Grants and Contributions

iENSO is developing a next-generation intelligent camera platform that merges multiple imaging sensors with advanced on-device AI to deliver faster, more accurate insights directly at the edge. This project will create compact, low-power camera modules capable of combining visual and thermal data for real-time analysis in industrial, robotics, security, and defence applications. The resulting technology will enhance iENSO’s cloud-connected vision platform, enabling new levels of efficiency, automation, and data intelligence for global customers.

Effective management of organic waste reduces pollution and helps to preserve land and water resources. Organic waste (primarily food waste) in Northern landfills generate leachates polluting fragile Northern ecosystems. At the same time, such waste represents the only available source of renewable biomass in the North. The Project proposes to develop and demonstrate an innovative bioprocess for enhanced biomethane production (bioenergy) from organic food waste, which also results in the production of organic fertilizer and clean water. The proposed bioprocess is based on a combination of food waste pre-treatment in a leach-bed reactor (LBR) and enhanced biomethane production in a bioelectrochemically enhanced anaerobic digestion (BEAD) reactor based on the BEAST (bioelectrochemical anaerobic sewage treatment) concept. This concept will be tested at a pilot scale and is expected to result in a simple, robust, and low-cost system that is optimized for operation in Northern and remote communities. To reduce energy consumption and optimize BEAD performance at low temperatures, local knowledge will be used to determine locally available sources of organic waste and local microbial inoculum for BEAD operation.

The Youth Hire will be assigned to build out key AI elements in the Larus portfolio of software products.

Pilot a dual-use airfield pavement inspection solution for civilian airports and defence airstrips by integrating vehicle-mounted high-resolution optical data collection with an automated processing pipeline and AI-based distress detection and quantification aligned with standard pavement condition survey practices.

The Project will support pilot testing of AI strategies, determine target architecture, and quantitative technical objectives for implementation of an AI solution.

This project develops a "Grid-to-Asset" intelligence framework designed to deliver Automated Emissions Reduction (AER) across residential and commercial building portfolios. By automatically shifting energy usage to periods of peak grid cleanliness, the framework achieves measurable carbon reductions without manual intervention. A primary technical focus is establishing AI readiness by correlating energy consumption with environmental and emissions datasets, delivering clear, actionable, and data-driven insights for both tenants and building managers.

Effective management of organic waste reduces pollution and helps to preserve land and water resources. Organic waste (primarily food waste) in Northern landfills generate leachates polluting fragile Northern ecosystems. At the same time, such waste represents the only available source of renewable biomass in the North. The Project will develop and demonstrate an innovative bioprocess for enhanced biomethane production (bioenergy) from organic food waste, which also results in the production of organic fertilizer and clean water. The Project’s bioprocess is based on a combination of food waste pre-treatment in a leach-bed reactor (LBR) and enhanced biomethane production in a bioelectrochemically enhanced anaerobic digestion (BEAD) reactor based on the BEAST (bioelectrochemical anaerobic sewage treatment) concept. This concept will be tested at a pilot scale and is expected to result in a simple, robust, and low-cost system that is optimized for operation in Northern and remote communities. To reduce energy consumption and optimize BEAD performance at low temperatures, local knowledge will be used to determine locally available sources of organic waste and local microbial inoculum for BEAD operation.

Kapoose Creek Bio will adapt our existing AI-enabled phenotypic profiling platform for chemical threat assessment. Unlike chemistry-first detection that requires knowing what to look for, our activity-based approach identifies biological mechanism of action regardless of chemical novelty detecting even never-before-seen agents, such as novel derivatives that would evade traditional methods. The platform uses high-content cell imaging combined with Random Forest machine learning, leveraging a training set of 13,000+ bioactive compounds with known activity and mechanism. We will build a reference atlas of ~100 threat-relevant agents to further train mechanism classification. This capability will provide actionable intelligence within 48h.

Fiber Frame Incorporated has invented a novel framing method and product, called FiberLok. This project will complete the product development and will generate product performance data needed to provide guidance to engineers, designers, contractors, and owners.

GameStrat will advance its Tactical Instant Replay system from an early prototype to a more robust solution capable of supporting the demands of law enforcement and special operations training environments.