Innovative Projects Realized

Interactive, place-based mapping of climate analogs in Yukon’s mountain regions to inform tree species selection in areas of rapid climate change.

Research problem: Reclamation practitioners must consider seed source for revegetation measures that maximize long-term resilience of reclaimed sites in regions undergoing rapid climate change. Climate analog mapping, relating climate of a space at a point in time to that of another, can help identify location of seed sources best adapted for current and future climates. Climate analogs published for northern mountain regions have not incorporated topo-edaphic (slope and aspect) factors that can modulate climate sensitivity and growth of canopy forming tree species.
Objectives: The goal of this internship is to develop an interactive, place-based, climate analog tool specific to Yukon’s boreal tree species using biologically meaningful climate variables determined from tree-ring, climate, and topo-edaphic relationships.
Anticipated Outcome: This research will inform selection of seed stock for forest ecosystem reclamation by linking historic growth response of tree species to their contemporary and future climate analogs.

Clutched redundant Rotary Motors for Aircraft Primary Flight Control

Graduate students working on the NSERC RDC program titled: Clutched redundant Rotary Motors for

Aircraft Primary Flight Control will have the opportunity to pursue their respective research projects at

the locations of the two industrial partners supporting the project. Bell Helicopter will accept 5 fulltime

4 months internships and Bombardier will accept 4 fulltime 4 months internships.

The research will develop novel electric actuator system architectures and new clutching technologies

required to develop all-electric aircraft control systems (a very important challenge for the aircraft

industry). In collaboration with industry, interns will develop engineering models and prototypes to be

used in their research work. They will conduct typical engineering tasks such as defining design

requirements, developing analytical and numerical models of complex mechanical systems, designing

mechanical systems and components using Computer Assisted Drawing tools, and conducting

structural analysis with Finite Element Analysis tools.

Promoting Water Stewardship Through Citizen Science with Water Rangers

Water Rangers uses citizen science to acquire baseline data for water bodies across Canada by giving and selling testkits to volunteers across Canada. This project will explore who is most likely to take part in water testing, what engagement strategies reduce dropout rates, and how testing water increase environmental concern by changing values and people’s connection with nature. Using a mixed method approach involving surveys and interview questions, 275 participants will be recruited. They will receive a free tiny testkit, a subsidized mini testkit or borrow a large testkit. Three different engagement strategies using reminder emails, social proof principles, and gamification principles will be tested. The surveys will help build a profile of participants, understand how testing the water impacted their values and their relationship to their local watershed. Finally, some participants will be interviewed to get a deeper understanding of what motivated them to test water. Overall the project will help Water Rangers understand how to focus their resources when recruiting participants, reduce dropout rates, and how their work can train people to be better water stewards.

PUBLIC PARTICIPATION IN MINE PLANNING AND DESIGN

Canada is a leader in the global mining industry. At a time of increasing scrutiny of the role of business in society, mining executives now cite social risk as a key challenge to providing resources for global growth and sustainable development. This proposed research aims to assess approaches for mining companies to engage with stakeholders and better manage social performance during the mine life cycle. Specifically, the research seeks to determine whether and how mining can be a catalyst for sustainable development in resource rich regions. The research will produce a case study focussed on stakeholder engagement and local economic development associated with a proposed gold mine in Mongolia. The mine will be the largest in Bayankhongor province, generating employment and supply opportunities for local residents and businesses. The findings will assist the partner – Erdene Resource Development – to strengthen its engagement with communities during design and permitting of the proposed mine.

Caractérisation objective de l’acouphène chez l’individu acouphénique.

A l’aide d’une recherche psychoacoustique préliminaire afin de caractériser subjectivement l’acouphène, l’idée est de créer une bande son centré sur la fréquence de l’acouphène et d’y ajouter des gaps. Cette bande son est alors administrée au patient acouphénique et un patient témoin. Le but est le suivant, au travers d’observation électroencephalographique, nous observons si les gaps sont comblés par l’acouphène afin de prouver que le son a été bien calibré et que l’analyse psycho acoustique préliminaire a été bien réalisée.

L’automatisation du suivi comportemental et biométrique des porcs dans les fermes porcines

Dans les fermes porcines, le suivi du comportement et l’analyse des données biométriques sont indispensables pour l’amélioration du bien-être, du niveau de santé, de l’efficacité des traitements, la prise de décision en matière de reproduction et plus généralement, la durabilité de la production porcine. Additionnellement, avec la pénurie de main d’œuvre au Québec, les fermes d’ici doivent se doter de solutions autonomes.

Dans le but de répondre à ces besoins au niveau de l’industrie et de la recherche, l’objectif du projet de recherche est d’élaborer des preuves de concept pour le développement d’un système de collecte de données biométriques et de suivi du comportement des porcs dans les fermes. En plus de répondre aux enjeux énumérés, le système entrevu se démarque des produits commerciaux actuels puisqu’il fonctionne en temps réel et de façon autonome, qu’il est non-intrusif et qu’il est très abordable.

Using RTLS and Computer Vision to Extend Worksite Safety

The project aims to extend worksite safety of construction projects at Hydro-Quebec (HQ) using computer vision and a Real-Time Location System (RTLS). The case study is a substation construction project near Montreal. The main safety risks that will be targeted in the case study are related to equipment mobility (struck-by accidents) and not wearing Personal Protection Equipment. The concept of the method is to have a priori information about the types of expected risks in the planning phase, and then to monitor the site using video cameras and the RTLS. Artificial Intelligence (AI) and computer vision techniques are used to detect the location and other attributes of the workers and equipment with respect to the identified risks. The workers will be equipped with a wristband that can generate vibration safety alerts in case of proximity to equipment. In addition to safety support, the system can provide the following side benefits: (1) improved security by detecting potential intruders to the construction site by using infrared cameras and night vision; and (2) generating time-lapse video of the project.

Deep learning based approaches for hard and soft data fusion towards better maritime domain awareness

In this project, we apply deep learning methods to analyze and obtain useful information from text data that are collected from social media, and combine these information with numerical data from physical sensors. We then develop new deep learning based solutions that exploit the combined data in order to track the ships in the open sea with more accuracy. The primary strength of our work is that social media data provides additional information when the usual physical sensors like radars and satellites can not provide enough data. Our work is integrated into the partner organization’s commercial software to illustrate the improved performance of ship tracking.

NetRepAIr: Making networks reliable for next-generation applications using AI/ML techniques

Networks have grown from small topologies connecting a dozen of devices to large, shared infrastructures supporting primary needs of our society. Today, we count on networked services for trading, commuting, monitoring weather conditions, meeting people. In order to provide reliable services, network operators need to cope with the daunting challenge of ensuring millions of flows from heterogeneous devices arrive at their destination on time and showing a reasonable throughput. Despite the significant advances recent Software Defined Networks (SDNs) provided towards managing large scale network infrastructures, they still fall short to enable fault-tolerant, performance-guaranteed data transmissions to the level next-generation applications such as 5G, smart cities, augmented reality and the Tactile Internet demand. In this project, we propose a new view to the problem of network reliability. Through Artificial Intelligence (AI) and Machine Learning (ML) techniques, we look for building a smart, highly scalable and robust network repair system. Our design will combine state-of-the-art machine learning techniques such as deep reinforcement learning and graph neural networks with high-performance and flexible network devices (e.g., P4 switches, NetFPGAs, and SmartNICs) to detect and correct network faults with high accuracy and in extremely short timescales.

Design of foot orthosis with customized variable stiffness structure using 3D printing techniques

There is a large need for custom orthotic insoles that meet user’s needs in terms of comfort, pressure distribution correction and impact absorption integrated a in more extensive and flexible way. The limited reliable control of these factors in current manufacturing processes has led to frequent orthotic adjustments, reduced device compliance, lowered effectiveness and increased time and expense from both the orthotic provider and the patient. Using 3D printing technology, this proposal aims to develop the next generation of custom orthotic insoles through tuning the mechanical characteristics of the insole within its structure. Upon project completion, results will lay a framework for designing optimized custom orthotic insoles.

Where do we want to go? Have we arrived? Improving transparency, rigourand knowledge in complex multi-stakeholder planning processes

This research aims to address to knowledge gaps in complex multi-stakeholder planning

contexts. First, it involves better understanding engagement methods and value elicitation

techniques with the purpose of decision making in multiple urban/rural planning contexts.

Second, it will research and develop a participatory monitoring and evaluation (M&E)

framework to be applied in a First Nations context, specifically to address the lack of

knowledge and application of M&E in the ‘Comprehensive Community Plans’ that are been

developed across the province supported by Indian and Northern Affairs Canada (INAC).

This research project is of relevance to the partner since it will greatly enhance the current

planning approaches and methods it is using. It will also deliver higher impacts for the clients

of the partner firm and hence be of benefit to Canada. Finally it has the potential to positively

influence a broader professional planning audience, particularly First Nations.

Multivariable PID Controller for Search and Rescue UAV Operations Based on Static Output Feedback

The research proposed in this document will build upon and extend the previously funded CRIAQ (AUT-1701) and MITACS (IT12130) projects on the development of a UAV platform for search and rescue activities in the ski facilities of Domain Saint Bernard in Mont Tremblant in collaboration with SII Canada. The goal of this research is to develop a synthesis methodology for a multivariable PID flight controller to steer a rescuing UAV to a person in danger using output feedback.