Grants and Contributions

The goal of this project is to develop a fully-automated product for use in the quality inspection of turbine blades using the X-ray Laue technique. For some time, the missing link in bringing such a product to market has been the intervention of an operator when determining the orientations of the grain(s) being imaged. This project would build upon proof-of-concept examples in the literature, whereby a feed-forward neural network is trained to extract the orientations of one or more crystal grains from a synchrotron Laue image, and apply this approach to the analysis of laboratory Laue images (which are qualitatively lower-resolution).

Provides a recent "BSc, Interactive Arts and Technology" graduate with career development opportunity in CX/UX Design & Management, and provide high quality training in Mobile App Development to develop UX Workflows/Refinements skills.

Project aims to provide a chemistry-focused review for AI model inputs and outputs across several AI models.

This project will test hypotheses and experiment with new mechanisms to design a repeatable and scalable business process, which may result in new business methods for linear accelerator simulator training platforms.

Isolating and refining Omega-3 oil without the use of high heat. Followed by removal of contaminants ensuring purity and regulatory compliance.

This project will develop a self-onboarding system for the Firm's AI-enabled document intelligence platform that lays the foundation for scaleable purchase order processing and broader market adoption.

The purpose of this CanExport Innovation agreement is to support Canadian organizations to carry out activities related to pursuing opportunities that would lead to establishing new collaborative Research and Development (R&D) partnerships between the Recipient and foreign organizations in order to support R&D on the Recipient’s technology and advance its commercialization.

The primary goal of this initiative is to minimize risks associated with individual chip chemistries, ensuring their accuracy and reliability in measuring urine albumin and creatinine and serum cystatin-c and creatinine. By validating the performance of each biomarker against a well-established predicate device, we aim to confirm their suitability for clinical use.

Atmose Ltd. is an Edmonton-based startup pioneering affordable and deployable aerosol technologies for monitoring air quality. Their novel charging discoveries allow for the principles of laboratory instruments to be incorporated into a miniaturized, low-cost, and accessible form, thus overcoming critical challenges in multiple markets. Building upon Atmose’s significant development of its patented sensor (current at TRL 6), the proposed project will further advance the manufacturing process, optimize the design for scalability, increase Atmose’s production capacity and evaluate the sensor in increasingly challenging environments (TRL 7 to 9) to prepare for certifications and customer trials/sales.

As Armilla scales to a larger number of platform users, a productivity-enhancing feature is to use LLMs as risk copilots to assist in AI risk reporting, recommending testing controls, and keeping abreast with AI regulatory changes.