Grants and Contributions
About this information
In June 2016, as part of the Open Government Action Plan, the Treasury Board of Canada Secretariat (TBS) committed to increasing the transparency and usefulness of grants and contribution data and subsequently launched the Guidelines on the Reporting of Grants and Contributions Awards, effective April 1, 2018.
The rules and principles governing government grants and contributions are outlined in the Treasury Board Policy on Transfer Payments. Transfer payments are transfers of money, goods, services or assets made from an appropriation to individuals, organizations or other levels of government, without the federal government directly receiving goods or services in return, but which may require the recipient to provide a report or other information subsequent to receiving payment. These expenditures are reported in the Public Accounts of Canada. The major types of transfer payments are grants, contributions and \'other transfer payments\'.
Included in this category, but not to be reported under proactive disclosure of awards, are (1) transfers to other levels of government such as Equalization payments as well as Canada Health and Social Transfer payments. (2) Grants and contributions reallocated or otherwise redistributed by the recipient to third parties; and (3) information that would normally be withheld under the Access to Information Act and the Privacy Act.
$72,000.00
Jul 1, 2022
For-profit organization
L-Spark IVY AI for perception
993712
Develop proof of concept for sensor fusion with BlackBerry IVY platform.
$50,000.00
Jan 6, 2020
For-profit organization
GVC - L-SPARKS/QNX Sensor Cortek
941321
GVC - L-SPARKS/QNX - Corporate Accelerator Program. Sensor Cortek is developing smart sensor solutions for the automotive industry.
$30,000.00
Feb 15, 2019
For-profit organization
YOUTH - Dhanvin Kolhatkar
922663
This project aims at the development of smarter sensors to improve both (a) their reliability and (b) their ability to extract the higher level information required for the deployment of self-driving cars. More specifically, in the context of this project, we intend to develop more competitive radar solutions that will integrate advanced features not available in current radar sensor technology. The introduction of Artificial Intelligence and in particular deep neural networks (DNN), has drastically improved the performance of many sensors such as laser scanners and cameras. Our objective is therefore to develop an embedded solution that will allow radars to produce more accurate information.