Grants and Contributions

This project will engage a recent graduate engineer to develop and implement a structured quality assurance and reliability program to reduce unplanned downtime and ensure consistent performance of automated feather sexing machines prior to and after deployment.

The project is to support two internal staff members to address two main bottlenecks: a cultural localization gap and manual process inefficiency in sales and marketing functions across Nunavut's 25 communities. Those issues remain relevant, but Nunafab's current stage now requires a broader technical-commercial response that addresses a more critical commercialization bottleneck: the gap between product interest and buyer-ready adoption packages for northern housing and defense related applications.

Electric vehicle adoption and battery second-life applications require efficient tools for battery degradation monitoring and state-of-health estimation. Electrochemical Impedance Spectroscopy (EIS) is widely used for this purpose; however, University of Windsor research has identified a critical limitation in conventional measurements: open?circuit-voltage (OCV) induced bias can distort impedance estimates, particularly at low frequencies. The Project will advance a corrected EIS excitation and analysis method that combines a tailored excitation waveform with an observation model to estimate and remove the OCV contribution from the measured response. Initial proof-of-concept experiments have demonstrated feasibility at TRL 3. The Project will advance the technology to TRL 4 through systematic laboratory validation, benchmarking against conventional EIS, and evaluation across different cells, chemistries, states of charge, and measurement conditions. The NRC will conduct parallel validation and contribute to documenting the methodology and results.

Development of a method and equipment for optimizing container collection.

Develop conversational interface on Software as a Service (SaaS) platform, enabling aviation operators to query emissions and related Sustainable Aircraft Fuel (SAF) compliance data across numerous regulatory frameworks.

LeapAP will develop advanced AI technologies to improve automation within accounts payable and utility invoice workflows. The project will focus on increasing automated matching and data extraction accuracy, reducing manual intervention in invoice processing, improving anomaly detection, and accelerating the creation and maintenance of utility portal automations. The work will combine machine learning, large language models, and workflow automation techniques to address challenges associated with unstructured financial documents, changing utility portals, and complex operational data.

Virtual Hallway Consults Inc. will develop context-aware intelligence capabilities for its clinical AI tool, improving the safety, reliability, and clinical relevance of AI-generated responses for Canadian physicians.

Improved noise attenuation performance of our systems for underwater noise mitigation.

Development of an AI-powered service layer that ingests and analyzes mixed-fleet telematics and product-usage data to deliver actionable intelligence, digests, and engagement signals to customer success teams and end customers — improving fleet adoption, retention, and operational outcomes across direct and channel customers.

The project will support PCT1 and PCT2 filing by Hill & Schumacher.