Innovative Projects Realized

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

29670 Completed Projects

2811
AB
4990
BC
801
MB
663
NL
825
SK
8841
ON
9197
QC
95
PE
568
NB
1088
NS

Projects by Category

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

Autonomous Surveillance and Inspection using Drone for Oil and Gas Pipelines

Using drones and sensors to conduct autonomous monitoring has shown benefits in many applications, including urban infrastructure planning and maintenance. In this research, we focus on a less studied problem of detecting hazardous and illegal events along oil and gas pipelines. We aim to deliver a real-time Internet of Drones (IoD) system, which integrates the latest computer vision techniques, including the learning-based approaches, and cloud networks, in collaboration with companies like TELUS and TC Energy Canada. Differing from vehicles and humans, that can already be detected robustly by a number of current tools, our objects or events of interest, e.g., leakages and oil splits along a pipeline corridor, are not frequently seen, such as leakage or oil spills. The system can be applied not only to the oil and gas industry across Canada, but also on other applications, which analyze aerial sensor data.

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Faculty Supervisor:

Irene Cheng

Student:

Partner:

AirMarket

Discipline:

Computer science

Sector:

Manufacturing; Professional, scientific and technical services; Transportation and warehousing

University:

University of Alberta

Program:

Accelerate

Development of a Broadband low Resolution Spectral Irradiance Monitoring Device

The proposed device, that has been requested to Sciencetech Inc. (SCI) during the past few years, is an innovative way to analyze the light generated by modern high intensity light sources in many areas of research, and in particular in those using Solar Simulation (space research, agriculture, biology, medical, semiconductors solar cells and renewable energy devices).

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Faculty Supervisor:

François Lagugné-Labarthet

Student:

Partner:

Sciencetech Inc

Discipline:

Physics

Sector:

Professional, scientific and technical services

University:

The University of Western Ontario

Program:

Accelerate

Factors Affecting Nursing Retention in Rural Canada: Managing nursing shortages in Rural Alberta

In this research, we will be identifying the factors that affect retention of nurses in the rural areas, and particularly we will be exploring how such factors including the organizational culture, and the academic preparation of the nurses affect the retention of nurses in the rural areas. It is the objective of this research to explore identify the strategies that have been utilized or are currently being utilized by organizations to deal with this retention challenge. Finally, we will be exploring any new strategies that may be utilized to deal with the retention of nurses in rural areas.

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Faculty Supervisor:

Carolin Rekar Munro

Student:

Partner:

CYNKON

Discipline:

Business

Sector:

Professional, scientific and technical services

University:

Royal Roads University

Program:

Accelerate

Developing a Revolutionary Technology for the Niche Tool Grinding Sector

During the intended internship, the industry partner aims to develop machinery for the niche tool grinding sector that can potentially boost current industry technology. This machinery targets immediate launch to market a unique CNC grinding system to be characterized by an unsurpassed flexibility owed to its 14 motion controlled axes, with multiple serial manipulator robots custom designs for the specific application. To further enhance its performances, the grinding system will be coupled with a fully automated robotic loading cell to enable long term/overnight unsupervised operation of the machine. The primary goal of this internship is to investigate and review mechanical systems in detail and complete the design section. Applied research is required in combination with hard design and implementation. Most of the challenges associated with this structure that is unique in the world are a result of its kinematic complexity.

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Faculty Supervisor:

Remus Tutunea-Fatan

Student:

Partner:

Williams and White Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

Western University

Program:

Accelerate

Application of ultrasonic technology for converting residual feedstocks into humic acids

New innovative technology is proposed for converting residual feedstocks, like petcoke and asphaltenes, into valuable products such as humic acids. This novel approach process employs an ultrasound technique in an aqueous alkaline media to create OH radicals at low reaction conditions and thus enhancing the formation of humic acids. The process has high efficiency, conversion and selectivity for humic with zero CO2 emissions, thus making the process environmentally friendly. Considering the large amount of feedstocks in Canada, the success of this project helps Carbon OxyTech Inc. to commercialize this technology and sell a high price humic acids which will increase their revenue that can later be leveraged to further advance this project.

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Faculty Supervisor:

Nashaat Nassar

Student:

Partner:

Carbon OxyTech Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

University of Calgary

Program:

Accelerate

5G-DetNet/TSN for Industrial Automation

The 4th industrial revolution, Industry 4.0, promises to create completely flexible production lines with automated-guided mobile robots. To enable this transformation, 1/0 devices (e.g., sensors and actuators) require not only a 5G Ultra-Reliable Low-Latency Communication (URLLC) but also a deterministic and bounded low latency with a guaranteed data delivery and extremely low data loss. In this regard, IEEE 802.1 Time Sensitive Networking (TSN) and IETF Deterministic Networking (DetNet) become the de facto wireline technologies to complement 5G communication for industrial automation systems. Despite all efforts on 5G-TSN integration for industrial automation, still a unified end-to-end integration has not been realized. This research project will look at different aspects of this end-to-end integration

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Faculty Supervisor:

Marc St-Hilaire

Student:

Partner:

Ericsson Canada Inc (Quebec)

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Carleton University

Program:

Accelerate

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