Grants and Contributions

The project will lay the groundwork for the development of an ML-based predictive model to identify and manage bleeding complications in surgical patients, particularly following colorectal and hepatopancreatobiliary (HPB) procedures.

To achieve this goal, FPrimeC will expand its current platform to include additional non-destructive testing (NDT) concrete testing variables analyzed by a deep learning AI model. This expansion will improve the condition assessment of existing structures and align with FPrimeC’s mission to preserve existing structures in support of sustainability.

The core goal of this project is to integrate AI driven capabilities into LAMTA to assist users in creating, editing, and managing end-to-end test scripts, as well as to provide intelligent log analysis during test execution. The AI will be aware of all the lab's resources—including radios, chambers, RF attenuation arrays and matrices—and will leverage this knowledge to take natural language prompts from users and transform them into fully functional test scripts. It will significantly simplify the test development process, enabling users to describe their testing needs in plain language while the AI configures the necessary components and scripts for execution.

The Project will support the assessment of data quality and data sources for AI project development. The project may also characterize and document aspects of data for AI and Data Governance.

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

The project will enable AI strategies to be tested, the ideal architecture to be established and quantitative technical objectives to be achieved in preparation for the implementation of an AI solution.

Use of deep neural networks to optimize the synthesis of biomolecules by genetically modifying microorganisms, focusing on creating high-performance synthetic enzymes tailored for diverse industrial applications.

Summary:
This project focuses on developing in-house AI tools to create high-quality visual effects for television production. By leveraging Canadian talent and training AI models on project-specific data, the initiative aims to enhance the efficiency and accuracy of AI-generated imagery. The goal is to complement existing platforms and integrate innovative workflows into production, driving new capabilities in visual storytelling while boosting Canadian expertise in AI and digital media.

Prototype a traffic data collection processing system that incorporates AI / LLM algorithms to transform raw data into actionable insights.

A project to develop an AI-driven RF spectrum intelligence platform to provide real-time detection, classification, and tracking of RF anomalies and unauthorized transmissions.