Innovative Projects Realized

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.

IoT device fingerprinting and anomaly detection using ML

The number of Internet of Things (IoT) devices is expected to reach 50 billion devices by 2020 and the devices are increasingly diverse. They are disrupting traditional security measures. Mobile Network Operators (MNOs) have limited control over customers’ IoT devices, as they are deployed on the customer premises. MNOs need to deploy effective security controls at their end to protect their assets. Huge amounts of data are generated by IoT devices, which can be exploited to understand device behaviours. The proposed research program aims at finding novel solutions to the problem of detecting abnormal behaviour in IoT environments. When abnormal network traffic is detected, two solutions can be adopted: blocking the traffic, or sending it for deeper analysis. The first solution may disconnect legitimate IoT devices, as certain behavior deviations are quite normal, e.g., bandwidth fluctuation. The second solution attempts to learn more about IoT devices and refine the learned behaviour model. This is a real-time and continuous learning process that adapts the model to a changing environment, e.g., new device types. Therefore, sophisticated IoT fingerprinting exploiting machine learning algorithms is the ultimate objective to achieve.

Creative Artificial Intelligence in Interactive Mobile Systems

The large amount of information available today on the web brings many challenges to the information retrieval and artificial Intelligence communities. Moreover, personalization is a key component in today’s successful mobile websites and interactive applications. In order to be effective, these websites are required to provide visitors with the information they need without the complexity in finding it. Developing intelligent and interactive systems with visual user interfaces is therefore essential for any mobile device. In this regard, our goal is to develop a new interactive mobile tool that elicit and lean users’ requirements and preferences, in order to provide them with what they actually need. This will be achieved by taking advantage of the advancement of artificial intelligence as well as the new 5G technologies.