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

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Exploring the feasibility of ion beam gyroscope based on corona discharge

Author: 
Date created: 
2017-09-01
Abstract: 

An ion beam gyroscope has been proposed to measure the speed of rotating, which is potentially inexpensive and free of the mechanical stress. The characteristics of the low-pressure corona gas discharge in a compact discharge tube are studied to obtain a sufficient and stable ion beam source for ion beam gyroscopes. Ions moving to the cathode by the effect of an electric field can produce a current signal. If a dual-cathode discharge device is used, currents going to the two cathodes are denoted by I1and I2 respectively. If the device is placed on a rotating platform, ions will be deflected by the Coriolis effect. Thus, I1 and I2 change accordingly with rotating speeds. Consequently, the differential current between the variation of each cathode is a function of the Coriolis acceleration rate. Since a magnetic field can deflect ions in the same way as the Coriolis effect, a magnetic field is used to simulate the Coriolis effect to avoid the rotating platform in the early exploring stage. This replacement allows us to have a simpler experimental setup, and the magnetic field is also easier to control. A theoretical model has been derived to describe the motion of ions in the discharge tube. Emulation experiments were conducted to explore the correlation between speed of rotating and differential current, relevant variables and their impacts on the sensitivity of ion beam gyroscopes. These experiments demonstrate the ion beam gyroscope is feasible, although the device's sensitivity is limited by 1.6 pA/rpm.

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

Precision and reliability of application specific designs on FPGA

Date created: 
2017-04-20
Abstract: 

Application-specific designs can use hardware, such as Field Programmable Gate Array(FPGA)s, to have complete freedom. This includes defining data types as opposed to usingthe predefined standard data types supported in software that are restricted by the architectureof the target processor. Moreover FPGAs are extremely popular for computationallyintensive applications that use either integer or fixed-point calculations and have a limitedpower budget. Static Random-Access Memory (SRAM)-based FPGAs also have the potentialfor a high level of reliability by leveraging their ability to be reconfigured at runtime.In this thesis, we have selected two case studies for hardware acceleration: 1) a biomedicalimaging system for our investigations on precision and reliability Fourier Domain OpticalCoherence Tomography (FD-OCT), and 2) and a Synthetic Aperture Radar (SAR) systemfor our investigations on reliability.Fixed-point arithmetic provides faster and smaller hardware implementations for DigitalSignal Processing (DSP) applications at the expense of accuracy. Especially, when a FastFourier Transform (FFT)-Inverse Fast Fourier Transform (IFFT) pair are required as partof the calculation, the error introduced into the calculations can be significant. This errormostly affects the phase information of the processing signal. Thus, for phase sensitiveapplications, such as FD-OCT, this degradation is unacceptable. For example, a 32-bitfixed-point implementation of FD-OCT on an FPGA could result in a 78% of error comparedto double precision calculations. In order to retain the accuracy of both SAR andFD-OCT implementations, we numerically analyzed the algorithm versus fixed-point, integerand floating point numbers and introduced the adaptability of integer transforms forsuch applications, specially FD-OCT. Then, by using our custom designed Integer SplitRadix Fast Fourier Transform (Int-SRFFT) and Integer Radix-22 FFT, we decreased themaximum peak error from 78% to less than 1 percent. Moreover, a mathematical reliabilitymodel has been developed for an on-board SAR processor that informs the appropriatetechniques for increasing the reliability of the final SAR processor implementation. Variousupset mitigation strategies are introduced and two customized strategies are proposed forthis specific application. The proposed methods are based on truth vectoring and scheduledscrubbing that achieved an increase of robustness of the system by the factor of 3.8 and 4.8respectively.

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

Detecting upper extremity activity with force myography

Author: 
Date created: 
2017-09-12
Abstract: 

A novel technique named force myography (FMG) is shown to be able to detect some upper extremity (UE) status. It holds the potential to be a cost-effective solution to detect complex UE movements, which can be used for UE status monitoring and human-machine interface applications. In this thesis, a novel FMG system to capture the forearm muscle movement information was proposed. The system’s capability to detect complex UE postures was investigated. Also, its feasibility to be used in real-time UE posture detection applications, and its applicability to be utilized in a non-real-time UE activity tracking scenario were examined. Specifically, the capability of using FMG to predict the elbow, forearm, and wrist positions was investigated by studying the corresponding FMG signal patterns and classification performances. This study also used the more established surface electromyography (sEMG) method to identify the strength and weakness of FMG. Elbow, forearm, and wrist position predictions using FMG achieved 84%, 82%, and 71% accuracies respectively. The sEMG method yielded 75%, 83% and 92% accuracies for predicting the same respective positions. The feasibility of using FMG to predict a set of complex UE postures in real-time was investigated using a custom designed classification system. An experiment which required volunteers to perform a sequence of UE postures simulating a functional action, i.e., drinking from a cup, was conducted to examine the classification performance. An average accuracy of 92% with standard deviation of 3% was obtained from 6 volunteers. Also, the same system was used for controlling a robotic device that assists the forearm rotation. A 96% accuracy for predicting five forearm positions for controlling the device was obtained. Finally, the applicability of using FMG to count grasping actions during a pick-and-place (PAP) exercise was investigated. Two wireless FMG straps were prototyped to enable the capturing of FMG signal during the arm movement. A median percentage error of 1% with an interquartile range of 5% was achieved for counting 120 PAP actions with ten volunteers.

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

Improving EEG Classification Accuracy of Single Limb Imaginary Movements by Leveraging an Established EMG Pattern Recognition Scheme

Author: 
Date created: 
2017-09-27
Abstract: 

Brain-computer interfaces may enable the collaboration between human and machines. They can in fact potentially translate the electrical activity of the brain to understandable commands to operate a machine or a device.In this thesis, a method is proposed to improve the accuracy of a BCI by leveraging an established electromyography (EMG) pattern recognition scheme. Such a pattern recognition scheme extracts time-domain features, which include autoregressive (AR) model coefficients, root mean square (RMS), waveform length (WL) and classifies them using an optimized support vector machine (SVM) with a radial basis kernel function (RBF).Upon validating that such a method can indeed process EMG signals to classify different fifteen movements of the arm with high accuracy (> 90%), this thesis investigates the possibility of implementing it for the design of a BCI based on electroencephalographic (EEG) signals. The discrimination of rest, imaginary grasp and imaginary elbow movement of the same limb was selected as test case to validate the designed BCI. This classification task is particularly challenging because imaginary movements within the same limb have close spatial representations on the motor cortex area.The use of the proposed method was demonstrated to be suitable for identifying imaginary movements using EEG signal. In fact, the investigated method achieved an average accuracy of 91.8 % and 90 % for identifying the user intention corresponding to imaginary grasps and imaginary elbow movements (2-class BCI). An average classification accuracy of 74.2 % was achieved for classification of rest versus imaginary grasps versus imaginary elbow movements (3-class BCI).The investigated method outperformed methods that are widely used in the BCI literature such as common spatial patterns (CSP), filter bank CSP (FBCSP), and band power methods. These results are encouraging and the proposed method could potentially be used in the future in BCI-driven robotic devices to assist individuals with impaired upper extremity functions in performing daily tasks.

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

USB 3.0 machine vision camera hardware design and FPGA implementation

Author: 
Date created: 
2017-05-31
Abstract: 

Machine vision camera is becoming more popular in the industry. USB 3.0 interface support 5Gbps transmission, and is a low-cost and fast way to transmit video signals from sensors to computers. However, most image sensors are incompatible with USB 3.0 protocol, and cannot directly connect to USB 3.0. In this report, we present the development of the hardware design of a USB 3.0 multi-purpose camera using the Cypress microprocessor. Our camera also has a FPGA module as an adaptive part to provide protocol translation, voltage level conversion, serial to parallel conversion, multi-channel data collection, and clock synthesis. We also discuss some important principles of system and schematic design, and emphasize a few critical PCB routing rules for assurance of PCB integrity. Some testing outcomes are provided to illustrate the hardware functionality and algorithm running results, which verify the success of the design and the performance of the camera.

Document type: 
Graduating extended essay / Research project
File(s): 
Supervisor(s): 
Jie Liang
Marinko Sarunic Jianbing Wu
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Project) M.Eng.

Wearable sensory system for a motorized compression bandage

Author: 
Date created: 
2017-08-09
Abstract: 

Disorders associated with excessive swelling of the legs are common. This swelling can be associated with pain, the production of varicose veins, reduced blood pressure (hypotension) when standing and cause light-headedness, fainting, and falls. These events can significantly affect the quality of life and, in severe cases, lead to death. It is well documented that up to 30% of the elderly have standing hypotension. Swelling is common during pregnancy ranks highly as one of the causes of varicose veins. Current physical remedies to these disorders include air compression leg massagers, which do not allow for ambulatory use, and compression stockings, which attempt to limit blood pooling and fluid build-up in the legs during walking. However, neither of these devices is able to adapt to the changing physiological conditions of the patient while compression stockings can provide only passive assistance to edema.One of the developed technology, a motorized bandage, which is wrapped around the lower leg, has recently been prototyped. It uses an actuator and thin cables to apply a fully controlled and desired compression profile on the lower leg. The device is battery operated and is designed to be utilized for ambulatory situations. The main goal of this MEng project is to develop and test a sensor system for the motorized compression bandage. This sensor system should be able to detect lower leg motion and trigger the compression bandage when a user is inactive.

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

Novel geometry and function based topological data analysis in neuroimaging data

Author: 
Date created: 
2017-06-14
Abstract: 

Parkinson's disease (PD) is the most common movement disorder and the second most common neurodegenerative disorder. The diagnosis of PD commonly relies on clinical examination with limited number of non-invasive imaging based methods available for clinical diagnosis. Likewise, preterm birth is a growing global issue with increasing incidence and has been commonly associated with cognitive and functional deficits in later years of life. Non-invasive assessment of morphology change in the brain due to preterm birth can potentially aid proper clinical decision process. As a first step in this direction, this thesis presents novel geometry and function feature based topological data analysis in neuroimaging data. Efficacy of these methods to capture the subtle changes in brain due to nueurological conditions at the beginning and later end of human life cycle show promise in their clinical utility. These topology features are able to discriminate between PD patients and healthy groups and preterm born and term born children. First, we present a novel framework to quantify the brain geometry change with brain abnormalities in an algebraic topology approach to obtain persistent homology features (chapter 3). In chapter 4, we model the whole brain geometrical arrangement of cortical and subcortical structures to obtain topology features and show their potential to discriminate between disease and healthy groups. Subsequently, we study the topology of function indexed on the brain geometry. In chapters 5 & 6 we present a novel surface deformation based surface displacement shape feature to identify change in shape of the subcortical structures due to PD and preterm birth and study the topology of the shape feature in chapter 7. In chapters 8, 9 & 10 we present the study of cortical atrophy in PD, cortical abnormality in preterm born children and the topology of the cortical thickness change in the disease groups. Lastly, in the appendix A we present a library of brain MRI templates with ground truth labels for subcortical structures that was built to obtain accurate segmentation of these structures in pediatric brain MRI images.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Mirza Faisal Beg
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

A new, low-cost, PDMS metallization process for highly conductive flexible and stretchable electronics

Date created: 
2017-03-17
Abstract: 

This thesis describes a novel microfabrication process to produce thick-film copper microstructures that are embedded in polydimethylsiloxane (PDMS). This process has reduced fabrication complexity and cost compared to existing techniques, and enables rapid prototyping of designs using minimal microfabrication equipment. This technology differs from others in its use of a conductive copper paint seed layer and a unique infrared-assisted transfer process. The resulting microstructures are embedded flush with the PDMS surface, rather than on top, and adhere to PDMS without the need of surface modifications. The 70-micrometers-thick copper layer has a surface roughness of approximately 5 micrometers, a low film resistivity of 2.5-3 micro-Ohm-cm, and can be patterned with feature sizes of 100 micrometers. The low-cost, thick metal films demonstrate a comparative advantage in high-current, low-power applications, with feature sizes and metal layer properties that are otherwise comparable to similar processes. Several applications are fabricated, including stretchable interconnects integrated with fabrics for wearable devices and a multi-layer electromagnetic microactuator with a soft magnetic nanocomposite polymer core for large magnetic field generation. The interconnects can accommodate strains of 57 percent before conductive failure, which is similar to existing technology, and demonstrate a significantly lower resistance of less than 0.5 Ohm per device. The actuator produces an average magnetic field of 2.5 milli-Tesla per volt applied within a cylindrical volume of 34 cubic millimeters. Simulations indicate that fields of up to 1 Tesla are possible for 200 micro-second input pulses, and that significantly larger fields are achievable through simple design modifications. These results are comparable to existing devices, while our device has the advantage of being fully flexible, low-cost, and is easily integrated with various substrates and polymer microfabrication processes.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Bonnie Gray
Lesley Shannon
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

Human embryo component detection using computer vision

Date created: 
2017-04-24
Abstract: 

This thesis focuses on automatic identification of various components of human embryos in Hoffman Modulation Contrast (HMC) microscopic embryo images at early stages of growth from Day-1 to Day-5. Our primary motivation is to develop an automated system that would assist embryologists to study and analyze the behavior of developing preimplantated embryos in an attempt to improve In-Vitro Fertilization (IVF) outcomes. Through this thesis, we propose three novel methods for identification of various parts of human embryo. The main contribution of this thesis is to efficiently and reliably determine the boundaries of embryonic cells in Day-1 to Day-3 of HMC human embryo images. The proposed method is a model-based one that utilizes global ellipsoidal models conforming to the local image features such as edges and normals. It is an iterative approach through which image features contribute only to one candidate and will be retired once associated with that model candidate. An overall Precision and Sensitivity of 92% and 88% are achieved. Another contribution of this thesis is to segment different components of Day-5 embryos (also known as blastocysts) in HMC images as size and properties of these regions play an important role in grading and selecting viable embryos. A new method, called Segmentation using Neural Network in Compressed Domain (SNNCD), is developed to segment all three regions (Zona Pellucida (ZP), Trophectoderm (TE) and Inner Cell Mass (ICM)) in compressed blastocyst images. We exploit valuable features of a DCT transform to train a 2-layer feedforward backpropagation neural network. The overall Precision of 0.80, 0.69 and 0.76 and Sensitivity of 0.81, 0.80 and 0.56 for the ZP, TE and ICM detection in test data are achieved, respectively. Last, we propose a two-stage pipeline, called Segmentation using Fully Convolutional Network (SFCN) that first uses a preprocessing step to remove artifacts from the input images, which are then used by the Fully Convolutional Networks (FCN) to identify ICM regions. We also propose a data augmentation technique to avoid overfitting. The performance of the proposed pipeline is evaluated based on Accuracy and Overall Quality (OQ). This method improves SNNCD results on ICM segmentation by about 28% on OQ.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Parvaneh Saeedi
Ivan Bajic
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

FPGA to the cloud

Author: 
Date created: 
2017-03-29
Abstract: 

FPGAs are enabling more applications to be put to the market at a fraction of the cost of ASICs and with a much faster deployment rate. However, the wide range of FPGA brands and types currently available on the market; could overwhelm first time users when choosing a suitable FPGA for a given application. Furthermore, intermediate-to-advanced FPGA users may desire to evaluate some new FPGAs before committing to a purchase. FPGA to the Cloud is a web application that allows users to interact with FPGA evaluation kits remotely on a try-before-you-buy or pay-per-use model. The end user would access a web site where the web application is hosted. The end user would select an FPGA evaluation board from a list, and would be given direct remote access to said FPGA board; with programming tools. The user could use available sample FPGA design files, or upload user-created FPGA design files; for testing and evaluation. The project-prototype is based on the ZedBoard which uses Xilinx’s Zynq-7000 FPGA. The web application was developed using Laravel’s PHP framework.

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