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

Nutrient removal using using a glass-base engineered adsorbent for treating public effluence and agricultural wastewater: designing of a portable continuous setup and study of an agricultural application of the saturated adsorbent

Agricultural wastewater and public effluents often contain elevated levels of phosphorous and nitrogen that limit its ability to be directly repurposed as crop fertilizer or irrigation spray. Removal of soluble nutrients from wastewater is difficult. Current treatment options have high investment costs and are often not well suited for smaller farm sizes common in Canada. This research intends to characterize the utility of a solid-state adsorbent material engineered by NPower Clean Tech Corporation that shows promise for removing anionic forms of phosphorous and nitrogen. Successful application of the saturated (spent) adsorbent for crop growing purposes (as soil amender and slow release fertilizer) will add more value to the whole process particularly for small size farms. If the product economically recovers these nutrients, then this research may directly benefit Canadian farmers, public sewer utilities, and waste generating industries by offering an additional means of controlling their waste stream profiles.

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Faculty Supervisor:

Hossein Kazemian

Student:

Dorna Sobhani;Simisola Idim

Partner:

NPower Clean Tech Corporation

Discipline:

Environmental sciences

Sector:

Manufacturing

University:

University of Northern British Columbia

Program:

Accelerate

AI enabled audio-visual annotation for endoscopic procedures

Video endoscopy is the main diagnostic and screening method for cancer screening in the gastrointestinal (GI) tract. Currently, the endoscopic video is not sufficiently documented, which makes reviewing of these videos difficult. The research team will partner with the company to develop an audio/video recording technology to combine physician voice annotation of the video during the endoscopy procedure. This project will produce more detailed documented endoscopic video and allow more accurate comparison of videos recorded at different time to monitor disease progress.

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Faculty Supervisor:

Qiyin Fang

Student:

Ian Phillips

Partner:

AzadMedica Inc.

Discipline:

Engineering - biomedical

Sector:

Professional, scientific and technical services

University:

McMaster University

Program:

Accelerate

High Performance Cold Spray Process Development

PolyCSAM is a new industrial-scale cold spray additive manufacturing (CSAM) facility created by Polycontrols to address production and industrialization challenges. It serves as a demonstrator and an innovation/development platform. A recent strengths, weaknesses, opportunities, and threats (SWOT) analysis revealed that the supply chain is solid and diversified for all key components, with the exception of the cold spray (CS) gun itself. There is a clear business opportunity for Polycontrols to come up with innovations that have the potential to be disruptive in the world of CS additive manufacturing. The objective of this project is to develop a new high-performance CS gun that exploits the potential innovations in the following areas: recurrent nozzle clogging, nozzle designs maximizing particle’s impact velocity & temperature, coating internal diameters, and efficient powder preheating. Specific activities that will develop new innovative solutions to allow for the production of a new gun that reaches a technology readiness level of 5 will be performed using CFD and modern engineering design tools.

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Faculty Supervisor:

Bertrand Jodoin

Student:

Aleksandra Nastic;Daniel MacDonald;Saeed Rahmati;Roberto Ortiz Fernandez

Partner:

Polycontrols

Discipline:

Engineering - mechanical

Sector:

Manufacturing

University:

University of Ottawa

Program:

Accelerate

Flexible and semitransparent solar cells

Solar power is the fastest growing source of renewable energy worldwide. Developing low cost, high efficiency and clean solar energy technologies will be of significant long-term interests. All around the world, silicon solar cells dominate the rooftop solar energy production market due to their high efficiency and stability. However, silicon modules have limited use cases as they are bulky, opaque and difficult to apply to complex surfaces. Perovskite solar cells have gained widespread interest in recent years as they are solution processed at low temperatures and can therefore be deposited on flexible and lightweight substrates. Additionally, oerovskite has achieved high efficiencies and is a highly tunable material, where the chemical and crystal properties can be altered to tune the transparency. Semitransparent, lightweight, and flexible solar cells can be applied in several novel applications including electric vehicle autobodies; building windows and facades; powering electronic devices (loT); or attached on top of existing silicon based solar cells to boost the overall solar cell efficiency. The objective of the partner organization is to commercialize perovskite solar cell technology for above mentioned applications and the proposed project will help advance their goal by developing flexible and semi-transparent solar cells.

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Faculty Supervisor:

Ian Hill

Student:

Irina Valitova

Partner:

Rayleigh Solar Tech Inc.

Discipline:

Geography / Geology / Earth science

Sector:

University:

Dalhousie University

Program:

Sustainable and Reusable Face Masks for Combating the Spread of COVID-19

This project seeks to develop a novel, sustainable, self-sanitizing face masks for purpose of providing more robust solutions and mitigate the health risks for the front line workers and general public during this, as well as any future pandemics. This is accomplished by design of novel filters composing of cellulose derived nanofibrils and electrochemically exfoliated graphene, and 3D printing the lightweight, customizable face mask body. Cellulose nanofibrils are made of renewable, recyclable natural resources—pulp is the main source material—and testing to date suggests that it is non-toxic and its production poses no serious environmental risks. Graphene is chemically identical to graphite (widely used in pencil lead), and safe to dispose. anada has become a leader in production of new materials from cellulose, and the proposed project directly supports the development of Canada renewable sector while combating the spread of COVID 19 pandemic and climate change.

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Faculty Supervisor:

Milana Trifkovic;Edward Roberts

Student:

Saqr Abuhatab

Partner:

RECYCL3D

Discipline:

Engineering - chemical / biological

Sector:

Manufacturing

University:

University of Calgary

Program:

Accelerate

Workers’ Specialty Legal Clinic (WSLC): Ensuring Access to Justice for Disadvantaged Workers

There are three legal clinics funded by Legal Aid Ontario that addressed the needs of workers. There is a need to better understand the work of the legal clinics and the needs of the populations they serve in order to guide their ongoing work. The objective of this project is to co-design a framework of collaboration, partnership and coordination to guide the work of the three legal clinics to ensure access to justice for disadvantaged workers. Interns will be involved in reviewing the literature, consulting with staff, clients and other stakeholders, and engaging in a co-design process. The findings will identify the needs of workers in Ontario, and highlight how the clinics can work together to ensure access to justice for disadvantaged workers.

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Faculty Supervisor:

Rebecca Gewurtz

Student:

Nazlim Bilgi;Jesse Anne Sonoda

Partner:

IWCLC

Discipline:

Other

Sector:

Professional, scientific and technical services

University:

McMaster University

Program:

Accelerate

Exploring the state of the art deep learning algorithms in computer visionand voice recognition for applications in RF signal processing

Drones popularity has been rapidly increasing for a wide class of applications including commercial delivery, photography, fire-fighting and environmental monitoring due to their low cost and high commercial availability as unmanned aerial vehicles. In the light of this growth, anti-drone technology has become a significant research topic of investigation due to unauthorized flying of drones in sensitive airspace where their presence is regarded as a potential threat. In this project, we will modify and use existing well-established computer vision and voice recognition deep learning models for signal classification to remotely detect the presence of rogue drones in the air. We will analyze several datasets of radio signals transmitted by popular drones in the market to extract unique fingerprints hidden in the drone signals. Deep learning models will be designed to make use of the extracted fingerprints for detecting the presence of a drone signal.

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Faculty Supervisor:

Ian Frigaard

Student:

Alireza Sarraf Shirazi

Partner:

Skycope Technologies Inc

Discipline:

Engineering - mechanical

Sector:

Professional, scientific and technical services

University:

University of British Columbia

Program:

Accelerate

Gulo Gulo Vision-based navigation for autonomous vehicles

The Gulo Gulo project objective is to improve the navigation algorithm utilized by the onboard motion sensors of AV/CVs to achieve positioning accuracy of less than 1% of the distance travelled with standalone INS solutions. The standalone solution will be aided with visions sensors (camera, LIDAR and RADAR) in addition to integrated system fusion with AI HD map-aiding visual odometry solution.
The solution will be 5G ready so that, in the future, the whole computation effort can be done on the cloud and will not depend on powerful local GPUs.
Utilizing the intern’s research and development efforts in navigation and HD mapping, the partner organization , METI, will be able to achieve the project objectives, attract business partners, expand to and compete within the automotive industry.

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Faculty Supervisor:

Yang Gao

Student:

Mohamed Moussa

Partner:

Micro Engineering Tech Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Calgary

Program:

Accelerate

Electrophysiological studies of medical cannabinoids on 3D human cerebral organoids and neuroglial cultures

Epilepsy, characterized by recurrent seizures, affects 1% of the population. More than 30% of patients are drug-resistant, a significant burden to their lives. New innovative ways to treat the disorder are needed. Cannabidiol (CBD), a nonpsychoactive compound derived from the cannabis plant, has been shown to be a promising new treatment for epilepsy. In collaboration with Avicanna, we will test the antiepileptic effects of CBD and other cannabinoids alone and in combination with common anticonvulsant drugs on both mouse and human brain tissue. In addition, we will also develop a high-throughput multi-well brain slice recording system.

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Faculty Supervisor:

Peter Carlen

Student:

Yasaman Javadzadeh;Alexandra Santos

Partner:

Avicanna Inc

Discipline:

Engineering - biomedical

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

AI-enhanced Automated and Large-scale Real Estate Valuation

House price valuation and forecasting are considered as critical urban problems that aid individuals and organizations to execute a variety of property-related practices such as property purchase and selling, taxation, and mortgage property. This project aims to collect appropriate features for house price modeling and to develop a combined machine learning-graph modeling approach to have an accurate property value assessment model. Then, a spatial-temporal prediction model based on deep learning is designed for future house price forecasting. The intern involved in this project encounters a real-world project with plenty of practical instructive experiences. Additionally, he can attain a priceless experience of working on industry research. The partnering academic institutions will gain valuable insight into new and accurate house price modeling and prediction, which is one of the most trending real estate problems.

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Faculty Supervisor:

Zheng Liu

Student:

Amirhossein Zaji

Partner:

Offerland

Discipline:

Engineering

Sector:

University:

Program:

Accelerate

Oilseed with Depleted Glycosides

Flax seed is of interest as a health food product with high omega-3 fatty acid, protein and fiber content. The seeds naturally contain compound that are anti-nutritive and have a bitter flavor. We have developed a process to safely and cheaply remove these compounds from whole flax seed. We intend to investigate the nutritive qualities, flavor, and shelf life of this flax seed and to scale up the process for commercial use. We will develop recipes for the seed and process co-products. This project will increase the marketability and export of Canadian flax seed and improve the flavor and nutrition of flax-based food products for the health food market.

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Faculty Supervisor:

Martin Reaney

Student:

Chao Huang

Partner:

Bioriginal Food and Science Corp

Discipline:

Forestry

Sector:

Agriculture

University:

University of Saskatchewan

Program:

Accelerate

A hybrid two-way recommender system for candidate and job search

The recruitment industry faces information overload when matching qualified individuals with competitive jobs. A recommender system can effectively solve this problem by filtering relevant content in order to make recommendations, in this case matching candidates with jobs. The objective of this project is to develop such a system for two-way search and ranking of candidates given job description and vice versa; put simply this system will recommend candidates to employer and will recommend job opportunities to candidates. Existing recommender systems have their own unique benefits. This work will build upon earlier research in this field by combining the enhanced interpretability and superior performance of Knowledge Graph recommender systems with the inferencing ability of Fuzzy Logic recommender systems, ultimately developing a two-way, hybrid recommender system with increased accuracy and explainable recommendations—an innovation that will provide competitive advantage in the recruitment industry.

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Faculty Supervisor:

Uzair Ahmad

Student:

Ashish Kutchi;Pruthvi Ignole;Sukhpreet Kaur

Partner:

Eggdemy Inc.

Discipline:

Business

Sector:

Professional, scientific and technical services

University:

Durham College

Program:

Accelerate

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