Grants and Contributions

To develop a machine learning augmented healthcare enterprise search service for improving the exploration of patient data within clinical & research platforms.
Enterprise search is an intelligent exploration of patient records that incorporates several data sources while allowing for a dynamic and customized search experience.
Compared to a standard full-text search, such a system provides highly relevant results to clinical or research queries (i.e. COVID long-term impacts, such as reduced lung function, memory loss, and neurological disorders, which require correlating multiple data sources to identify).

The project has for aim to transform TUIO into an all-in-one financial app for the education industry by allow the company to build an ecosystem of financial features.

Project supports the integration of a number of functions currently offered in an effort to streamline the delivery of integrated digital marketing and managed IT services.

The project is to extend the firm's current DAFS (Data Analysis Facilitation Suite) platform to include segmentation of MRI images.

RidgeTech will be supporting Sina (Colin) Yazdani in his growth as a control system specialist. Colin will be working in the maintenance department at a London poultry processing plant. He will be exposed to the processing environment and multiple trades people on a daily basis. Colin will report directly to the plant personnel. He will be involved in monthly updates with the RidgeTech team.

The proposed project is to design a self-contained on-line equipment, able to identify the drifts of water quality of cooling systems in real time.

The design of federally regulated transportation infrastructure and service plays a critical role in serving older adults. By 2051, one quarter of Canadians (almost 12M people) is expected to be aged 65 and older. Older adults may be more focused on goods and services related to travel (e.g., recreation, health) compared to other age groups, yet face risks related to long-distance transport PAGE 4 such as standing, sitting and walking for extended periods; or low experience with complex, poorly integrated travel systems; factors which may intensify pre?existing health conditions. Currently there is no framework that systematically identifies risks, barriers, gaps, and recommendations to better support adults using federally regulated transportation. We’re developing and evaluating this framework with active participation from older adult stakeholders. In addressing the benefits travel brings through recreation, reuniting people, accessing healthcare or enabling work in later life, older adults can age better both in their communities and beyond.

This project will develop growing components used in indoor vertical farming by exploring the constraints and requirements placed on the parts by various processes from cradle to grave including manufacturing, shipping, operational use, safety, and decommissioning.

In this project, we aim to adapt an evidence-supported psychotherapeutic caregiver (CG) intervention called the CARERS program into a virtual reality (VR) simulation training platform. The project is co-led by a clinical research team with extensive expertise in dementia care and CG interventions and the NRC team with extensive expertise in health?care technological solution development and testing. We draw upon a large network of collaborators with clinical, simulation, implementation, knowledge mobilization (KMb), and healthcare technology expertise to collaborate with knowledge users (KUs), CGs and care stakeholders. The project goes through an evidence-based process of co-design, clinical content development, content validation, and VR development, followed by testing of the minimum viable product (MVP) while addressing implementation challenges. This project answers the call for Aging-in-Place for dementia CGs and persons living with dementia in a time when limited access to resources is exacerbated by the pandemic.

The Project focuses on building resilient and secure machine learning (ML)
techniques for connected health-care systems.