computer science, has recently attracted much attention in the information theory and coding communities. The goal of PIR is to allow a user to download a message from a …

Experimental And Computational Studies Of Functionalized Carbon Nanotubes For Use In Energy Storage Devices And Membranes, Emine S. Karaman

Dissertations

Electrolytes with good interfacial stability are a crucial component of any electrochemical device. The development of novel gel polymer electrolytes (GEs) with good interface stability and better manufacturability is important for the development of the next generation electrochemical devices. Gel electrolytes are hybrid electrolyte materials, combining benefits of both liquid and solid systems. Compared with liquid and solid electrolytes, GEs open new design opportunities and do not require rigorous encapsulation methods. In this dissertation, studies on functionalized carbon nanotubes (fCNTs) and graphene oxide (GO) doped polyvinyl alcohol (PVA) based gel electrolytes (GEs) are reported. The ionic conductivity and mechanical strength …

Development Of Sensor, Sensory System And Signal Processing Algorithm For Intelligent Sensing Applications, Xingzhe Zhang

Dissertations

Sensors have been receiving significant attention in the last decade and the demand for sensory systems has increased in recent years due to the rapid growth in the field of artificial intelligence (AI). Sensors can improve people’s awareness by providing them with real-time information on the environment and their immediate health conditions. This dissertation presents the fulfilment of three main projects and focuses on the development of a sensor, a sensory system, and a sensor signal recognition system for AI applications by employing printed electronics, analog circuit design, and digital signal processing techniques.

In the first project, a multi-channel stethograph …

Colloidal Quantum Dot (Cqd) Based Mid-Wavelength Infrared Optoelectronics, Shihab Bin Hafiz

Dissertations

Colloidal quantum dot (CQD) photodetectors are a rapidly emerging technology with a potential to significantly impact today’s infrared sensing and imaging technologies. To date, CQD photodetector research is primarily focused on lead-chalcogenide semiconductor CQDs which have spectral response fundamentally limited by the bulk bandgap of the constituent material, confining their applications to near-infrared (NIR, 0.7-1.0 um) and short-wavelength infrared (SWIR, 1-2.5 um) spectral regions. The overall goal of this dissertation is to investigate a new generation of CQD materials and devices that advances the current CQD photodetector research toward the technologically important thermal infrared region of 3-5 ?m, known as …

Intelligent And Secure Fog-Aided Internet Of Drones, Jingjing Yao

Dissertations

Internet of drones (IoD), which utilize drones as Internet of Things (IoT) devices, deploys several drones in the air to collect ground information and send them to the IoD gateway for further processing. Computing tasks are usually offloaded to the cloud data center for intensive processing. However, many IoD applications require real-time processing and event response (e.g., disaster response and virtual reality applications). Hence, data processing by the remote cloud may not satisfy the strict latency requirement. Fog computing attaches fog nodes, which are equipped with computing, storage and networking resources, to IoD gateways to assume a substantial amount of …

Coordination, Adaptation, And Complexity In Decision Fusion, Weiqiang Dong

Dissertations

A parallel decentralized binary decision fusion architecture employs a bank of local detectors (LDs) that access a commonly-observed phenomenon. The system makes a binary decision about the phenomenon, accepting one of two hypotheses (H0 (“absent”) or H1 (“present”)). The k 1 LD uses a local decision rule to compress its local observations yk into a binary local decision uk; uk = 0 if the k 1 LD accepts H0 and uk = 1 if it accepts H1. The k 1 LD sends its decision uk over a noiseless dedicated channel to a Data Fusion Center (DFC). The DFC combines the …

Drone-Assisted Emergency Communications, Di Wu

Dissertations

Drone-mounted base stations (DBSs) have been proposed to extend coverage and improve communications between mobile users (MUs) and their corresponding macro base stations (MBSs). Different from the base stations on the ground, DBSs can flexibly fly over and close to MUs to establish a better vantage for communications. Thus, the pathloss between a DBS and an MU can be much smaller than that between the MU and MBS. In addition, by hovering in the air, the DBS can likely establish a Line-of-Sight link to the MBS. DBSs can be leveraged to recover communications in a large natural disaster struck area …

Countering Internet Packet Classifiers To Improve User Online Privacy, Sina Fathi-Kazerooni

Dissertations

Internet traffic classification or packet classification is the act of classifying packets using the extracted statistical data from the transmitted packets on a computer network. Internet traffic classification is an essential tool for Internet service providers to manage network traffic, provide users with the intended quality of service (QoS), and perform surveillance. QoS measures prioritize a network's traffic type over other traffic based on preset criteria; for instance, it gives higher priority or bandwidth to video traffic over website browsing traffic. Internet packet classification methods are also used for automated intrusion detection. They analyze incoming traffic patterns and identify malicious …

Energy And Performance-Optimized Scheduling Of Tasks In Distributed Cloud And Edge Computing Systems, Haitao Yuan

Dissertations

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival …

Subspace Portfolios: Design And Performance Comparison, Anqi Xiong

Dissertations

Data processing and engineering techniques enable people to observe and better understand the natural and human-made systems and processes that generate huge amounts of various data types. Data engineers collect data created in almost all fields and formats, such as images, audio, and text streams, biological and financial signals, sensing and many others. They develop and implement state-of-the art machine learning (ML) and artificial intelligence (AI) algorithms using big data to infer valuable information with social and economic value. Furthermore, ML/AI methodologies lead to automate many decision making processes with real-time applications serving people and businesses. As an example, mathematical …

Efficient Hardware Implementations Of Bio-Inspired Networks, Anakha Vasanthakumaribabu

Dissertations

The human brain, with its massive computational capability and power efficiency in small form factor, continues to inspire the ultimate goal of building machines that can perform tasks without being explicitly programmed. In an effort to mimic the natural information processing paradigms observed in the brain, several neural network generations have been proposed over the years. Among the neural networks inspired by biology, second-generation Artificial or Deep Neural Networks (ANNs/DNNs) use memoryless neuron models and have shown unprecedented success surpassing humans in a wide variety of tasks. Unlike ANNs, third-generation Spiking Neural Networks (SNNs) closely mimic biological neurons by operating …

Coding Against Stragglers In Distributed Computation Scenarios, Malihe Aliasgari

Dissertations

Data and analytics capabilities have made a leap forward in recent years. The volume of available data has grown exponentially. The huge amount of data needs to be transferred and stored with extremely high reliability. The concept of "coded computing", or a distributed computing paradigm that utilizes coding theory to smartly inject and leverage data/computation redundancy into distributed computing systems, mitigates the fundamental performance bottlenecks for running large-scale data analytics.

In this dissertation, a distributed computing framework, first for input files distributedly stored on the uplink of a cloud radio access network architecture, is studied. It focuses on that decoding …

Bio-Inspired Learning And Hardware Acceleration With Emerging Memories, Shruti R. Kulkarni

Dissertations

Machine Learning has permeated many aspects of engineering, ranging from the Internet of Things (IoT) applications to big data analytics. While computing resources available to implement these algorithms have become more powerful, both in terms of the complexity of problems that can be solved and the overall computing speed, the huge energy costs involved remains a significant challenge. The human brain, which has evolved over millions of years, is widely accepted as the most efficient control and cognitive processing platform. Neuro-biological studies have established that information processing in the human brain relies on impulse like signals emitted by neurons called …

Blind Separation For Intermittent Sources Via Sparse Dictionary Learning, Annan Dong

Dissertations

Radio frequency sources are observed at a fusion center via sensor measurements made over slow flat-fading channels. The number of sources may be larger than the number of sensors, but their activity is sparse and intermittent with bursty transmission patterns. To account for this, sources are modeled as hidden Markov models with known or unknown parameters. The problem of blind source estimation in the absence of channel state information is tackled via a novel algorithm, consisting of a dictionary learning (DL) stage and a per-source stochastic filtering (PSF) stage. The two stages work in tandem, with the latter operating on …

Probabilistic Spiking Neural Networks : Supervised, Unsupervised And Adversarial Trainings, Alireza Bagheri

Dissertations

Spiking Neural Networks (SNNs), or third-generation neural networks, are networks of computation units, called neurons, in which each neuron with internal analogue dynamics receives as input and produces as output spiking, that is, binary sparse, signals. In contrast, second-generation neural networks, termed as Artificial Neural Networks (ANNs), rely on simple static non-linear neurons that are known to be energy-intensive, hindering their implementations on energy-limited processors such as mobile devices. The sparse event-based characteristics of SNNs for information transmission and encoding have made them more feasible for highly energy-efficient neuromorphic computing architectures. The most existing training algorithms for SNNs are based …

Supercapacitors With Gate Electrodes, Tazima Selim Chowdhury

Dissertations

A new approach to improve the capacitance of supercapacitors (SC) is proposed in this study. A typical SC is composed of an anode and a cathode; a separator in between them assures an unintentional discharge of the capacitor. The study focuses on a family of structured separators, either electronically active or passive which are called gates. An active structured separator layer has been fabricated and analyzed. The structured separator has characteristics of electrical diode and is fabricated out of functionalized carbon nanotubes (CNT). Improvement of the overall capacitance of SC, equipped with either active or passive structured separators demonstrated a …

Surface-Enhanced Raman Detection Of Glucose On Several Novel Substrates For Biosensing Applications, Laila Saad Alqarni

Dissertations

The small normal Raman cross-section of glucose is considered to be a major challenge for its detection by Surface Enhanced Raman Spectroscopy (SERS) for medical applications. These applications include blood glucose level monitoring of diabetic patients and evaluation of patients with other medical conditions, since glucose is a marker for many human diseases. This dissertation focuses on Surface-Enhanced Raman Scattering primarily for the detection of glucose. Some experiments also are carried out for the detection of the corresponding enzyme glucose oxidase that is used in electrochemical glucose sensors and in biofuel cells. This project explores the possibility of utilizing Surface …

Workload Allocation In Mobile Edge Computing Empowered Internet Of Things, Qiang Fan

Dissertations

In the past few years, a tremendous number of smart devices and objects, such as smart phones, wearable devices, industrial and utility components, are equipped with sensors to sense the real-time physical information from the environment. Hence, Internet of Things (IoT) is introduced, where various smart devices are connected with each other via the internet and empowered with data analytics. Owing to the high volume and fast velocity of data streams generated by IoT devices, the cloud that can provision flexible and efficient computing resources is employed as a smart "brain" to process and store the big data generated from …

Storage Systems For Mobile-Cloud Applications, Nafize R. Paiker

Dissertations

Mobile devices have become the major computing platform in todays world. However, some apps on mobile devices still suffer from insufficient computing and energy resources. A key solution is to offload resource-demanding computing tasks from mobile devices to the cloud. This leads to a scenario where computing tasks in the same application run concurrently on both the mobile device and the cloud.

This dissertation aims to ensure that the tasks in a mobile app that employs offloading can access and share files concurrently on the mobile and the cloud in a manner that is efficient, consistent, and transparent to locations. …

The Influence Of The Electric Supply Industry On Economic Growth In Less Developed Countries, Edward Richard Bee

Dissertations

This study measures the impact that electrical outages have on manufacturing production in 135 less developed countries using stochastic frontier analysis and data from World Bank’s Investment Climate surveys. Outages of electricity, for firms with and without backup power sources, are the most frequently cited constraint on manufacturing growth in these surveys.

Outages are shown to reduce output below the production frontier by almost five percent in Africa and by a lower percentage in South Asia, Southeast Asia and the Middle East and North Africa. Production response to outages is quadratic in form. Outages also increase labor cost, reduce exports …

A Novel Approach For Identifying Cloud Clusters Developing Into Tropical Cyclones, Chaunte' W. Lacewell

Dissertations

Providing advance notice of rare events, such as a cloud cluster (CC) developing into a tropical cyclone (TC), is of great importance. Having advance warning of such rare events possibly can help avoid or reduce the risk of damages and allow emergency responders and the affected community enough time to respond appropriately. Considering this, forecasters need better data mining and data driven techniques to identify developing CCs. Prior studies have attempted to predict the formation of TCs using numerical weather prediction models as well as satellite and radar data. However, refined observational data and forecasting techniques are not always available …

Tropical Cyclone Intensity Estimation Using Temporal And Spatial Features From Satellite Data, Gholamreza Fetanat Haghighi

Dissertations

Accurate intensity estimation of tropical cyclones (TC) is an important topic of research due to its economic impact and public safety concerns. An accurate measure of the current wind strength is necessary to accurately predict TC intensity. We have developed and tested automated method to estimate TC intensity based on the existing historical satellite images alone. The Hurricane Satellite data (HURSAT-B1) is used to develop the algorithm, which focuses on the North Atlantic from 1978-2009. The algorithm is trained and validated using aircraft reconnaissance-based data. Here, the data is restricted to include only fixes that are over water and are …

Adaptive Security-Aware Scheduling For Packet Switched Networks Using Real-Time Multi-Agent Systems, Ma'en Saleh Saleh

Dissertations

Conventional real-time scheduling algorithms are in care of timing constraints; they don’t pay any attention to enhance or optimize the real-time packet’s security performance. In this work, we propose an adaptive security-aware scheduling with congestion control mechanism for packet switching networks using real-time agentbased systems. The proposed system combines the functionality of real-time scheduling with the security service enhancement, where the real-time scheduling unit uses the differentiated-earliest-deadline-first (Diff-EDF) scheduler, while the security service enhancement scheme adopts a congestion control mechanism based on a resource estimation methodology.

The security service enhancement unit was designed based on two models: singlelayer and weighted …