Grants and Contributions

Intelligent engine for automatic real-time incident detection and optimized resource allocation in a mass transit environment.

The project intends to complete the development of various neurofeedback and AI/ML algorithms for sleep regulation in preparation for commercial launch.

Vendasta will establish a new executive role, Vice President of Security, to lead the development of a company-wide security strategy that enables secure deployment of AI solutions for small businesses in an environment that incorporates key organizational Controls.

This project provides a feasibility analysis of developing a cost-effective apple harvesting robot. The outcomes will highlight critical technical challenges to address in future phases.

AgriAssure will develop foundational business development capabilities through structured mentorship of a recent graduate in lead generation, partner research, CRM management and the creation of internal sales tools to support market expansion in Canada and LATAM, contributing to agricultural technology compliance solution growth.

Development of an alcohol-free, low-sugar Asian wine-type beverage.

This project represents a comprehensive artificial intelligence initiative that will be strategically integrated across the entire IBRA product and service platform, significantly enhancing functionality, accuracy, and market value. Ratings IQ represents a transformative evolution in biomass supply chain assessment, combining advanced machine learning, predictive analytics, and natural language processing to dramatically enhance and expand the efficiency, market value and catalogue of IBRA’s rating services, and solidify Ecostrat’s position as the undisputed global leader for feedstock ratings in the cleantech sector.

The project introduces a new product to add to Beck’s Broth existing line of functional beverages.

This is an R&D project to develop Ai functionality for our core technology, the L Squared Hub to make it easier for customers to present critical real time information from their business operations to their staff in Manufacturing plants.

The use of automated prognostic imaging biomarkers in clinical practice may enhance (re-) staging and response assessment, as it could inform more personalized treatment selection and patient monitoring, which in turn could lead to improved patient outcomes. The purpose of this project is to develop automated, deep learning-based imaging biomarkers that predict outcomes of breast cancer patients with solid tumors based on various modalities of medical imaging.
By enabling an artificial neural network to “learn” what visual features in thousands of imaging studies are associated with respective patients’ actual future outcomes, the resulting deep learning algorithm will be able to predict survival for a new patient’s imaging study.