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

A ubiquitous positioning solution for head-mounted sensors – Year two

Recent advancement in computer vision and sensing technology has shown great potential for autonomous vehicles. This work aims to studies using an Augmented Reality heads-up display to improve the reliability of Advanced Driver Assistance Systems (ADAS). The algorithms developed will help drivers detect obstacles e.g. pedestrians crossings, improve current lane departure warnings to allow a car to navigate safely without, as well as estimate distance to nearby vehicles for collision avoidance.

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Faculty Supervisor:

Edward Park

Student:

Ahmed Arafa

Partner:

Elves Technologies Inc

Discipline:

Engineering - mechanical

Sector:

Information and communications technologies

University:

Program:

Elevate

Mathematical modeling of B-vitamin supply in dairy cows – Year two

The B vitamin requirements of cattle were traditionally satisfied via rumen microbial synthesis. However, the B vitamin demands of the modern high producing dairy cow now exceed the synthesis rate by rumen microbes, leading to sub-optimal milk production and efficiency. An increased understanding of dietary factors driving ruminal synthesis and use of B vitamins will help identify when supplementation will benefit the cow. Although B vitamin kinetics in the dairy cow have not previously been modelled, data on concentrations and flows are available from extant sources. This information will be used to develop models in order to increase our overall understanding of the factors affecting B vitamin synthesis in the rumen. Such a model will support the development of nutritional strategies to meet modern dairy cow requirements for B vitamins, delivering improved metabolic efficiency and health.

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Faculty Supervisor:

James France

Student:

Douglas Castagnino

Partner:

University of Guelph

Discipline:

Animal science

Sector:

Agriculture

University:

Program:

Elevate

Secured Cell Broadcasting technology in M2M communication for energy specific application

This project will focus on researching Cell Broadcast solutions for Machine-to-Machine (M2M) communications (command and control operations). As an alternative to the Internet, Cell Broadcast is expected to offer great
advantages to both Utility providers as well as Commercial/Residential HVAC consumers due to its characteristics and it’s broadcasting nature over cellular control channels. Adopting Cell Broadcast will generate several communications-related challenges that will be studied and researched throughout the grant period. Comprehensive proposed solutions for such challenges will be also addressed. The Mitacs program will assist NexGen Controls with expertise, knowledge and various skill-sets that the interns will provide to accelerate the research and development for the proposed project. The research outcomes will enable NexGen Controls customers to optimize and control energy resources from all utility providers through to their HVAC mechanical equipment, etc. Moreover, the project results will create a transition for NexGen Controls from a consumerfocused service provider, to a proven controls energy communications solution provider, offering unmatched, Internet resistant, secure command and control, for utility companies, commercial/industrial as well as residential consumers.

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Faculty Supervisor:

Jahangir Hossain

Student:

Hassan Mohammadnavazi

Partner:

NxGen Controls Corp

Discipline:

Engineering

Sector:

Energy

University:

Program:

Accelerate

Development of Combined Building Integrated Photovoltaic/Thermal (BIPV/T) System for Net-Zero Energy Building Applications – Year two

Building integrated photovoltaic–thermal array (BIPV/T) incorporated within a building structure is a system that combines the roof/facade, photovoltaic cells and thermal collector as an all-in-one product instead of installing each individually. BIPV/T effectively replaces conventional building materials and is more cost-effective than having several separate products, and installation of the BIPV/T system can be implemented during initial building construction. BIPV/T serves to not only produce electricity, but can also generate thermal energy, and act as protection against noise and the weather. With this in mind, BIPV/T functions as a new disruptive technology. This project will benefit the partner(s)’ understanding of BIPV/T benefits, challenges, and system design/installation improvements, which could potentially reduce costs, help advance commercial solar energy system and contribute to the economic benefits of Ontario and Canada.

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Faculty Supervisor:

Alan Fung

Student:

Raghad Kamel

Partner:

Toronto and Region Conservation Authority

Discipline:

Engineering - mechanical

Sector:

Alternative energy

University:

Program:

Elevate

The Guelph Civic Accelerator

This project will evaluate the Civic Accelerator program in Guelph and research key features of the program. The Civic Accelerator is an innovative approach to economic development, using public sector procurement and challenge competitions to support “civic tech” entrepreneurs, startups, students and companies. This project will support the on-going development of the Civic Accelerator program in Guelph as well as adoption of the model more broadly. Specifically, this project will look at how topics for the challenges are identified, how the participating businesses are supported, how ideas are evaluated in the “challenge” and alternative ways in which the program could be delivered. Answers to these questions will directly inform future iterations of the Accelerator in Guelph as well as CODX’s efforts to seed “Open Data Innovation Challenges” in municipalities across Canada.

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Faculty Supervisor:

Harry Cummings

Student:

Josephine Bamanya

Partner:

Canadian Open Data Exchange

Discipline:

Environmental sciences

Sector:

Management of companies and enterprises

University:

Program:

Accelerate

Harnessing the power of horizontal gene transfer for yeast strain development

Yeast are used on a massive scale in many industrial settings, such as production of food and beverages, nutrient supplements, pharmaceuticals, and others, totaling over $5 billion dollars in annual market value. Most yeasts currently used for industrial purposes are taken directly from nature and not optimized for the specific process requirements of industry. Currently, many tools for strain improvements require genetic modification of organisms, which does not allow for a non-GMO “clean” labeling. Yeast strain improvements can also be derived by classical methods – such as selective breeding and evolution – however the scope is limited to traits already present in the broader yeast species capable of mating. Yeasts also acquire new traits by horizontal gene transfer (HGT), which is the acquisition of genetic material by mechanisms other than reproduction. TO BE CONT’D

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Faculty Supervisor:

John Smit

Student:

Cedric Brimacombe

Partner:

Renaissance BioScience Corporation

Discipline:

Biology

Sector:

Life sciences

University:

Program:

Elevate

Technology and Tools for Quantitative Neurodiagnostics Using Ultra-High Resolution Magnetic Resonance Imaging

The project aims to translate developments in ultra-sensitive MRI sensors to a clinically-relevant setting. To create high-sensitivity sensors for better images, we aim to create a tight-fitting system which places the sensors—akin to antennas—closer to the brain. This will improve the quality of the signals that we can extract from the brain, and allow us to use these improvements to capture images that have higher resolution and better contrast. Using this imaging improvement, we aim to then create a large normative dataset of grey matter thicknesses. This dataset will tell us what is “normal” for thickness in every part of the brain, and let us capture differences accurately and sensitivity. Ultimately, we aim to become sensitive to even subtle changes in grey matter loss, which may permit us to detect certain neurodegenerative diseases earlier, allowing us to treat them better.

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Faculty Supervisor:

Reza Farivar-Mohseni

Student:

William Mathieu

Partner:

Siemens Healthcare Ltd.

Discipline:

Engineering - mechanical

Sector:

Medical devices

University:

Program:

Accelerate

Applications of deep learning to large-scale data analysis in mass spectrometry-based proteomics

As a result of recent advances in high-throughput technologies, rapidly increasing amounts of mass spectrometry (MS) data pose new opportunities as well as challenges to existing analysis methods. Novel computational approaches are needed to take advantage of latest breakthroughs in high-performance computing for the large-scale analysis of big data from MS-based proteomics. In this project, we aim to develop new applications of deep learning and neural networks for the analysis of MS data. In particular, we focus on three fundamental problems in a typical MS analysis workflow: peptide feature detection and quantification, de novo peptide sequencing, and protein identification and quantification. Once successfully evaluated, the proposed techniques will be implemented and integrated to PEAKS Studio, the current MS analysis platform of the partner. We believe that the project results will contribute major advances to the research field of MS-based proteomics and substantially improve the performance of the partner’s software products.

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Faculty Supervisor:

Mark Giesbrecht

Student:

Ngoc Hieu Tran

Partner:

Bioinformatics Solutions Inc.

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Elevate

Next Generation Catalyst Layer Design for PEM Fuel Cells

The performance of non-precious metal catalysts (NPMCs) for proton exchange membrane fuel cell (PEMFC) has now reached a stage at which they can be considered as possible alternatives to expensive Pt, especially for low power applications. However, despite significant efforts on catalyst development in the past, only limited studies have been performed on NPMC-based electrode designs. Thus, it is required to develop an effective NPMC-based electrode that can correctly balance the complex parameters to maximize the performance it can bring. Herein, we propose the research and development of cost-effective NPMC electrodes with enhanced performance from hierarchical nanoarchitectures. This work entails a unique and promising approach in the advanced manufacturing of nanostructured NPMCs and electrodes with accurate engineering of their nanostructures.

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Faculty Supervisor:

Zhongwei Chen

Student:

Ja-Yeon Choi

Partner:

Ballard Power Systems Inc.

Discipline:

Engineering - chemical / biological

Sector:

Energy

University:

Program:

Accelerate

Longitudinal Weak Labeling for Lung Cancer Prognosis and Treatment Response Prediction

This project aims at evaluating whether recent results in deep learning models, trained to exploit weak labels (Hwang, 2016) can serve to extract meaningful lesion localizations from image-level labels, either from individual scans or given a (longitudinal) sequence thereof. To this end, we will scale up existing models that have been shown to work on 2D images to a 3D context, studying labeling performance as the dataset size grows. If successful, this work will assert the usefulness of DCNNs to provide a general modeling framework to integrate imaging with other clinical patient data into a predictive system that could help support clinical decisions and ultimately improve patient care. The proposed research project fits within the partner’s scientific roadmap, which is to develop deep learning models suitable to processing clinical data that arises in a sequential fashion at the patient level (longitudinal data), wherein the set of available clinical modalities can be highly variable (heteromodality). The industrial partner has an existing team of full-time researchers dedicated to studying these questions; the intern will attack complementary questions with the help of the team.

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Faculty Supervisor:

Yoshua Bengio

Student:

Michal Drozdzal

Partner:

Imagia Cybernetics Inc

Discipline:

Computer science

Sector:

Medical devices

University:

Program:

Accelerate

Analysis of TGF-? traps as effective immunostimulating cancer treatments

AVID200 is a TGF-? trap that specifically sequesters TGF-?I and TGF-?III to enhance antitumour immunity to inhibit tumor growth. AVID200 also avoids adverse side effect of depleted of TGF-?II. AVID200 is relatively short-lived in circulation, decreasing its capacity to exert desirable enhancement of anti-tumour immunity. To increase the effectiveness of TGF-? traps, a panel of candidate molecules that retain TGF-? isoform specificity and inactivating capacity, but with projected increased stability, have been generated by Formation Biologics. To support an investigational new drug (IND) application, mouse models of breast, melanoma and ovarian cancers will be treated with these TGF-? traps. Their effect on the immune system and tumor inhibition will be determined both as single agents and in combination with immune stimulating drugs. These experiments will provide preclinical evidence of the effectiveness of candidate TGF-? inhibitors on mouse models of cancer in preparation for an IND application and clinical trials.

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Faculty Supervisor:

James Koropatnick

Student:

Michael Thwaites

Partner:

Formation Biologics

Discipline:

Biochemistry / Molecular biology

Sector:

Pharmaceuticals

University:

Program:

Accelerate

Visualization, understanding and engineering of machine learning models for entity recognition

Machine learning is a discipline of teaching computers repeatable tasks that humans do well but slowly. At Interdata we are on a mission to use Artificial intelligence to understand the data being stored by organizations and the relationships between those data assets. As such Darrell will be working on methodologies and tools to expand our understanding of the algorithms we develop in order to improve them. He will then use those methodologies and tools to engineer new algorithms to be used by the organization to categorize and tranform data.

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Faculty Supervisor:

David Duvenaud

Student:

Darrell Aucoin

Partner:

Interdata Laboratories Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

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