Innovative Projects Realized

Quantum Magnetic Fingerprinting for GNSS-Denied Navigation in Urban Air Mobility: A Machine Learning Approach from Ground to Altitude

GPS works poorly in dense city environments because tall buildings block satellite signals. This project will test whether a new type of ultra-sensitive sensor—a quantum magnetometer that measures tiny changes in the Earth’s magnetic field—can help drones and future flying taxis (Urban Air Mobility) figure out where they are when GPS fails. The research team will first build detailed magnetic “fingerprint” maps at ground level in outdoor urban spaces, then fly a drone at different heights (up to 100 meters) to see if those same magnetic patterns can still be recognized from the air. Using machine learning, they will develop software that predicts a drone’s location based on the magnetic field it measures. The expected benefit for the participating institutions is twofold: the host university (in Canada) will gain unique datasets and advance its leadership in quantum sensing and drone navigation, while the partner university (in South Korea) will bring back specialized expertise in field-testing quantum sensors and integrating them with artificial intelligence—creating a lasting research collaboration between the two countries.

Alta Construction Group – Business Strategy Internship

This project will support Alta Construction Group, a Canadian for-profit organization operating in the housing and built-environment sector, by strengthening its business strategy and operational decision-making. Two undergraduate interns will conduct market research, analyze data, and assess internal processes to help the organization better understand market demand, improve efficiency, and identify growth opportunities. The insights and recommendations generated through this project will enable the partner organization to make more informed, data-driven decisions, improve productivity, and scale its operations more effectively, ultimately contributing to more efficient delivery of innovative housing in Canada.

Whisperloop – model evaluation metrics and edge deployment

Sensor Technology Ltd. develops advanced underwater sensing systems, including towed hydrophone arrays and related signal-processing/analytics used in maritime monitoring. The WhisperLoop project is SensorTech’s initiative to operationalize AI-enabled detection, classification, and localization on compact, low-power edge hardware integrated with thin-line towed arrays. The project addresses key challenges SensorTech faces for deploying a field-ready capability: (1) establishing a robust, metric-driven evaluation approach for quantitative model performance, (2) validating inference on constrained edge platforms suitable for uncrewed/autonomous operations, and (3) reducing dependence on high-bandwidth connectivity by enabling reliable, largely autonomous onboard processing. Through the internship, SensorTech will gain practical tools and evidence to support deployment decisions (accuracy, robustness, compute/power constraints), de-risking product development and accelerating transition to commercial offerings. Anticipated benefits include improved competitiveness for real-time maritime monitoring solutions, reduced operating costs for persistent deployments, and strengthened internal capability in edge AI evaluation and deployment workflows.

Development of Reference Application Models for an FPGA-Based Real-Time System

This project aims to develop a set of application-oriented example models demonstrating the capabilities of the NOA Arch1 FPGA platform using a model-based MATLAB/Simulink workflow. The developed examples will cover multiple engineering domains, including power electronics, biomedical signal generation, artificial intelligence inference, and avionics laboratory signal validation. By producing validated and reusable demonstration models, the project supports technology transfer, training, and broader adoption of advanced real-time hardware platforms in academic and industrial environments.

Stagiaire en marketing numérique — stratégie de contenu, production vidéo et gestion des réseaux sociaux

Dans un contexte où la présence numérique est devenue un levier incontournable pour le développement des petites et moyennes entreprises, Optimise Action souhaite structurer et renforcer sa stratégie de marketing numérique. La stagiaire, étudiante au baccalauréat en administration des affaires avec une double concentration en marketing et en gestion des ressources humaines, mettra à profit cette formation polyvalente pour contribuer à l’élaboration et à la mise en œuvre d’un plan de contenu adapté aux plateformes numériques — notamment LinkedIn, Facebook et Instagram. Elle produira des visuels et des vidéos promotionnelles, établira un calendrier éditorial opérationnel pour assurer la continuité des efforts au-delà du stage, et procédera à un audit des comptes existants afin d’identifier des pistes d’amélioration concrètes. Elle développera également des outils réutilisables — gabarits graphiques et guide de gestion — qui permettront à l’équipe de maintenir une présence cohérente et professionnelle de façon autonome. Sa double formation représente un atout particulier dans ce contexte : sa compréhension des dynamiques organisationnelles et humaines viendra enrichir la qualité des contenus produits et favoriser une intégration fluide des pratiques marketing au sein de l’équipe. À terme, ce projet permettra à Optimise Action de disposer d’une infrastructure marketing numérique fonctionnelle, documentée et directement exploitable, contribuant ainsi à accroître sa visibilité et à soutenir sa croissance dans la région de l’Abitibi-Témiscamingue.

Designing and Validating a Scalable Marketing and Operations System for Upfront Sports

This project will help Upfront Sports, a Fredericton-based youth hockey development organization, build a more structured and scalable operating model for its training programs, leagues, and mentorship offerings. As demand has grown, we’ve reached a point where informal marketing and founder-led operations are no longer enough to support sustainable growth.

The project focuses on two areas that can become a real competitive advantage for Upfront Sports: a more performance-measured digital marketing system and more consistent operational systems behind program delivery. Interns will help test and strengthen how we attract athletes and families, while also refining workflows related to registration, parent communication, onboarding, and logistics. Together, these improvements will allow us to grow more intentionally, respond faster, and deliver a more reliable experience than groups still relying mainly on informal processes.

The goal is to create a repeatable system that supports growth without sacrificing coaching quality or overloading founder capacity. In doing so, Upfront Sports will strengthen its position in the market by pairing high-quality coaching with stronger systems, better communication, and more informed decision-making. That combination will help us stand out, scale more effectively, and deliver a better overall experience for athletes and families in the Fredericton community.

Content Creator AI-powered Monetization Platform Development

This project will build an AI-powered question-and-answer platform that helps creators and expert-led brands respond to customer questions more efficiently across channels like social media and a dedicated app. The system will organize incoming questions, support payment and answer workflows, and turn approved answers into a searchable knowledge base that can be reused over time. The expected benefit to the partner organization is a more scalable way to serve their audience, create new digital revenue streams, reduce repetitive manual work, and improve the overall customer experience by making expert knowledge easier to deliver and access.

Développement d’une méthodologie et d’une infrastructure expérimentale pour l’étude approfondie du couplage capacitif résonant dans des conditions variables

Bien que la technologie d’AWL-Électricité permette une distance de transmission supérieure à celle des solutions sur le marché, la performance du système est fortement affectée par le désalignement des interfaces de couplage (émetteur-récepteur). Actuellement, la conception et l’optimisation des interfaces constituent le goulot d’étranglement le plus important en termes de temps (délai de prototypage) et de ressources, en raison de la nécessité de personnalisation pour chaque cas d’application (taille, distance, environnement). Ce facteur réduit considérablement la vitesse de mise-en-marché d’AWL-Électricité. Le défi scientifique réside dans la caractérisation précise, répétable et tridimensionnelle des champs de puissance et des performances du système en fonction des degrés de liberté de désalignement.
Le projet vise à développer une méthodologie et une infrastructure expérimentale pour l’étude approfondie du couplage capacitif résonant dans des conditions variables, afin de transformer le processus empirique de conception en une approche prédictive et robuste. Le projet vise à développer une méthodologie et une infrastructure expérimentale pour l’étude approfondie du couplage capacitif résonnant dans des conditions variables, afin de transformer le processus empirique de conception en une approche prédictive et robuste.

Research & Policy Internship – SHIELD Institute for Public Policy

The internship will cover two projects: Qualitative Analysis of Personalized Algorithmic Pricing in a Canadian and Provincial Context and Watching the Checkout: Surveillance, Loyalty Programs and Consumer Power in Canada

Security Analysis for Github Actions

Modern software projects rely on automated pipelines (like GitHub Actions) to build, test, and deploy code. While these systems make development faster, small mistakes in their setup can create serious security risks, such as leaking sensitive data or allowing attackers to change how software behaves.
This project focuses on identifying and understanding these hidden risks. Today, there is no reliable collection of real-world examples showing how such misconfigurations happen, which makes it difficult to improve security tools.
To solve this, we will create a carefully curated dataset of GitHub Actions workflows, each labeled with known security issues. This dataset will be built using a mix of expert review and automated analysis tools developed by our industry partner, BoostSecurity.
The result will help researchers and companies better evaluate and improve security tools, making software development pipelines safer. Ultimately, this project aims to reduce real-world security risks and strengthen trust in modern software systems.

Exploration efficace pour la conception de systèmes de transmission de données

Description grand public: Les codes de correction d’erreurs (CCE) sont utilisés dans la plupart des systèmes de transmission et de stockage de données, car ils permettent une réduction significative de la puissance du signal émis (ou stocké), ce qui permet à l’émetteur de consommer moins d’énergie. Cependant, les CCE efficaces nécessitent une quantité d’énergie importante pour être décodés à la réception. Il existe donc un compromis entre l’énergie économisée à l’émission et l’énergie additionnelle requise à la réception. Notre projet de recherche se concentre sur le cas fréquent où il est primordial de minimiser conjointement la consommation de l’émetteur et du récepteur, par exemple dans le cas de deux objets alimentés par batterie qui doivent communiquer. À cette fin, nous développons un outil d’exploration efficace pour la conception de systèmes de transmission de données à énergie minimale, basé sur des modèles énergétiques des circuits émetteurs et récepteurs. Cet outil permettra ensuite de développer des solutions de transmission de données à plus faible consommation d’énergie pour diverses applications, contribuant ainsi à réduire l’empreinte énergétique du secteur des technologies de l’information.

Design and Development of a Web-Based Dispatching Interface, Integration of AI Model, and Deployment

EffectiveAI Inc. is an Ontario-based company dedicated to revolutionizing the Emergency Services industry using advanced Artificial Intelligence (AI) and Neural Network technology. Their mission is to enhance operational efficiency and effectiveness in public safety, currently engaging with five municipalities across Ontario.
In a previous collaboration, the WIMTACH team developed and optimized an AI model to provide data-driven assessments for optimal stand-by locations for emergency services. The main challenge the partner aims to solve through this project is the lack of a user-friendly, real-time mechanism for dispatchers to view and interact with the AI model’s information and insights.
This project will deliver a professional, web-based dispatching interface that integrates the AI model, bridging the gap between research and commercial application. The anticipated economic benefit is the commercialization of the AI model as a deployable product, strengthening EffectiveAI’s market position. The social benefit is the enhanced operational efficiency in emergency services, leading to faster, more data-driven decisions regarding ambulance deployment, which ultimately improves public safety and response times.