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

Eco-efficiency Evaluation of Cybersecurity Solutions

Industries around the world have been leaning towards cleaner solutions to mitigate their environmental footprint. IT is one industry that has been rapidly growing during the recent past. Hence, it is important to enhance the environmental performance of IT solutions through a life cycle thinking lens. Eco-efficiency evaluation enables analyzing the environmental and economic performance of a product in focus and has been a popular ratio for comparing alternative products. This research performs eco-efficiency assessment for cybersecurity solutions. The operational and embodied carbon footprint of cybersecurity solutions will be estimated using ISO standards. Life cycle costing and carbon footprint will be used for calculating the eco-efficiency of a cybersecurity solution. The findings of this research support identifying environmentally acceptable and economically viable cybersecurity solutions.

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Faculty Supervisor:

Rajeev Ruparathna;Ikjot Saini

Student:

John Akana

Partner:

Crypto4A Technologies

Discipline:

Engineering - civil

Sector:

Information and cultural industries

University:

University of Windsor

Program:

Accelerate

Assessing Regenerative Energy Technologies for Electric Vehicles

Electric vehicle (EV) is the future of sustainable transportation to phase out the reliance on petroleum fuels. Despite the multibillion-dollar market potential, wide deployment of EV is challenging due to limited energy storage. Regenerative energy generation can be implemented to compensate for the energy consumption in EV to provide the much-needed extra mileage. Apart from regenerative braking, other energy harvesting options such as solar panels, wind turbines, and vibration/shock energy harvesting have yet to be implemented at larger
scales. This research focuses on systematic evaluation and engineering of regenerative energy harvesting systems with computational approach, leading to market analysis and cost performance index comparison of hybrid energy harvesting systems, thus proposing a feasible high-performing regenerative energy harvesting system that is projected to worth at least $37.5B within the next 5 years for an annual sale of 350,000 EV units when successfully implemented in a leading EV company (Tesla) market alone.

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Faculty Supervisor:

Kirk H Bevan

Student:

Yee Wei Foong;Salvador Valtierra Rodriguez

Partner:

Mega Range Extender

Discipline:

Engineering

Sector:

Information and cultural industries

University:

McGill University

Program:

Accelerate

Understanding the Path from Real to Virtual Economy Adoption: The Unity Engine, Design Theory & Future Directions

This project examines virtual economy design practices within Unity Technologies Canada Company’s software engine/editor. Analysis of game design choices found within proprietary data from the company is used to determine the predominant design practices within the system and identify underlying ideological assumptions that affect the design, creation, and maintenance of virtual economies. Using a combination of this design data provided by Unity Technologies Canada Company and theory from the field of game studies, the goal of the project is to further understandings of how virtual economies interact with real economies in the present and to shape future virtual economy design within the Unity engine/editor.

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Faculty Supervisor:

Mia Consalvo

Student:

Ryan Scheiding

Partner:

Unity Technologies

Discipline:

Journalism / Media studies and communication

Sector:

Professional, scientific and technical services

University:

Concordia University

Program:

In vitro evaluation and understanding of fabrics interaction with skin cells and dominant skin microbiome: a concurrent material degradation and health implication

Textile industry has been revolutionized over the years with inventions and creative innovations on fabrics to improve performance, comfort and durability. Developing such high performance and durable textiles without compromising human safety is a major challenge. This project will investigate the interactions between fabric systems with human skin and major skin colonizing bacteria. In addition, durability of these fabrics when exposed to skin bacteria under moist conditions be investigated through this project. Three interns will look into these aspects in collaboration with lululemon inc. Interns will use the fabric systems (natural, synthetic, semisynthetic blends) developed by lululemon inc. towards developing biocompatible/ safe textiles without comprising its performance and durability.

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Faculty Supervisor:

Sepideh Pakpour;Abbas Sadeghzadeh Milani;Apurva Narayan

Student:

Iman Jalilvand;Taif Anjum

Partner:

Lululemon Athletica

Discipline:

Computer science

Sector:

Manufacturing

University:

University of British Columbia Okanagan

Program:

Accelerate

Application of a novel cryptographic filesystem to high-security domains

Aerial full-motion video, marine systems and space-based earth observation share key characteristics: they involve critical infrastructure, they rely on sensitive information and they require strong data provenance. We have applied cryptographic techniques — derived from both historic security protocols and newer blockchain systems — to create a novel research cryptographic filesystem in a previous Mitacs project, and now we will apply that filesystem to these three problem domains. This will require the development of new research ideas and new software based on cutting-edge tools and techniques, and it will enable us to secure critical data for the safeguarding of Canada and its interests. It will also position for future commercialization, generating significant economic activity in the high-tech, aerospace and marine systems sectors.

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Faculty Supervisor:

Jonathan Anderson

Student:

Arastoo Bozorgi;Samir Dharar

Partner:

C-CORE

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Memorial University of Newfoundland

Program:

Accelerate

A Predictive Cluster-based Machine Learning Pricing Model

Dynamic pricing models create price by assessing total cost, demand, and timing to customize the price to the moment. The models enable both buyers and sellers to settle a price that is very custom to their specific needs. Bison Transport Inc. has a network model that monitors profit and a pricing engine that monitors margin. The network model needs to evolve in critical ways to facilitate dynamic pricing. The current model allows viewing of the network from a variety of vantage points- region, customer, driver, asset, service type and time (day of week, time of day, season of year). The pricing engine, however, is not flexible. The current direct programming-based solutions incorporated into the pricing engine for dynamic pricing cannot adapt to the changing and unpredictable market conditions. Deletion or addition of information are not possible unless the programming code is directly modified. This is tedious. The solution is, therefore, automating the software. We propose to use computer automating tools from machine learning, that will allow the computer to learn from the input data set and predict future prices without human intervention. These tools can perform real time data analysis and optimize prices to changing demand and market conditions.

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Faculty Supervisor:

Parimala Thulasiraman

Student:

Emanuel Wiens

Partner:

Bison Transport Inc

Discipline:

Computer science

Sector:

Transportation and warehousing

University:

University of Manitoba

Program:

Accelerate

A Social Welfare Maximization Matching Framework for Supplemental Nurse Staffing

The adequacy of hospital nurse staffing in Canada is essential for the delivery of quality health care to Canadians. In light of permanent nursing staff shortage in most of Canadian hospitals, using of supplemental nurses to bolster permanent nursing staff is widespread. Having suitably qualified staff on duty at the right time is a large determinant of service organization efficiency in providing continuity of care. On the other hand, attractive schedules are an important factor leading to successful recruiting and retaining valuable nursing personnel. Computing mutually beneficial staffing schedules in a dynamic supplemental nurse staffing environment at larger scale is a big challenge facing healthcare staffing agencies. In this project, we will develop machine learning algorithms, optimization models, dynamic scheduling structures to tackle the challenge. These proposed methods will be implemented in software modules and integrated in a software platform which can be hosted in Medialpha’s cloud computing environment. By using the proposed supplemental nurse staffing system, Medialpha will attract more nurses and hospitals to their platform. In addition, the integrated system will streamline Medialpha’s business process and lower its operational costs.

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Faculty Supervisor:

Chun Wang

Student:

Xinkai Xu;Jie Gao;Andy Ta

Partner:

Medialpha Laboratories Inc

Discipline:

Engineering

Sector:

Health care and social assistance

University:

Concordia University

Program:

Accelerate

Hybrid Energy Harvesting Yarns Based on Piezo-Photoelectric Nanofibers for Wearable Electronics

The rapid development of wearable electronics and smart textiles demands a more flexible energy supply. Compared with charging from a rigid and bulky battery/capacitor, harvesting the energy from the environment and converting the energy to electricity is a more sustainable and user-friendly approach. A yarn-shaped energy harvester, which is able to convert solar and mechanical energy into electricity simultaneously, will be fabricated. The dual harvesting capability can also compensate the energy shortage when the device is not illuminated by sun. The flexible yarn structure enables the harvester to be woven or knitted into a fabric. The power fabric can then be connected to the wearable device and make the self-power device feasible. Our work can help Texavie develop more reliable wearable electronics by providing a flexible energy harvesting and converting device.

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Faculty Supervisor:

Frank Ko

Student:

Siying Wu

Partner:

Texavie Technologies Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

University of British Columbia

Program:

Parallel multibody solver coupling algorithms

This project concerns the efficient simulation of constrained-multi body systems with applications in training simulations. For instance, a crane on a construction site can be modeled and simulated as a collection of rigid bodies connected by rotational joints. Simulation of contact and friction is similar but a challenge because the force is bounded (i.e., forces are not allowed to act like glue and can only push objects apart). When there are large numbers of bodies in a simulation, with many frictional contacts, these systems can be challenging to
solve. In this work, we allow the systems to be partitioned, solved in parallel, and then coupled with a solve of the interface forces. This work specifically aims to improve the efficiency of this interface solve, with the main benefit being that much larger training systems can be solved both accurately, and at speeds that are useful for interactive training simulations.

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Faculty Supervisor:

Paul Kry;Sheldon Andrews

Student:

Henry Ho

Partner:

CMLabs

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Program:

Accelerate

Smart Brush for Cattle

Cattle in commercial farming systems are highly motivated to use brushes to perform grooming and improve coat cleanliness. A change in brushing behavior may also serve as an early indicator of health and welfare problems. Farmers often provide rotating motorized brushes for cows but the currently available devices do not collect any data on which animal is using them and for how long, limiting the use of brushing behavior to inform management decisions. This project aims to develop and validate a smart brush that gives information on individual- and group-level brush use and can be used on commercial farms to integrate brushing behavior with other on-farm data to improve animal welfare.

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Faculty Supervisor:

Daniel Weary;Marina von Keyserlingk

Student:

Borbala Foris

Partner:

Artex

Discipline:

Food science

Sector:

Manufacturing

University:

University of British Columbia

Program:

Accelerate

Development of a Welding Robotic Systems for Pipeline Welding

Pipelines are a vital part of safe drinking water networks and hygienic facilities, and any improvement in facilitating their development affects decreasing the disease and death and increasing the prosperity of the community economy. This project aims to develop a welder robot for massive pipeline connections, which will expedite the execution phases of welding. Compared with the current bulky and expensive welding robot, the developed robotic system will be more helpful in pipeline development because it will be portable and affordable. Furthermore, labor cost is allocated a significant portion of pipeline development cost, and weld reliability is highly affected by welder skills when done manually. The proposed project aims at designing a robotic platform that can achieve a high-quality uniform weld bead with minimum operator interference, which will decrease the labor and repair cost.

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Faculty Supervisor:

Mehrdad Moallem

Student:

Jalal Taheri Kahnamouei

Partner:

Jewel Holdings LTD

Discipline:

Engineering - mechanical

Sector:

Other

University:

Simon Fraser University

Program:

Microstructure Engineering of Aluminum Extrusions for the Automotive Sector

The use of aluminum alloys in automotive applications is increasing since the weight of the vehicle can be decreased. This is beneficial for both internal combustion and battery powered vehicles, to increase fuel economy and increase vehicle range, respectively. However, in general, aluminum alloys are more difficult to form and their performance during a vehicle crash may be challenging. Thus, it is necessary to understand the linkages between the production of the components and their performance. In many cases, components can be made by extrusion followed by heat treatment. The cooling conditions after extrusion and before heat treatment are important. Here, better mechanical properties are found with high cooling rates but higher cooling rates can lead to distortion of component shape which is not acceptable. The goal of this work is to develop models to predict performance based on processing conditions.

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Faculty Supervisor:

Warren Poole

Student:

Andrew Zang;Jared Uramowski;Gwenaelle Meyruey

Partner:

Rio Tinto Alcan

Discipline:

Engineering

Sector:

Manufacturing

University:

University of British Columbia

Program:

Accelerate

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