Electronic Theses and Dissertations

Permanent URI for this collectionhttps://hdl.handle.net/10155/5

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Development of synthetic fiber-based reinforcements for thermoplastics as alternative to steel-based analogs for industrial applications
(2022-08-01) Syed, Nabeel Ahmed; Rizvi, Ghaus; Pop-Iliev, Remon
Despite being robust and inexpensive, steel cords can often become a source of risk to user health and safety when incorporated in industrial applications such as escalator handrails and rubber conveyor belts. Due to their inherent creep accompanied by cyclic thermal expansion and contraction of steel cords, the overall stability and performance of the application can reduce over time. In this context, the present research focuses on an innovative approach to replace steel cords by designing, developing and processing synthetic fibers of high specific strength (based on carbon, glass, and/or Kevlar fibers) to develop Thermoplastic Polyurethane (TPU) composites with superior mechanical properties and enhanced safety characteristics. The experimental work proposed in this thesis is divided into 3 distinct experimental research phases. The experimental results of phase 1 show that the epoxy coating on synthetic fibers significantly increases its load-bearing capacities due to improved compatibility between the fibers and the TPU matrix caused by chemical interaction and enhanced interlocking. Experimental results from phase 2 revealed that the injection molding process parameter related to temperature was the most significant process control variable compared to the injection pressure and the processing time in achieving high tensile properties. The ANOVA test confirmed the significance of temperature at a 99 % confidence level. The mathematical model generated through Response Surface Methodology (RSM) predicted a model with an accuracy of 98.06 %. Phase 3 results emphasized that the electrolytic treatment of carbon fibers created active sites and roughened the surface. When coated with a silane coupling agent, this electrolytically modified carbon fiber (with sulphuric acid electrolyte) increased 39 % in load-bearing capacity against raw carbon/TPU.
The impact of subclinical neck pain and its treatment on cerebellar processing as measured by the cervico-ocular and vestibulo-ocular reflexes
(2022-08-01) Campbell, Devonte; Yielder, Paul; Murphy, Bernadette
Alterations in cerebellar processing, associated with subclinical neck pain (SCNP), have been demonstrated to improve following spinal manipulation. These alterations and improvements have only been demonstrated utilizing indirect measures of the cerebellum. The cervico-ocular and vestibulo-ocular reflexes (COR & VOR) are two measures that may be utilized to directly assess changes within the cerebellum. Utilizing two eye-tracking protocols this thesis aimed examine differences in COR gain and VOR gain adaptation in a SCNP population prior to, and following, an 8-week chiropractic intervention. SCNP was demonstrated to alter COR gain but have a limited impact upon the VOR. These alterations within the COR gain were also observed to normalized following the chiropractic intervention. This may reflect that those with SCNP may have alterations in their proprioceptive input towards the cerebellum that may be normalized following spinal manipulation. However, SCNP may have a minimal impact on vestibular input towards the cerebellum.
QoS-aware energy efficient time-slotted channel schedule for heterogeneous IoT sensor networks
(2024-10-01) Vatankhah, Aida; Liscano, Ramiro
The emergence of the Internet of Things (IoT) has attracted significant attention in industrial environments, where applications must meet specified Quality of Service (QoS) requirements for latency, throughput, and packet loss. To address this, the IEEE 802.15.4e standard introduced the Time Slotted Channel Hopping (TSCH) Medium Access Control (MAC) protocol. However, the protocol does not specify any particular MAC schedule. Designing a centralized scheduling system that simultaneously achieves the required QoS is challenging due to the multi-objective optimization nature of the problem. Additionally, managing the energy consumption of IoT devices is also crucial while achieving the QoS requirements of the sensing applications. This thesis presents a novel QoS-aware Energy Efficient optimized TSCH scheduling algorithm (QoE-TSCH), designed to meet QoS requirements such as delay and packet loss for multiple services within a heterogeneous sensor network, while also achieving the expected throughput. The QoE-TSCH algorithm incorporates a padding strategy to increase the duty cycle, resulting in reduced energy consumption. The QoE-TSCH algorithm was implemented in MATLAB and evaluated within a co-simulation environment that integrates both MATLAB and TSCH, focusing on a range of sensor network topologies and industrial QoS scenarios as defined by the ISA SP100 standard. The evaluation results indicate that the optimum schedules produced by the QoE-TSCH algorithm effectively support both “open-loop” and “monitoring” industrial services specified in the ISA SP100 standard for sensor networks comprising 16 to 36 nodes. While the delay requirements are met in scenarios involving 64 nodes, the packet loss rates in these cases exceed the maximum acceptable threshold by an average of 0.5%. Additionally, the algorithm’s energy-saving strategy significantly improves the scheduling duty cycle. The reduction in the duty cycle enhances energy efficiency across sensor network configurations ranging from 16 to 64 nodes. Specifically, for scenarios with 16 to 36 nodes, the duty cycle was reduced by approximately 80%, while for scenarios with 64 nodes, the reduction was around 15%.
Enhanced wireless power transfer system modeling using reflection theory and magnetic circuit analysis
(2024-09-01) Son, Jeonggi; Williamson, Sheldon
This thesis presents an advanced model for wireless power transfer (WPT) systems, designed to optimize efficiency in high-power applications. By incorporating reflection theory and imaginary gyrator to address impedance mismatches between systems, the research refines conventional approaches. The model integrates Faraday's law, reflection theory, circuit analysis, and magnetic circuit theory, validated through both simulations and experiments. Several coil configurations, including circular and hexagonal designs, are analyzed, and a planar coil self-inductance model is developed using magnetic circuit theory. Tested at 3.7 kW, the model identifies peak efficiency points under varying load conditions by combining reflection theory with circuit analysis. Unlike previous models, it adapts to load variations within a 10 Ω tolerance range. The proposed method also optimizes mutual inductance for different power levels and load conditions. This research offers significant advancements for WPT systems in electric vehicle and drone charging, improving efficiency and addressing limitations of existing designs.
Producing transition metal oxides as cathodes for aqueous zinc-ion batteries via Ultra-Short Laser Pulses for In-situ Nanostructure Generation (ULPING)
(2024-09-01) Shiam, Suben Sri; Kiani, Amirkianoosh
Aqueous Zinc-ion Batteries (AZIBs) offer a compelling alternative to conventional lithium-ion batteries thanks to their high energy density, cost-effectiveness, and safety profile. Nonetheless, developing high-performance cathode materials poses challenges due to the need to balance electrochemical efficiency with environmental and health considerations. This thesis introduces a cutting-edge laser processing method to fabricate battery electrodes using a fiber-pulsed laser. This approach is efficient and environmentally friendly, as it avoids the use of chemical precursors and eliminates the need for post-processing. The study explores various parameters of pulsed lasers, particularly laser power, in creating an oxide layer directly on the surface of a transition metal. Fabrication was carried out using the Ultra-short Laser Pulses for In-situ Nanostructure Generation (ULPING) system. The effectiveness of the synthesized active surface was validated through material characterization and electrochemical testing. The research examines how the electrochemical performance relates to the physical properties of the treated surface, such as oxidation and surface area. Overall, the study supports sustainable synthesis and cost-effective production methods for aqueous zinc-ion batteries.
Cluster detection in general Markov chains with applications to directed networks
(2024-10-01) Sands, Darryen; Breen, Jane
Many community detection algorithms rely on information provided by the eigenvalues of matrices of the associated network. These techniques cannot be extended for directed networks since the matrices required are symmetric, diagonalizable, and have only real eigenvalues. Directed networks do not necessarily have these properties, which makes their analysis difficult. In this thesis, we created a community detection algorithm that utilizes the eigenvalues and eigenvectors of a transition matrix to find communities within Markov chains and directed networks. We test our community detection algorithm on various benchmarks, such as an implementation of the stochastic block model, Lancichinetti-Fortunato benchmarks, and real-world networks. We score the algorithm’s performance against other detection algorithms using validation metrics such as the Rand index. Our findings indicate that our algorithm’s performance depends on the strength of the clusters as measured by weight and structure ratios and that its performance is comparable to other community detection algorithms.
LLM-powered active learning for cost-effective text classification
(2024-10-01) Rouzegar, Hamidreza; Makrehchi, Masoud
This thesis presents an LLM-powered active learning framework for cost-effective text classification, addressing the challenge of potential LLM annotation errors while balancing annotation quality and model accuracy. Our methodology combines human and large language model (LLM) annotations using uncertainty sampling and confidence scoring. Starting with a small, labeled seed set, the model iteratively selects the most informative data points for annotation, reducing labeling costs while maximizing performance. To simulate real-world scenarios, a dynamically updated proxy validation set mirrors the distribution of the unlabeled pool, enabling reliable performance estimation throughout training. The Performance Improvement Cost Ratio (PICR) is introduced as an objective stopping criterion to optimize the balance between costs and accuracy gains. Additionally, role-based prompting enhances annotation quality, creating a scalable framework adaptable to diverse text classification tasks. Experimental results demonstrate that the proposed approach achieves human-comparable performance at reduced costs, underscoring its potential for practical applications.
Smart monitoring of cybersecurity incidents using machine learning
(2024-10-01) Page, Austin; Azim, Akramul
As cyber crime and Internet usage increase, cybersecurity solutions must evolve rapidly to keep pace. Despite significant advancements, these systems remain imperfect and are used daily by security analysts to monitor large networks, necessitating further improvements. Machine learning has gained traction in software development for added functionality, but its adoption in cybersecurity has been slow. This thesis introduces smart monitoring modules that employ machine learning to enhance cybersecurity tools and assist analysts in monitoring, investigating, and prioritizing threats. The anomaly detection module transforms log data into time series to detect abnormal activity, achieving an average F1 score of 87.24% across eight real-world datasets. Additionally, the threat assistance module utilizes historical threat tickets and state-of-the-art language models to classify and summarize threats, earning an F1 score of 85% across 38 cases and effectively summarizing relevant information in each instance.
Strategic modification of oligonucleotides with carboxylic acids containing small molecules
(2024-11-01) Mariscal, Kevin; Desaulniers, Jean-Paul
Small interfering RNA (siRNA) are double-stranded RNA molecules used to manipulate gene regulation, by silencing gene expression. SiRNA molecules have been integrated into the therapeutic field, with several FDA-approved medications targeting liver-specific diseases. While effective, these therapeutics have challenges, such as increasing costs, off-target effects, and delivery systems to other organs. To combat these issues, new formulation strategies must be implemented. One method is to discover new cost-efficient delivery systems that help guide the RNA to target cells increasing the specificity and stability of the RNA. Carboxylic acids such as folic and retinoic acid have been used extensively in RNA disease and cancer research due to their strong binding affinities to folate and retinoic acid receptors in the body. This has allowed us to tag various carboxylic acids containing small molecules onto RNA using a novel, and cost-efficient chemistry protocols to investigate unique delivery systems to further increase the silencing and stability capabilities in HeLa cells.
“Seeing the word ‘police’ can be a huge point of recoil”: a qualitative examination of trans people’s perceptions and questions of police legitimacy
(2024-11-01) Ginsley, Victoria; Cesaroni, Carla; Perry, Barbara
This dissertation examines transgender people’s experiences and perceptions of police within Canada. Transgender people experience a wide variety of negative interactions with police, including disrespectful language, rude and inappropriate behaviour, verbal and physical harassment, and the denial of police services (Dwyer, 2011, 2014, 2015; Wolff & Clokely, 2007; Shields, 2021). This study investigates the need for a transgender lens when examining the concepts of procedural justice and police legitimacy. Additionally, this study contributes to the growing literature on gender and legitimacy by emphasizing the need for transgender-focused analyses. I answer how trans people construct police legitimacy using in-depth, qualitative interviews. The following research questions guided this research project: What does the relationship between the trans community and police look like according to the trans community? Why do trans individuals believe the relationship is the way that it is? According to the trans community, what are the proposed best practices for police engagement with the trans community? What is justice to you, and how can police reflect that back to you? A total of 14 trans people were interviewed through online one-on-one and focus group interviews. The research questions were used as the interview questions. Findings show that trans people have limited or no trust in the police, as individual officers or in the institution of policing. Additionally, trans people feel the only way to address police mistreatment is to abolish the policing system. This study contributes to the criminological literature by combining transgender theory and procedural justice theory, including a transgender analysis of how police legitimacy is formed. This research’s significance and further contributions encourage future researchers to examine how trans people express their understanding of procedural justice and police legitimacy, and add to the growing literature on gender and legitimacy.
Discovering trade-offs between fairness and accuracy in machine learning systems: a multi-objective approach
(2024-09-01) Chowdhry, Arsh; Lewis, Peter
In recent years, the focus of machine learning has expanded from solely emphasizing accuracy to adopting a more comprehensive and human-centred perspective that includes privacy, fairness, and transparency. These aspects are frequently perceived as conflicting; for instance, there can be a trade-off between the accuracy and fairness of predictive models. Fairness analysis aims to ensure that machine learning models do not exhibit discrimination based on protected or sensitive attributes such as race, gender, age, or religion. However, multiple notions of fairness exist, and not all are compatible with each other. This thesis investigates the relationship between accuracy and various fairness metrics using three datasets, demonstrating that standard training techniques can lead to biased predictions. We examine the relationship between accuracy and six distinct fairness metrics through a multi-objective training approach designed to optimize both accuracy and fairness. Our findings reveal that while some scenarios exhibit a trade-off between accuracy and fairness, the multi-objective approach offers a range of models that balance these trade-offs. In other cases, the approach facilitates the development of models that are both accurate and fair, a result not achievable with single-objective methods. Consequently, our research highlights that explicitly incorporating fairness into the training process enables decision makers to access a spectrum of models meeting both accuracy and fairness criteria and identifies scenarios where a trade-off between fairness and accuracy is not necessary.
The use of mobile devices to help young children learn about Indigenous perspectives through environmental inquiry
(2024-11-01) Walsh, Angela; LeSage, Ann
Technology is part of everyday life for many young children in Canada, and the use of technology in early childhood education has been progressing. This research explores how mobile devices can support young children in learning about Indigenous perspectives through environmental inquiry. Learning about Indigenous perspectives is an ongoing effort towards truth and reconciliation in Canada. Early childhood educators (ECEs) can gain confidence in planning and implementing teaching practices related to Indigenous content when they understand teaching about Indigenous perspectives. A case study design was used to investigate ECEs’ perceptions of mobile device use in early childhood education. Semi-structured interviews were used to gather information from the seven ECEs who took part in this study. The findings revealed meaningful experiences and barriers for integrating mobile devices in early learning. This research contributes to a new space in early childhood education, with technology, Indigenous perspectives, and environmental inquiry being considered collectively.
Experimental investigation of integrated systems for biohydrogen and biomethane production
(2024-07-01) Kilicaslan, Ahmet Faruk; Dincer, Ibrahim
This thesis study presents two unique systems for biohydrogen (bioH2) and biomethane (bioCH4) production. The first system consists of a single-chamber cylindrical membraneless the microbial electrolysis cell (MEC), while the second system is an MEC-anaerobic digestion system. The system 1 investigates the effects of various parameters on the bioH₂ production, whereas the system 2 examines simultaneous production of the bioH2 and the bioCH4 under different operation conditions. The aluminum electrodes provide the maximum bioH2 production of 854 mg/L at 2.0 V, outperforming other electrodes. The optimum pH value and temperature for bioH2 production in the MEC systems are 6 and 40°C, respectively. The bioH2 production is enhanced by CO2 and N2 sparging, resulting in a 40% and 35% increase, respectively, under improved conditions. The highest H2 efficiency of 92.5% is achieved using an aluminum plate while sparging CO2 gases at a rate of 400 mL/min. The peak productions of the bioH2 and the bioCH4 are 830.9 mL and 720 mL in batch operation mode, respectively, and 843.3 mL and 760 mL in continuous operation mode after 24 hours. During the continuous flow operation, bioH2 production increases by 53% compared to batch mode.
Contextual topics: advancing text segmentation through pre-trained models and contextual keywords
(2024-09-01) Maraj, Amit; Vargas Martin, Miguel; Makrehchi, Masoud
Text Segmentation (TS) is a Natural Language Processing based task that is aimed to divide paragraphs and bodies of text into topical, semantically aligned blocks of text. This can play an important role in creating structured, searchable text-based representations after digitizing paper-based documents. Traditionally, TS has been approached with sub-optimal feature engineering efforts and heuristic modelling. In this work, we explore novel supervised training procedures with a labeled text corpus along with a neural Deep Learning model for improved predictions. Results are evaluated with the Pĸ and WindowDiff metrics and show performance improvements beyond any previous unsupervised TS systems evaluated on similar datasets. The proposed system utilizes Bidirectional Encoder Representations from Transformers (BERT) as an encoding mechanism, which feeds to several downstream layers with a final classification output layer, and even shows promise for improved results with future iterations of BERT. It is also found that infusing sentence embeddings with unsupervised features, such as the ones gathered from Latent Dirichlet Allocation (LDA), provides comparable results to current state-of-the-art (SOTA) TS systems. In addition to this, unsupervised features derived from LDA give the proposed system the ability to generalize better than previous supervised systems in the space. Furthermore, it is shown that with the use of novel language models such as Generative Pre-trained Transformers (GPT) for text augmentation, training data can be multiplied, while continuing to see performance improvements. Although the proposed systems are supervised in nature, they have the capability of fine-tuning a threshold variable that allows the system to predict segments more frequently or sparingly, further bolstering the practical usability of it. Due to the increasing competition in the supervised TS space, creating competitive systems often see contributions from larger research companies with more available resources (e.g., Google, Meta, etc.). However, unsupervised TS has been relatively unexplored in comparison with supervised efforts, since it is much more challenging to build a generalizable TS system. To this end, strong word and sentence embeddings are used to create an unsupervised TS system called “Coherence”, that blends the best of pre-trained models and unsupervised features to create a system that is capable of generalizing across various datasets, while achieving competitive results in the space. Since Coherence is unsupervised, inference is quick and requires no upfront investment (i.e., this technique can be picked up and applied to a domain without the need for fine-tuning).
Development of an AI-driven robotic manipulation framework
(2024-09-01) Liu, Xiaolong; Ren, Jing; Lang, Haoxiang
This paper introduces a manipulator control system that leverages state-of-the-art artificial intelligence (AI), advanced robotics, and the latest computer vision techniques to enable intuitive and universal robotic control across different robotic manipulators. The intuitive user command interpretation is deployed in the system using ChatGPT API. The vision capabilities of the system rely on YOLOv7 for object detection, Semi-Global Block Matching (SGBM) for object localization, and Principal Component Analysis (PCA) for object orientation. Moreover, MoveIt is utilized for motion planning, and the simulation environment is powered by Gazebo. These components are seamlessly coordinated by a custom state machine integrating into a ROS-based framework. This system allows users to control manipulators completing complex tasks by commands without expert knowledge of robotics. Additionally, a cross-platform kinematics solver architecture is introduced to assess inverse kinematics algorithms for manipulators. Compared to the traditional kinematics solver implemented as ROS plugins, this architecture integrates the sophisticated mathematical capabilities of Matlab with the feasibility of ROS.
Mothers of children with autism: current challenges and intricate dynamics influencing the quality of life and mental health of mothers caring for children with autism
(2024-09-01) Kokkoros, Peter; Alvi, Shahid
This study investigates the social, personal and institutional challenges faced by mothers of children with Autism Spectrum Disorder (ASD) from their perspectives, aiming to understand these challenges, their strategies for negotiating these challenges and the impact on their mental health and quality of life. Employing a qualitative approach, the study utilizes Interpretative Phenomenological Analysis (IPA) and Young's (1999) framework on social inclusion and exclusion to contextualize societal attitudes towards autism over time. The analysis revealed four main themes: high caregiver burden, stress from the emotional toll of advocating for their child, difficulties in accessing resources, and a lack of coping strategies for dealing with the diagnosis. Despite increased awareness and advocacy for autism, mothers still experience significant stress and life disruptions due to societal acceptance issues and resource limitations. The findings highlight the need for a deeper understanding of caregivers' daily struggles to inform more effective support strategies and emphasize the necessity for tailored support systems to address these unique challenges.
Perspectives on dementia education: a phenomenological investigation in institutional and community-based settings
(2024-10-01) Gabel, Glory; Sun, Winnie
Introduction: This study explores the factors influencing the knowledge uptake of dementia education among health care workers (HCWs) in institutional and community-based settings. It also examines the current barriers and facilitators within dementia education programs. Methods: An interpretive phenomenological approach using in-depth interviews with a diverse sample HCWs (n=10) from institutional and community-based settings. Braun and Clarke’s (2006) thematic analysis was used to identify the main themes and sub-themes related to HCWs’ experiences with dementia education and working with people with dementia (PWD). Results: Four major themes were identified from the interview data, including learning styles of HCWs; facilitators to dementia education; barriers to dementia education and future recommendations. Conclusion: Findings will guide improvements in the training of HCWs by creating engaging and specialized needs-based dementia education programs to enhance knowledge uptake and application of knowledge in healthcare settings, and ultimately address the diverse needs of PWD.
Optimizing training designs and elevating reporting standards in simulation-based medical education
(2024-09-01) Elliott, Leanne K.; Wattie, Nick
Introduction: This thesis explores key elements of simulation-based education for healthcare professionals, focusing on protocol reporting, technical skill retention/transfer, and training microstructures. Study 1 developed and assessed new protocol reporting and evaluation tools (PRT and PET) to measure the quality of reporting (QOR) in simulation studies. Using a modified Delphi method, the PRT and PET were created and applied alongside the TIDieR Checklist and CONSORT Statement to evaluate 17 randomized controlled trials. Results revealed significant differences in QOR scores across the tools, highlighting strengths and areas for improvement in reporting practices. The PRT and PET have the potential to enhance QOR, enable accurate study replication, and assist in the identification of optimal training designs. Study 2 examined the long-term impact of a simulation-based mastery learning (SBML) curriculum versus a competency-based curriculum on pediatric emergency medicine (PEM) physicians’ video laryngoscopy (VL) skill retention and transfer six months post-training. A multidisciplinary panel set a minimum passing score (MPS) of 32/36 (89%) using the Mastery Angoff method. The mastery group outperformed the competency group in skill retention and transfer, suggesting that SBML better sustains VL skills amongst PEM physicians. Study 3 analyzed the microstructures of competency-based and mastery-based training interventions for VL. Behavior coding software determined that mastery learners took longer to reach their MPS, engaged in more partial versus whole practice trials, spent more time verbalizing preparatory steps and aftercare plans, and received more feedback from their instructor compared to learners in the competency group. These differences likely contributed to differences in VL skill learning, as observed in Study 2. Conclusions: This thesis emphasizes the importance of the microstructure in simulation-based training interventions. Detailed and accurate reporting and evaluation of training microstructures is necessary for the advancement of the field, and the newly developed PRT and PET provide a means to do so. The microstructure of the SBML intervention more effectively facilitated VL skill learning, compared to the competency-based intervention. These findings offer valuable insights for educators, researchers, and program facilitators in medical education.
Exploring the lived experience of aging among older adults who are chronically homeless
(2024-09-01) Peters, Volletta M.; Sun, Winnie
A rise in the number of older adults in Canada living longer and healthier lives has been lauded as a medical and public health success. In response, the Canadian government developed directives promoting healthy aging and aging in place. Missing from these directives is a subset of older adults, many of whom are entering homelessness later in life and are aging with chronic homelessness. While there is an increasing number of studies focusing on older adults who are homeless, there remains a dearth of scholarly literature on those experiencing chronic homelessness. Due to the lack of empirical data, it is challenging to devise solutions to address the healthcare, social support and housing gaps experienced by older adults who are homeless. The purpose of this interpretative phenomenological study was to explore the conceptualization of older adults who were chronically homeless, and their lived experiences related to aging. The study was guided by the social constructivism philosophical paradigm to more fully understand how the participants constructed and applied meaning to their lived experiences of the phenomenon. During 3 months of fieldwork, I met with 18 older adults who were chronically homeless and purposefully selected from study settings that provided homeless services. Data collection included semistructured interviews and unstructured observations. Through member checking, participants reviewed and confirmed their transcribed interview notes. The data were transcribed, summarized and analyzed using phenomenological analysis. Six significant themes and 21 related subthemes emerged from the data. The major themes included: (a) suddenly, everything gets taken away from you; (b) physiological and psychosocial changes in the past five years; (c) aging describes life; (d) COVID-19 changed everything; (e) experiences with healthcare and social services; and (f) participants recommendations to address current needs and fill healthcare, housing and social service gaps. Despite their desire to move out of the homeless shelters into permanent housing, participants were hampered by a lack of material and immaterial resources. Policymakers and decision-makers in healthcare, social services, and housing can utilize the findings to fill public policy and service gaps that contribute to and perpetuate homelessness among older adults.
Signal and power integrity analysis of bidirectional DC-DC converters for hybrid energy storage systems with EMI/EMC optimization
(2024-09-01) Ladhar, Manraj Singh; Williamson, Sheldon
Hybrid Energy Storage System (HESS) utilizes multiple energy storage architectures to achieve a broader range of characteristics in terms of power density, energy density and calendar life. A 500W bidirectional synchronous DC-DC converter design is implemented for the active topology of HESS. This thesis examines optimal design principles and practices essential for the printed circuit board (PCB) layout of switching regulators with fast dv/dt and di/dt edge rates to achieve electromagnetic interference (EMI) compliance standards. Furthermore, it focuses on comparative analysis and investigation of near-field noise emissions measured from three PCB designs each sharing the same schematic but different layout design rules and stackup configurations. Reflections, crosstalk, and transmission line management techniques along with power delivery network/system (PDN/PDS) design are implemented for enhanced signal and power integrity. 4-layer board designed with embedded interplane capacitance, controlled impedance traces, proper signal termination and crosstalk management exhibits lowest noise emissions. The experimental results show good consistency with electromagnetic field theory and simulations.

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