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

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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): 
Senior supervisor: 
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): 
Senior supervisor: 
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): 
Senior supervisor: 
Craig Scratchley
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Project) M.Eng.

Cloud-assisted real-time free viewpoint video rendering and streaming System

Author: 
Date created: 
2017-03-27
Abstract: 

Free Viewpoint Video (FVV) is an emerging type of video which allows user to choose viewpoint freely in three-dimensional scenes. Depth-image-based Rendering (DIBR) is a common method to generate FVV using both texture and depth information. However, FVV rendering is more time-consuming than the original video since it has higher computational complexity. In order to make FVV rendering in real-time, a cloud-assisted system is proposed, which leverages cloud and distributed computing. In addition, we use multithread programming to take full advantage of cloud resources. As a result, by deploying our system on the WestGrid cluster, the FVV generation speed can be over 30 fps. Furthermore, to achieve the optimal trade-off between economic cost and user experience, we formulate and build mathematical models for the cloud-based FVV rendering and streaming system. Based on that, dynamic resource allocation algorithms are designed, which can provide the optimal resource allocation scheme according to users’ requests. The performance of the system is demonstrated by various experiments. To the best of our knowledge, this is the first cloud-assisted real-time FVV rendering and streaming system.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Jie Liang
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Development of a Point-Of-Care Lensless Birefringent Molecule Detection System

Date created: 
2017-03-10
Abstract: 

This thesis is a proof-of-concept exploration of an optical birefringent Point-Of-Care (POC) detection device. Birefringent detection can be useful for monitoring glucose and cholesterol, as well as diagnoses of diseases such as malaria, Ebola, bacterial infection and AIDS. Many diseases cause optical birefringent materials to precipitate in blood. These precipitates can be used as a biomarker to diagnose the disease. In this thesis, we will focus on the development of a device for detection of a birefringent phantom, called Tetrabutylammonium (TA), suspend in deionized water. We will show a method for, a low cost, Point-Of-Care, and easy to use birefringent detection platform. This thesis builds on the concepts of flow cytometry for detection of depolarized light and uses these concepts for the development of a miniaturized optical birefringent detection setup, utilizing a lensless design, for a sample flowing through a microchannel. A microfluidic channel with a serpentine shape was developed in order to increase the volume of sample present within the detection area, while also decreasing the total volume used per measurement by reducing the cross sectional area of the channel. To demonstrate the concept of birefringent detection, a bulk optic setup was developed which used two detection channels. The two detection channels were a 2 dimensional (2D) Charged Coupled Device (CCD) and a 1 dimensional (1D) Avalanche Photodiode (APD). Using the bulk optic setup, we compared 2D imaging with 1D sensing and compared the ability of the two detectors to identify detection events. We identify detection events at a concentration of 1 µg/mL of TA using both 1D sensing and 2D imaging in the bulk optic setup, before using a 1D APD detector for the miniaturized optical setup. In the miniaturized optical setup, we detected events at the same concentration limit.

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

Implementation of machine learning on an innovative processor for IoT

Date created: 
2016-12-19
Abstract: 

The “Internet of Things” (IoT) is a very rapidly increasing market segment for electronics, and it holds the promise to be one of the most significant drivers for innovation in the semiconductor industry in the near future. “IoT” is providing new and different specifications to the design of embedded systems, and such specifications are likely to change the constraints that drive embedded systems design. In particular, “IoT” is introducing a wave of innovation on the design of embedded microprocessors that are the heart and soul of such systems. This report took place in the context of larger investigation on innovative embedded processor architectures for “IoT”. The work started from an existing processor design, developed in Simon Fraser University in form of a Hardware Description Language (HDL) open source library. Such processor design advantages on the RISC-V instruction set distributed since 2011 by the University of California at Berkley. This work focused on analyzing a reference algorithmic application of relevance for the “IoT” (Linear Discriminant Analysis, a well-known Machine Learning tool for data classification), that is currently being utilized in two different research projects in Simon Fraser University. This report contributed to the larger project by: 1) Porting a C version of the LDA algorithm developed for ARM cores on the newly proposed processor architecture. 2) Evaluating the performance of the LDA algorithm on the proposed architecture in terms of available data sample rates and required energy consumption. 3) Profiling the LDA algorithm on the proposed processor in order to determine the critical operation kernels that mostly affect the performance. 4) Defining the hardware configuration for the proposed processor that leads to the most efficient implementation of LDA.

Document type: 
Graduating extended essay / Research project
File(s): 
Senior supervisor: 
Lakshman One
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Project) M.Eng.

Clinical optical coherence tomography angiography registration and analysis

Date created: 
2017-04-20
Abstract: 

Optical Coherence Tomography Angiography (OCT-A) is an emerging imaging modality with which the retinal circulation can be visualized by computing the decorrelation signal on a pixel-by-pixel basis. This non-invasive, in vivo visualization of the retinal microvasculature can be instrumental in studying the onset and development of retinal vascular diseases. Quantitative measurements, such as capillary density, can be used to stratify the risk of disease progression, visual loss, and also for monitoring the course of disease. Due to projection artifact and poor contrast, it is often difficult to trace individual vessels when only one en face image is visualized. Averaging of up to 10 serially acquired OCT-A images with parallel strip-wise microsaccadic noise removal and localized nonrigid registration is presented. Additionally, the use of a deep learning method for the quantification of Foveal Avascular Zone (FAZ) parameters and perifoveal capillary density of prototype and commercial OCT-A platforms in both healthy and diabetic eyes is evaluated.

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

Exploiting side information and scalability in compressed sensing and deep learning

Author: 
Date created: 
2016-11-02
Abstract: 

There is a tremendous demand for increasingly efficient ways of both capturing and processing high-dimensional datasets of large size. When capturing such datasets, a promising recent trend has developed based on the recognition that, many high-dimensional datasets have low-dimensional structures. For example, the notion of sparsity is a requisite in the compressed sensing (CS) field, which allows for accurate signal reconstruction from sub-Nyquist sampled measurements given certain conditions. When processing such datasets, the recently developed deep learning is a powerful tool, able to extract high-level and complex abstractions from massive amounts of data. CS has a wide range of applications that include imaging, radar and many more. Much effort has been put on developing more accurate and efficient reconstruction algorithms. In this thesis, first, we are interested in how to incorporate the side information into CS reconstruction when there is an initial estimation of the sparse signal available from other sources. Rigorous theoretical analysis was proposed for the first time in this field. Sufficient number of measurements is required for accurate CS reconstruction. We may have to wait for a long time to do the reconstruction until we receive enough measurements, which could incur undesired delays. Moreover, state-of-the-art CS reconstruction algorithms are still inefficient for signals of large size, e.g., images. Inspired by the multi-resolution or scalable reconstruction in multimedia transmission, such as JPEG 2000 and H.264/SVC, in the second part of this thesis, we analyzed scalable CS reconstruction problem and proposed to reconstruct a low-resolution signal if the number of measurements is too small. Deep learning or deep neural networks (DNNs) has evolved into the state-of-the-art technique for many artificial intelligence tasks including computer vision, speech recognition and natural language processing. However, DNNs generally involve many layers with millions of parameters, making them difficult to be deployed and updated on devices with limited resources such as mobile phones and other smart embedded systems. Moreover, if the DNN needs to be updated, usually via wireless communications, downloading the large amount of network parameters will cause excessive delay. In the final part of this thesis, we propose a scalable representation of the network parameters, so that different applications can select the most suitable bit rate of the network based on their own storage constraints.

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

Multi-GPU accelerated real-time retinal image segmentation

Author: 
Date created: 
2016-11-10
Abstract: 

In recent years, Optical Coherence Tomography (OCT) has become one of the dominant imaging technologies for ophthalmic diagnostics and vision research. The fast and high-resolution cross-sectional data that OCT provides has brought a new possibility in the role of intra-operative imaging. However, existing commercial OCT systems lack the automated real-time functionality for providing immediate feedback of changes in anatomical configuration as the result of surgical actions. The predominant reason for lacking such functionality is because high complexity algorithms are hard to implement in real-time imaging due to their computationally expensive nature. In this thesis, we will present a Graphics Processing Unit (GPU) accelerated retinal layer segmentation for real-time intra-operative imaging applications. Modern GPUs has emerged as a strong tool for mass computation in scientific researches. The computational power of the GPU outpaces Central Processing Unit (CPU) significantly when the processing task is parallelizable. Image segmentation is a computationally expensive algorithm and traditionally implemented in sequential instructions. An example of a parallelizable segmentation algorithm is Push-Relabel (PR) Graph-Cut(GC), which can be implemented using GPU. The GPU Retinal Segmentation (GRS) presented in this thesis is built upon such an algorithm. To ensure the run time of the GRS meets the real-time requirement for its application, multiple GPUs are used to accelerate the segmentation processing further in parallel. As a result of using GRS, we were able to achieve the visualization of the retinal thickness measurement and the enhancement of retinal vasculature networks in real-time.

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

Automatic and Non-Invasive Delineation of the Seismocardiogram Signal for the Estimation of Cardiac Time Intervals with Applications in Diastolic Timed Vibration and Early Stage Hemorrhage Detection

Date created: 
2016-10-12
Abstract: 

Seismocardiography is the non-invasive measurement of the heart vibration by placing an accelerometer on the human chest. Due to its non-invasive nature, the seismocardiogram signal could be embedded inside portable devices for the purpose of health monitoring and remote diagnosis. With the combination of the electrocardiogram (ECG) signal, cardiac time intervals (CTI) could be extracted. CTIs are timing intervals that are associated with specific events of the cardiac cycle. The research community has explored the potential of CTI in the diagnosis of chronic myocardial disease, ischemic and coronary artery disease, arterial hypertension, cardiac resynchronization therapy, and implantable cardioverter de-fibrillator. For the extracted CTIs to be useful in a medical device, the seismocardiogram signal (SCG) has to be automatically delineated. Upon the automatic delineation of CTIs, the timing parameters could be either combined with other physiological signals to create new indices that have unique physiological interpretations or to be used as a complimentary technology. Hence, The present dissertation has three main objectives: (1) automatic SCG delineation algorithm, (2) application of cardiac time intervals (extracted from SCG) for generating aunique index for early stage hemorrhage detection, and (3) complementary technology for optimization of the diastolic timed vibration therapy. For the first objective, the proposed delineation algorithm had the capability to correctly estimate the CTIs while discarding low-quality cardiac cycles, which are the ones thatdon’t have identifiable fiducial points. For the second objective, the combination of the electrocardiogram, seismocardiogram, and photoplethysmogram signals was used to design a hemorrhage progression index, which ultimately was applied for early stage detection. For the last objective, the extracted CTIs were applied to the “diastolic timed vibration”, which is a potential therapy for patients with acute ischemia during the pre-hospitalization phase. A calibration methodology was proposed for diastole detection in real-time.

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