Grants and Contributions

The objectives of the program are to:.•Support greater influence and control over Indigenous ELCC programs and services by Indigenous peoples;.•Improve quality and accessibility of Indigenous ELCC with an emphasis on cultural and language content;.•Support a more holistic and integrated system of quality ELCC services for Indigenous peoples; and.•Adapt and improve existing federal programs to be more flexible, adaptable and horizontal across federal departments as a first step prior to transferring control..

The objectives of the program are to:.•Support greater influence and control over Indigenous ELCC programs and services by Indigenous peoples;.•Improve quality and accessibility of Indigenous ELCC with an emphasis on cultural and language content;.•Support a more holistic and integrated system of quality ELCC services for Indigenous peoples; and.•Adapt and improve existing federal programs to be more flexible, adaptable and horizontal across federal departments as a first step prior to transferring control..

The objectives of the program are to:.•Support greater influence and control over Indigenous ELCC programs and services by Indigenous peoples;.•Improve quality and accessibility of Indigenous ELCC with an emphasis on cultural and language content;.•Support a more holistic and integrated system of quality ELCC services for Indigenous peoples; and.•Adapt and improve existing federal programs to be more flexible, adaptable and horizontal across federal departments as a first step prior to transferring control..

These results are derived from object-level data using machine learning technologies and by fully exploiting the available very large and diverse EO data archives.

PeaPod is able to generate any desired environment to grow any crop, while collecting data on plant growth to optimize food products over time via machine learning. The insulated growth environment is extendable and modular, and is easily adapted to suit plant selection and mission requirements.

This project aims to enhance Canada's training capacity in satellite-based Earth observation to meet the growing demand for skilled professionals capable of handling large datasets and utilizing technologies like cloud computing and machine learning. Collaborating with industry and government partners across various application areas, the project will revise existing courses and develop new training materials to address emerging gaps, ensuring a mix of traditional university courses and flexible workshops accessible to professionals and students, ultimately providing long-lasting benefits to Canadians beyond the project's duration.

This project will support the development of Assistability's novel 3D-printed customized wheelchair cushion and AI algorithm in the following areas:
• Materials selection and testing
• Development of material property database to complement machine learning algorithm (currently being developed at RDP and UofA) considering stress-strain data for various unique lattice cell structures
• Quality control of cushions fabricated at RDP in terms of productivity, traceability, and optimization of digital process
• Develop procedure document and prepare documents required ISO 13485 certification

Within this data-lake, apply machine learning to identify variances within assessed risk (frequency, probability, severity) as well as the level of risk mitigation provided by proper controls, in relationship to incidents resulting from risks as well as field/site assessments.

To achieve the required performance, we will exploit machine learning techniques to develop a flexible design methodology for the optimization of device geometries with a large number of design parameters and incorporating subwavelength metamaterials.

Within this data-lake, apply machine learning to identify variances within assessed risk (frequency, probability, severity) as well as the level of risk mitigation provided by proper controls, in relationship to incidents resulting from risks as well as field/site assessments.