Innovative Projects Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

30508 Completed Projects

2882
AB
5105
BC
825
MB
681
NL
860
SK
9051
ON
9491
QC
97
PE
586
NB
1141
NS

Projects by Category

Diagnostic, Prognostic and Health Monitoring of Aircraft Flight Control System

The reduction of aircraft life-cycle cost and environmental footprint, increasingly desired by the aerospace industry, requires innovative ideas not only at design level but also at maintenance level. Aircraft manufacturers are looking for highly reliable equipment; however, degradations due to wear and tear and faults/failures are inevitable. Thus, when degradations occur, aircraft owners/operators want to be able to identify (i.e. detect and isolate/localize) them as early as possible and replace the faulty unit as quickly as required (and feasible). Also, it is highly desired to evolve from time-based to condition-based maintenance (CBM). This requires the capability to predict degradation progression time profile and thus estimate time-to-failure (TTF) or remaining-useful-life (RUL) of components. This will help optimize maintenance scheduling and reduce time dedicated to maintenance.

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Faculty Supervisor:

Khashayar Khorasani;Ruxandra Botez;Chahé Nerguizian

Student:

Partner:

GlobVision

Discipline:

Earth science

Sector:

Professional, scientific and technical services

University:

Concordia University; École de technologie supérieure; Polytechnique Montréal

Program:

Accelerate

Nutrition and management strategies to improve Canadian pork production

The Canadian swine industry must adapt to current and emerging challenges to remain competitive. In general, the industry is focused on improving efficiencies and reducing costs of production. However, the industry also faces many concerns regarding environmental sustainability and societal acceptance of production that need to be continually addressed. Feed represents approximately 70% of the cost of production and plays a critical role in maintaining animal health and performance and in reducing the environmental impact of pork production. The main research projects included in this proposed program aim to identify nutrition and management strategies to improve competitiveness of Canadian pork producers.

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Faculty Supervisor:

Denise Beaulieu;Andrew Van Kessel;Anna Kate Shoveller

Student:

Partner:

Prairie Swine Centre Inc

Discipline:

Life Sciences

Sector:

Agriculture

University:

University of Guelph; University of Saskatchewan

Program:

Accelerate

Childhood Healthy Weights Early Intervention Program

The Early Intervention Program (EIP) is a family-based intervention targeting families of children who are off the healthy weight trajectory. The EIP is a 10-week program offered at community centers across BC where children and their families meet once a week for 90 minutes. Parents will be provided with healthy lifestyle content and will engage in discussions on how to engage in health behaviours, and children will participate in physical activities aiming to enhance their motor skills. Families will also have access to a web portal with content and suggestions of activities to be completed. The Childhood Obesity Foundation will benefit by having an experienced post doctoral fellow leading and overseeing the evaluation team to ensure the research is following best standards. The intern will contribute with previous experience and innovative perspectives on the use of technologies to promote behaviour change in low income and diverse populations.

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Faculty Supervisor:

Patti-Jean Naylor;Sam Liu

Student:

Partner:

Childhood Obesity Foundation

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

University of Victoria

Program:

Accelerate

Anomaly Detection in transactions volumes

The objective of the project is to investigate how machine learning techniques can be used to detect anomalies in volumes of transactions. This requires the student to conduct a literature review about the topic as well as experimenting with a subset of selected machine learning techniques. The results from the research could help the partner organization in improving in place mechanisms used to detect anomalies in volume of transactions.

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Faculty Supervisor:

Anthony Bonner

Student:

Partner:

Ethoca Technologies

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

L’adoption des pratiques de menus durables dans les établissements de santé québécois : une étude de faisabilité

L’alimentation durable peut avoir des répercussions bénéfiques sur l’environnement, la santé, l’économie, et la société. L’introduction de la durabilité dans l’alimentation des individus est un processus long et complexe, et les leaders de changement doivent être en mesure de comprendre les perceptions des gens qui œuvrent dans le domaine de l’offre alimentaire. Le projet compte analyser la faisabilité d’adopter des pratiques de menus durables dans les établissements de santé québécois, en partenariat avec l’organisme Nourrir la Santé, de la Fondation McConnell. Les résultats tirés de cette étude seront directement réinvestis dans la production et la diffusion d’un Guide de Menus Durables, un projet tenant à cœur une gestionnaire innovateur du programme Nourrir la Santé, afin de supporter les gestionnaires de services alimentaires en milieu de santé.

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Faculty Supervisor:

Geneviève Mercille

Student:

Partner:

Fondation McConnell

Discipline:

Life Sciences

Sector:

Other services (except public administration)

University:

Université de Montréal

Program:

Accelerate

Robust WiFi-based Indoor Presence Detection and Localization

In this project, we are interested in device-free methods that passively sense, monitor, and track people’s indoor presence, location, and movement using off-the-shelf Wi-Fi-enabled devices. We use information extracted from the physical layer of wireless links to detect and interpret human presence, location, and physical activities. The current design and implementation of Wi-Fi-based systems exhibit some temporal inconsistencies and limitations due to the complexity of the wireless signal propagation in indoor environment and the challenging nature of human’s behavior itself. This project focus on feature extraction techniques to reduce data inconsistencies and improving the performance of classical machine learning algorithms and deep learning models, for building robust smart-home applications such as presence detection and indoor localization.

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Faculty Supervisor:

Xue (Steve) Liu

Student:

Partner:

Aerial Technologies Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

Scalable Secure Authentication in Mesh-enabled Networks for Smart Cities

The proposed solution will address the aforementioned challenges by attempting to provide scalable authentication and encryption mechanisms. A combination of software and hardware based approaches can be used to provide enhanced security to constrained IoT nodes with respect to their timing and power demands. Technologies such as Bluetooth or 802.11ax mesh networking could be critical to smart city implementations, and will be investigated. We are proposing a smart city friendly complete proof-of-concept implementation.

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Faculty Supervisor:

Zeljko Zilic

Student:

Partner:

Ericsson Canada Inc (Montreal, QC)

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

Machine learning towards intelligent steel refining processes

In the steelmaking industry, process control models need to be based on a sound physical understanding of the process but should also account for many uncertainties due to the nature and complexity of the environment in which the process is carried out. As a result, it is crucial to extract useful process control information from the raw data stream acquired by the industrial sensors. The proposed project aims at developing advanced algorithms to improve the estimation of key control parameters in the Argon-Oxygen Decarburization (AOD) process, by leveraging on Machine Learning approaches and tools applied to manufacturing data. This research, while being a valuable training for a high-talented student in Canada, will help the partner organization Tenova Goodfellow Inc. in maintaining its leadership in process optimization applied to steel making furnaces.

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Faculty Supervisor:

Abdallah Shami

Student:

Partner:

Tenova Goodfellow Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Western University

Program:

Accelerate

Etude des interactions entre entités biologiques et substrats nano et microstructurés pour un diagnostic précoce de sepsis

il s’agira d’étudier les interactions entre entités biologiques, notamment des cellules, et des substrats nano-micro-structurés servant par ailleurs à la bio détection plasmonique. Cette étude sera centrée sur la nature et les propriétés des interactions qui gouvernent la fixation, le positionnement relatif des entités et leurs mobilités éventuelles. Elle pourra déboucher sur de nombreuses applications, notamment le développement d’un biocapteur dévolu au diagnostic précoce du sepsis via le contrôle simultané des propriétés bio-mécaniques et photoniques. En particulier, dans le cadre des études relatives au diagnostic du sepsis, nous aimerions pouvoir démontrer le concept auto-assemblage organisé de cellules sur les substrats afin de rendre le diagnostic tout à la fois plus reproductible et robuste ainsi que plus sensible à la fois au niveau du mécanisme biologique et au niveau de la mesure physique. TO BE CONT’D

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Faculty Supervisor:

Paul Charette

Student:

Partner:

Institut d'Optique Graduate School

Discipline:

Engineering

Sector:

Education

University:

Université de Sherbrooke

Program:

Globalink Research Award

Predicting recovery from concussion during military cadet training using multimodal MRI data and machine learning

In the military, concussions are common and many occur while non-deployed, including during cadet training exercises. For the majority of those with concussions, symptoms resolve on their own but for a “miserable minority” symptoms persist beyond the typical 3-month recovery period, impacting quality of life. Most concussion research produces group level inferences which cannot be used to make individual predictions. We propose a supervised machine learning approach to build a model to predict symptom recovery from multiple MRI brain measures. The ability to identify those in the acute phase likely to have poor symptom recovery at 6 months post injury is incredibly useful for clinical decision making, concussion management, optimized treatment and personalized medicine. This project will contribute to bridging the gap between research and clinical use, by adapting and validating machine learning applications in neuroimaging. TO BE CONT’D

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Faculty Supervisor:

Douglas J Cook

Student:

Partner:

Synaptive Medical Inc

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Manufacturing; Professional, scientific and technical services

University:

Queen's University

Program:

Accelerate

Drivers of woodland caribou calf survival in the Rocky Mountain foothills: a landscape with anthropogenic disturbance and multi-carnivore predation risk

Throughout western Canada, declines in woodland caribou (Rangifer tarandus) continue at unprecedented rates. Caribou calves are especially vulnerable in their first four weeks of life, after the calving period. During this time, mother caribou must effectively select habitat that is rich in food resources, but also minimizes likelihood of predation. In the Rocky Mountains of Alberta, the predator community is large, including
wolf, black bear, grizzly bear, cougar and wolverine, meaning avoidance of areas with high predation risk is important. Using an array of camera traps in the Rockies, I estimate predator distributions for the entire predator community, and combine this with telemetry data of two mountain caribou herds to investigate how female caribou select habitat to balance these costs, and the effect this has on the survival of their calves.
Understanding drivers of caribou calf survival will inform management and aid in the development of recovery plans for these herds.

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Faculty Supervisor:

John Volpe

Student:

Partner:

University of Victoria

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

University of Victoria

Program:

Accelerate

Forecast of User’s Water Consumption

The main objective and outcome of the proposed research project will try to develop a model that will be able to predict the water consumption level of users as well as the station. Deliverables of the project will be reports, presentation and a software solution that will include a model for the forecast of water consumption. Research will involve supervised and unsupervised data mining techniques. The solution will be in the R programming language that can be used by the Lowfoot Inc. to predict the actual water consumption in the Peterborough, ON. For the company the outcomes of the research has a quite applied value. The primary goal of the company is the cooperation with the utility suppliers for the prediction of a user consumption level and its further decrease during the consumption peaks by sending out notifications. All these results will help to extend the market of the company.

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Faculty Supervisor:

Sabine McConnell

Student:

Partner:

Lowfoot Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Trent University

Program:

Accelerate

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