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Partial Symbol Detection Based Energy-Efficient Receiver for Short-Reach Optical Communication
No full textThis thesis presents an energy-efficient architecture for optical receivers, targeting silicon photonic solutions for next-generation data centers. It introduces a partial symbol detection (MSB)-based equalization scheme that can significantly reduce hardware complexity for multilevel signaling. The design detects MSB bits from the incoming signal and uses MSB decisions to mitigate ISI from MSB transitions. This transforms the PAM-4 eye into a partially ISI-removed PAM-2 eye, reducing the number of comparators needed for a 1-tap PAM-4 DFE implementation from 12 to 4. Simulations show the equalizer can equalize up to 66% of ISI, enabling reliable operation at 112 Gb/s. The architecture, post-layout extracted and simulated in 12nm-FinFET technology consumes 108.84mW at 112Gb/s using PAM-4 modulation. The receiver features TIA and CTLE followed by 8-way time-interleaved signal processing unit that corrects pre- and post-cursor ISI. The inverter-based TIA topology provides 70dB gain, 29.65GHz bandwidth, and 1.594μA input-referred noise
Understanding the Role of Mitochondria in Driving Antiviral Inflammatory Responses
No full textAging is associated with immune dysfunction and increased risk of morbidity and mortality due to infectious diseases, as exemplified by the COVID-19 pandemic. To improve health outcomes in these populations we need novel therapeutics that can restore immune function. Targeting macrophages is a potential option. These cells are the first line of defence in most tissues and sense changes in the local microenvironment to initiate and regulate immune responses. Emerging evidence suggests aging induces an energy-deficient state that compromises macrophage function. It is unclear how these changes in cellular metabolism affect innate sensing and downstream antiviral responses. To investigate this, we developed young and old metabotypes for antiviral immune responses using murine bone marrow derived macrophages stimulated with viral ligands (TLR7 ligand; mimics ssRNA) and (TLR3 ligand; mimics dsRNA). In young cells, TLR7 engagement was associated with high levels of inflammatory cytokine production dependent on glycolysis for ATP production. TLR3 engagement was associated with a robust type I IFN response, requiring sustained OXPHOS activity for energy production. We found mitochondria superoxide production (mtROS) had differential effects on cytokine production following stimulation with these viral ligands, dampening cytokine production following TLR7 activation but increasing it following TLR3. In aging cells, reduced metabolic activity was associated with reduced cytokine production irrespective of the ligand used. This was linked to a reduced ability to modulate the main ROS producing ETC complexes and reduced mtROS production. We conducted a pilot study to evaluate if Bacillus Calmette-Guerin (BCG) can restore function in aged macrophages. BCG is a potent inducer of trained immunity and was used during the COVID-19 pandemic to protect against severe disease. Interestingly, we found that BCG treatment in vivo induced a trained phenotype in cells stimulated with TLR7 ligands and a tolerant phenotype following TLR3. In the old cells, these effects were more subtle, with indications of metabolic reprograming in favor of glycolysis. Collectively, our findings have the potential to inform novel therapeutic treatments to improve the innate response to viral infections, particularly in aging populations
Autonomous Aerial Drone Landing Site Selection on a Maritime Vessel
No full textThis thesis focuses on the development of an autonomous system capable of iden- tifying, tracking, and landing on suitable sites aboard a moving ship. Leveraging modifications to the Hazard-Aware Landing Optimization (HALO) algorithm, origi- nally designed for static terrain, the system integrates robust mapping, point cloud registration, and site selection algorithms to enable reliable performance in dynamic maritime conditions. A simulation environment was developed, utilizing Microsoft AirSim and ShipMo3D. This simulation incorporated a quadrotor equipped with Light Detec- tion and Ranging (LiDAR) to map ship decks and evaluate potential landing sites. Key innovations included dynamic point cloud registration using FilterReg and the integration of a modified Landing Period Indicator (LPI) algorithm. The results demonstrated the system’s ability to autonomously map ship decks, identify suitable landing sites, and execute landings on a ship moving under diffi- cult sea conditions. This work establishes a foundation for further development in autonomous maritime operations
Hardware Oriented Evolutionary Spiking Neural Network
Get PDFWith the ever-increasing complications of Artificial Neural Networks (ANNs), the power and resource costs demand more attention in the design process. Spiking Neural Networks (SNN) are a type of neural networks that promise simpler models which can be power and cost effective. However, these networks have complications regarding training and adaptation. Genetic algorithms are another nature inspired technique of finding the best solutions to a problem, advantageous in terms of ease of implementation and better control over network parameters. This work proposes a method of training SNNs using genetic algorithms for a navigational problem and optimize network parameters to reduce power and computational costs. GA results can be translated into FPGA implementable networks, using the LIF neuron model with optimized arithmetic and logical units, with 86 percent classification accuracy, 34.33 and 12.81 percent lower LUT and FF utilization respectively, and 397mW lower power usage compared to a network trained using backpropagation
Positioning Pharmaceuticals: The Emergence of Prescription Drug Advertising in Canada, 1987-2005
No full textThis dissertation examines how the pharmaceutical industry in Canada used advertising to encourage Canadians to reimagine their relationship to prescription drugs between 1987 and 2005. During this period, drug companies stopped promoting products exclusively to physicians and began advertising directly to consumers. Using a strategy I call “pharmaceutical positioning,” the industry leveraged the communicative affordances of advertising to construct a new subjectivity— the “somatic consumer”—which is centred on the use of pharmaceuticals as technologies for self-actualization. Pharmaceutical positioning has five key aspects, each of which is explained in the dissertation. These include: the creation of a transformational consumer subject position (the somatic consumer); provision of authoritative health information in an otherwise information-scarce environment; access to non-traditional lifestyle expertise; the promise of biological indeterminacy; and an ethical orientation to body-centred consumption. This study analyzes representative pharmaceutical advertisements collected from three Canadian magazines: Chatelaine, Maclean’s, and Reader’s Digest. Using multimodal critical discourse analysis, I show how drug companies used advertisements as part of an effort to recontextualize access to prescription medicines as a consumer practice. The resulting analysis illustrates a convergence of pharmaceutical business interests with rising health consumerism and the “responsibilization” of health (Osborne, 1997, p. 185). By situating Canadian pharmaceutical advertising within communication studies, this dissertation expands the scope of existing analysis of the topic. It brings the object of analysis beyond the public health policy field, where it has received the lion’s share of attention. It argues the industry used advertising as a key mechanism to establish a direct relationship with consumers, thereby reshaping the way Canadians think about pharmaceuticals. The legacy of these efforts lives on today in the way we conceptualize about and relate to prescription drugs in this country
Horncore Size as an Ecometric Indicator? Considerations from the Observation of North American Bison
Get PDFBison bison historically have been found across North America. This broad geographic distribution places them in a variety of climatic conditions, which may influence their morphology. The vascularised horncores for bison are overlain by a keratinous sheath, and it is possible that this results in preferential heat loss for the animal in this region. Larger horncores possess a larger surface through which body heat maybe lost, in warmer climates shedding excess heat maybe beneficial, but in colder climates maintaining body heat is critical. The purpose of this research is to evaluate the possibility that the horncore size for Bison bison can be used to infer climatic conditions. Regression analyses suggest a positive correlation between horn length and latitude in Bison, and negative correlation with temperature and precipitation. This suggests that thermoregulatory constraint does not act upon Bison horncores, but other selective pressures such as predation drive their elaboration
Single Sign-On (SSO) and its Intersection with Phishing Attacks: An Investigation
Get PDFUsers are increasingly prompted to click login links and login buttons from their emails and on websites, as services offer alternative login methods extending beyond traditional usernames and passwords. Single sign-on (SSO) simplifies password management by allowing users to login to services, like Spotify, Slack, Zoom, GitHub, Airbnb, and many more, using external identity providers (IDPs) like Google, Facebook, and Apple, to authenticate users using their already existing email address and accounts. We define a new phishing attack which is specifically targeted to SSO users, exploiting the “login with XYZ” button or link that takes the user to the malicious website. We then explore the possible consequences, specifically susceptibility to this new SSO-based phishing attack, questioning whether developing the habit of clicking on these buttons makes them disproportionately susceptible to this new type of phishing. To accomplish this, we created a user-study that included instances of SSO-based phishing
Immigration Detention and Release Decisions in Canada: Development and Preliminary Validation of a Risk Assessment Tool for Frontline Officers
Get PDFImmigration detention systems face mounting pressure to demonstrate transparent and defensible decision-making practices amid growing ethical concerns. The Canada Border Services Agency (CBSA) has drawn particular scrutiny in this area, largely due to its partnerships with correctional agencies. Critics challenge CBSA's framework for placing noncitizens in these facilities, characterizing its risk assessment processes as opaque and arbitrary. To address these limitations, we developed the Immigration Risk Assessment for Detention (IRAD), an empirically informed tool designed to meet CBSA's multiple decision-making needs—from release on community-based alternatives to detention (ATDs) to security classification level within detention facilities. This dissertation presents research conducted across three co-authored articles, each representing a distinct phase in the IRAD’s development and preliminary validation. First, we surveyed 92 CBSA employees to gather their insights on immigration detention risk assessment. Second, we developed a 30-item numerical IRAD prototype by integrating our survey results with CBSA’s operational guidance and correctional risk assessment research. We then conducted a longitudinal retrospective validation of the IRAD prototype using 301 case files, which provided preliminary support for its use. The IRAD's Danger to Public and Unlikely to Appear domains showed good to excellent interrater reliability, and the latter domain predicted ATD violations with moderate accuracy. Concordance analyses revealed misalignments between client risk and CBSA's purportedly risk-based decisions. The IRAD and CBSA's current security classification tool also showed similar concordance with security classification decisions. Finally, we adapted the IRAD prototype for operational use, creating a 21-item structured professional judgement tool. We then explored the potential operational utility of this tool in a mixed prospective-retrospective pilot with CBSA employees. Though low officer engagement prevented robust evaluation, we found further evidence of misalignment between risk and decisions. A noise audit with client vignettes also revealed inconsistencies among officers during the risk identification, risk analysis, and decision-making processes for ATD determinations. As the IRAD consolidates CBSA's operational resources, our research suggests that decisions are influenced by other factors, which may be extraneous given the inconsistencies identified in our noise audit. The IRAD's streamlined, empirically informed design and preliminary evidential support may therefore help CBSA better align its decisions with risk
A Comprehensive Energy Management Framework for Electric Vehicle Driving Range Extension Considering Battery Aging
Get PDFThe longer charging time and scarcity of fast-charging stations contribute to range anxiety, a significant obstacle to the widespread adoption of electric vehicles (EVs). In addition, the battery pack of an electric vehicle is both expensive and has a limited lifespan. Reduced range and lower resale value are concerns for potential EV owners due to the degradation of capacity over time. The thesis puts forward a novel Energy Management Strategy (EMS) framework that is specifically developed to optimize the speed profile in real-time, thereby extending the driving range and battery lifespan of EVs. The contributions of this study include the integration of battery degradation considerations into the EMS of EVs, eliminating the reliance on precise mathematical modeling through the use of machine learning (ML) techniques, the introduction of driver-adjustable power-saving modes, and the execution of long-term performance analysis simulating up to one year of driving under varying temperatures and driving cycles. The proposed EMS framework is developed based on an autonomous EV platform, facilitating the optimization of the vehicle’s speed profile. However, it can also be adapted for human-driven vehicles by translating the throttle angle into torque demand. The subject of this research is a single-source-powered Battery EV (BEV). The proposed EMS utilizes Multi-Objective Genetic Algorithm (MOGA), Model Predictive Control (MPC), and Pontryagin’s Minimum Principle (PMP) as optimization techniques in three distinct models. The study develops a longitudinal vehicle model, maps motor-inverter characteristics based on experimental tests, and builds battery State of Charge (SOC) and State of Health (SOH) models using ML algorithms. The EMS framework is tested on identical EVs across various driving cycles and temperatures, and is compared against a reference model to assess its effectiveness. Results demonstrate that the proposed EMS, in its moderate mode, can increase driving range by 10.9%, reduce power consumption by 11%, and mitigate battery aging by 15.3%. In aggressive power-saving mode, the EMS extends the driving range by up to 17.7%, minimizes consumption by 16.7%, and improves battery SOH by 21.9%. These findings confirm that the proposed EMS framework can significantly enhance EV’s performance without major compromises to driving experience