Innovative Projects Realized

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

31620 Completed Projects

2978
AB
5221
BC
856
MB
696
NL
899
SK
9419
ON
9858
QC
98
PE
619
NB
1192
NS

Projects by Category

Achieving Circular Wastewater Management with Machine Learning

Effective wastewater treatment is essential to the health of the environment and municipal wastewater treatment plants in Canada are required to achieve specific effluent water quality goals to minimize the impact of human generated wastewater on the surrounding environment. Most wastewater treatment plants include a combination of physical, chemical, and biological unit processes and therefore have several energy inputs to drive mixing, maintain ideal temperatures, and move water from one unit process to the next. Methane and other gases (biogas) and biosolids are generated during wastewater treatment. Both of these can be captured and repurposed for use within and outside of the wastewater treatment plant and can in some cases even be converted to revenue streams. Thus, biogas and biosolids are considered recoverable resources rather than waste products. Circular wastewater management (CWM) is an emerging approach that aims to optimize wastewater treatment, energy usage, and resource recovery. To achieve CWM, the operators of wastewater treatment plants must have a thorough understanding and reliable control of the different elements of the system. This is usually achieved using a combination of operator expertise, online sensors, and offline water quality measurements coupled with data collection, storage, and analysis software. TOBECONT’

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Faculty Supervisor:

Stephanie Gora

Student:

Partner:

Ontario Clean Water Agency

Discipline:

Engineering

Sector:

Construction and infrastructure; Utilities

University:

York University

Program:

Accelerate

Futures First Algorithmic Derivatives Trading

This internship research project is aimed at using state-of-the-art machine learning and AI techniques to create profitable trading strategies in the derivatives markets. This research is key to the business operations of the partner organization and gives interns invaluable experience and insights to the business.

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Faculty Supervisor:

Manuel Morales;Christian Dorion;Anatoliy Swishchuk;Manuel Morales

Student:

Partner:

Futures First Canada Inc

Discipline:

Mathematics

Sector:

Finance and Insurance

University:

HEC Montréal; Université de Montréal; University of Calgary

Program:

Accelerate

CONVERGENT CROSS MAPPING FOR DEMAND FORECASTING

The availability of inexspensive electricity in a constant and reliable fashion is critical to economic development and efficient resource consumption. To this end, accurate short term load forecasting (STLF) on an electrical grid enable the minimization of dispatch and running costs on the scale of seconds to a week. Models and approaches employed in STLF include multi-linear regression, Box-Jenkins Analysis, fuzzy systems, non-linear state space reconstruction (SSR), and various hybrid models. The domain of this project lies in pure and potentially hybrid non-linear state space methods where SSR have already been explored (5,7). Unexplored in this domain is a method called convergent cross mapping (CCM). The thrust of CCM is a potentially novel approach to making inferences regarding the causal drivers of a time series variable. The primary goal of this project is: use CCM to identify predictors of power grid demand and determine whether or not such predictors improve upon current demand forecast methods.

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Faculty Supervisor:

Simon Bonner

Student:

Partner:

University of California, San Diego

Discipline:

Mathematics

Sector:

Education

University:

The University of Western Ontario

Program:

Globalink Research Award

Modelling a 5G mmWave Cell Site to evaluate RF exposure

This project will produce a useful simulation of a 5G mmWave Cell site. This simulation will allow the Industrial Partner to evaluate and optimize the operation of a typical 5G Cell site. The simulation will model a 5G phased array antenna connected to a Base Station which will be able to create focused beam radiation patterns that will track simulated users moving through the cell site. The effectiveness of user tracking with beams can thus be studied. The simulation will also allow an evaluation of the RF exposure of the users within the cell site. It is critically important to the Canadian Economy that 5G Communication Systems are quickly adopted because 5G will be a key enabler of the new connected economy.

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Faculty Supervisor:

Raman Paranjape

Student:

Partner:

Saskatchewan Telecommunications

Discipline:

Engineering

Sector:

Information and cultural industries

University:

University of Regina

Program:

Accelerate

Occurrence of the Timiskaming-type sedimentary rocks in the North Caribou Greenstone belt

Jason Duff, who is completing MSc program at the University of Ottawa, recently found evidence for sedimentary rocks in the North Caribou greenstone belt that are 2680 million years old, younger than other rocks in the belt. The rocks elsewhere in the belt contain 3.0 billion year sedimentary and volcanic rocks including the host rocks for the Musselwhite mine. Similar young sedimentary rocks, called Timiskaming-type rocks, are known to be spatially associated with large gold deposits elsewhere in the Archean Superior Province (which forms a large part of “Canadian Shield”), including the Porcupine, Kirkland Lake, Larder Lake, Malartic, Pickle Lake, and Hemlo gold camps. The proposed study will determine the distribution of this young sedimentary rock unit in the belt and evaluate the source of the sedimentary rocks; outcrops in the area will be examined, and rock types identified and their ages determined by isotopic methods.

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Faculty Supervisor:

Keiko Hattori

Student:

Partner:

GoldCorp (Mussel White Mines)

Discipline:

Earth science

Sector:

Mining

University:

University of Ottawa

Program:

Accelerate

Development of bio-physical communication model predicting potential toxicity of polycyclic aromatic hydrocarbons

Polycyclic aromatic hydrocarbons (PAHs) are environmental pollutants that occur in various chemical forms across the terrestrial and marine environments and have long been of the subject of intense research worldwide due to their highly toxic properties. PAHs are uptaken by bio-organisms and activated to electrophilic metabolites to exert their toxic effects. Meanwhile, different toxicity of PAHs with similar structure and molecular weight could not been accurately explained by current in silico models. Using PAH target materials, we will develop bio-physical communication model based on two hypotheses: a) biological activity of a toxicant with the receptor ligand in bio-organism is influenced by electron-mediated reaction and b) when different toxicants with the same concentration react with the same receptor, their biological activity is solely dependent to the material’s physico-chemical properties. New toxicity prediction model would help us understand biological response against toxicant’s physico-chemical properties characterized using synchrotron-radiation X-ray spectroscopy. Upon successful development, this model will serve as an absolute toxicity assessment tool for screening chemicals and evaluating the safety of pharmaceutical products rapidly, which will result in significant abatement of traditional animal testing.

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Faculty Supervisor:

Gap Soo Chang

Student:

Partner:

Seoul National University

Discipline:

Physics

Sector:

Education

University:

University of Saskatchewan

Program:

Globalink Research Award

Information-based Public Transport Control Strategy Study Under Pandemic Situation

Health and safety are key concerns during the current public health crisis to individuals who rely on public transit. To lessen the risk of those individuals, the research aims to develop an information-based control strategy using real-time travel information as a meaningful non-pharmaceutical intervention strategy in the public transportation sector. To achieve this goal, the proposed research has four distinct but related parts that contribute to a safer and resilient public transportation system. First, this research will measure the risk level of infection. Second, the research will explore how information and travel recommendations change the traveler’s choice. Third, we will develop a realistic design of a real-time framework to control the target level of density by providing real-time travel information. Finally, we will take an integrated, user-centered approach based on advanced gamification design techniques to increase transit user’s compliance rate with the travel recommendation. The proposed models can assist in developing the effectiveness of continuing various policies to minimize the spreading of COVID-19.

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Faculty Supervisor:

Yong Hoon Kim

Student:

Partner:

University of Seoul

Discipline:

Engineering

Sector:

Transportation (excluding aerospace); Public Service, Policy, and Governance

University:

University of Windsor

Program:

Globalink Research Award

Integrated Circuit Architectures for Photonic Computing

Photonic integrated circuits allow the computations required for artificial neural networks to be performed using light. By doing so, they obviate many of the challenges associated with electronic computers, paving the way to a new class of information processing hardware. Such hardware could help address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. However, interfacing electronics are still required, at a minimum to control the photonic devices. This project researchers the microelectronic integrated circuits required to interface to photonic neural networks for computing. In collaboration with industry partners, the objective is to co-design the combined electronic/photonic neural network.

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Faculty Supervisor:

Tony Chan Carusone

Student:

Partner:

Huawei Technologies Canada Co Ltd (Markham, ON)

Discipline:

Engineering

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Eye tracking and ECG as flight crew workload indicators in a training context

Pilots are responsible for the safe operations of airplanes in complex environments. Therefore, is it essential that pilots receive high quality training. High quality training is dependent on instructor’s ability to provide constructive feedback and help pilots perform to the best of their abilities. The recent development of portable biometric sensors, such as the Apple Watch, open up a realm of possibilities for creating tools for instructors to provide quality feedback to pilot candidates. This partnership, between Keplr Intelligence and Concodia Vision Labs aims to combine the power of artificial intelligence with that of cognitive science to develop a tool which would enable instructors to better understand pilot candidates’ performance in the fast-paced, high-risk environment of flight training. This is expected to contribute to increasing flight safety and the quality of pilot training, all while strengthening Montreal’s position as a leader in aviation and artificial intelligence.

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Faculty Supervisor:

Aaron Johnson

Student:

Partner:

Keplr Intelligence

Discipline:

Sociology

Sector:

Education

University:

Concordia University

Program:

Accelerate

Developing Novel Biosensors for Remote Detection of Cardiac Arrest

When a citizen collapses from sudden cardiac arrest (SCA), it must be recognized before anyone can call 9-1-1 or bystanders can start cardiopulmonary resuscitation (CPR). In more than 75% of all SCAs, no one is there to witness the event, and resultant survival is near 0%. To address delays in detecting SCA and administering CPR, this project aims to develop wearable sensors to identify SCA and automatically call 9-1-1 with global positioning system (GPS) co-ordinates. Death from SCA is a significant and unrecognized epidemic in Canada affecting over 20,000 people annually. Immediate recognition of SCAs would have the potential to impact thousands of individuals and their families in Canada every year. This project is strategically aligned with the goals of our partner organization, CHÉOS,’ contributes to training the next generation of researchers and working cooperatively with other health research organizations, including those at the University of British Columbia and beyond, to develop and carry out research strategies that impact the health and wellbeing of Canadians.

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Faculty Supervisor:

Brian Grunau;Babak Shadgan;Calvin Kuo

Student:

Partner:

Providence Health Care

Discipline:

Engineering

Sector:

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

University:

The University of British Columbia

Program:

Accelerate

Improving technology for identifying environmental microplastics with machine learning

The average human consumes a credit card worth of plastic every week as a result of environmental microplastics. The tools and technology that are currently used to analyze chemical compound structures to identify polymer types in microplastics research are not well-calibrated for field-specific use. Raman spectroscopy data from microplastics samples is imperfect. Furthermore, plastics that have been weathered by environmental factors offer even less analytic certainty. Various environmental factors further skew the spectroscopy data. Machine learning tools and techniques can allow us to better calibrate the research tools for certainty in microplastics analysis.
This research study will not only improve our knowledge of microplastics spectroscopy data broadly, but also break new ground into understanding chemical compounds of plastics that have been weathered by various environmental processes. The significance of this project is to strive for a measurably improved predictive capacity of Raman spectroscopy data to help classify polymer types through an applied machine learning process.

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Faculty Supervisor:

Sheela Ramanna

Student:

Partner:

Compound Connect

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Winnipeg

Program:

Accelerate

Performance de l’utilisation des fertilisants liquides organiques en condition de serre biologiques de plein sol

Le secteur de production de légumes biologiques en serre de plein sol est en essor au Québec. Afin de répondre à cette demande et si l’on veut accroître et la productivité de ces cultures et augmenter les superficies en mesure de produire 10-12 mois par année, il est impératif de combler les lacunes sérieuses en matière de nos connaissances sur l’impact des pratiques actuelles de gestion des nutriments afin d’assurer le maintien de la santé des sols et la qualité de l’environnement. L’objectif de ce projet est de 1) documenter la dynamique de l’azote (N) et du phosphore (P) en culture de serre de plein sol en fonction des pratiques culturales biologiques actuelles et de 2) Proposer et tester une méthode durable de gestion de l’N et du P. Le projet contribuera à assurer la durabilité de la ressource sol et de la production de légumes de serre de plein sol qui en plus d’être en essor constitue un moteur de la vitalité de l’économie locale dans plusieurs régions du Québec.

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Faculty Supervisor:

Jacynthe Dessureault-Rompré

Student:

Partner:

Circulus AgTech Solutions Inc.

Discipline:

Earth science

Sector:

Agriculture and Food; Sustainability & the Environment; Other

University:

Université Laval

Program:

Accelerate

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Mitacs — Return on Innovation

Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.