Presented by: Jinshuai Gan

Date: Sunday, December 1, 2024
Time: 11:00 am
Place: Zoom, link below.

Abstract: 

Optical microcavities have emerged as powerful tools for detecting single molecules and nanoparticles due to their exceptional sensitivity and label-free operation. However, the performance of ultra-high-Q microcavities is highly vulnerable to factors such as temperature fluctuations, mechanical vibrations, and laser frequency drifts, all of which can destabilize laser-cavity detuning. Optomechanical oscillation (OMO), a phenomenon driven by radiation pressure within the cavity, offers significant advantages for liquid-based sensing, but requires precise conditions and a high cavity quality factor. In this thesis, we demonstrate stable, long-term OMO in an aqueous environment by implementing a Proportional-Integral (PI) locking scheme.

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Presented by: Saleh Mohammed Shahriar

Date: Monday, December 9, 2024
Time: 10:30 am
Place: Zoom, link below.

Abstract: 

Electric vehicles (EVs) play an important role in reducing the greenhouse gas emissions by providing an environment-friendly alternative to the fossil-fuel-based means of transportation. EVs are typically powered by Li-ion battery packs supported by a Battery Management System (BMS). The latter is tasked with monitoring and keeping the battery voltage, current, and temperature within safe operating limits, as well as estimating and improving the battery performance-related parameters, such as the battery state-of-charge and lifespan. In this thesis, we aim to extend the BMS capabilities by enabling battery voltage predictions under a given load profile (i.e., discharge/charge current varying over time). Such predictions are useful for proactive (as opposed to reactive) load management, as they allow a BMS to forecast the battery voltage behaviour under various anticipated load conditions.

Using a data-driven deep learning (DL) approach, we propose a novel model that generates battery voltage estimates given the battery current, temperature, and consumed charge over time. It has a V-shaped architecture that features two wings to enhance the model explainability. The first wing predicts the steady-state open-circuit voltage (OCV) component, based on the consumed battery charge information, while the second wing predicts the transient voltage component, based on the battery current and temperature information. The total number of the model parameters is under 2.6K.

A well-known experimental dataset was used in this study for training, validation, and testing purposes. This dataset contains measurements taken on a Li-ion battery subjected to various EV driving cycles interleaved with charging cycles. The mean absolute percentage error (between predicted and measured battery voltage values) was under 1%, demonstrating the accuracy of the proposed model.

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Presented by: Fatemeh Mazinani

Date: Monday, December 9, 2024
Time: 12:00 pm
Place: Zoom, link below.

Abstract: 

This seminar presents a novel approach for the detection and sizing of surface-breaking crack defects in pipes using 3D K-Means clustering of ultrasound imaging data. The proposed method processes volumetric ultrasound data (obtained from a moving transducer array inside a pipe) to identify distinct clusters, effectively reducing noise and isolating critical crack-related features. Experimental validation has been performed on three pipe samples with different crack sizes and locations. The results show that 3D K-Means clustering improves crack detection and sizing, outperforming 2D K-means clustering in most cases. This research contributes to the field of ultrasonic nondestructive testing by providing an efficient solution for assessing the structural integrity of critical infrastructure components, such as pipelines.

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Presented by: Kamran Janwiri

Date: Wednesday, December 11, 2024
Time: 9:30 am
Place: REMOTE Via Zoom

Abstract: As the adoption of Electric Vehicles (EVs) accelerates globally, with governments like Canada targeting 100% electric vehicle sales by 2035, the need for secure and reliable EV charging infrastructure becomes critical. EV charging stations (EVSEs) are increasingly targeted by cyberattacks such as reconnaissance, SYN floods, UDP floods, and backdoor intrusions, which can disrupt operations and compromise sensitive data.

This study explores the use of Machine Learning (ML) to enhance the security of EVSE systems. Using the CICEVSE2024 dataset and employs techniques such as Synthetic Minority Oversampling Technique (SMOTE) for data balancing and Principal Component Analysis (PCA) for feature selection. Multiple ML models, including Random Forest, k-Nearest Neighbors, and Gradient Boosting Machines, are evaluated to identify optimal solutions for cyberattack detection.

The findings demonstrate that Machine Learning can significantly improve EVSE security, ensuring robust, real-time threat detection while balancing performance and scalability. This work highlights the potential for ML to secure critical EV infrastructure, fostering confidence in the transition to electric mobility.

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Presented by: Sahand Mosayyebpour

Date: Friday, December 13, 2024
Time: 1:00 pm
Place: Zoom - see below.

Abstract: 

This research introduces a supervised multi-channel audio source separation system that integrates a video-based face detection system. The face detector identifies the nose position, aiding the multi-channel processing in isolating the primary speaker while suppressing environmental background noise and distracting secondary speakers. It is demonstrated that in far-field applications, multi-channel processing struggles with distracting secondary speakers when the primary speaker position is unknown. Utilizing video data provides valuable insights to identify the target speaker and assists the audio source separation system in directing its focus towards this speaker. Furthermore, it is shown that multi-channel processing benefits from speaker position information to improve noise reduction in noisy reverberant environments.

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Presented by: Alaa Alahmar

Date: Monday, December 16, 2024
Time: 11:30 am
Place: Remote via Zoom

Abstract: 

In the era of smart transportation, Vehicle Area Networks (VANs) are critical

in enabling secure communication between vehicles and infrastructure. This project

examines the security robustness of the PUFGuard protocol, a physically unclon-

able function (PUF)-based authentication framework designed to protect Vehicle-to-

Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications in VANs. PUFGuard

leverages the inherent uniqueness of PUFs for secure key generation and authentica-

tion, aiming to establish trust and resilience against adversarial attacks in dynamic,

multi-hop communication environments. To validate PUFGuard’s resilience, this re-

search employs formal verification tools—AVISPA and SPAN—to simulate and ana-

lyze its effectiveness against common network threats, including replay attacks, man-

in-the-middle attacks, and impersonation attacks. The protocol is modelled in the

High-Level Protocol Specification Language (HLPSL), where each component of the

V2I and V2V authentication processes is systematically represented. Results from the

AVISPA tests highlight the protocol’s strengths and potential vulnerabilities, provid-

ing insights into the adequacy of PUFGuard’s security measures in real-world VAN

applications. The findings of this study suggest refinements to fortify PUFGuard

further, offering a framework for secure, authenticated communication in modern

vehicular networks.

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Presented by: Alexandros Dimopoulos

Date: Friday, December 20, 2024
Time: 10:00 am
Place: Zoom, link below.

ABSTRACT:

The design of countermeasures against integrated circuit counterfeit recycling requires the ability to simulate aging in CMOS devices. Electronic design automation tools commonly provide this ability; however, their models must be tuned for use with a specific target technology. This requires data which is ideally provided by a fab. It may also be collected from a set of purpose-built test devices, a costly and time-consuming process. Here we describe a novel, low-cost, and rapid approach to tuning such models. Our iterative method leverages public domain data sourced from published studies to fit an aging model. Results are statistically validated against the target technology’s specification. We demonstrate our approach by fitting a compact hot carrier injection degradation model for use with both core and I/O nMOSFETs from a specific 65 nm technology. Our resulting model parameter values are validated with a maximum error of 0.5 % with a 99 % confidence bound.

LOCATION: Remote via Zoom

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Presented by: Alexandros Dimopoulos

Date: Monday, December 23, 2024
Time: 10:00 am
Place: Zoom, link below.

ABSTRACT:

Counterfeit electronic components are known to enter supply chains through recycling, with these already-aged components creating serious reliability risks, particularly for critical infrastructure systems. A number of recycled integrated circuit (IC) risk mitigation approaches have been proposed, but these generally lack pragmatic feasibility. This work proposes a novel real-world deployable on-chip sensor that: 1) is tamper-resistant by exploiting persistent changes caused by hot carrier injection (HCI); 2) generates a DC signal measurable by common low-cost test equipment; and 3) reuses an existing I/O interface, including existing pins; while 4) requiring a very small footprint. Combining this sensor with a random sample-based testing strategy allows for low-cost and time efficient detection of fraudulently recycled batches of ICs. Through simulation-based validation using process-accurate models of a 65 nm technology we show that employing a random sample size as small as 130 is sufficient for identifying such batches with a statistical significance level of 0.01.

LOCATION: Remote via Zoom

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