6,386 research outputs found

    Graduate Catalog of Studies, 2023-2024

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    A Broadband Mid-infrared Metasurface for Polarisation Manipulation and Utilisation

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    A pair of enantiomers are distinct from each other due to chiral structural arrangement which leads to selective interaction with chiral light. Vibrational circular dichroism spectroscopy in mid-infrared region provide a powerful label-free method to distinguish chiral enantiomers. Besides, mid-infrared sensing also significantly benefit from resolving compositional information of molecules due to molecular vibrational fingerprints which holds promising application in biological and medical sensing. However, the low signal-to-noise ratio associated with weak light-matter interaction is a continuing obstacle hindering the practical application. Recent demonstrations of chiral metamaterials have shown that due to the chirality of structure, local superchiral field can be produced in the vicinity of structure to interact with molecules and enhance vibrational circular dichroism response. However, a limitation factor in development of chiral structure is the narrow effective working bandwidth and the requirement of circular polarization excitation. This thesis introduces an achiral nanorod-based metausrface that enable to overcome these limitations. First, the nanorod-based metasurface is present to achieve high efficient linear-to-circular polarization conversion in a broadband mid-infrared wavelength range in reflection mode. The model was firstly studied and optimised through simulation tool based on Finite Difference Time Domain method. The device was fabricated in a top-down approach based on electron beam lithography and characterised using Fourier transform infrared spectroscopy. We identified two distinct resonances originated from gap-plasmon mode at 3.4Ī¼m and Fabry-Perot mode at 7.9Ī¼m. The demonstration of polarization state based on the measured Stokes parameters within off-resonance range from 4-7Ī¼m show that the reflected beam has converted into circular polarization state. For practical application of vibrational circular dichroism spectroscopy, we numerically demonstrate the induced chirality in near-field under excitation of linear polarization with various polarization angles. These analysis suggest that superchiral field can be produced by nanorod-based metasurface and distributed spatially under linear polarization excitation. When polarization is parallel or orthogonal to rod, namely the symmetry exist in the combination of rod and incident polarization, the absolute chirality is zero due to the fact that same amount of optical chirality density with opposite handedness offset by each other. However it is showed that one handedness of the optical chirality density is dominant when the symmetry is broken, hence, holds potential for circular dichroism spectroscopy sensing. In an experimental feasibility study, we measured the polarization states of light in far-field and demonstrate that the absolute chirality in the far-field show similar behaviour as that in near-field. Finally, we conduct a molecular sensing measurement based on the rod-shape metausrface for enantiomers (alanine) identification through circular dichroism spectroscopy. This thesis demonstrates with FDTD simulations that the metasurface can generate superchiral fields which enable to enhance interaction with molecules upon linear polarization excitation. By simply rotating sample with 90 degree, molecules can then interact with superchiral field with opposite handedness. The circular dichroism is to record the intensity of reflected beam and characterise the differential intensity between the two. Despite the measured data do not show inverse pattern for L- and D-alanine, we confirmed that metausrface enable to enhance the light-matter interaction

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Autonomous Radar-based Gait Monitoring System

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    Features related to gait are fundamental metrics of human motion [1]. Human gait has been shown to be a valuable and feasible clinical marker to determine the risk of physical and mental functional decline [2], [3]. Technologies that detect changes in peopleā€™s gait patterns, especially older adults, could support the detection, evaluation, and monitoring of parameters related to changes in mobility, cognition, and frailty. Gait assessment has the potential to be leveraged as a clinical measurement as it is not limited to a specific health care discipline and is a consistent and sensitive test [4]. A wireless technology that uses electromagnetic waves (i.e., radar) to continually measure gait parameters at home or in a hospital without a clinicianā€™s participation has been proposed as a suitable solution [3], [5]. This approach is based on the interaction between electromagnetic waves with humans and how their bodies impact the surrounding and scattered wireless signals. Since this approach uses wireless waves, people do not need to wear or carry a device on their bodies. Additionally, an electromagnetic wave wireless sensor has no privacy issues because there is no video-based camera. This thesis presents the design and testing of a radar-based contactless system that can monitor peopleā€™s gait patterns and recognize their activities in a range of indoor environments frequently and accurately. In this thesis, the use of commercially available radars for gait monitoring is investigated, which offers opportunities to implement unobtrusive and contactless gait monitoring and activity recognition. A novel fast and easy-to-implement gait extraction algorithm that enables an individualā€™s spatiotemporal gait parameter extraction at each gait cycle using a single FMCW (Frequency Modulated Continuous Wave) radar is proposed. The proposed system detects changes in gait that may be the signs of changes in mobility, cognition, and frailty, particularly for older adults in individualā€™s homes, retirement homes and long-term care facilities retirement homes. One of the straightforward applications for gait monitoring using radars is in corridors and hallways, which are commonly available in most residential homes, retirement, and long-term care homes. However, walls in the hallway have a strong ā€œclutterā€ impact, creating multipath due to the wide beam of commercially available radar antennas. The multipath reflections could result in an inaccurate gait measurement because gait extraction algorithms employ the assumption that the maximum reflected signals come from the torso of the walking person (rather than indirect reflections or multipath) [6]. To address the challenges of hallway gait monitoring, two approaches were used: (1) a novel signal processing method and (2) modifying the radar antenna using a hyperbolic lens. For the first approach, a novel algorithm based on radar signal processing, unsupervised learning, and a subject detection, association and tracking method is proposed. This proposed algorithm could be paired with any type of multiple-input multiple-output (MIMO) or single-input multiple-output (SIMO) FMCW radar to capture human gait in a highly cluttered environment without needing radar antenna alteration. The algorithm functionality was validated by capturing spatiotemporal gait values (e.g., speed, step points, step time, step length, and step count) of people walking in a hallway. The preliminary results demonstrate the promising potential of the algorithm to accurately monitor gait in hallways, which increases opportunities for its applications in institutional and home environments. For the second approach, an in-package hyperbola-based lens antenna was designed that can be integrated with a radar module package empowered by the fast and easy-to-implement gait extraction method. The system functionality was successfully validated by capturing the spatiotemporal gait values of people walking in a hallway filled with metallic cabinets. The results achieved in this work pave the way to explore the use of stand-alone radar-based sensors in long hallways for day-to-day long-term monitoring of gait parameters of older adults or other populations. The possibility of the coexistence of multiple walking subjects is high, especially in long-term care facilities where other people, including older adults, might need assistance during walking. GaitRite and wearables are not able to assess multiple peopleā€™s gait at the same time using only one device [7], [8]. In this thesis, a novel radar-based algorithm is proposed that is capable of tracking multiple people or extracting walking speed of a participant with the coexistence of other people. To address the problem of tracking and monitoring multiple walking people in a cluttered environment, a novel iterative framework based on unsupervised learning and advanced signal processing was developed and tested to analyze the reflected radio signals and extract walking movements and trajectories in a hallway environment. Advanced algorithms were developed to remove multipath effects or ghosts created due to the interaction between walking subjects and stationary objects, to identify and separate reflected signals of two participants walking at a close distance, and to track multiple subjects over time. This method allows the extraction of walking speed in multiple closely-spaced subjects simultaneously, which is distinct from previous approaches where the speed of only one subject was obtained. The proposed multiple-people gait monitoring was assessed with 22 participants who participated in a bedrest (BR) study conducted at McGill University Health Centre (MUHC). The system functionality also was assessed for in-home applications. In this regard, a cloud-based system is proposed for non-contact, real-time recognition and monitoring of physical activities and walking periods within a domestic environment. The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition and gait analysis. Range-Doppler maps generated from a dataset of real-life in-home activities are used to train deep learning models. The performance of several deep learning models was evaluated based on accuracy and prediction time, with the gated recurrent network (GRU) model selected for real-time deployment due to its balance of speed and accuracy compared to 2D Convolutional Neural Network Long Short-Term Memory (2D-CNNLSTM) and Long Short-Term Memory (LSTM) models. In addition to recognizing and differentiating various activities and walking periods, the system also records the subjectā€™s activity level over time, washroom use frequency, sleep/sedentary/active/out-of-home durations, current state, and gait parameters. Importantly, the system maintains privacy by not requiring the subject to wear or carry any additional devices

    Improving low latency applications for reconfigurable devices

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    This thesis seeks to improve low latency application performance via architectural improvements in reconfigurable devices. This is achieved by improving resource utilisation and access, and by exploiting the different environments within which reconfigurable devices are deployed. Our first contribution leverages devices deployed at the network level to enable the low latency processing of financial market data feeds. Financial exchanges transmit messages via two identical data feeds to reduce the chance of message loss. We present an approach to arbitrate these redundant feeds at the network level using a Field-Programmable Gate Array (FPGA). With support for any messaging protocol, we evaluate our design using the NASDAQ TotalView-ITCH, OPRA, and ARCA data feed protocols, and provide two simultaneous outputs: one prioritising low latency, and one prioritising high reliability with three dynamically configurable windowing methods. Our second contribution is a new ring-based architecture for low latency, parallel access to FPGA memory. Traditional FPGA memory is formed by grouping block memories (BRAMs) together and accessing them as a single device. Our architecture accesses these BRAMs independently and in parallel. Targeting memory-based computing, which stores pre-computed function results in memory, we benefit low latency applications that rely on: highly-complex functions; iterative computation; or many parallel accesses to a shared resource. We assess square root, power, trigonometric, and hyperbolic functions within the FPGA, and provide a tool to convert Python functions to our new architecture. Our third contribution extends the ring-based architecture to support any FPGA processing element. We unify E heterogeneous processing elements within compute pools, with each element implementing the same function, and the pool serving D parallel function calls. Our implementation-agnostic approach supports processing elements with different latencies, implementations, and pipeline lengths, as well as non-deterministic latencies. Compute pools evenly balance access to processing elements across the entire application, and are evaluated by implementing eight different neural network activation functions within an FPGA.Open Acces

    Implementation of a real time Hough transform using FPGA technology

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    This thesis is concerned with the modelling, design and implementation of efficient architectures for performing the Hough Transform (HT) on mega-pixel resolution real-time images using Field Programmable Gate Array (FPGA) technology. Although the HT has been around for many years and a number of algorithms have been developed it still remains a significant bottleneck in many image processing applications. Even though, the basic idea of the HT is to locate curves in an image that can be parameterized: e.g. straight lines, polynomials or circles, in a suitable parameter space, the research presented in this thesis will focus only on location of straight lines on binary images. The HT algorithm uses an accumulator array (accumulator bins) to detect the existence of a straight line on an image. As the image needs to be binarized, a novel generic synchronization circuit for windowing operations was designed to perform edge detection. An edge detection method of special interest, the canny method, is used and the design and implementation of it in hardware is achieved in this thesis. As each image pixel can be implemented independently, parallel processing can be performed. However, the main disadvantage of the HT is the large storage and computational requirements. This thesis presents new and state-of-the-art hardware implementations for the minimization of the computational cost, using the Hybrid-Logarithmic Number System (Hybrid-LNS) for calculating the HT for fixed bit-width architectures. It is shown that using the Hybrid-LNS the computational cost is minimized, while the precision of the HT algorithm is maintained. Advances in FPGA technology now make it possible to implement functions as the HT in reconfigurable fabrics. Methods for storing large arrays on FPGAā€™s are presented, where data from a 1024 x 1024 pixel camera at a rate of up to 25 frames per second are processed

    Kinetics of a dual nickel and iridium photocatalysed cross-coupling

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    Over the past decade, photoredox catalysis has developed into an important tool for synthetic chemists. Of particular importance has been the dual photoredox/nickel catalysis technique pioneered by the Doyle, MacMillan and Molander groups in 2014. While there has been much development of the synthetic applications of these techniques, in both industrial and academic settings, the mechanisms of this type of reaction remain poorly understood and few bulk-reaction kinetic studies have been conducted. This work takes the silane-mediated bromide-bromide sp2-sp3 cross-coupling developed by the MacMillan group in 2016 as a case study to probe the mechanism, both of this particular transformation and as representative of the class of reactions as a whole. In order to study this reaction, an in situ illumination 19F NMR spectroscopy (LED-NMR) apparatus was constructed in-house. Systematic variation of the reaction conditions allowed for the impact of each of the reaction components on the kinetics of the system to be observed. Four components (light, aryl bromide, nickel, and iridium photocatalyst) were found to control the rate of aryl bromide consumption, but not the product selectivity, while two components (silane and alkyl bromide), control the product selectivity, but not the rate. One particularly important outcome of this monitoring was the direct observation of a key aryl-Ni(II) intermediate that is the major resting state of the nickel catalyst throughout the cycle. Subsequent 13C isotope labelling studies demonstrated that this complex undergoes Ir-photocatalysed conversion to products in competition with degenerate release of aryl bromide. The experimental observations enabled development of a minimal kinetic model which allows simulation of the reaction evolution. This model provides useful insights for optimisation of these processes in the laboratory, as well as providing a framework for evaluating the validity of existing, and future, mechanistic proposals
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