Engineering Commons^™

Resource Allocation For Heterogeneous Wireless Networks Of Devices With Multi-Connectivity, Monika Prakash

Dissertations

This dissertation contributes to designing an effective resource allocation scheme for multi-connectivity (MC) networks. MC is a feature that allows devices to utilize the radio resources from more than one base station simultaneously. With MC anticipated to play a key role in satisfying the stringent QoS requirements of the next-generation networks (5G/6G), it is crucial to efficiently allocate the resources across multiple connections of one or more radio access technologies (RATs).
/="/">Currently, there is a huge demand on the availability of radio spectrum resources with a large volume of different types of data traffic (Internet-of-things, human-based, machine-machine) traversing the …

Behavior Of Steel Fiber Reinforced Concrete Made With Recycled Concrete Aggregates: Material Characterization And Deep Beam Shear Response, Nancy Kachouh

Dissertations

This research aims to examine the performance of concrete made with recycled concrete aggregates (RCAs) and steel fibers. It comprises material characterization tests, large-scale reinforced concrete (RC) deep beam tests, and numerical modeling. Material characterization test parameters included the RCA replacement percentage (30, 70, and 100%) and the steel fiber volume fraction (vf = 1, 2, and 3%). Fourteen large-scale deep beam numerical models with and without web openings made with 100% RCAs and steel fibers were developed and validated through a comparative analysis with test results. A numerical parametric study was conducted to investigate the effect of varying …

Efficient Routing Protocols For Cognitive Radio Networks Of Energy-Constrained Devices And Dissimilar Delay-Sensitive Levels, Rita Ahmad Abu Diab

Dissertations

As the Cognitive Radio (CR) overwhelms spectrum deficiency, it offers a substantial communication environment to accommodate IoT devices demanding connectivity and prompt arrival of their sensed data. The perceived shortage in CR routing protocols concerning delay and energy consumption promotes the novel proposal of two multi-hop routing protocols; Efficient Routing protocol for Cognitive Radio (ERCR) networks and Efficient Hybrid Routing protocol for Cognitive Radio (EHRCR) networks. A proposal for a D/M/1/K queuing model adapted to be applied in a CR network (CRN) is investigated with a retrial service system over a single licensed channel. …

Thermomechanics Of Semiconducting Polymers And Their Morphological Phenomena, Luke Galuska

Dissertations

In contrast to conventional silicon-based electronics, semiconducting polymers show great promise for emerging applications in soft, flexible, and ductile electronic technologies. This is due to their polymeric nature, tailorable structure, and sub-100 nm device thickness. Despite this mechanical novelty, there remains a poor understanding of their structure-property-processing relationships, which has hindered growth within the field. This dissertation elucidates these relationships through investigation of their thermomechanics, and morphological phenomena. This was accomplished through the following projects:

1) To demonstrate the impact of backbone rigidity on semiconducting polymer thermomechanics, we varied the backbone rigidity of an NDI-based polymer by inserting flexible methylene …

Utilization Of Triply Periodic Minimal Surface (Tpms) Based Architectures Impregnated With Phase Change Material For Heat Transfer Applications, Zahid Ahmed Qureshi

Dissertations

This dissertation is concerned with the utilization of mathematically architected Triply Periodic Minimal Surface (TPMS) based lattices for Latent Heat Thermal Energy Storage (LHTES) systems. With the advent of Additive Manufacturing (AM), TPMS structures can be readily manufactured. The objective of this dissertation was to investigate the heat transfer performance of TPMS structures vis-à-vis conventional metal foams represented by Kelvin cells while both were impregnated with a Phase Change Material (PCM). Numerical simulations were performed under various boundary conditions to assess the performance. It was found that TPMS structures outperformed conventional metal foam. Moreover, the effects of boundary conditions (isothermal …

Improving Dysarthric Speech Recognition By Enriching Training Datasets, Sophie Cullen

Dissertations

Dysarthria is a motor speech disorder that results from disruptions in the neuro-motor interface and is characterised by poor articulation of phonemes and hyper-nasality and is characteristically different from normal speech. Many modern automatic speech recognition systems focus on a narrow range of speech diversity therefore as a consequence of this they exclude a groups of speakers who deviate in aspects of gender, race, age and speech impairment when building training datasets. This study attempts to develop an automatic speech recognition system that deals with dysarthric speech with limited dysarthric speech data. Speech utterances collected from the TORGO database are …

Real-Time Stock Market Recommendation & Prediction Using Multi Source Data, Kalpana Konety

Dissertations

Stock investors must be cognizant of both the current price of their stock and the price at which they want to sell it in the future. This does not stop investors to monitor past price patterns and apply their knowledge to the present. ’Past performance is not an indicator of future success’, as the saying goes. To put it another way, historical stock data alone isn’t enough to forecast future stock prices. Another key factor to consider in a trading strategy is the impact of market psychology. Financial data, which is a type of multimedia data, provides a wealth of …

Measuring And Comparing Social Bias In Static And Contextual Word Embeddings, Alan Cueva Mora

Dissertations

Word embeddings have been considered one of the biggest breakthroughs of deep learning for natural language processing. They are learned numerical vector representations of words where similar words have similar representations. Contextual word embeddings are the promising second-generation of word embeddings assigning a representation to a word based on its context. This can result in different representations for the same word depending on the context (e.g. river bank and commercial bank). There is evidence of social bias (human-like implicit biases based on gender, race, and other social constructs) in word embeddings. While detecting bias in static (classical or non-contextual) word …

Hybridization Of Biologically Inspired Algorithms For Discrete Optimisation Problems, Elihu Essian-Thompson

Dissertations

In the field of Optimization Algorithms, despite the popularity of hybrid designs, not enough consideration has been given to hybridization strategies. This paper aims to raise awareness of the benefits that such a study can bring. It does this by conducting a systematic review of popular algorithms used for optimization, within the context of Combinatorial Optimization Problems. Then, a comparative analysis is performed between Hybrid and Base versions of the algorithms to demonstrate an increase in optimization performance when hybridization is employed.

Development Of An Explainability Scale To Evaluate Explainable Artificial Intelligence (Xai) Methods, Stephen Mccarthy

Dissertations

Explainable Artificial Intelligence (XAI) is an area of research that develops methods and techniques to make the results of artificial intelligence understood by humans. In recent years, there has been an increased demand for XAI methods to be developed due to model architectures getting more complicated and government regulations requiring transparency in machine learning models. With this increased demand has come an increased need for instruments to evaluate XAI methods. However, there are few, if none, valid and reliable instruments that take into account human opinion and cover all aspects of explainability. Therefore, this study developed an objective, human-centred questionnaire …

Evaluation Of Automated Eye Blink Artefact Removal Using Stacked Dense Autoencoder, Matthew Rigney

Dissertations

The presence of artefacts in Electroencephalograph (EEG) signals can have a considerable impact on the information they portray. In this comparative study, the automated removal of eye blink artefacts using the constrained latent representation of a stacked dense autoencoders (SDAE) and comparing its ability to that of the manual independent component analysis (ICA) approach was evaluated. A comparative evaluation of 5 stacked dense autoencoder architectures lead to a chosen architecture for which the ability to automatically detect and remove eye blink artefacts were both statistically and humanistically evaluated. The ability of the stacked dense autoencoder was statistically evaluated with the …

Scrolling Vs Paging: Reading Performance And Preference Of Reading Modes In Long-Form Online News, Richard Herlihy

Dissertations

This study explores the impact of scrolling and dynamic pagination in long-form online documents on reader performance and reader experience. Previous research has produced mixed results, indicating no difference between modes, or a positive effect favouring scrolling. Recent advances in web standards have enabled simpler, dynamic, performant methods of pagination to tailor content responsively to any screen, meriting renewed study in this area. This paper uses one such method to load subsequent online news pages instantly without buffering. In an online browser experiment with 38 participants, an increase in reading speed in the scrolling mode was found at a level …

Ensemble Approach To The Semantic Segmentation Of Satellite Images, Brendan Kent

Dissertations

Automatic classification and segmentation of land use land cover(LULC) is extremely important for understanding the relationship between humans and nature. Human pressures on the environment have drastically accelerated in the last decades, risking biodiversity and ecosystem services. Remote sensing via satellite imagery is an excellent tool to study LULC. Research has shown that deep learning encoder-decoder architectures have achieved worthy results in the area of LULC, however the application of an ensemble approach has not been well quantified. Studies have shown it to be useful in the area of medical imaging. Ensembling by pooling together predictions to produce better predictions …

An Analysis On Network Flow-Based Iot Botnet Detection Using Weka, Cian Porteous

Dissertations

Botnets pose a significant and growing risk to modern networks. Detection of botnets remains an important area of open research in order to prevent the proliferation of botnets and to mitigate the damage that can be caused by botnets that have already been established. Botnet detection can be broadly categorised into two main categories: signature-based detection and anomaly-based detection. This paper sets out to measure the accuracy, false-positive rate, and false-negative rate of four algorithms that are available in Weka for anomaly-based detection of a dataset of HTTP and IRC botnet data. The algorithms that were selected to detect botnets …

Dark Patterns: Effect On Overall User Experience And Site Revisitation, Deon Soul Calawen

Dissertations

Dark patterns are user interfaces purposefully designed to manipulate users into doing something they might not otherwise do for the benefit of an online service. This study investigates the impact of dark patterns on overall user experience and site revisitation in the context of airline websites. In order to assess potential dark pattern effects, two versions of the same airline website were compared: a dark version containing dark pattern elements and a bright version free of manipulative interfaces. User experience for both websites were assessed quantitatively through a survey containing a User Experience Questionnaire (UEQ) and a System Usability Scale …

Evaluating The Performance Of Vision Transformer Architecture For Deepfake Image Classification, Devesan Govindasamy

Dissertations

Deepfake classification has seen some impressive results lately, with the experimentation of various deep learning methodologies, researchers were able to design some state-of-the art techniques. This study attempts to use an existing technology “Transformers” in the field of Natural Language Processing (NLP) which has been a de-facto standard in text processing for the purposes of Computer Vision. Transformers use a mechanism called “self-attention”, which is different from CNN and LSTM. This study uses a novel technique that considers images as 16x16 words (Dosovitskiy et al., 2021) to train a deep neural network with “self-attention” blocks to detect deepfakes. It creates …

An Investigation Of The Relationship Between Subjective Mental Workload And Objective Indicators Of User Activity, Greg Byrne

Dissertations

Whilst the concept of physical workload is intuitively understood and readily applicable in system design, the same cannot be said of mental workload (MWL), despite its importance in our increasingly technological society. Despite its origin in the mid 20th century, the very concept of ”mental workload” is still a topic of debate in the literature, although it can be loosely defined as “the amount of mental work necessary for a person to complete a task” (Miller, 1956; Longo, 2014). Several methods have been utilized to measure of MWL, including physiological methods such as neuro-imagery, performance-based metrics, and subjective measures via …

Direct And Constructivist Approaches For The Design Of Instruction In Well-Structured Domains: A Comparison Of Efficiency Via Mental Workload And Performance., Giuliano Orru

Dissertations

This doctoral research investigates the efficiency of two instructional designs: a design based on the direct-instruction approach to learning and its extension with a collaborative activity based upon the community of inquiry approach to learning. This is motivated by the educational challenge associated with the improvement of the learning phase. The goal is to investigate the extent to which highly guided communities of inquiry, when added to direct-instruction teaching methods, can actually improve the efficiency of learners. A total of 577 students participated in the experiments across 24 third-level classes that were divided into two groups. A control group of …

Kg-Cnn: Augmenting Convolutional Neural Networks With Knowledge Graphs For Multi-Class Image Classification, Aidan O'Neill

Dissertations

Computer vision is slowly becoming more and more prevalent in daily life. Tesla has recently announced that it plans to scale up the manufacturing of their Robotaxis by 2024, with this increase in self-driving vehicles being just one example, the importance of computer vision is growing year by year. Vision can be easy to take for granted, as most humans grow up using vision as their primary way of absorbing environmental information. The way humans process and classify visual information differs significantly from how current computer vision systems process and organise visual information. The human brain can use its past …

The Impact Of Emotion Focused Features On Svm And Mlr Models For Depression Detection, Alexandria Mulligan

Dissertations

Major depressive disorder (MDD) is a common mental health diagnosis with estimates upwards of 25% of the United States population remain undiagnosed. Psychomotor symptoms of MDD impacts speed of control of the vocal tract, glottal source features and the rhythm of speech. Speech enables people to perceive the emotion of the speaker and MDD decreases the mood magnitudes expressed by an individual. This study asks the questions: “if high level features deigned to combine acoustic features related to emotion detection are added to glottal source features and mean response time in support vector machines and multivariate logistic regression models, would …

Evaluating The Performance Impact Of Fine-Tuning Optimization Strategies On Pre-Trained Distilbert Models Towards Hate Speech Detection In Social Media, Aidan Mcgovern

Dissertations

Hate speech can be defined as forms of expression that incite hatred or encourage violence towards a person or group based on race, religion, gender, or sexual orientation. Hate speech has gravitated towards social media as its primary platform, and its propagation represents profound risks to both the mental well-being and physical safety of targeted groups. Countermeasures to moderate hate speech face challenges due to the volumes of data generated in social media, leading companies, and the research community to evaluate methods to automate its detection. The emergence of BERT and other pre-trained transformer-based models for transfer learning in the …

Performance Evaluation Of An Edge Computing Implementation Of Hyperledger Sawtooth For Iot Data Security, Sean Connolly

Dissertations

Blockchain offers a potential solution to some of the security challenges faced by the internet-of-things (IoT) by using its practically immutable ledger to store data transactions. However, past applications of blockchain in IoT encountered limitations in the rate at which transactions were committed to the chain as new blocks. These limitations were often the result of the time-consuming and computationally expensive consensus mechanisms found in public blockchains. Hyperledger Sawtooth is an open-source private blockchain platform that offers an efficient proof-of-elapsed-time (PoET) consensus mechanism. Sawtooth has performed well in benchmarks against other blockchains. However, a performance evaluation for a practical application …

Examining The Effects Of Disabilities On Vr Usage And Accessibility Issues For Persons With Disabilities, Sean Williams

Dissertations

Virtual Reality (VR) is an emerging technology that’s popularity has been increasing at a yearly rate. Despite this, concerns about the accessibility of VR devices are ever-growing as many users struggle to use the technology, especially users with disabilities. This study analyses how different types of disabilities affect how often a user uses VR and any associated re-occurring difficulties that are related to specific types of disability. To do this, a previous survey regarding VR accessibility run by Disability Visibility Project and ILMxLAB is examined. In this survey, 79 participants who identify as having a disability answered questions related to …

Development Of An Explainability Scale To Evaluate Explainable Artificial Intelligence (Xai) Methods, Stephen Mccarthy

Dissertations

Explainable Artificial Intelligence (XAI) is an area of research that develops methods and techniques to make the results of artificial intelligence understood by humans. In recent years, there has been an increased demand for XAI methods to be developed due to model architectures getting more complicated and government regulations requiring transparency in machine learning models. With this increased demand has come an increased need for instruments to evaluate XAI methods. However, there are few, if none, valid and reliable instruments that take into account human opinion and cover all aspects of explainability. Therefore, this study developed an objective, human-centred questionnaire …

An Investigation Of The Relationship Between Subjective Mental Workload And Objective Indicators Of User Activity, Greg Byrne

Dissertations

Whilst the concept of physical workload is intuitively understood and readily applicable in system design, the same cannot be said of mental workload (MWL), despite its importance in our increasingly technological society. Despite its origin in the mid 20th century, the very concept of ”mental workload” is still a topic of debate in the literature, although it can be loosely defined as “the amount of mental work necessary for a person to complete a task” (Miller, 1956; Longo, 2014). Several methods have been utilized to measure of MWL, including physiological methods such as neuro-imagery, performance-based metrics, and subjective measures via …

Crash Injury Severity Prediction With Artificial Neural Networks, Rima Abisaad

Dissertations

Motor vehicle crashes are one of our nation's most serious social, economic and health issues. They are the leading cause of death among children and young adults, killing approximately 1.35 million people each year. Providing a safe and efficient transportation system is the primary goal of transportation engineering and planning. To help reduce traffic fatalities and injuries on roadways, crash prediction models are used to forecast the injury severity of potential crashes and apply precautionary countermeasures accordingly. Most of these models are reactive as they use historical crash data to categorize crash-related factors. Recently, advancements have been made in developing …

Statistics-Based Anomaly Detection And Correction Method For Amazon Customer Reviews, Ishani Chatterjee

Dissertations

People nowadays use the Internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source of gathering information for data analytics, sentiment analysis, natural language processing, etc. The most critical challenge is interpreting this data and capturing the sentiment behind these expressions. Sentiment analysis is analyzing, processing, concluding, and inferencing subjective texts with the views. Companies use sentiment analysis to understand public opinions, perform market research, analyze brand reputation, recognize customer experiences, and study social media influence. According to the different needs for aspect granularity, …

Machine Learning Techniques For Network Analysis, Irfan Lateef

Dissertations

The network's size and the traffic on it are both increasing exponentially, making it difficult to look at its behavior holistically and address challenges by looking at link level behavior. It is possible that there are casual relationships between links of a network that are not directly connected and which may not be obvious to observe. The goal of this dissertation is to study and characterize the behavior of the entire network by using eigensubspace based techniques and apply them to network traffic engineering applications.

A new method that uses the joint time-frequency interpretation of eigensubspace representation for network statistics …

Coherent Control Of Dispersive Waves, Jimmie Adriazola

Dissertations

This dissertation addresses some of the various issues which can arise when posing and solving optimization problems constrained by dispersive physics. Considered here are four technologically relevant experiments, each having their own unique challenges and physical settings including ultra-cold quantum fluids trapped by an external field, paraxial light propagation through a gradient index of refraction, light propagation in periodic photonic crystals, and surface gravity water waves over shallow and variable seabeds. In each of these settings, the physics can be modeled by dispersive wave equations, and the technological objective is to design the external trapping fields or propagation media such …

Iii-Nitride Nanostructures: Photonics And Memory Device Applications, Barsha Jain

Dissertations

III-nitride materials are extensively studied for various applications. Particularly, III-nitride-based light-emitting diodes (LEDs) have become the major component of the current solid-state lighting (SSL) technology. Current III-nitride-based phosphor-free white color LEDs (White LEDs) require an electron blocking layer (EBL) between the device active region and p-GaN to control the electron overflow from the active region, which has been identified as one of the primary reasons to adversely affect the hole injection process. In this dissertation, the effect of electronically coupled quantum well (QW) is investigated to reduce electron overflow in the InGaN/GaN dot-in-a-wire phosphor-free white LEDs and to improve the …