Innovative Projects Realized

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

30156 Completed Projects

2861
AB
5059
BC
812
MB
673
NL
842
SK
8957
ON
9368
QC
96
PE
579
NB
1120
NS

Projects by Category

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.

View Full Project Description
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.

View Full Project Description
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.

View Full Project Description
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.

View Full Project Description
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

View Full Project Description
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

View Full Project Description
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.

View Full Project Description
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.

View Full Project Description
Faculty Supervisor:

Sabine McConnell

Student:

Partner:

Lowfoot Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Trent University

Program:

Accelerate

Intelligent mini-unmanned Aerial Vehicles (UAVs) for automated skin cancer screenings

The main goal of this project is to design a UAV-based image acquisition system to capture high quality full body images which will be integrated into DermEngine Full Body Imaging (FBI) module. With this system, we intend to achieve consistency between different images that FBI needs to analyze. We also plan to deliver an easy-to-use, affordable, compact, and automated system for experts, physicians, and even patients.
At a very high level, we are looking at a mini-UAV that can be programmed and controlled by sensors to fly in a specific path around the patient body and captures high quality images at different angles. At this stage, we just want to conduct a feasibility study to specify and customize a vision-based indoor navigation system for this application. In future, we plan to develop the complete UAV-based TBP system which will be smart, autonomous, and able to capture 3D images.

View Full Project Description
Faculty Supervisor:

Siamak Arzanpour

Student:

Partner:

MetaOptima Technology Inc

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

Simon Fraser University

Program:

Accelerate

Novel membrane mimetics in HT antibody screening and structural biology

Many therapeutic targets are proteins embedded in the membrane that surrounds the cell. Traditionally, such targets present major challenges, because they required the use of detergents to extract them from the membrane and to purify them. Such detergents can cause artefacts, hampering the development of novel therapeutics. Here we will test new methods that get rid of detergents during extraction, purification, or both. The membrane proteins thus isolated can then be used for screening of therapeutic antibodies; for example the binding strength of an antibody against the target protein can be determined. In a different part of the project the purified protein can be used to determine the 3D-structure of the protein. The more native environment provided by the novel approaches is likely to improve stability of the sample and therefore increase the chances of success.

View Full Project Description
Faculty Supervisor:

Filip Van Petegem;Karen Cheung;Anne Condon;Corey Nislow;Khanh Dao Duc

Student:

Partner:

Amgen British Columbia

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

The University of British Columbia

Program:

Accelerate

3D visualizations for neuroscience education, research, and clinical applications

As new technologies are rapidly being integrated in postsecondary institutions, the question is often where to focus investment and how to facilitate adoption of new technologies by faculty and students. The advent of an era that will increasingly rely on the use of augmented and virtual reality in the instructional arena necessitates the creation of easy to use technologies and exploration as to whether these tools are applicable and practical in education and research. While there is extensive interest in using augment reality (AR) in the educational and research settings, challenges exist including development of models that are not only visually interesting but precise and usable for data analysis and visualization.
UBC and Microsoft will continue their collaboration, which started with the “Holographic Brain Lecture”, which looked at the educational impact of AR.TO BE CONT’D

View Full Project Description
Faculty Supervisor:

Claudia Krebs

Student:

Partner:

Microsoft Canada;Microsoft HQ

Discipline:

Life Sciences

Sector:

Technology; Education; Health and Related Sciences & Technology

University:

The University of British Columbia

Program:

Accelerate

The Genetics of Blood Biomarkers in COPD

COPD is a progressive inflammatory airway disease characterized by persistent and progressive airway inflammation. It is a major cause of global morbidity and mortality and is predicted to become the third leading cause of death by 2020. Biomarkers may be useful for diagnosing disease considering that the usually used lung function measures have poor correlation with both symptoms and other measures of disease progression. However, the relationship between biomarkers and COPD is still elusive. Establishing causality for selected proteins and pathways is a promising step toward their development as both biomarkers and therapeutic targets. Our group has found surfactant protein D is a novel biomarker, plays a causal role in the pathogenesis of COPD and its progression. TO BE CONT’D

View Full Project Description
Faculty Supervisor:

Xuekui Zhang

Student:

Partner:

Providence Health Care

Discipline:

Mathematics

Sector:

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

University:

University of Victoria

Program:

Accelerate

Previous
1…2,3602,3612,3622,3632,3642,3652,366…2,513
Next

Join a thriving innovation ecosystem.

Subscribe to our newsletter now!

Subscribe