Engineering Commons^™

Atomistic Insights Into The Mechanisms Of Ultrasonic Bonding, Milad Khajehvand

Engineering Ph.D. Theses

This thesis research uses a combination of computational and experimental approaches to provide atomistic insights into the mechanisms of ultrasonic bonding (UB), a family of solid-state metal-metal joining techniques, including ultrasonic wire bonding, ultrasonic flip-chip bonding, ultrasonic additive manufacturing, and ultrasonic spot welding. The work investigates the atomic-scale contact formation (i.e., the so-called Jump-to-Contact (JC) mechanism) between the UB counterparts, generation of a network of dislocations (i.e., one-dimensional crystallographic defects) at their interface, and the evolution of the network when the interface is under ultrasonic vibration. In particular, the thesis delivers invaluable insights on the mechanisms for contact formation, bond …

On Sparse Coding As An Alternate Transform In Video Coding, Michael G. Schimpf

Engineering Ph.D. Theses

In video compression, specifically in the prediction process, a residual signal is calculated by subtracting the predicted from the original signal, which represents the error of this process. This residual signal is usually transformed by a discrete cosine transform (DCT) from the pixel, into the frequency domain. It is then quantized, which filters more or less high frequencies (depending on a quality parameter). The quantized signal is then entropy encoded usually by a context-adaptive binary arithmetic coding engine (CABAC), and written into a bitstream. In the decoding phase the process is reversed. DCT and quantization in combination are efficient tools, …

Efficient Hardware Implementation Of Deep Learning Networks Based On The Convolutional Neural Network, Anaam Ansari

Engineering Ph.D. Theses

Image classification, speech processing, autonomous driving, and medical diagnosis have made the adoption of Deep Neural Networks (DNN) mainstream. Many deep networks such as AlexNet, GoogleNet, ResidualNet, MobileNet, YOLOv3 and Transformers have achieved immense success and popularity. However, implementing these deep and complex networks in hardware is a challenging feat. The growing demand of DNN applications in mobile devices and data centers have led the researchers to explore application specific hardware accelerators for DNNs. There have been numerous hardware and software based solutions to improve DNN throughput, latency, performance and accuracy. Any solution for hardware acceleration needs to optimize in …

A Submodular Optimization Framework For Imbalanced Text Classification With Data Augmentation, Eyor Alemayehu

Engineering Ph.D. Theses

In the domain of text classification, imbalanced datasets are a common occurrence. The skewed distribution of the labels of these datasets poses a great challenge to the performance of text classifiers. One popular way to mitigate this challenge is to augment underwhelmingly represented labels with synthesized items. The synthesized items are generated by data augmentation methods that can typically generate an unbounded number of items. To select the synthesized items that maximize the performance of text classifiers, we introduce a novel method that selects items that jointly maximize the likelihood of the items belonging to their respective labels and the …

Reactive Particle Swarm Control Architecture And Application For Scalar Field Adaptive Navigation, Shae Taylor Hart

Engineering Ph.D. Theses

Adaptive navigation is a subcategory of navigation techniques that attempts to identify goal locations that satisfy specific criteria in an unknown area. In 2D scalar field adaptive navigation (SFAN), primitives navigate to or along features of interest in an unknown, possibly time-varying, planar scalar field. Features include extrema, contours, and fronts. This work solves the 2D SFAN problem using swarm robotic techniques. Robotic swarms are a subset of multi-robot systems that use decentralized control of simple interchangeable robots to perform collective actions. A subgroup of swarms is the Reactive Particle Swarm (RPS), characterized based on its simplicity, reactivity to its …

Metascriptura: A General Data Provenance Framework, Maria Joseph Israel

Engineering Ph.D. Theses

Digital technology makes it easy to generate and distribute large volumes of data. However, it has also complicated the process of verifying and validating sources of data and their derivatives risking obfuscation of truth amidst the deluge of data. To address this issue, I trace and develop an approach based on data provenance tracking. Specifically, I make it possible to deep trace the origins and lineages of data, by applying state-of-the-art data provenance technologies, which I extend beyond traditional data provenance applications. In this dissertation, I demonstrate that with the right data infrastructure it is feasible to grant greater agency …

Experimentation And Modeling Of Laser Radiation Scattering Through Carbon Fiber Reinforced Polymers, Steven P. Shepard

Engineering Ph.D. Theses

With the prevalence of carbon fiber reinforced polymers (CFRPs) in aerospace platforms, there is a need to better understand radiative heat transport through the material. A laboratory experiment was constructed and a computational zonal Monte Carlo (ZMC) model developed to quantify and understand the laser scattering properties of CFRPs. The ZMC model builds off of the zonal method (ZM)—developed by Hottel et al. and expanded by researchers such as Yuen et al.—by incorporating Monte Carlo techniques into the ZM. The ZMC method is superior in efficiency to the ZM and alternative ray tracing methods, which enables larger mediums of exchange …

Enhancing The Quality Of Service And Energy Efficiency Of Wifi-Based Iot Networks, Jaykumar Sheth

Engineering Ph.D. Theses

The 802.11 standard, known as WiFi, is currently being used for a wide variety of applications including Internet of Things (IoT). However, the contention between the traffic of IoT stations (STAs) as well as the contention between these flows and regular user-generated traffic reduces the energy efficiency and timeliness of IoT communication. To remedy this problem, in this thesis, we take the following approaches for mitigating the challenges faced by WiFi-based IoT networks: First, we highlight the importance of observability with respect to WiFi networks and how it helps the researchers to better examine the dynamics of issues and its …

Personalized Memory Transfer For Conversational Recommendation Systems, Naga Archana Godavarthy

Engineering Ph.D. Theses

Dialogue systems are becoming an increasingly common part of many users' daily routines. Natural language serves as a convenient interface to express our preferences with the underlying systems. In this work, we implement a full-fledged Conversational Recommendation System, mainly focusing on learning user preferences through online conversations. Compared to the traditional collaborative filtering setting where feedback is provided quantitatively, conversational users may only indicate their preferences at a high level with inexact item mentions in the form of natural language chit-chat. This makes it harder for the system to correctly interpret user intent and in turn provide useful recommendations to …

Deep Learning-Based Low Complexity And High Efficiency Moving Object Detection Methods, Bingxin Hou

Engineering Ph.D. Theses

Moving object detection (MOD) is the process of extracting dynamic foreground content from the video frames, such as moving vehicles or pedestrians, while discarding the nonmoving background. It plays an essential role in computer vision field. The traditional methods meet difficulties when applied in complex scenarios, such as videos with illumination changes, shadows, night scenes,and dynamic backgrounds. Deep learning methods have been actively applied to moving object detection in recent years and demonstrated impressive results. However, many existing models render superior detection accuracy at the cost of high computational complexity and slow inference speed. This fact has hindered the development …

Adaptive Navigation Of Three-Dimensional Scalar Fields With Multiple Uavs, Robert Ka-Hing Lee

Engineering Ph.D. Theses

Adaptive Navigation (AN) control strategies allow an agent to autonomously alter its trajectory based on realtime measurements of its environment. Compared to conventional navigation methods, AN techniques can potentially reduce the time and energy needed to explore scalar characteristics of unknown and dynamic regions of interest (e.g., temperature, concentration level). Multiple Uncrewed Aerial Vehicle (UAV) approaches to AN can improve performance by exploiting synchronized spatially-dispersed measurements to generate realtime information regarding the structure of the local scalar field for use in navigation decisions. This dissertation presents initial results of a comprehensive program to develop, verify, and experimentally implement mission-level AN …

Cost Effective And Non-Intrusive Occupancy Detection In Residential Building Through Machine Learning Algorithm, Chenli Wang

Engineering Ph.D. Theses

Residential and commercial buildings consume more than 40% of energy and 76% of electricity in the U.S. Buildings also emit more than one-third of U.S. greenhouse gas emissions, which is the largest sector. A significant portion of the energy is wasted by unnecessary operations on heating, ventilation, and air conditioning (HVAC) systems, such as overheating/overcooling or operation without occupants. Wasteful behaviors consume twice the amount of energy compared to energy-conscious behaviors. Many commercial buildings utilize a building management system (BMS) and occupancy sensors to better control and monitor the HVAC and lighting system based on occupancy information. However, the complicated …

An Input Power-Aware Maximum Efficiency Tracking Technique For Energy Harvesting In Iot Applications, Sanad Fares Yousef Kawar

Engineering Ph.D. Theses

The Internet of Things (IoT) enables intelligent monitoring and management in many applications such as industrial and biomedical systems as well as environmental and infrastructure monitoring. As a result, IoT requires billions of wireless sensor network (WSN) nodes equipped with a microcontroller and transceiver. As many of these WSN nodes are off-grid and small-sized, their limited-capacity batteries need periodic replacement. To mitigate the high costs and challenges of these battery replacements, energy harvesting from ambient sources is vital to achieve energy-autonomous operation. Energy harvesting for WSNs is challenging because the available energy varies significantly with ambient conditions and in many …

Deep Generative Models For Semantic Text Hashing, Suthee Chaidaroon

Engineering Ph.D. Theses

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of …

Mission-Oriented Multirobot Adaptive Navigation Of Scalar Fields, Robert Mcdonald

Engineering Ph.D. Theses

Scalar fields are spatial regions where each point has an associated physical value. These fields often contain features of interest, such as local extrema and contours with a value of significance. Traditional navigation techniques require robots to exhaustively search these regions to find the areas of significance, while adaptive navigation allows them to move directly to the points of interest based on measurements of the field taken during the navigation process. This work expands existing adaptive navigation techniques by adding a finite state machine layer to the control architecture, and using it as a discrete mode controller; the state machine …

A Drift-Resilient And Degeneracy-Aware Loop Closure Detection Method For Localization And Mapping In Perceptually-Degraded Environments, Kamak Ebadi

Engineering Ph.D. Theses

Enabling fully autonomous robots capable of navigating and exploring unknown and complex environments has been at the core of robotics research for several decades. Mobile robots rely on a model of the environment for functions like manipulation, collision avoidance and path planning. In GPS-denied and unknown environments where a prior map of the environment is not available, robots need to rely on the onboard sensing to obtain locally accurate maps to operate in their local environment. A global map of an unknown environment can be constructed from fusion of local maps of temporally or spatially distributed mobile robots in the …

Metamaterial Absorbers For Mitigating Unintentional Radiated Emissions, Ali Khoshniat

Engineering Ph.D. Theses

Unintentional radiated emission spikes are one of the causes of failure in electromagnetic compliance tests of high-speed systems. In this thesis, a new absorber solution for mitigating such emissions is proposed using the concept of metamaterial structures. The absorber is placed inside the high-speed system shield box to match the low (almost zero) impedance of the metal walls to the wave impedance of unwanted radiations. As a result, waves reflected from the shield box are attenuated which eventually reduces the emissions leaked outside of the box. The effectiveness of the proposed solution is demonstrated through simulations and experimental evaluations of …

Mnews: A Study Of Multilingual News Search Interfaces, Chenjun Ling

Engineering Ph.D. Theses

With the global expansion of the Internet and the World Wide Web, users are becoming increasingly diverse, particularly in terms of languages. In fact, the number of polyglot Web users across the globe has increased dramatically.

However, even such multilingual users often continue to suffer from unbalanced and fragmented news information, as traditional news access systems seldom allow users to simultaneously search for and/or compare news in different languages, even though prior research results have shown that multilingual users make significant use of each of their languages when searching for information online.

Relatively little human-centered research has been conducted to …

A Flexible Bch Decoder For Flash Memory Systems Using Cascaded Bch Codes, Arul K. Subbiah

Engineering Ph.D. Theses

NAND ash memories are widely used in consumer electronics, such as tablets, personal computers, smartphones, and gaming systems. However, unlike other standard storage devices, these ash memories suffer from various random errors. In order to address these reliability issues, various error correction codes (ECC) are employed. Bose-Chaudhuri Hocquenghem (BCH) code is the most common ECC used to address the errors in modern ash memories. Because of the limitation of the realization of the BCH codes for more extensive error correction, the modern ash memory devices use Low-density parity-check (LDPC) codes for error correction scheme. The realization of the LDPC decoders …

Deep Learning For Recommender Systems, Travis Akira Ebesu

Engineering Ph.D. Theses

The widespread adoption of the Internet has led to an explosion in the number of choices available to consumers. Users begin to expect personalized content in modern E-commerce, entertainment and social media platforms. Recommender Systems (RS) provide a critical solution to this problem by maintaining user engagement and satisfaction with personalized content.

Traditional RS techniques are often linear limiting the expressivity required to model complex user-item interactions and require extensive handcrafted features from domain experts. Deep learning demonstrated significant breakthroughs in solving problems that have alluded the artificial intelligence community for many years advancing state-of-the-art results in domains such as …

Design And Measurement Of A Millimeter-Wave 2d Beam Switching Planar Antenna Array, Benjamin Horwath

Engineering Ph.D. Theses

A millimeter-wave 2-D beam switching microstrip patch antenna array excited by a 4x4 substrate integrated waveguide (SIW) Modified Butler Matrix is designed and experimentally evaluated in this thesis. A novel architecture is introduced for the Butler Matrix feed network to give designers a choice for phase shifter location to pursue a smaller circuit area. In addition, it enables the designer to control the BM phased outputs for achieving a set of desired 2-D beam directions, e.g., ϕ₀=45°, 135°, 225°, and 315° at θ₀=45°, with a passive beam switching network for a given array geometry. Full-wave simulation …

Shingled Magnetic Recording Disks For Mass Storage Systems, Quoc Minh Le

Engineering Ph.D. Theses

Disk drives have seen a dramatic increase in storage density over the last five decades, but to continue the growth seems difficult if not impossible because of physical limitations. One way to increase storage density is using a shingled magnetic recording (SMR) disk. Shingled writing is a promising technique that trades off the inability to update in-place for narrower tracks and thus a much higher data density. It is particularly appealing as it can be adopted while utilizing essentially the same physical recording mechanisms currently in use. Because of its manner of writing, an SMR disk would be unable to …

Quaternion Information Theoretic Learning Adaptive Algorithms For Nonlinear Adaptive, Carlo Safarian

Engineering Ph.D. Theses

Information Theoretic Learning (ITL) is gaining popularity for designing adaptive filters for a non-stationary or non-Gaussian environment [1] [2] . ITL cost functions such as the Minimum Error Entropy (MEE) have been applied to both linear and nonlinear adaptive filtering with better overall performance compared with the typical mean squared error (MSE) and least-squares type adaptive filtering, especially for nonlinear systems in higher-order statistic noise environments [3].

Quaternion valued data processing is beneficial in applications such as robotics and image processing, particularly for performing transformations in 3-dimensional space. Particularly the benefit for quaternion valued processing includes performing data transformations in …

Fuzzy-Model-Based (Fmb) Control Of A Spacecraft With Fuel Sloshing Dynamics, Lilit Mazmanyan

Engineering Ph.D. Theses

During the upper-stage separation and orbit injection, orbital control, and attitude maneuver, propellant slosh in partially-filled fuel tanks can cause dynamical instability or pointing errors. The spacecraft dynamics combined with propellant sloshing results in a highly nonlinear and coupled dynamic system that requires a complicated control law. This problem has been a long-standing concern for space missions. The purpose of this research is two fold. The first part is to investigate and develop nonlinear Takagi-Sugeno (T-S) fuzzy model-based controllers for a spacecraft with fuel sloshing considering the input constraints on the actuators. It includes i) a fuzzy controller/observer with a …

Human Attention Region Of Interest In Video Compression, Olayinka Sylvia N’Guessan

Engineering Ph.D. Theses

In this thesis, we propose a generic human attention region-of-interest (Generic- HAROI) algorithm to improve video compression while preserving subjective quality. Precisely, this algorithm performs a perceptual adaptive quantization algorithm on video frames as a function of the distribution of their luminance, motion vector, and color saturation. Our research incorporates a psycho-visual study that demonstrated that human attention automatically enhanced perceived saturation. As a result, the adaptive quantization phase of our compression algorithm is characterized by a luminance and saturation-aware just noticeable distortion (JND) function. After running multiple experiments on 18 videos with various resolutions ranging from QCIF to 4K, …

Machine Learning Models For Context-Aware Recommender Systems, Yogesh Jhamb

Engineering Ph.D. Theses

The mass adoption of the internet has resulted in the exponential growth of products and services on the world wide web. An individual consumer, faced with this data deluge, is expected to make reasonable choices saving time and money. Organizations are facing increased competition, and they are looking for innovative ways to increase revenue and customer loyalty. A business wants to target the right product or service to an individual consumer, and this drives personalized recommendation. Recommender systems, designed to provide personalized recommendations, initially focused only on the user-item interaction. However, these systems evolved to provide a context-aware recommendations. Context-aware …

Carbon Nanotube Ultracapacitor Characteristics And Cell Design, Antonis A. Orphanou

Engineering Ph.D. Theses

A model of carbon nanotube (CNT) ultracapacitor (CNU) as a high-performance energy storage device is developed based on simulations of electrolyte ion motions between cathode and anode. Using a molecular dynamics (MD) approach, the equilibrium positions of electrode charges interacting through Coulomb potential are determined, which in turn yield the equipotential surface and electric field associated with the capacitor. With an applied AC voltage, the current is computed from the nanotube and electrolyte particle distribution and interaction, resulting in a frequency-dependent CNU impedance. From the current and impedance profiles, the Nyquist and Cyclic Voltammetry plots are then extracted. Results of …

Towards Efficient Resource Provisioning In Hadoop, Peter P. Nghiem

Engineering Ph.D. Theses

Considering recent exponential growth in the amount of information processed in Big Data, the high energy consumed by data processing engines in datacenters has become a major issue, underlining the need for efficient resource allocation for better energy-efficient computing. This thesis proposes the Best Trade-off Point (BToP) method which provides a general approach and techniques based on an algorithm with mathematical formulas to find the best trade-off point on an elbow curve of performance vs. resources for efficient resource provisioning in Hadoop MapReduce and Apache Spark. Our novel BToP method is expected to work for any applications and systems which …

Supernode Transformation On Parallel Systems With Distributed Memory – An Analytical Approach, Yong Chen

Engineering Ph.D. Theses

Supernode transformation, or tiling, is a technique that partitions algorithms to improve data locality and parallelism by balancing computation and inter-processor communication costs to achieve shortest execution or running time. It groups multiple iterations of nested loops into supernodes to be assigned to processors for processing in parallel. A supernode transformation can be described by supernode size and shape. This research focuses on supernode transformation on multi-processor architectures with distributed memory, including computer cluster systems and General Purpose Graphic Processing Units (GPGPUs). The research involves supernode scheduling, supernode mapping to processors, and the finding of the optimal supernode size, for …

Diffraction Model Of Thermoreflectance Data, Sahida Rahimbhai Kureshi

Engineering Ph.D. Theses

Diffraction based mathematical model is developed to address the issue of spatial resolution in thermoreflectance imaging at the scale of 1 and 10 μm. Thermoreflectance imaging provided non-contact temperature measurement at micro and nano scale but the spatial resolution is limited by diffraction. By virtue of this work mathematical model is developed for the analysis of thermoreflectance data. In the development of model both the diffraction occurring at sample and substrate is combined to calculate intensity of thermoreflectance signal. This model takes into account the effective optical distance, sample width, wavelength, signal phase shift and reflectance intensity. Model shows qualitative …