Engineering Commons^™

Structural Studies To Determine The Mechanisms Supporting Multiferroic And Ferroelectric Properties Of Complex Oxides, Han Zhang

Dissertations

Multiferroics are a class of materials which possess both magnetic and electrical polarization with possible coupling between them. They show promise to enable new sensors and data storage devices with novel features, such as the possibility of writing polarization bits with magnetic fields at low power. The coexisting magnetic and ferroelectric order parameters are usually weakly coupled, preventing practical use. The development and study of new classes of materials with large magnetoelectric couplings is of high importance. Understanding the structure of these materials is key to this effort.

As one class of these systems, the RX3(BO₃)₄ has …

Microfluidic Biosensor With Functionalized Gold Nano Particles On Interdigitated Electrodes, Bharath Babu Nunna

Dissertations

The integration of the microfluidics to the biosensor has growing demand with favorable conditions such as reduced processing time and low reagent consumption. The immuno biosensing with the microfluidic platform helped to make the electrochemical biosensing assays portable due to which this sensing mechanism can be easily implemented in point of care devices. The implementation of the biosensing in the microchannels significantly reduces the sample requirement form milli liter (mL) to micro liter (uL), and thus leads to low volume sample requirement during the sensing. The primary factors contributing to the microfluidic biosensors performance are probe immobilization, specific binding and …

Experiments And Multi-Field Modeling Of Inelastic Soft Materials, Shuolun Wang

Dissertations

Soft dielectrics are electrically-insulating elastomeric materials, which are capable of large deformation and electrical polarization, and are used as smart transducers for converting between mechanical and electrical energy. While much theoretical and computational modeling effort has gone into describing the ideal, time-independent behavior of these materials, viscoelasticity is a crucial component of the observed mechanical response and hence has a significant effect on electromechanical actuation. This thesis reports on a constitutive theory and numerical modeling capability for dielectric viscoelastomers, able to describe electromechanical coupling, large- deformations, large-stretch chain-locking, and a time-dependent mechanical response. This approach is calibrated to the widely-used …

Antimicrobial Materials Via Thiol-Ene Chemistry, Dahlia Amato

Dissertations

With the increasing prevalence of antimicrobial resistance, the escalation of opportunistic/pathogenic infections is a looming global crisis. To avoid the pitfalls of conventional antibiotics, this dissertation focuses on developing macromolecular solutions to develop novel antimicrobial materials based on essential oils (thymol, carvacrol, and aldehydes). It is well established that essential oil derivatives exhibit high potency towards a wide range of pathogenic microbes. The rapid photopolymerization kinetics, limited by-products, and homogeneous network formation afforded by thiol-ene photopolymerization are utilized to either encapsulate essential oil derivatives or convert them into monomers which can subsequently be incorporated into new antimicrobial materials with new …

Polymer Nanocomposites Containing High Aspect Ratio Particulates: Innovation In Co-Extruded Multilayer Barrier Films, Kevin Meyers

Dissertations

Delaminated montmorillonite (MMT) clay/ maleic anhydride grafted LLDPE nanocomposite multilayer films with alternating layers of LDPE were produced through multilayer co-extrusion. The MMT concentration within the nanocomposite layers was increased through annealing the films in the melt due to a mismatch in interdiffusion rates of the polymer layers. Analysis of the nanocomposite layers upon annealing revealed that the platelets impinged upon one another resulting in significant improvement in oxygen barrier in the multilayer system, exceeding the results of bulk nanocomposites.

Model analysis demonstrated that increasing the nanoplatelet aspect ratio or initial concentration in the filled layers would lead to even …

Electric-Field Assisted Manipulation And Self-Assembly Of Particle Suspensions, Edison Chijioke Amah

Dissertations

The aim of this dissertation is to model the processes by which particles suspended in liquids and at liquid surfaces self-assemble when they are subjected to uniform and non-uniform electric fields. To understand the role of electric forces, three related problems were studied numerically and experimentally.

In the first problem, particles are assumed to be suspended inside a liquid and a nonuniform electric field is applied using electrodes mounted in the domain walls which causes positively polarized particles to collect in the regions where the electric field intensity is locally maximal and the negatively polarized particles collect in the regions …

A Methodological Framework For Quantifying Impacts Of Truck Traffic On Regional Network With Implications To Transport Policy, Chaitanya Narendra Pathak

Dissertations

Increased global trade has promoted the importance of shipping industry and the introduction of mega-ships has created an opportunity to be more cost-effective. Because of this, the expected change in freight transportation influences the operating regimes and schedules at the port terminals. Trucks being the predominant mode of transportation used to carry the freight transport, there is a growing concern about the impact of trucks in the region. The problems are further expected to grow as the improvements to resolve them are hindered by funding shortfalls. Public agencies are therefore involved in developing comprehensive state freight plans that outline immediate …

Combustion Of Nanocomposite Thermite Powders, Ian Monk

Dissertations

This work investigates combustion of nanocomposite thermite powders prepared by arrested reactive milling (ARM). The focus is on how ARM as a top-down approach to nano-thermite building generating fully-dense nanocomposite particles with dimensions of 1-100 µm affects the rates and mechanism of their combustion. A variety of thermites are milled using both aluminum and zirconium as fuels combined with several oxidizers (WoO₃, MoO₃, CuO, Fe₂O₃, and Bi₂O₃). The powders are ignited using both an electrostatic discharge (ESD) and a CO₂ laser beam.

A range of parameters vary …

Functionalized Nanoporous Carbon Scaffolds For Hydrogen Storage Applications, Christopher Carr

Dissertations

Recent efforts have demonstrated confinement in porous scaffolds at the nanoscale can alter the hydrogen sorption properties of metal hydrides, though not to an extent feasible for use in onboard hydrogen storage applications, proposing the need for a method allowing further modifications. The work presented here explores how the functionalization of nanoporous carbon scaffold surfaces with heteroatoms can modify the hydrogen sorption properties of confined metal hydrides in relation to non-functionalized scaffolds (FS). Investigations of nanoconfined LiBH₄and NaAlH₄indicate functionalizing the carbon scaffold surface with nitrogen can shift the activation energy of hydrogen desorption in excess of …

Spoilage Detection In Raspberry Fruit Based On Spectral Imaging Using Convolutional Neural Networks, Karthik Kuchangi Jothi Prakash

Dissertations

Effective spoilage detection of perishable food items like fruits and vegetables is essential for retailers who stock and sell large quantities of these items. This research is aimed at developing a non-destructive, rapid and accurate method which is based on Spectral Imaging (SI) used in tandem with Convolutional Neural Network (CNN) to predict whether the fruit is fresh or rotten. The study also aims to determine the number of days before which the fruit rots. This research employs a primary, quantitative and inductive methods to investigate the Deep Learning based approach to detect fruit spoilage. Raspberry fruit in particular has …

A Comparison Of Real Time Stream Processing Frameworks, Jonathan Curtis

Dissertations

The need to process the ever-expanding volumes of information being generated daily in the modern world is driving radical changes in traditional data analysis techniques. As a result of this, a number of open source tools for handling real-time data streams has become available in recent years. Four, in particular, have gained significant traction: Apache Flink, Apache Samza, Apache Spark and Apache Storm. Despite the rising popularity of these frameworks, however, there are few studies that analyse their performance in terms of important metrics, such as throughput and latency. This study aims to correct this, by running several benchmarks against …

Comparing The Effectiveness Of Different Classification Techniques In Predicting Dns Tunnels, Patrick Walsh

Dissertations

DNS is one of the most widely used protocols on the internet and is used in the translation of domain names into IP address in order to correctly route messages between computers. It presents an attractive attack vector for criminals as the service is not as closely monitored by security experts as other protocols such as HTTP or FTP. Its use as a covert means of communication has increased with the availability of tools that allow for the creation of DNS tunnels using the protocol. One of the primary motivations for using DNS tunnels is the illegal extraction of information …

Application Of Synthetic Informative Minority Over-Sampling (Simo) Algorithm Leveraging Support Vector Machine (Svm) On Small Datasets With Class Imbalance, Akshatha Fakkeriah Kallappanamatt

Dissertations

Developing predictive models for classification problems considering imbalanced datasets is one of the basic difficulties in data mining and decision-analytics. A classifier’s performance will decline dramatically when applied to an imbalanced dataset. Standard classifiers such as logistic regression, Support Vector Machine (SVM) are appropriate for balanced training sets whereas provides suboptimal classification results when used on unbalanced dataset. Performance metric with prediction accuracy encourages a bias towards the majority class, while the rare instances remain unknown though the model contributes a high overall precision. There are chances where minority instances might be treated as noise and vice versa. (Haixiang et …

Protocol For Energy Efficient Cluster Head Election For Collaborative Cluster Head Elections., David Otu

Dissertations

In wireless sensor networks (WSNs) , energy is a major constrain.Energy efficient network protocols is required to maintain reliable sensing of the sensing field . WSNs that use LEACH protocol, have node in a cluster periodically take trials to become cluster - head,such that the nodes in the cluster becomes cluster - head evenly. Nodes with greater transmission distance to the base station in the cluster dies out faster because it performs more work in transmitting data when it is cluster-head. The distance-based algorithm presented in this project, ensures that work is allocated to the nodes in the cluster more …

An Application Of Natural Language Processing For Triangulation Of Cognitive Load Assessments In Third Level Education, Luis Alfredo Contreras

Dissertations

Work has been done to measure Mental Workload based on applications mainly related to ergonomics, human factors, and Machine Learning. The influence of Machine Learning is a reflection of an increased use of new technologies applied to areas conventionally dominated by theoretical approaches. However, collaboration between MWL and Natural Language Processing techniques seems to happen rarely. In this sense, the objective of this research is to make use of Natural Languages Processing techniques to contribute to the analysis of the relationship between Mental Workload subjective measures and Relative Frequency Ratios of keywords gathered during pre-tasks and post-tasks of MWL activities …

Can Threshold-Based Sensor Alerts Be Analysed To Detect Faults In A District Heating Network?, Liam Cantwell

Dissertations

Older IoT “smart sensors” create system alerts from threshold rules on reading values. These simple thresholds are not very flexible to changes in the network. Due to the large number of false positives generated, these alerts are often ignored by network operators. Current state-of-the-art analytical models typically create alerts using raw sensor readings as the primary input. However, as greater numbers of sensors are being deployed, the growth in the number of readings that must be processed becomes problematic. The number of analytic models deployed to each of these systems is also increasing as analysis is broadened. This study aims …

Elasticity Measurement In Caas Environments - Extending The Existing Bungee Elasticity Benchmark To Aws's Elastic Container Service, Nora Limbourg

Dissertations

Rapid elasticity and automatic scaling are core concepts of most current cloud computing systems. Elasticity describes how well and how fast cloud systems adapt to increases and decreases in workload. In parallel, software architectures are moving towards employing containerised microservices running on systems managed by container orchestration platforms. Cloud users who employ such container-based systems may want to compare the elasticity of different systems or system settings to ensure rapid elasticity and maintain service level objectives while avoiding over-provisioning. Previous research has established a variety of metrics to measure elasticity. Some existing benchmark tools are designed to measure elasticity in …

Use Of Hyperspectral Images (Hsi) And Convolutional Neural Network (Cnn) To Identify Normal, Precancerous And Cancerous Tissues, Pallavi Jain

Dissertations

Cancer detection has been a great topic of research for a long time, as early detection of cancer can help in increasing the survival rate of patients by providing on time better treatment. A robust system is required in order to detect early-stage cancer as its difficult to identify early-stage cancer from the normal clinical process. The computer vision techniques provide a new way to understand the challenges related to the medical image analysis. This thesis presents the medical image analysis using a combination of Convolutional Neural Network and Hyperspectral Images of cancer patient's tissues. The idea behind choosing the …

From Business Understanding To Deployment: An Application Of Machine Learning Algorithms To Forecast Customer Visits Per Hour To A Fast-Casual Restaurant In Dublin, Odunayo David Adedeji

Dissertations

This research project identifies the significant factors that affects the number of customer visits to a fast-casual restaurant every hour and proceeds to develop several machine learning models to forecast customer visits. The core value proposition of fast-casual restaurants is quality food delivered at speed which means they have to prepare meals in advance of customers visit but the problem with this approach is in forecasting future demand, under estimating demand could lead to inadequate meal preparation which would leave customers unsatisfied while over estimation of demand could lead to wastage especially with restaurants having to comply with food safety …

Can Machine Learning Beat Physics At Modeling Car Crashes?, Gavin Byrne

Dissertations

This study aimed to look at a traditional method used for measuring the severity and principle direction of force of a car crash and see if it could be improved on using machine learning models. The data used was publicly available from the NHTSA database and included descriptions of the vehicle, test and sensors as well as the accelerometer data over the period of the crashes. The models built were SVM classifiers and multinomial regression models. Although the SVM and Regression models were built successfully and gave higher levels of accuracy than the momentum models in terms of the severity, …

Supervised Learning Models To Predict Stock Direction Within Different Sectors In A Bull And Bear Market, Tiffany Razy

Dissertations

Forecasting stock market price movement is a well researched and an alluring topic within the machine learning and financial realm. Supervised machine learning algorithms such as Random Forest (RF) and Support Vector Machines (SVM) have been used independently to gain insight on the market. With such volatility in the market the scope of this study will utilized the RF and SVM in a very volatility market to determine if these models will perform at a high level or outperform each other in both markets. This relative study is performed on 16 stocks in 4 different sectors over the bear market …

Identifying Expert Investors On Financial Microblog Via Artificial Neural Networks, Pierluca Del Buono

Dissertations

In the recent years, thanks to social media platform, a plethora of information has been available to financial investors, that were traditionally dependent from financial institutions advisors. Strategies are now shared among web users, performances of stocks are commented in web communities and hints and suggestions are travelling on the internet with a fast pace, in a way that was unthinkable few years before. Several attempts have been made in the recent past, to predict Market movements and trends from activity of Financial Social Networks participants, and to evaluate if contributions from individuals with high level of expertise distinguish themselves …

Investigating The Application Of Deep Convolutional Neural Networks In Semi-Supervised Video Object Segmentation, Jayadeep Sasikumar

Dissertations

This thesis investigates the different approaches to video object segmentation and the current state-of-the-art in the discipline, focusing on the different deep learning techniques used to solve the problem. The primary contribution of the thesis is the investigation of usefulness of Exponential Linear Units as activation functions for deep convolutional neural architectures trained to perform object semi-supervised segmentation in videos. Mask R-CNN was chosen as the base convolutional neural architecture, with the view of extending the image segmentation algorithm to videos. Two models were created, one with Rectified Linear Units and the other with Exponential Linear Units as the respective …

Beef Cattle Instance Segmentation Using Mask R-Convolutional Neural Network, Mohammad Danish

Dissertations

Maintaining the cattle farm along with the wellbeing of every heifer has been the major concern in dairy farm. A robust system is required which can tackle the problem of continuous monitoring of cows. the computer vision techniques provide a new way to understand the challenges related to the identification and welfare of the cows. This paper presents a state-of-art instance segmentation mask RCNN algorithm to train and build a model on a very challenging cow dataset that is captured during the winter season. The dataset poses many challenges such as overlapping of cows, partial occlusion, similarity between cows and …

Evaluating The Impact Of Adopting 3d Printing Services On The Retailers, Sharareh Rajaei Dehkordi

Dissertations

As additive manufacturing technology becomes more responsive to consumers’ demand, one important question for the retailers is whether they should provide 3D printing services in their brick-and-mortar store in addition to the traditional off-the-shelf product? If so, what should be the retailers pricing scheme to achieve a higher profit? What should be the optimal inventory level of off-the-shelf products? What is the optimal capacity of 3D printers? In this study, stochastic models are examined to capture the joint optimal 3D product price and capacity of 3D printers to maximize retailer’s expected profit while considering consumer product choices. Moreover, a stochastic …

Black Silicon – Optical Properties And Applications, Sita Rajyalaxmi Marthi

Dissertations

Crystalline Silicon (c-Si) is one of the most abundant and widely used semiconductors. Si is a semiconductor with indirect bandgap. The average reflectance of Si is about 30% in the visible range of wavelengths. Standard Si solar cells are not entirely useful in the infrared spectrum region. In order to make Si useful in a wide spectral range, the surface of Si is modified to reduce the reflectance of Si. The silicon thus modified is called Black Silicon (BSi).

This dissertation describes the model based on effective medium approximation to determine the effective optical constants, refractive index and extinction coefficient …

Interference Unmixing And Estimation Technique For Improvement Of Speech Separation Performance, Ankur Maurya

Dissertations

Presence of noise in the speech can sometimes become annoying as it can lead to loss of important data or create misunderstandings between the communications area which can lead to major problems associated to loss of time and money. This thesis focuses to filter out noise form a speech signal which is simulated in Matlab/Octave software while making a comparison between temporal resolution of signal with respect to the spectral resolution of the signal in which the parameters such as the size of window length are varied in order to obtain the best speech separation performance. To get the best …

Distributed Model Predictive Control Of Power Converters In Microsrids Under Different Modes Of Operation, Riyadh Toman Thahab Toman

Dissertations

Microgrids consist of many distributed energy sources (DERs) and the overall system is viewed as a number of subsystems. In this work, a finite control set model predictive control is proposed to control power converters in both grid and island modes of operation of the microgrid. Each subsystem is assigned a local controller that relies on measurements of common coupling point (PCC) and/or terminal quantities to provide accurate regulation of current or voltage components. This meets the local objective of each subsystem and contributes to the global objective of the microgrid.

In the first part of the work, a microgrid …

Self-Generated Absorption Filter Based Optical Densitometry Calculations For Expanded Gamut And Spot Color Printing On Paperboard For Packaging Applications And Proposed Oba Index, Awadhoot Vijaykant Shendye

Dissertations

In the printing industry, process color printing is a very common way of reproduction of color photographic images. As continuous tone reproduction is not possible, images are converted to halftone format. Process control of a halftone image quality is carried out by measuring optical density of solid patches. ISO 5 defines measurement of optical density, formula, filters and requirements. As per ISO document optical density can be calculated only for the 4 colors cyan, magenta, yellow and black, known as process colors. To enhance quality of color reproduction of images, the number of reproducible colors are increased by gamut expansion. …

Bio-Socially Inspired Strategies In Support Of Dynamic Spectrum Access: An Evolutionary Game Theory Perspective, Mohammad Ali Marzoug Abu Shattal

Dissertations

Dynamic Spectrum Access (DSA) has been introduced to fulfill the expanded need for spectrum by different wireless networks and applications. Within the realm of spectrum access, the problem of "spectrum crunch" is present since some of the spectrum bands are overcrowded and others are underutilized. DSA aims to alleviate the problem of spectrum crunch. In DSA, Primary Users (PUs) allow Secondary Users (SUs) to access the spectrum as long as they do not interfere with PU transmissions beyond a pre-agreed acceptable level. In this work, a bio-socially inspired approach is proposed for SU interactions in support of better throughput for …