Grants and Contributions

This project will advance the DMU from TRL7 to TRL9. This project will undertake post laboratory trial validation of the minimal viable product and mathematical model through field deployments on operational dredging sites. The firm will also focus on transitioning to more robust and futureproof hardware in order to accommodate modern enterprise ecosystems and contemporary embedded system engineering practices. Additional field trialing will allow for the collection of large amounts of data to use in the development of an edge inference machine learning interface to improve system accuracy.

The project is focused on advancing the development of the Enclave platform from a proof of concept presently, to a low fidelity functional prototype. The platform will enable informed decisions regarding product selection and measure performance against goals that reduce costs, accelerate build speed and improve sustainability. The result is better more sustainable homes and affordable micro-communities, built more quickly at a lower cost to purchasers. The platform is scalable and is expected to evolve eventually to a SaaS model that would benefit the entire home building industry, however this is outside of the scope of this project.

The project involves designing for manufacturing of various CathPilot system components to enable commercialization feasibility and success. This includes the redesign of various components for molding and optimizing for assembly to help reduce costs, assembly time, and improve quality.

This project will test hypotheses and experiment with new mechanisms to optimize a repeatable and scalable business process, which may result in new business methods for RCI’s Digital Marketplace.

This project will test hypotheses and experiment with new mechanisms to design (or optimize) a repeatable and scalable business process, which may result in new business methods for electronic health records or new digital health products.

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

The youth will be responsible for the readiness and quality of VR kits, including simulations, and provide troubleshooting support to clients.

This student will be working on a range of tasks (development, new evaluations, analysis, supporting infrastructure, systems to operationalize it and reporting tools) to accelerate the development of an AI system to evaluate call center call recordings to predict customer satisfaction and call resolution.

Leaky blood vessels in the retina are the main cause of vision loss in diseases like age-related macular degeneration and diabetic retinopathy – which collectively affect 380 million individuals worldwide. While there are drugs that can stop retinal leakage, the most reliable test for detecting it involves an injection of dye and necessitates emergency readiness in case of an allergic reaction. Hence, this test is invasive, complex, costly, and infrequently used. To address this challenge, we propose an innovative artificial intelligence (AI) approach that will simulate the invasive test from a non-invasive, injection-free photograph commonly collected in routine eye exams.

8 Bit Cortex has leveraged bootstrapping and non-dilutive funding to develop an MVP gamified learning management system (LMS) to enhance engagement and tracking of wellness data. 8 Bit Cortex has validated the market need for an LMS to host addiction recovery programs, enabling automated measurement and reporting of Addiction Recovery Capital consistent with the Alberta Recovery Model for stakeholders, such as government and insurers.