Numerical Study Of Ozone Decomposition Reaction Behaviours In Gas-Solids Circulating Fluidized Bed Reactors, Zhengyuan Deng

Electronic Thesis and Dissertation Repository

This study numerically investigated the reaction behaviours of catalytic ozone decomposition reaction in a 10.2-meter-tall gas-solids circulating fluidized bed (CFB) reactor. A pseudo-homogeneous reactive transport model for ozone decomposition, integrated with the two-fluid model, was developed and validated using experimental data. Based on the model, the impacts of turbulence models, specularity coefficients, and simulation methods on reaction behaviours in the CFB riser reactor were explored. These three factors were found to significantly affect the hydrodynamic characteristics and the reaction behaviours in the riser. A comparative study of CFB riser and downer reactors was conducted. Operations in the direction of and …

Computer Vision-Based Hand Tracking And 3d Reconstruction As A Human-Computer Input Modality With Clinical Application, Tania Banerjee

Electronic Thesis and Dissertation Repository

The recent pandemic has impeded patients with hand injuries from connecting in person with their therapists. To address this challenge and improve hand telerehabilitation, we propose two computer vision-based technologies, photogrammetry and augmented reality as alternative and affordable solutions for visualization and remote monitoring of hand trauma without costly equipment. In this thesis, we extend the application of 3D rendering and virtual reality-based user interface to hand therapy. We compare the performance of four popular photogrammetry software in reconstructing a 3D model of a synthetic human hand from videos captured through a smartphone. The visual quality, reconstruction time and geometric …

Pressure Drop And Heat Transfer In Flow Over An Array Of Blocks Of Varying Heights: A Statistical And Ai Analysis On The Effect Of Block Height Variation, Ali Navidi

Electronic Thesis and Dissertation Repository

The presence of a stiff obstruction in the path of fluid causes the creation of a boundary layer over and around the obstruction. The flow over an idealized, two-dimensional series of blocks is numerically investigated to determine how statistical blocks height variation, such as standard deviation, mean, and skewness, influence pressure drop and heat flux. These data sets serve as a foundation for developing models for estimating the heat transfer coefficient of each block using machine learning (ML) methods. The results show that the pressure drop increased by 60% when the standard deviation of heights of blocks increased from 0.1 …

A Framework For Stable Robot-Environment Interaction Based On The Generalized Scattering Transformation, Kanstantsin Pachkouski

Electronic Thesis and Dissertation Repository

This thesis deals with development and experimental evaluation of control algorithms for stabilization of robot-environment interaction based on the conic systems formalism and scattering transformation techniques. A framework for stable robot-environment interaction is presented and evaluated on a real physical system. The proposed algorithm fundamentally generalizes the conventional passivity-based approaches to the coupled stability problem. In particular, it allows for stabilization of not necessarily passive robot-environment interaction. The framework is based on the recently developed non-planar conic systems formalism and generalized scattering-based stabilization methods. A comprehensive theoretical background on the scattering transformation techniques, planar and non-planar conic systems is presented. …

Efficient Discovery And Utilization Of Radio Information In Ultra-Dense Heterogeneous 3d Wireless Networks, Mattaka Gamage Samantha Sriyananda

Electronic Thesis and Dissertation Repository

Emergence of new applications, industrial automation and the explosive boost of smart concepts have led to an environment with rapidly increasing device densification and service diversification. This revolutionary upward trend has led the upcoming 6th-Generation (6G) and beyond communication systems to be globally available communication, computing and intelligent systems seamlessly connecting devices, services and infrastructure facilities. In this kind of environment, scarcity of radio resources would be upshot to an unimaginably high level compelling them to be very efficiently utilized. In this case, timely action is taken to deviate from approximate site-specific 2-Dimensional (2D) network concepts in radio resource utilization …

Large-Scale Analysis And Automated Detection Of Trunnion Corrosion On Hip Arthroplasty Devices, Anastasia M. Codirenzi

Electronic Thesis and Dissertation Repository

Corrosion at the modular head-neck taper interface of total and hemiarthroplasty hip implants (trunnionosis) is a cause of implant failure and thus a clinical concern. Patient and device factors contributing to the occurrence of trunnionosis have been investigated in prior implant retrieval studies. The Goldberg corrosion scoring method is considered the gold standard for observing trunnionosis, but it is labour-intensive. As a result, previous studies have generally looked at under 250 implants for analysis. The purpose of this thesis was to do a large-scale analysis of trunnionosis and explore its relationship to device and patient factors and compare to previously …

Wastewater Aeration Process Dynamic Modelling: Combined Mechanistic And Machine Learning Approach, Yuehe Pan

Electronic Thesis and Dissertation Repository

The aeration process is the largest energy consumer in wastewater treatment plants (WWTPs), and the optimization of the process based on computational models can offer significant savings for the plant. Recent theoretical developments have revealed that many of the parameters commonly assumed as constants in aeration modelling, in fact, have a dynamic nature; however, there still lacks a universal way to model these factors in an easy, accurate and timely manner. This work proposed a machine learning-based modelling approach to offer real-time estimations of the oxygen transfer rate, airflow demand, and energy consumption.

Utilizing the field data collected from Adelaide …

Mixed-Reality Visualization Environments To Facilitate Ultrasound-Guided Vascular Access, Leah Groves

Electronic Thesis and Dissertation Repository

Ultrasound-guided needle insertions at the site of the internal jugular vein (IJV) are routinely performed to access the central venous system. Ultrasound-guided insertions maintain high rates of carotid artery puncture, as clinicians rely on 2D information to perform a 3D procedure. The limitations of 2D ultrasound-guidance motivated the research question: “Do 3D ultrasound-based environments improve IJV needle insertion accuracy”. We addressed this by developing advanced surgical navigation systems based on tracked surgical tools and ultrasound with various visualizations. The point-to-line ultrasound calibration enables the use of tracked ultrasound. We automated the fiducial localization required for this calibration method such that …

Data And Sensor Fusion Using Fmg, Semg And Imu Sensors For Upper Limb Prosthesis Control, Jason S. Gharibo

Electronic Thesis and Dissertation Repository

Whether someone is born with a missing limb or an amputation occurs later in life, living with this disability can be extremely challenging. The robotic prosthetic devices available today are capable of giving users more functionality, but the methods available to control these prostheses restrict their use to simple actions, and are part of the reason why users often reject prosthetic technologies. Using multiple myography modalities has been a promising approach to address these control limitations; however, only two myography modalities have been rigorously tested so far, and while the results have shown improvements, they have not been robust enough …

Generative Learning In Smart Grid, Samer M. El Kababji

Electronic Thesis and Dissertation Repository

If a smart grid is to be described in one word, that word would be ’connectivity’. While electricity production and consumption still depend on a limited number of physical connections, exchanging data is growing enormously. Customers, utilities, sensors, and markets are all different sources of data that are exchanged in a ubiquitous digital setup. To deal with data complexity, many researchers recently focused on machine learning (ML) applications in smart grids. Much of the success in ML is attributed to discriminative learning where models define boundaries to categorize data. Generative learning, however, reveals how data is generated by learning the …

In-Clinic Functional Measurement And Analysis Of Knee Osteoarthritis Patients Undergoing Total Knee Replacement, Riley A. Bloomfield

Electronic Thesis and Dissertation Repository

Prevalence of osteoarthritis is increasing as individuals are remaining active later in life. Since the knee is one of the most commonly affected joints and is involved in almost all daily activities, functional impairment has a substantial impact on overall health. Despite this increase, there currently exists no disease modifying drugs or treatments. Mild cases are managed with physiotherapeutic exercises and common anti-inflammatories but surgical intervention is required for more severe disease progression.

Total knee replacement as a treatment for osteoarthritis is a highly successful surgery that is effective at restoring knee function and reducing pain but still requires further …

Machine Learning Prediction Of Structural Response For Reinforced Concrete Members Under Blast Loading, Monjee Almustafa

Electronic Thesis and Dissertation Repository

With increasing accidental and intentional explosions and blast events inflicting life loss and economic damage to civil infrastructure, greater attention is given to the analysis and design of blast-resistant structures. Accordingly, this thesis introduces state-of-the-art machine learning models dedicated to predicting the structural behavior of various reinforced concrete (RC) members under blast loading, including slabs, columns, and beams. Moreover, extended prediction models were developed for RC members that employ fiber-reinforced polymer (FRP) retrofitting and steel fiber-reinforced concrete as blast mitigation strategies. For each model, extensive validation was conducted through statistical performance measures and comparisons to existing prediction methods. Additionally, feature …

Machine Learning Prediction Of Shear Capacity Of Steel Fiber Reinforced Concrete, Wassim Ben Chaabene

Electronic Thesis and Dissertation Repository

The use of steel fibers for concrete reinforcement has been growing in recent years owing to the improved shear strength and post-cracking toughness imparted by fiber inclusion. Yet, there is still lack of design provisions for steel fiber-reinforced concrete (SFRC) in building codes. This is mainly due to the complex shear transfer mechanism in SFRC. Existing empirical equations for SFRC shear strength have been developed with relatively limited data examples, making their accuracy restricted to specific ranges. To overcome this drawback, the present study suggests novel machine learning models based on artificial neural network (ANN) and genetic programming (GP) to …

Machine Learning Prediction Of Mechanical And Durability Properties Of Recycled Aggregates Concrete, Itzel Rosalia Nunez Vargas

Electronic Thesis and Dissertation Repository

Whilst recycled aggregate (RA) can alleviate the environmental footprint of concrete production and the landfilling of colossal amounts of demolition waste, there need for robust predictive tools for its effects on mechanical and durability properties. In this thesis, state-of-the-art machine learning (ML) models were deployed to predict properties of recycled aggregate concrete (RAC). A systematic review was performed to analyze pertinent ML techniques previously applied in the concrete technology field. Accordingly, three different ML methods were selected to determine the compressive strength of RAC and perform mixture proportioning optimization. Furthermore, a gradient boosting regression tree was used to study the …

Hyperspectral Image Classification For Remote Sensing, Hadis Madani

Electronic Thesis and Dissertation Repository

This thesis is focused on deep learning-based, pixel-wise classification of hyperspectral images (HSI) in remote sensing. Although presence of many spectral bands in an HSI provides a valuable source of features, dimensionality reduction is often performed in the pre-processing step to reduce the correlation between bands. Most of the deep learning-based classification algorithms use unsupervised dimensionality reduction methods such as principal component analysis (PCA).

However, in this thesis in order to take advantage of class discriminatory information in the dimensionality reduction step as well as power of deep neural network we propose a new method that combines a supervised dimensionality …

Machine Learning Towards General Medical Image Segmentation, Clara Tam

Electronic Thesis and Dissertation Repository

The quality of patient care associated with diagnostic radiology is proportionate to a physician's workload. Segmentation is a fundamental limiting precursor to diagnostic and therapeutic procedures. Advances in machine learning aims to increase diagnostic efficiency to replace single applications with generalized algorithms. We approached segmentation as a multitask shape regression problem, simultaneously predicting coordinates on an object's contour while jointly capturing global shape information. Shape regression models inherent point correlations to recover ambiguous boundaries not supported by clear edges and region homogeneity. Its capabilities was investigated using multi-output support vector regression (MSVR) on head and neck (HaN) CT images. Subsequently, …

Parkinsonian Speech And Voice Quality: Assessment And Improvement, Amr Gaballah

Electronic Thesis and Dissertation Repository

Parkinson’s disease (PD) is the second most common neurodegenerative disease. Statistics show that nearly 90% of people impaired with PD develop voice and speech disorders. Speech production impairments in PD subjects typically result in hypophonia and consequently, poor speech signal-to-noise ratio (SNR) in noisy environments and inferior speech intelligibility and quality. Assessment, monitoring, and improvement of the perceived quality and intelligibility of Parkinsonian voice and speech are, therefore, paramount. In the first study of this thesis, the perceived quality of sustained vowels produced by PD patients was assessed through objective predictors. Subjective quality ratings of sustained vowels were collected from …

Data Analytics And Performance Enhancement In Edge-Cloud Collaborative Internet Of Things Systems, Tianqi Yu

Electronic Thesis and Dissertation Repository

Based on the evolving communications, computing and embedded systems technologies, Internet of Things (IoT) systems can interconnect not only physical users and devices but also virtual services and objects, which have already been applied to many different application scenarios, such as smart home, smart healthcare, and intelligent transportation. With the rapid development, the number of involving devices increases tremendously. The huge number of devices and correspondingly generated data bring critical challenges to the IoT systems. To enhance the overall performance, this thesis aims to address the related technical issues on IoT data processing and physical topology discovery of the subnets …

Towards Efficient Intrusion Detection Using Hybrid Data Mining Techniques, Fadi Salo

Electronic Thesis and Dissertation Repository

The enormous development in the connectivity among different type of networks poses significant concerns in terms of privacy and security. As such, the exponential expansion in the deployment of cloud technology has produced a massive amount of data from a variety of applications, resources and platforms. In turn, the rapid rate and volume of data creation in high-dimension has begun to pose significant challenges for data management and security. Handling redundant and irrelevant features in high-dimensional space has caused a long-term challenge for network anomaly detection. Eliminating such features with spectral information not only speeds up the classification process, but …

Objective Assessment Of Machine Learning Algorithms For Speech Enhancement In Hearing Aids, Krishnan Parameswaran

Electronic Thesis and Dissertation Repository

Speech enhancement in assistive hearing devices has been an area of research for many decades. Noise reduction is particularly challenging because of the wide variety of noise sources and the non-stationarity of speech and noise. Digital signal processing (DSP) algorithms deployed in modern hearing aids for noise reduction rely on certain assumptions on the statistical properties of undesired signals. This could be disadvantageous in accurate estimation of different noise types, which subsequently leads to suboptimal noise reduction. In this research, a relatively unexplored technique based on deep learning, i.e. Recurrent Neural Network (RNN), is used to perform noise reduction and …

Optimization Modeling And Machine Learning Techniques Towards Smarter Systems And Processes, Abdallah Moubayed

Electronic Thesis and Dissertation Repository

The continued penetration of technology in our daily lives has led to the emergence of the concept of Internet-of-Things (IoT) systems and networks. An increasing number of enterprises and businesses are adopting IoT-based initiatives expecting that it will result in higher return on investment (ROI) [1]. However, adopting such technologies poses many challenges. One challenge is improving the performance and efficiency of such systems by properly allocating the available and scarce resources [2, 3]. A second challenge is making use of the massive amount of data generated to help make smarter and more informed decisions [4]. A third challenge is …

Multivariate Analysis Of Mr Images In Temporal Lobe Epilepsy, Diego H. Cantor-Rivera

Electronic Thesis and Dissertation Repository

Epilepsy stands aside from other neurological diseases because clinical patterns of progression are unknown: The etiology of each epilepsy case is unique and so it is the individual prognosis. Temporal lobe epilepsy (TLE) is the most frequent type of focal epilepsy and the surgical excision of the hippocampus and the surrounding tissue is an accepted treatment in refractory cases, specially when seizures become frequent increasingly affecting the performance of daily tasks and significantly decreasing the quality of life of the patient. The sensitivity of clinical imaging is poor for patients with no hippocampal involvement and invasive procedures such as the …

Automated Image Interpretation For Science Autonomy In Robotic Planetary Exploration, Raymond Francis

Electronic Thesis and Dissertation Repository

Advances in the capabilities of robotic planetary exploration missions have increased the wealth of scientific data they produce, presenting challenges for mission science and operations imposed by the limits of interplanetary radio communications. These data budget pressures can be relieved by increased robotic autonomy, both for onboard operations tasks and for decision- making in response to science data.

This thesis presents new techniques in automated image interpretation for natural scenes of relevance to planetary science and exploration, and elaborates autonomy scenarios under which they could be used to extend the reach and performance of exploration missions on planetary surfaces.

Two …

Automated Segmentation Of Left And Right Ventricles In Mri And Classification Of The Myocarfium Abnormalities, Cyrus (Mohammad Saleh) Nambakhsh

Electronic Thesis and Dissertation Repository

A fundamental step in diagnosis of cardiovascular diseases, automated left and right ventricle (LV and RV) segmentation in cardiac magnetic resonance images (MRI) is still acknowledged to be a difficult problem. Although algorithms for LV segmentation do exist, they require either extensive training or intensive user inputs. RV segmentation in MRI has yet to be solved and is still acknowledged a completely unsolved problem because its shape is not symmetric and circular, its deformations are complex and varies extensively over the cardiac phases, and it includes papillary muscles. In this thesis, I investigate fast detection of the LV endo- and …