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

Junior hockey competence analytics

Analytics is about awareness of the states of knowledge of users. Users can become aware of their owns states of knowledge at different levels. Analytics measures such levels of each user, and engages them in taking initiatives to hop from one knowledge state to the next.
The hops happen mostly gradually, depending on the capacity of the user, punctuated by dramatic jumps. Analytics identifies such scenarios where dramatic jumps are necessary and offers the information needed to enact such jumps.
Learning Analytics, in the context of Ice Hockey, is the study of detection, analysis, and generation of moments of progress awareness about skater, goaltender, and team experiences.
By employing recent advances in statistics, machine learning algorithms and sensor technologies, this research aims to build a big data learning analytics solution that provides progress awareness scenarios and means to improve junior hockey player competency and self-reflection. TO BE CONT’D

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Faculty Supervisor:

Vivekanandan Suresh Kumar

Student:

Kannan Govindarajan

Partner:

Eighty Seven Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Elevate

Collaborative Service Robot in a Group Home Environment of People with Developmental Disabilities

The vision of this proposed project is to design and build a collaborative service robot that will help people with developmental disabilities (DD) reach their personal goals and achieve greater independence, using existing and new technologies. This will allow the industry partner, JDQ, to advance the type of services provided to people with DD and their caregivers in a group home environment, through its partnership with Developmental Disabilities Association (DDA). This project will support the overall intention of contributing to the creation of better collaborative service robots than exist today, specifically designed to support adults with DD and their families. Ultimately, JDQ and DDA expect the project outcomes to be transferable to health and elder care environments.

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Faculty Supervisor:

Ahmad Rad

Student:

Sina Radmard

Partner:

Developmental Disabilities Association

Discipline:

Computer science

Sector:

Medical devices

University:

Program:

Accelerate

Effects of Dietary Fatty Acids on Overwinter Survival of Underyearling Striped Bass (Morone Saxatilis)

To achieve the goal of commercializing striped bass aquaculture in Nova Scotia, the obstacle of high (up to 100%) overwinter mortality among young-of-year fish needs to be overcome. Mortality is restricted to fish that are under one year old (underyearlings, <100g) transferred to cages in freshwater ponds in the late fall. Larger bass (>500g) suffer negligible mortality. Potential factors for such low survival rates identified through four years of trials by researchers from Dalhousie include low oxygen levels and exhaustion of lipid energy reserves. This project uses the concept of homeoviscous adaptation to test the hypothesis that changing the proportions of dietary saturated and unsaturated fats will affect overwinter survival. Homeoviscous adaptation proposes that proper cell function depends on cells remaining fluid no matter the temperature of the fish’s environment. TO BE CONT’D

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Faculty Supervisor:

Peter Tyedmers

Student:

Kare Tonning

Partner:

Aquaculture Association of Nova Scotia

Discipline:

Environmental sciences

Sector:

Fisheries and wildlife

University:

Program:

Accelerate

Machine-to-Machine Remote Asset Monitoring & Optimal Inspection and Repair Logistics

Cyber-physical systems or Industrial Internet of Things (IIoT) applications are more advanced than commercial IoT devices/applications mainly because of the prevalence of connected sensors and embedded systems in the industrial world. The objective of this project is to develop and package a low range, low power (LoRa Technology) remote asset monitoring and control system for “remote fixed utility” IIoT applications. Such developed system has the following advantages: 1) more cost effective than traditional wired approach; 2) immediate interpretation of data to actionable information; 3) risk reductions applying corrective control action. The common challenge is the extreme environments/remoteness of sites to be serviced. An IIoT-based maintenance solution supported by techniques of Fault Tree Analysis (FTA), Failure Mode, Effects, and Analysis (FMEA), and optimal planning scheduling will be developed. Validation using three test cases will help generalize and commercialize the technology for a wide range of applications.

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Faculty Supervisor:

Ahmed Azab

Student:

Mehmmood Abdullah

Partner:

ONYX Engineering Ltd

Discipline:

Engineering - mechanical

Sector:

Information and communications technologies

University:

Program:

Accelerate

Machine learning in fluid composition quantification

A critical issue in the oil and gas industry is to quantify the composition of fluids flowing back from the hydraulic fracturing process. This quantification is usually carried out by a manual process (frequently via a visual test) to estimate the water and oil produced from a well flow back process. A sample of these onsite tests are sent to laboratories for chemical analysis. This process has been the status quo for decades. This approach is manual, prone to error, and does not lend itself to sophisticated real time analysis. Machine learning techniques have significantly developed in the last decades, and combining with in-depth mathematical basis, it is now capable of producing a revolutionary impact to almost every industrial application. This research project aims at developing a machine learning framework, that can detect the fluid composition based on the sensor data as well as referencing chemical analysis results.

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Faculty Supervisor:

Yau Shu Wong

Student:

Yile Zhang

Partner:

MaxFleet Solutions

Discipline:

Mathematics

Sector:

Information and communications technologies

University:

Program:

Accelerate

Development of a hybrid seismic data inversion method for determining well-drilling location at complex geophysical area

Due to the current economic downturn, especially the lower crude oil price, the drilling success rate become the most important goal for any oil/gas company. For a start-up company, any failure in drilling will be a disaster. To this end, the Deep Treasure Corp wishes that through the combination of mature hydrocarbon prediction techniques and new research results in seismic inversion,  the success rate of hydrocarbon prediction, the theoretical basis for well placement can be provided  in Roncott field, which will improve the success rate in drilling. The University of Calgary is one of Canada’s top research institutes, especially in the areas of exploration geophysics, seismic data processing and  petroleum engineering. On the other hand, Deep Treasure Corp, with a  short operating history,  is lack of expertise  in . The company will collaborate with researchers from University of Calgary to access the most up-to-date research results in seismic waveform inversion, and the most advanced technology available in precise well placement, so that the drilling success rate can be improved to reduced drilling cost and environment impact. 

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Faculty Supervisor:

Wenyuan Liao

Student:

Yaoting Lin

Partner:

University of Calgary

Discipline:

Mathematics

Sector:

Oil and gas

University:

Program:

Elevate

Alberta High Resolution Wetland Inventory Methodology Development

This project aims to operationalize innovative methods for developing cost effective wetland inventories across Alberta by use of numerous sources of remote sensing data, namely light detection and ranging (LiDAR), synthetic aperture Radar (SAR), and optical imagery. The project will formalize a mapping specification, develop training and validation datasets (available in-kind from the academic supervisor and industry partner), and  review literature to identify candidate data platforms and mapping methodologies that have the potential to meet the requirements of the Alberta Wetland Classification System (AWCS) and the Canadian Wetland Inventory (CWI) data model.  A workflow will be developed to integrate candidate data sources and methodologies to yield high resolution wetland mapping and attribution. Project deliverables will support  the implementation of Alberta Wetland Policy and North American Waterfowl Management Plan (NAWMP) Habitat Restoration and mitigation programs.

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Faculty Supervisor:

Christopher Hopkinson

Student:

Craig Mahoney

Partner:

University of Calgary

Discipline:

Geography / Geology / Earth science

Sector:

Natural resources

University:

Program:

Elevate

Super Varnishes for Aerospace Coatings

Organic coatings are used to protect surfaces in many prominent industries such as automotive and aerospace. The industrial partner has found it imperative to develop their varnish for aircraft interiors in-house. They have identified a “super varnish” that must have exceptional resistance to cracking, prevention of shrinkage and adhesion to wood substrates (grouped herein as material properties). Secondary “must-have” properties are non-flammability, self-healing characteristics and greener processes. Flammability resistance is critical in airplane parts; thus lowering of flammability via reduction of volatile organic content (VOC) or the use of additives is key. Reduction of VOC lends itself to greener processes (eg. water-borne coatings) such as suspension or emulsion polymerization. Also, low viscosity coating solutions that are afforded by our chemistry allows less solvent to be used, thereby reducing VOC. This proposal aims to use copolymers incorporating various functionalities to improve adhesion via controlled radical polymerization as the coating resin.

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Faculty Supervisor:

Milan Maric

Student:

Faezeh Hajiali

Partner:

C&D Aerospace Canada Co

Discipline:

Engineering - chemical / biological

Sector:

Aerospace and defense

University:

Program:

Accelerate

Developing durable and electrical conductive concrete composites for Quebec

This project focuses on the development of Electric Conductive Concrete (ECC) pavements which can efficiently reduce the cost of snow removal of Quebec. Recent research has proven that temperature regulated pavements can melt snow and ice. By taking advantage of all latest technology in conductive inclusions, the project aims will develop an optimized ECC mix design and characterize its durability performances. The technology transfer to a local company of such knowledge will foster the implementation of new solutions for heating pavements in Quebec.

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Faculty Supervisor:

Luca Sorelli

Student:

Raphael Fulham-Lebrasseur

Partner:

Discipline:

Engineering - civil

Sector:

Construction and infrastructure

University:

Program:

Accelerate

NUR77 AND RXR: NEW TARGETS IN DRUG-INDUCED DYSKINESIA

Drug-induced dyskinesia is a debilitating side effect induced by anti-parkinsonian and antipsychotic drugs. Frequency can reach 80% in Parkinson’ disease and around 50% in schizophrenia patients treated with typical antipsychotics. Treatments for dyskinesia currently available have a very limited impact and generate important side effects. We have identified a new pharmacological target that may offer a new perspective in the treatment of these conditions. The target is composed of two nuclear receptors involved in the modulation of gene expression. We developed a sensitive biosensor that can monitor the activity of this complex. We used this assay to screen library of compounds and identified novel molecules (hits) that interact with this cellular target.
In the work proposed in the current application, we will now generate small libraries of hit analogues (hit expansion) in order to improve properties as lead compounds. Lead compounds will then be tested in animal models of drug-induced dyskinesia.

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Faculty Supervisor:

Sylvie Mader

Student:

David Cotnoir-White

Partner:

Merck Canada Inc.

Discipline:

Biochemistry / Molecular biology

Sector:

Pharmaceuticals

University:

Program:

Accelerate

Atomic Layer Deposition Tool – Testing and Process Development

Currently, no Canadian-based companies are involved in the manufacturing of state-of-the-art research tools for atomic layer deposition (ALD). Angstrom Engineering is a leading manufacturer of similar technologies – physical vapour deposition and chemical vapour deposition – and have identified an opportunity to produce the first ALD research tool manufactured entirely in Canada. ALD is a vapour phase technique that offers sequential, self-limiting surface reactions to deposit thin films with exceptional control over thickness and composition, as well as conformality and uniformity. The demand for ALD is primarily driven by fast emerging sophisticated technological demonstrations in materials science and engineering, nanotechnology, microelectronics, displays, storage devices and bio-integrated electronics. The development of this cutting-edge manufacturing equipment will further advance the growth and opportunities in this field, especially in the uncapitalized emerging markets (i.e. Asia and Latin America). TO BE CONT’D

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Faculty Supervisor:

Sean Barry

Student:

Akhil Vohra

Partner:

Angstrom Engineering Inc.

Discipline:

Chemistry

Sector:

Nanotechnologies

University:

Program:

Elevate

Implementation of a Machine Vision-based System for the Recognition of Indian Coins

Counting coins, with speed and accuracy, has been a challenging issue for banks and stores. People used to count coins manually before the arrival of coin counting machines. The process of counting coins manually is a very time consuming and tedious job. Moreover, mistakes are likely to occur due to various reasons such as fatigue, eye tiredness and too many coins of nearly same shape and size cause confusion in sorting and counting. Coin sorters are common in North America and can be found in most commercial banks and even some grocery stores. By contrast, they are not available in India, where the number and similarity of the coins make for a very challenging problem. The objective of this project is to determine whether advanced machine vision techniques are able to sort coins from India with acceptable speed and accuracy. TO BE CONT’D

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Faculty Supervisor:

Brian Surgenor

Student:

Vedang Chauhan

Partner:

Queen's University

Discipline:

Engineering - mechanical

Sector:

Advanced manufacturing

University:

Program:

Accelerate

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