Engineering Science - Theses, Dissertations, and other Required Graduate Degree Essays

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Atomistic and Continuum Modelling of Strength and Adhesion of Graphene

Date created: 
2016-04-07
Abstract: 

Exceptional electromechanical properties of graphene (a single layer of graphite) have been used to develop the next generation of nanodevices. These state-of-the-art nanodevices, such as sensors and transistors, have a profound impact in numerous engineering disciplines, ranging from biomedicine to aerospace. Recent experiments show that graphene could also be used as an ultra-strong reinforcement for composite materials. In both nanodevices and composite materials, graphene is in contact with adjoining materials, creating mechanically weak interfaces between them. Therefore, understanding the mechanical behaviour of both graphene and graphene interfaces is critically important in designing reliable graphene-based systems. In this thesis, molecular dynamics simulation studies are conducted to gain a basic understanding of the mechanics of graphene-based systems. Then, based on this knowledge, computationally efficient continuum-based models are developed in order to further investigate the strength and adhesion of nanoscale systems. The continuum-based models are accurate and around one million times faster than the molecular dynamics simulations. In addition, using the concepts of kinetic analysis, an analytical model is developed to estimate the strength of defective graphene. Finally, a nonlinear spring model is developed to characterize the adhesion properties of defective graphene interfaces. Results show that defects and temperature significantly reduce the strength of graphene. Low concentrations of hydrogen adatoms degrade the interfacial adhesion of graphene interfaces, and highly hydrogen functionalized graphene completely loses its strength when subjected to higher temperatures. It is also found that molecular dynamics simulations, conducted at elevated temperatures and high strain rates, significantly over predict the strength. Furthermore, the study reveal that graphene with vacancy defects shows a singular stress field as in continuum fracture mechanics and moderate amount of lattice trapping prevails in graphene.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Nimal Rajapakse
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Dissertation) Ph.D.

Distortion Estimation and Graph-based Transform for Visual Communications

Author: 
Date created: 
2016-04-29
Abstract: 

In this thesis, we study several visual communications problems, including joint source-channel coding for single view video transmission, transmission distortion estimation for multiview video coding, and depth video coding for multiview video applications. The first contribution in this thesis is the design and implementation of an error-resilient video conferencing system. We first develop an algorithm to estimate the decoder-side distortion in the presence of packet loss. We then design a family of very short systematic forward error correction (FEC) codes to recover lost packets. Finally, FEC codes are dynamically optimized to minimize the distortion from packet loss. The proposed scheme is demonstrated on a real-time embedded video conferencing system. A similar joint source channel coding framework can also be applied to multiview video coding applications such as free-viewpoint TV. Therefore an algorithm is needed for the encoder to estimate the distortion of the synthesized virtual view. We first derive a graphical model to analyze how random errors in the reference depth image affect the synthesized virtual view. We then consider the case where packet loss occurs in both the encoded texture and depth images during transmission, and develop a recursive algorithm to calculate the pixel level texture and depth probability distributions in the reference views. The recursive algorithm is then integrated with the graphical model method to estimate the distortion in the synthesized view. The graph-based transform has been extensively used for depth image coding in multiview video applications. In this thesis, we aim to develop a single graph-based transform for a class of depth signals. We first propose a 2-D first-order autoregression (2-D AR1) model and a 2-D graph to analyze depth signals with deterministic discontinuities. We show that the inverse of the biased Laplacian matrix of the proposed 2-D graph is exactly the covariance matrix of the proposed 2-D AR1 model. Therefore the optimal transform are the eigenvectors of the proposed graph Laplacian. Next, we show that similar results hold when the locations of the discontinuities are randomly distributed within a confined region. The theory in this thesis can be used to design both pre-computed and signal-dependent transforms.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Jie Liang
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

A customizable, scalable control solution for digitally-based reconfigurable magnetic microfluidic systems

Author: 
Date created: 
2015-10-06
Abstract: 

Recent trends in microfluidic technologies are leaning to more streamlined and integratedplatforms that can perform a variety of tasks. In order to achieve this, a continuous-flowintegrated microfluidics system needs to be made portable through the use of componentsthat are digitally controllable. The proposed device will use magnetic-based microfluidicscomponents, such as valves and mixers, which will require an electromagnetic based modelof actuation.The scope of this thesis is to design and optimize an FPGA-based control system comprisedof a user interface, device libraries and circuitry to connect to the physical components.Particular focus is given to optimizing the actuation system for magnetic microvalves toensure power efficiency, a trait that is paramount for a portable device such as the proposedmicrofluidics platform. Theoretical models and simulations are evaluated throughexperimentation to determine which best correlate with the physical system. This enablesthe selection of a set of parameters that result in a power-efficient actuation system. Thesimulations and evaluations are used to define a procedure for parameter selection.The selection criteria for these parameters are evaluated for an example system and theresulting actuation system behaves as predicted in a physical demonstration. The actuationsystem is integrated with the user interface through a software framework designed to bemodular, scalable and easy to upgrade.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Lesley Shannon
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Robo-Wrist Controller

Date created: 
2015-04-23
Abstract: 

Robo-wrist controller is a Graphical User Interface (GUI) based application designed and developed to control, and monitor a wrist exoskeleton driven by micro-controllers. The software application is developed to better assist a physiotherapist in administering physical therapy to stroke patients with the help of the exoskeleton. Since the micro-controller device that directly controls the exoskeleton is not very intuitive to use and lacks visual feedback, a software application fills these gaps and makes the device more user-friendly. It also provides the benefits of a wireless controller. The software application comprises two major aspects: an Android application for use in smartphones and a Windows PC version. The major differences between these two are the number of devices that can be controlled simultaneously and the communication media. The android application can control at most one device at any given time via Bluetooth, while the PC version is capable of controlling multiple devices simultaneously because it uses both Bluetooth and Wi-Fi communication media. Both types of software applications have features such as: ability to add new devices, view the added devices, create an exercise protocol and store it, view stored protocols, a data visualizer to plot incoming feedback data from the exoskeleton, and multiple user profiles with different levels of administrative privileges. Each module in the applications is independent of each other as well allowing for easy modifications and additions to the application in the future. The GUI of the software makes it very easy for the therapist to use the exoskeleton using visual controls and feedback allowing for easy administering of physical therapy using the exoskeleton to his/ her patients.

Document type: 
Graduating extended essay / Research project
File(s): 
Supervisor(s): 
Carlo Menon
Fabio Campi
Department: 
Applied Sciences:
Thesis type: 
(Project) M.Eng.

Implementation of a Wearable Feedback System Monitoring the Activities of Upper-extremities

Author: 
Date created: 
2015-04-21
Abstract: 

In the previous work done by ZG.Xiao and C.Menon, the novel idea of using a strap with 8 Force Sensing Resistor (FSR) sensors for monitoring activities of upper-extremities was proposed. The goal of my research is to implement such a system for a low-cost, low power embedded system, a band module, together with a hand-held user interface for rehabilitation related application. For hand gesture classification, the Linear Discriminate Analysis (LDA) method is used. The training and predicting can be done on either a band module or a hand-held user interface. Two system configurations are proposed: real-time band module data sampling and hand-held user interface data analysis, or real-time band module data sampling and analysis. On top of this, the LDA algorithm in C language used in our system has been profiled on an Intel Galileo Gen2 board in order to evaluate its performance on 32-bit embedded platforms for the next generation of our system.

Document type: 
Graduating extended essay / Research project
File(s): 
Supervisor(s): 
Carlo Menon
Department: 
Applied Sciences:
Thesis type: 
(Project) M.Eng.

Faster Tuberculosis Diagnosis with Low Cost Public Health Devices

Date created: 
2015-04-07
Abstract: 

Tuberculosis (TB) is a deadly infectious disease that usually affects the lungs, but may affect almost any tissue of the body. The primary symptoms of TB include cough of more than three weeks duration, weight loss, fever, blood-stained sputum, chest pain, and if left untreated, can result in death. In 2013 alone, nine million people fell ill with TB, and one point five million people died from the disease, making TB the second greatest killer worldwide as a single infectious agent. TB is both preventable by implementing appropriate infection control measures, and is treatable through the use of specific antibacterial drugs. A common method for detecting active tuberculosis and measuring its bacillary load is through the use of acid fast staining, such as Ziehl Neelsen (ZN) staining, which involves applying specific chemicals to a sputum sample or other smear from a patient, and analyzing the smear under a microscope. In the case of ZN staining, TB bacteria appear as pink rod-shaped objects against a bright blue background. By counting the number of bacteria visible in the stained smear, the examiner can deduce approximately how advanced the TB infection is, and act upon it appropriately. At this time, it is the norm for the bacteria to be counted manually by lab technicians working in TB clinics. The goal of this thesis is to identify the problem that is TB, and explain a set of methods and devices which can be used to further reduce the global impact of TB. Using a combination of image processing methods and image acquisition devices combined with microscopy, we are able to rapidly count the number of bacteria present in a ZN stained slide. The result is a portable, low cost, hand-held device that can perform hours of manual analysis in under a minute, while providing ivgreater consistency across individual tests. This has the potential to decrease the lead time on diagnosis and treatment of TB in the field from weeks to minutes, helping to reduce the impact of this deadly disease.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Dr. Mirza Faisal Beg
Dr. Pankaj Sadaphal
Department: 
Applied Sciences:
Thesis type: 
(Thesis) M.A.Sc.

Rapid Production of Nano-pixel-based Color Imageries

Author: 
Date created: 
2015-03-31
Abstract: 

Structural color pixels have attracted many interests as an alternate for colorant pigmentation. The structural coloration occurs as a result of light interaction with a physical structure containing nanometric features. In this study, novel techniques are presented to rapidly originate image master stamps for any given color image from a generic pixelated stamp named nanosubstrate. The nanosubstrate has gratings structure which gives colors due to light diffraction. Micro-patterning techniques were implemented to activate pixels of nanosubstrate and acquire image master stamps. The prepared master stamp can be replicated to provide several copies of image with the same quality. The given grating structure contains periodic hole arrays, which after replication convert to periodic pillars or vice versa. Both visible and invisible images can be easily stored and read using this method. This technique can be used for security and media applications, and can be complemented by combined information storage capabilities.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Bozena Kaminska
Department: 
Applied Sciences:
Thesis type: 
(Thesis) M.A.Sc.

Wavefront Sensorless Adaptive Optics Swept-Source Optical Coherence Tomography at 1060nm

Author: 
Date created: 
2015-12-23
Abstract: 

Optical Coherence Tomography (OCT) has revolutionized modern ophthalmology, providing depth resolved images of the retinal layers in a system that is suited to a clinical environment. A limitation on the performance and utilization of the OCT systems has been the lateral resolution; whereas the axial resolution is a function of the light source, the lateral resolution is dependent on the delivery optics. In this thesis, a compact lens based OCT system is presented that is capable of imaging the different retinal layers at a cellular lateral resolution with the combination of wavefront sensorless adaptive optics with dual variable optical elements. The central operating imaging wavelength of the wavelength swept OCT engine was 1060nm, close to the dispersion minimum of water (and the vitreous humor in the eye). A commercially available variable focal length lens is utilized to correct for a wide range of defocus commonly found in patient’s eyes, and a multi-actuator deformable lens for aberration correction to obtain near diffraction limited imaging at the retina. A parallel processing computational platform permitted real-time image acquisition and display. Cross-sectional images of the retinal layers and en face images of the cone photoreceptor mosaic acquired in vivo from research subjects are presented.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Marinko V. Sarunic
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Applications of Reinforcement Learning to Routing and Virtualization in Computer Networks

Author: 
Date created: 
2016-03-17
Abstract: 

Computer networks and reinforcement learning algorithms have substantially advanced over the past decade. The Internet is a complex collection of inter-connected networks with a numerous of inter-operable technologies and protocols. Current trend to decouple the network intelligence from the network devices enabled by Software-Defined Networking (SDN) provides a centralized implementation of network intelligence. This offers great computational power and memory to network logic processing units where the network intelligence is implemented. Hence, reinforcement learning algorithms viable options for addressing a variety of computer networking challenges. In this dissertation, we propose two applications of reinforcement learning algorithms in computer networks.We first investigate the applications of reinforcement learning for deflection routing in buffer-less networks. Deflection routing is employed to ameliorate packet loss caused by contention in buffer-less architectures such as optical burst-switched (OBS) networks. We present a framework that introduces intelligence to deflection routing (iDef). The iDef framework decouples design of the signaling infrastructure from the underlying learning algorithm. It is implemented in the ns-3 network simulator and is made publicly available. We propose the predictive Q-learning deflection routing (PQDR) algorithm that enables path recovery and reselection, which improves the decision making ability of the node in high load conditions. We also introduce the Node Degree Dependent (NDD) signaling algorithm. The complexity of the algorithm only depends on the degree of the node that is NDD compliant while the complexity of the currently available reinforcement learning-based deflection routing algorithms depends on the size of the network. Therefore, NDD is better suited for larger networks. Simulation results show that NDD-based deflection routing algorithms scale well with the size of the network and outperform the existing algorithms. We also propose a feed-forward neural network (NN) and a feed-forward neural network with episodic updates (ENN). They employ a single hidden layer and update their weights using an associative learning algorithm. Current reinforcement learning-based deflection routing algorithms employ Q-learning, which does not efficiently utilize the received feedback signals. We introduce the NN and ENN decision-making algorithms to address the deficiency of Q-learning. The NN-based deflection routing algorithms achieve better results than Q-learning-based algorithms in networks with low to moderate loads.The second application of reinforcement learning that we consider in this dissertation is for modeling the Virtual Network Embedding (VNE) problem. We develop a VNE simulator (VNE-Sim) that is also made publicly available. We define a novel VNE objective function and prove its upper bound. We then formulate the VNE as a reinforcement learning problem using the Markov Decision Process (MDP) framework and then propose two algorithms (MaVEn-M and MaVEn-S) that employ Monte Carlo Tree Search (MCTS) for solving the VNE problem. In order to further improve the performance, we parallelize the algorithms by employing MCTS root parallelization. The advantage of the proposed algorithms is that, time permitting, they search for more profitable embeddings compared to the available algorithms that find only a single embedding solution. The simulation results show that proposed algorithms achieve superior performance.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Ljiljana Trajkovic
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Dissertation) Ph.D.

Photo-controlled bio-mimicking dry adhesive

Author: 
Date created: 
2016-01-29
Abstract: 

The goal of this work is to develop a photo-switchable dry adhesive. Spiropyran doped PDMS polymer was moulded into biomimetic mushroom-shaped fibrillar adhesive microstructures characterized using a variety of measurement techniques and compared with a flat control surface made of the same material. Using UV light to generate of charged merocyanine molecules within ‘mushroom’-shaped micro-structured PDMS films enhanced the adhesion of the film to glass surfaces. The strength of the dry adhesive property can be lowered back to the original state using visible light. Quick and efficient switching in the polymer was observed. Integrating this molecule increased normal adhesion of unstructured samples by a factor of ~4 when polymer was in the neutral spiropyran form and ~5 for the merocyanine zwitterionic isomer, which demonstrated that control over the adhesion strength was possible. Surface charge and contact angle measurements further confirmed the proper functionality of the switch inside the PDMS polymer.

Document type: 
Graduating extended essay / Research project
File(s): 
Supervisor(s): 
Carlo Menon
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Project) M.Eng.