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Impact
CNERG: Charting a path to net zero homes through optimized retrofits
Single-detached homes account for more than half of Canada’s residential buildings and consume more than twice the energy per capita compared to apartments. To help meet Canada’s 2030 and 2050 emissions reduction targets, this research focused on identifying the most effective retrofit strategies to reduce energy use in these homes, using a case study modelled on a house in Cambridge, Ontario.
Turning raw data into real-time insights for advanced manufacturing
Conestoga researchers collaborated with Karmax to develop a machine learning and artificial intelligence prototype that integrates directly into the company’s existing digital twin software.
CNERG: Pedestrian data collection at Conestoga's Waterloo campus
This research project leveraged innovative data collection methods to map pedestrian movement at the Waterloo campus
CNERG: Analysis and predictions of land use/land coverage changes with the CA-Markov model
This study examined historical land use and land cover changes in Guelph from 2001 to 2022 and forecasted future trends up to 2037 using the Cellular Automata-Markov model.
Innovative energy management for electric vehicle charging and storage at Conestoga's SMART Centre
The project was developed in two phases to enhance system intelligence and usability. In the first phase, communication data is integrated into a centralized database, while an interface is established to connect the system with IT management tools, enabling real-time monitoring and greater visibility.
Enhancing efficiency and care with AI-powered document sorting for clinics
In Canada’s medical clinics, managing the steady flow of incoming electronic documents is a significant, time-intensive task. Addressing these gaps, Spring Medical Corp. sought to develop a secure, open-source AI solution that could integrate seamlessly into OSCAR-based EMR systems.
Advancing machine learning for industrial automation at Archronix
Archronix sought to enhance the capabilities of its industrial control unit by developing advanced machine learning functionalities for automotive feature recognition. The existing control unit had the potential to integrate TensorFlow-driven image processing through third-party accelerators.
Automating safety and efficiency in Eaton's paint line operations
Eaton’s Milton facility faced a critical challenge in managing the manual loading and unloading of its paint line, especially with larger, heavier parts that presented safety risks for operators.
Developing an AI model to identify rust
ServiceEcho aimed to further develop their application solution by integrating AR features to create a more immersive user experience, demonstrating the use of AR and AI to assist users in efficiently completing tasks.
Novel security model for automotive SoCs
Partnering with Conestoga’s SMART Centre, ETAS Embedded Systems and the SMART Centre explored the creation of a novel security approach that maintained the high performance needed by advanced driver assistance systems.