Grants and Contributions

This project will test hypotheses and experiment with new mechanisms to design or improve a repeatable and scalable business process, which may result in new business methods in eye care.

The project involves optimising the performance of our robotic agricultural equipment for precision mechanical weeding of root vegetable crops.

Make the ORG networks and expertise available to assess, connect and validate mining companies’ interest in the technological innovation projects of Canadian SMEs to partner on a technology demonstration project that would be funded under the IRAP ecosystem initiative: Enabling the transition to Mining 4.0.

The project will test AI strategies applicable to automatic anomaly detection for turbomachinery, establish the ideal architecture and achieve quantitative technical objectives in preparation for the implementation of an AI solution.

This project will ensure that soluble microbicidal diperiodatoargentates developed at ILC can be made consistently with a scalable manufacturing method. The compounds will be characterized for antimicrobial activity, stability, and compatibility. The most promising compounds will be further developed towards commercialization, including formulation work, finalizing/scaling up the manufacturing process, and performing proof-of-concept/pilot testing of the materials for specific applications, such as sprays, flushes, dips or wipes in medical, industrial, and agricultural settings. The results of the work will be used to promote collaborations with other companies and support registration of final products with appropriate regulatory agencies.

The project aims to help small and medium-sized businesses in the beverage industry become more innovative and competitive.

The project will deliver a new integrated LED micro-array driver and control network, which represents a key building block in the firm's optical switch technology, for future applications (antenna structures).

Our AI-powered Quality Control for Production & Structuring Frontier Data Project focuses on developing and testing AI-powered Quality Assurance (QA) algorithms that can be customized to various data types and annotation tasks, enabling more flexible and targeted quality assessment. These algorithms will leverage existing machine learning models and novel QA techniques to automatically evaluate and enhance the quality of data annotations produced by our Human AI Trainers, resulting in an increase in data annotation accuracy and decreased data processing time.

The aim is to develop an adhesive tape application machine for automotive seals.

This project focuses on brokerages' FINTRAC compliance reporting obligations and tools for perational efficiencies.