Innovative Projects Realized

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

13270 Completed Projects

1072
AB
2795
BC
430
MB
106
NF
348
SK
4184
ON
2671
QC
43
PE
209
NB
474
NS

Projects by Category

10%
Computer science
9%
Engineering
1%
Engineering - biomedical
4%
Engineering - chemical / biological

Feature selection for Deep Learning applied to the identification of impaired drivers

DriveABLE Inc uses a set of simple video tasks to identify the impaired drivers. Video tasks come in the form of simple games and measure cognitive ability. The test results are analysed by AI powered algorithm that predicts the impairment level of the driver. Our project’s main objective is to redesign the AI in such a way that it can cover more use cases with fewer tasks. In particular we will redesign the algorithm so that it will accept incomplete tests. We will also identify redundant games in order to make overall test shorter. In addition this analysis will allow to highlight important characteristic of tasks which will lead to new generation of improved tasks.

View Full Project Description
Faculty Supervisor:

Linglong Kong

Student:

Borislav Mavrin

Partner:

DriveABLE Inc

Discipline:

Mathematics

Sector:

Medical devices

University:

Program:

Accelerate

Machine Learning for the Telecommunication Industry – Year two

Ericsson is an industry leader in offering telecommunication solutions and products. As an important step on the path towards the automatic and autonomous management of next generation networks, Ericsson is developing technology in machine learning and artificial intelligence that will benefit operators around the world, including in Canada where Ericsson supplies technology to most of the major telecommunication network operators. In the proposed project, through the exploration of concrete telecommunication industry use cases, the Ericsson researchers in Canada and their academic partners will evolve the start-of-the-art in machine learning and artificial intelligence for the analysis of telecommunication data and operation of telecommunication networks. This will allow Ericsson to develop new products and services, which will allow Canadian network operators to offer improved communication services to Canadian customers. The proposed project will lead to new methodologies for processing complex communication network data, addressing significant imbalances in data sets, and performing anomaly detection.

View Full Project Description
Faculty Supervisor:

Mark Coates

Student:

Mathew Goonewardena

Partner:

Ericsson Canada

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Program:

Elevate

Scale-up of a Green Plasticizer

Plasticizers are used to increase the flexibility and workability of relatively rigid polymers such as poly(vinyl chloride) (PVC). For decades, di-2-ethylhexyl phthalate (DEHP and sometimes termed as dioctyl phthalate (DOP)) was the most common plasticizer used in PVC formulations. However, DEHP has been shown to have negative health effects, such as being an endocrine disruptor. Recently, DEHP has been banned for use in children’s toys and phthalate alternatives are rapidly being offered. We have been developing succinate-based esters that biodegrade to non-toxic metabolites after being discarded while also performing as well or better than DEHP in blend formulations. The previous internship established a promising formulation based on diheptyl succinate (DHPS) with sustainable building blocks and a streamlined process that obeys almost all the 12 principles of green chemistry. 

View Full Project Description
Faculty Supervisor:

Milan Maric

Student:

Roya Jamarani

Partner:

CG Towers

Discipline:

Engineering - chemical / biological

Sector:

Information and communications technologies

University:

Program:

Accelerate

Development of an Electromagnetic-Transient (EMT) Compatible Model for Power Transmission Line Tower and Tower-Footing Grounding System

Transmission lines expand over hundreds of kilometers and are the most vulnerable component of a power system to lightning strikes. Damages to equipment, high repair costs, and loss of revenue could happen when the lightning hits the transmission tower or conductors. Tower grounding systems in transmission towers play a major role for mitigating the over-voltages. The impedance of the tower’s structure contributes to the total impedance of the system as well when subjected to lightning surges. Time-domain macro-models will be developed that are compatible with power system simulators (such as PSCAD/EMTDC) and enable the calculation of currents and voltages along the tower structure considering several geometric factors and ground parameters. PSCAD/EMTDC, a simulation software used widely in the world, has been developed at MHI/Manitoba HVDC Research Centre. Collaboration with academia (University of Manitoba) on this project will provide PSCAD/EMTDC with advanced simulation tools calculate lightning-induced overvoltages more accurately.

View Full Project Description
Faculty Supervisor:

Behzad Kordi

Student:

Bamdad Salarieh

Partner:

Manitoba Hydro International Ltd

Discipline:

Engineering - computer / electrical

Sector:

Energy

University:

Program:

Modeling atmospheric gas dispersion using computational fluid dynamics

Although rare, industrial facilities can suffer from minor to catastrophic failures, commonly referred to as loss of primary containment (LOPC), which can result in the release of hazardous gases and/or liquids. In order to protect the safety of the public, companies must provide a means of mitigating the potential damage to people and the environment. Since these measures are determined beforehand, the ability to develop and produce accurate modeling techniques is of the utmost importance.
The modeling of gas dispersion in the atmosphere following industrial releases (industrial stacks, containment breaches, etc.) relies on the ability to simulate accurate atmospheric flow close to the Earth’s surface. These flows are generally divided into three stability classes. Although neutral stability is well understood and applies to a number of real world scenarios, accurate prediction of dispersion under stable and unstable atmospheric flow would expand upon the current number of applicable cases. TO BE CONT’D

View Full Project Description
Faculty Supervisor:

Jan Haelssig

Student:

Devin O'Malley

Partner:

Stantec Consulting Ltd.

Discipline:

Engineering

Sector:

Natural resources

University:

Program:

Accelerate

Exploring Deep Learning Architectures for Automatic Casting from Movies

Automatic casting applications aim is to accurately recognise facial regions that correspond to a same actor appearing in a movie to produce described video. This project will focus on the tasks of re-identify the face of each principal actors when they appear in different scenes of a movie. This is a challenging task because although recent movies are typically high resolution, the faces are often occluded and their appearance varies significantly according to pose, scale, illumination, blur, etc. This project will focus on developing and evaluating convolutional neural network (CNN) architectures that are suitable for accurate face re-identification in automatic casting applications. Deep learning architectures have recently been shown to provide a significantly higher level of accuracy compared to conventional methods on many challenging visual recognition problems. However, these architectures are complex, and the unlabelled facial trajectories captured in a movie provide a limited reference data to adapt or fine-tune CNNs. 

View Full Project Description
Faculty Supervisor:

Hugo Lemoine St-André

Student:

Hugo Lemoine St-André

Partner:

Centre de recherche informatique de Montréal

Discipline:

Visual arts

Sector:

Information and communications technologies

University:

École de technologie supérieure

Program:

Accelerate

Use of 3-D Virtual Reality Displays while in Flight: The Effects of Vestibular-Ocular Mismatch on Situation Awareness – Year Two

This PDF will further our understanding of how augmented reality, an emerging technology, can improve collaborative situation awareness. In particular, this project will investigate the best ways for information to be shared when two or more individuals each use an augmented reality device. For example, in maritime environments there is always the risk of man-overboard events. Depending on the light and sea conditions it can be difficult to spot and then to share the location information with others responsible for navigation. Augmented reality devices that resemble glasses or goggles can overlay holographic information onto your field of view. This holographic information may not be otherwise perceptible in the real world. It is believed that situation awareness, such as the knowledge of the location of important entities in the environment, (people, other ships, aircraft, or terrain) can be enhanced by the use of augmented reality. 

View Full Project Description
Faculty Supervisor:

Chris Herdman

Student:

Kathleen Van Benthem

Partner:

General Dynamics Canada

Discipline:

Visual arts

Sector:

Aerospace and defense

University:

Carleton University

Program:

Elevate

Green technology use in gold recovery: Deer Cove

The Deer Cove gold mine is located approximately 4.2 km north of Ming’s Bight near Baie Verte, north-central Newfoundland. Free gold has also known to occur in the surrounding till. While preliminary studies have been done it is still unknown if the gold in the till can be economically and more importantly environmentally recovered using green technologies. This project will investigate the possibility of developing an economically viable green process to extract gold from the till in Deer Cove, leaving natural habitat intact.  The final product will be a feasibility study that covers the potential to economically mine using green technology. The work will set up the possibility and framework for field trials.

View Full Project Description
Faculty Supervisor:

Gary Thompson

Student:

Christina Burke

Partner:

Anaconda Mining Inc.

Discipline:

Engineering

Sector:

Mining and quarrying

University:

College of the North Atlantic

Program:

Accelerate

Net Zero Energy Strategies and Quick Assessment Tool for multiunit residential buildings (MURBs) in Toronto

Buildings are responsible for a third of the total carbon emissions worldwide. Net zero building is a type of building that produces as much energy as it consumes, and it has a great potential for energy and carbon reductions. This research aims to study how multiunit residential buildings (MURBs) (e.g. condominiums) in Toronto can become net zero, as well as to create a simple tool in order to quickly assess whether becoming net zero is feasible. The partner organizations are among the leaders in pushing the residential development industry to higher energy performance standards. This project will provide comprehensive information relevant to the design of low-energy or net zero condominiums. It will also equip the partner organizations to better meet the challenges in the coming transition to a zero or low-carbon economy.

View Full Project Description
Faculty Supervisor:

Danny Harvey

Student:

Li Miao

Partner:

Sustainable Buildings Canada

Discipline:

Geography / Geology / Earth science

Sector:

Construction and infrastructure

University:

University of Toronto

Program:

Accelerate

New High Precision GNSS Positioning and Navigation System Using Triple-frequency GNSS Signals – Year two

Precise Point Positioning (PPP) is a next generation precise positioning technology using GNSS and is expected to be adopted in more and more applications. To date, all PPP systems available on the market are dual-frequency based systems and long positioning convergence time has limited its wide applications. With the progress of the GNSS modernization, triple-frequency signals are becoming available, which will shed light to overcome this challenge. It is expected all GNSS satellites will broadcast triple-frequency signals by 2020, which offer opportunities for technology innovation and new product development. This project aims to develop a real-time triple-frequency GNSS PPP system with fast positioning convergence capability. The major tasks include the investigation of triple-frequency signal bias variations, the development of processing strategies for observable-specific signal bias (OSB) determination, and fast triple-frequency PPP ambiguity resolution. A prototype system will be developed to assess the performance of the system for product development.

View Full Project Description
Faculty Supervisor:

Abu Sesay

Student:

Zhixi Nie

Partner:

Profound Positioning Inc

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Program:

Elevate

Virtual testing of composites in aerospace

Canada’s aerospace industry relies on expensive and time-consuming testing campaigns to introduce new composites or obtain baseline material properties. Design allowables (e.g., strength used in design phases) are obtained from empirical knock-down factors that account for temperature, environment, etc. This approach is over-conservative and disconnected from physical grounds; it also leads to situations where different teams in the same company (e.g., Bombardier) use different factors for the same part. Having accurate predictive models could reduce testing and decrease knock-down factors. Composites failure is a complex multi-scale process and no “simple” theory can accurately predict composites failure. At this stage, no framework seems to emerge as more suitable.
The principal objective of this project is to plan how using existing and novel composite failure predictive models and techniques can reduce testing and determine more accurate design factors. TO BE CONT’D

View Full Project Description
Faculty Supervisor:

Martin Levesque

Student:

Ilyass Tabiai

Partner:

Bombardier Aeronautic Inc

Discipline:

Engineering - mechanical

Sector:

Aerospace and defense

University:

Program:

Elevate

An investigation into the psychometric properties of patient-oriented measures in frailty and dementia – Year Two

Patient centred measures can facilitate better health outcome$ by focusing on symptoms considered important to the patient. Goal attainment scaling (GAS), a system of goal setting which places the emphasis on the symptoms of individual patients, has been utftlzed In dementia research. However, It Ia unclear whether GAS Is atilt sensitive to symptom changes (responsiveness) when patients set one goal Instead of the recommended three or more goals. The Pictorial Fit-Frail Scale (PFFS) was developed in response to some of the limitations of commonly used frailty measures (e.g., small numbers of listed symptoms; Inappropriateness with severely frail), and Its psychometric properties (reliability, validity, and responsiveness) are unclear. As such, investigations Into the psychometric properties of one-goal GAS and the newly developed PFFS are needed. This research will help to solidify the industry partner’s position as a global leader In the field of patient-centric outcome measurement for clinical monitoring and research.

View Full Project Description
Faculty Supervisor:

Olga Theou

Student:

Lisa McGarrigle

Partner:

DGI Clinical Inc

Discipline:

Medicine

Sector:

Medical devices

University:

Program:

Elevate

Previous
1…745746747748749750751…1,106
Next

Join a thriving innovation ecosystem.

Subscribe to our newsletter now!

Subscribe