Innovative Projects Realized

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

31620 Completed Projects

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899
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Projects by Category

Optimization and automation of the imaging and analysis of Ananda Device’s high-throughput NeuroHTSTM microplate

Ananda Devices has developed an innovative technology to produce high-throughput organ-on-chip technology for commercialization in the pharma industry and cosmetic industry. For cost effective and fast commercializing the device, semi automation/automation is required for the high throughput data analysis. Further validation of the automation algorithm is required for data accuracy. So, our aim is to develop and validate the automation strategies to be used for analysis of high through put imaging of the Neuro-HTS device, more specifically analysis of neuronal growth, cell counts, connectivity and degeneration.

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Faculty Supervisor:

Dan Nicolau

Student:

Partner:

Ananda Devices

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

Design, Development of an Automated Hipot and Continuity tester for Novel Catheter

A novel medical device is designed and built by the partner organization. The medical device must go under certain safety tests during the manufacturing process, which are currently performed manually. However, manual tests are too time consuming due to the special structure of the device. On the other hand, the device is now being approved to be used in in Europe and will start a controlled Clinical study in the USA under a PMA filling. Because of this, the partner organization wants the intern to undertake a research project whose objective is to decrees the testing time of the device. The main benefit to the company is that the total production time is going to be decreased, enabling the company to make the supply meet the high demand in the future.

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Faculty Supervisor:

Denis Giannacopoulos

Student:

Partner:

AgileMV

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

Membrane filtration for reuse of greenhouse wastewater

Most operations of horticulture are intensive users of water and nutrients. Growers recognize the importance of water conservation and recycling, and environmental protection and significant research dollars have been invested to develop integrated pest-management systems to reduce pesticide use, to optimize water and nutrient use within the operation, and encourage recirculation. The proposed project seeks to evaluate membrane filtration to treat and re-use irrigation water runoff, leachate, and other nutrient rich wastewaters. This technology has the potential to cost-effectively reduce water and nutrient use on the farm and eliminate or greatly reduce environmental impacts. It will be evaluated for effectiveness, cost/benefit, advantages, disadvantages, and suitability for different production systems. While this project is directed towards small and medium greenhouse operations and container nurseries, other groups producing high nutrient wastewater will benefit from the information derived.

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Faculty Supervisor:

Hongde Zhou

Student:

Partner:

The Soil Resource Group

Discipline:

Engineering

Sector:

Agriculture

University:

University of Guelph

Program:

Accelerate

Multi-sensors Error Modeling and Integration for Future Autonomous Car Navigation

Nowadays, there is a rapid increase in the use of low-cost inertial navigation sensors for commercial and civil applications. Fully autonomous or remotely controlled vehicles requires a reliable and continuous navigation system providing meter level accuracy. The cost, size, and power demand of navigation systems providing this level of accuracy impose a limiting factor to numerous applications. To provide a viable and alternative option, this research will focus on developing error models for multiple dead reckoning low-cost sensors, using innovative approaches, to mitigate the limitations on the performance of Global Navigation Satellite Systems (GNSS) for navigation in urban environments. Furthermore, the research entails developing a set of methods and algorithms for both data acquisition and processing, respectively. The main outcome of the proposed research will be a robust navigation system that entails hardware and software that is capable of providing accurate navigation solutions for autonomous and self-driving car navigation

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Faculty Supervisor:

Steve Liang

Student:

Partner:

Profound Positioning Inc

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Calgary

Program:

Elevate

Graphene Ammonium Ion Sensitive Field Effect Transistor Reliability Testing

This is a partnership between ABB, an international leader in water quality monitoring, and McGill University, where the objective is to test the reliability of an emerging technology in ion concentration measurement. The technology combines state-of-the-art electronics, composed of the atomically thin material graphene, and ionsensitive polymers, developed in the research group of Prof. Szkopek at McGill University. The immediate goal is to assess the viability of the technology for ammonium monitoring in waste-water treatment plants, where harsh conditions prevail and a suitable technology solution remains elusive. The project involves an internship of a
graduate student working with Prof. Szkopek who will be responsible for conducting reliability tests. The intern will receive industrially relevant training while facilitating knowledge transfer from McGill University to ABB.

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Faculty Supervisor:

Thomas Szkopek

Student:

Partner:

ABB Inc.

Discipline:

Engineering

Sector:

Water; Nanotechnology; Advanced Manufacturing

University:

McGill University

Program:

Accelerate

Generating Contextually Appropriate Followup Questions

The goal of this internship is to design and build a model which can generate contextually appropriate questions during a market research survey. This project will build on existing work, and will extend it by incorporating commonsense reasoning, long-range memory, and possibly other features The model will be tested with real humans using an online chatbot, and the results will be compared against previous work. The resulting model will be incorporated into the partner organization’s chatbot-based product, where it will be used as a premium feature for market research surveys.

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Faculty Supervisor:

Suzanne Stevenson

Student:

Partner:

Nexxt Intelligence

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Assessing neuromuscular responses following a single session of whole-body electrical myostimulation exercise

WB-EMS exercise is an alternative method to traditional exercise which improves strength and endurance. Currently, it is unclear how much fatigue is produced following one session of WB-EMS exercise and how long it takes for muscles to recover. Additionally, WB-EMS exercise can use a range of frequencies for electrical stimulation (1-100 Hz), but it is unclear if different frequencies produce more
fatigue and require longer recovery. Thus, we aim to determine how much fatigue if produced in WB EMS exercise, how long recover takes, and if there are differences between high (~85 Hz) and low (~25 Hz) frequencies of electrical stimulation. By understanding how much fatigue is induced by WB-EMS exercise, how long it takes to recover, and if there are differences between stimulation frequencies,
Torus Health Inc. will be able to better plan and design WB-EMS exercise training programs for clients undergoing rehabilitation or those wanting to improve fitness.

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Faculty Supervisor:

Brian Dalton

Student:

Partner:

Torus Health Inc

Discipline:

Life Sciences

Sector:

Arts, entertainment and recreation

University:

The University of British Columbia - Okanagan

Program:

Accelerate

Data synthesis using generative adversarial network

This project is about synthesizing data using generative adversarial network (GAN). Unlike conventional studies which use anonymization techniques for removing private information of individuals, we use variants of GAN architectures for crafting new records contextually similar to real records in the legitimate dataset. We plan to run exploratory experiments on public datasets to provide enough grounds for the viability of GANs in synthesizing information. The objective is to develop a proof of concept that shows if synthetic data could be used with similar results than original data.

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Faculty Supervisor:

Patrick Cardinal

Student:

Partner:

Mouvement des caisses Desjardins

Discipline:

Computer science

Sector:

Finance and Insurance

University:

École de technologie supérieure

Program:

Accelerate

Semi-Supervised Learning for NLP Text Classification

Insurance companies collect huge volumes of text on a daily basis and through multiple channels, which can be used for lots of different analyses, including identifying “cause of death”. It is difficult to overestimate the importance of an insurance company’s need to understand the facts and circumstances surrounding an insured individual’s death. These facts, including the manner and cause of death, along with other data about the decedent, are critical to an insurance company’s ability to measure mortality rates. Considering the huge volume of the data, it is very time-consuming and manual data labelling by human experts is barely possible. The main objective of the proposed research is to develop a semi-supervised model that best suits the unstructured text data. The goal is to develop and validate a generalizable unsupervised deep Natural Language Processing (NLP) model to label the data, identify, and classify “cause of death” from unstructured obituary text.

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Faculty Supervisor:

Hadis Karimipour

Student:

Partner:

Munich Re

Discipline:

Engineering

Sector:

Finance and Insurance

University:

University of Calgary; University of Guelph

Program:

Accelerate

Microelectromechanical Low-power Strain Sensor for structural health monitoring applications – Phase 2

Structural health monitoring (SHM) of airplanes requires very compact and low-power stain sensors. Therefore, IPR wants to investigate how a commercial micro-fabrication process can be used to implement its MEMS sensor design, particularly using the electro-conductive properties of doped silicon vs. metal-coated crystalline silicon or polysilicon. The project will consist of a conceptual study of the current design provided by IPR and the design and evaluation through simulations of that design implemented in different technology. Moreover, design variants of different geometries will be investigated.
This project aims at the design of an adapted sensor for the PiezoMUMPS and PolyMUMPS offered by the MEMSCAP foundry and the investigation of several design variants to improve yield and performance. Moreover, it will also provide a custom process flow that could be implemented as an alternate approach to fabrication at the C2MI.

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Faculty Supervisor:

Frédéric Nabki

Student:

Partner:

IPR Innovative Products Resources Inc.

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

École de technologie supérieure

Program:

Accelerate

Surveying the population structure and resistance to pesticides in soybean two-spotted spider mite populations

The two-spotted spider mite (TSSM) is a global pest that feeds on more than 150 crops including soybean. In Ontario, its pest pressure is especially high in dry years. With climate change, the TSSM pest pressure will increase, predicting the need for the effective pesticide control of TSSM. Dimethoate (Lagon® or Cygon®), an organophosphate pesticide, is currently the only active ingredient registered for the mite control on soybean. However, our pilot analysis identified mite resistance to dimethoate in ALL field TSSM populations tested, which correlates with dimethoate inefficiency reported by farmers. If alternative pesticides are to be registered for mite control, it is not clear which pesticides are expected to be effective, due to the multi-resistance status of most mite populations. In this project we will survey the pesticide resistance status of TSSM populations across soybean production areas in Ontario and will inform the future pesticide registration processes.

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Faculty Supervisor:

Vojislava Grbic;Daniel Lizotte

Student:

Partner:

Grain Farmers of Ontario

Discipline:

Life Sciences

Sector:

Agriculture

University:

The University of Western Ontario

Program:

Accelerate

Module for characterizing the patient response to pain

As chronic pain affects a large portion of the population, caregivers cannot locate the optimum pain site, there is a demand for the development of a pain scanner device to provide accurate information regarding the areas of pain. During this internship, a Patient Response Module (PRM) used by the patient to provide information about the pain felt with the application of different amount of pressure on unhealthy inflammatory soft tissue. As some individuals may have difficulty describing the amount or the location of pain, the PRM will provide the means to the caregivers to pinpoint the pain location and evaluate the pain intensity. This information can then be used to treat the pain effectively. The PRM will communicate the pain-related data wireless to a mobile device or a computer to further analyses. The data will be displayed on a separate module then saved and used track of the patient’s improvement.

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Faculty Supervisor:

Edmond Cretu

Student:

Partner:

ASSESSx Technology Ltd

Discipline:

Engineering

Sector:

Manufacturing

University:

The University of British Columbia

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.