This project will develop appropriate strategies for a “Smart Community” of the future with respect to the integration of food growing and processing spaces into the community. This may range from rooftop greenhouses, residential growing sunspaces, community gardens, food producing building components, etc. As well this proposal will investigate how food production will interact with […]

L’aménagement de larges bandes riveraines végétalisées en bord de champ est une pratique agroenvironnementale qui contribue à réduire les impacts environnementaux causés par les activités agricoles. Par contre, ce retrait de surface cultivée représente une perte de revenu pour un producteur agricole et ceci nuit à une adhésion généralisée à cette nouvelle pratique à l’échelle […]

This research will explore the connections between nature and people in Indigenous Protected and Conserved Areas (IPCAs). These values will be articulated by identifying the ecosystem services (i.e. the goods and services provided to people by nature) and determining which of these hold significant cultural values. These values will be compared using a variety of […]

The recording of brain activity through neural Implantable Medical Devices (IMDs) has a wide range of applications, such as studying brain patterns for seizure detection or brain stimulation to alleviate motor disorders. Some IMDs communicate with an external interface platform, which offloads the recorded data from, programs, and supplies power to the IMD. However, existing […]

Around 4 billion tonnes per year of crop and forest residues (biomass) are burned in open air, because they are often loose, wet, bulky, and too expensive to collect and centralize for subsequent conversion into useful products. This results in air pollution, and in some cases, exacerbated wildfires. In this project, we are applying machine […]

It is extremely challenging for clinicians and health researchers to extract insights from unstructured text data at scale. Recently, there has been a significant advancement in Natural Language Processing. Large deep-learning models, most famous of which are the transformers, generate pre-trained embeddings and are developed to extract insights from massive amounts of text. This project […]

Rapid growth of cryptocurrencies has drawn the attention of practitioners, regulators, and scholars. The trading venues for these digital assets display a number of features that make them a sui generis laboratory for studying price formation and market efficiency. One notable feature of cryptocurrency is high and strongly time-varying yields, whether measured by APRs offered […]

This purpose of this research project is it implement creative access affordances at Centre CLARK, an artist-run gallery in Montreal’s Mile End Neighbourhood, to increase the participation of visitors with various disabilities and access needs. Divided into four sub-projects, this research will use interviews, access audits and participatory workshops to find creative solutions to access […]

Downsview Open-source Asset Manager (DOsAM) is a research collaboration between the Carleton Immersive Media Studio (CIMS) and Northcrest Developments. The objective of the project is to develop an online, opensource asset information modelling and public engagement tool to facilitate democratized urban development.

Public art brings numerous economic and socio-cultural benefits to main street communities, however, it can be challenging for communities to effectively measure these impacts. Therefore, this research project involves the development of a toolkit of instruments to be used within main street communities to aid in measuring the impact of public art. This will involve […]

City parks and outdoor recreational opportunities are among Toronto’s most valued resources. The Discovery Walks Program has been one of Toronto Parks, Forestry and Recreation’s flagship outreach efforts to engage residents and tourists alike in learning about culturally and environmentally significant areas across the city. The purpose of this project is to develop and implement […]

Deep reinforcement learning has achieved great successes in recent years. One of the primary challenges faced by such methods is the high cost involved in training a system that demonstrates the desired competency and performance. This project aims to study and compare the available techniques for improving the training efficiency and effectiveness of reinforcement learning […]