Anonymous Age Verification Using Electrocardiogram (ECG) Obtained from Smart Wearables

Age-verification mandatory procedure for delivering certain services and products. Traditionally, identification documents have been a common mechanism of age-verification. However, this current strategy is subject to certain risks regarding privacy protection and online forgery. This demonstrates the value in anonymous age verification schemes using biometrics. Considering its age-dependent attributes, Electrocardiogram (ECG) is a potential solution. Preliminary experiments have been conducted regarding age estimation- classification from ECG obtained in clinical settings.

MEV protection through delayed execution and time-locked puzzles

Blockchain technology is, by nature, transparent and decentralized. However, transparency can sometimes be a direct threat to the system's decentralization when most of the profitable transactions get stolen by several high-power entities. This de-incentivizes new users from using the system and renders the whole system useless. In this project, we aim to guard decentralization against destructive and unfair effects of transparency. We reason that transparency can harm the system when it enables users to easily front-run each other.

OCTA

Maya HTT développe actuellement une solution de corrélation automatique des modèles numériques thermiques complexes permettant d’améliorer la précision et la fiabilité des modèles et de créer des jumeaux numériques temps réel et fiables, en particulier pour les applications aérospatiales (moteurs d’avion et satellites).
Dans le cadre du développement de cet outil plusieurs défis technologiques importants ont été identifiées, dont la réduction des modèles numériques (« Reduced Order Models ») et l’utilisation de la technologie « Graphic Processing Units (GPU) » pour réaliser les calculs scienti

Design and development of electrochemical sensors for detection of specific cannabis terpene constitutes validated by liquid chromatography hyphenated with tandem mass spectrometry (LC-MS/MS)

The fast growth of the cannabis industry has been impacting the regulatory sector. Governments and private regulatory organizations have been working hard to make sure the industry grows based on proper regulations and testing procedures. The problem is that all of the regulations that are currently designed for the cannabis industry are based on procedures and testing methods that have been used in the pharmaceutical industry.

Partial Discharge Propagation Model for Power Transformer Winding

Transformers are one of the key elements in electric power systems whose health ensures reliable, stable electric power for the economy and society. Partial discharge (PD) is known to be a symptom of defects in electrical insulation systems, such as those employed in transformers. Detection and localization of PD in transformers are critical to develop a mitigation strategy. The main objective of the proposed research is to develop an artificial intelligence model that is able to predict the location of PD in transformers.

Sales Micro-Patterns: ML-driven detection of deviant Store/SKU sales patterns

For almost a century, Canadian Tire has been offering products and services to help Canadians with the jobs and joys of life in Canada. Today, with a network of over 1,700 retail locations, Canadian Tire is dedicated to improving products and services offered to the community they serve. With this project, the company aims to examine the patterns of sales among its stores to identify anomalous product sales patterns. The objective is to design and develop a system capable of generating high-confidence automated notification to the stores.

Predicting falls based on a 2-minute walk test

Falls are the leading cause of injuries in older adults. Identifying older adults with risk for falls prior to discharge home from the Emergency Department (ED) could help direct fall prevention interventions, yet ED-based tools to assist risk stratification are under-developed. The aim of this study was to compare the performance of our proposed machine learning algorithms with existing screening tools to predict future falls in the 90-days post ED discharge for 150 older adults aged 65 years and older.

Next generation single-photon detectors

The ability to detect light at low signal levels is advantageous for numerous applications in defence, health, secure communication, imaging and sensing. The lowest light signal that we can detect are single particles of light (i.e., photons). However, portable single-photon detectors have limited efficiencies in the mid-wave infrared, which are colours (frequencies) we can’t see with our eye.

Scalable Data Science Systems for Federated Financial Applications Based on Knowledge Graph Technologies

This grant application introduces scalable data science systems for federated financial applications. Our systems are based on knowledge graph, graph machine-learning (GML), and natural language processing (NLP) technologies to automate several aspects of data science projects on federated financial datasets. We focus mainly on two main systems related to data science: A) automatic data annotation and augmentation for financial datasets, B) a GML-Enabled Knowledge Graph Engine for
financial decentralized knowledge graphs.

Inferring Subjective Ratings for In-car Speech Using Objective Measures

This project explores how computer algorithms will be used to predict the intelligibility and quality of in-car speech processed by hearing aids. Hearing impaired listeners graded in-car speech for a set of conditions. The conditions include seating position of talker, seating position of speaker, levels of background noise, and hearing aid processing methods. Each hearing impaired participant graded processed speech using multiple criteria. For each assessment criteria, a function is generated that maps the assessed criteria to the result of each computer algorithm.

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