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

Proof of concept testing of a bead-on-string polyacrylonitrile nanofibrous air filter for indoor air cleaners

Indoor air quality of buildings is becoming even more important today, with the persistence of COVID-19 pandemic. A new type of air filter, based on bead-on-string polyacrylonitrile, offers high ultrafine particles removal (above 99%) as well as low pressure drop. Environmental and Power Solutions and Canada Water Technology eXchange are joining forces to undertake research to investigate options to control COVID-19 virus. The objective of this project is to develop a proof-of-concept testing protocol for the bead-on-string filter (BOSF), conduct the experiments of the BOSF samples at the Microcellular Plastics Manufacturing Laboratory (MPML) at the University of Toronto, analyze the performance and pressure drop, and identify commercialization potential and certification testing opportunities of the new filter.

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Faculty Supervisor:

Edward McBean;Chul Park

Student:

Yifeng Huang

Partner:

CanadaWTX Inc

Discipline:

Other

Sector:

Professional, scientific and technical services

University:

University of Guelph

Program:

Accelerate

Optimizing the productivity of the whooping cough, diphtheria, and tetanus vaccines’ manufacturing processes: software and hardware-based solutions

The current key challenges in manufacturing of pharmaceuticals in general and vaccines in particular is the lack of rapid measurements for monitoring the processes in real time, lack of understanding of the correlation between operating conditions to the productivity of antigens composing the vaccines and contaminations that affect the purification processes. This proposed research program addresses these challenges through 3 projects as follows i) development of model based estimators in combination with rapid measurements for monitoring upstream and downstream manufacturing processes; (ii) elucidation of correlations between media composition and productivity in upstream operations by a combination of models and metabolic flux data (iii) Statistical control for vaccine upstream and downstream manufacturing processes using a combination of models and data. The research focuses on 3 vaccines: Tetanus, Diphtheria and Whooping Cough.

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Faculty Supervisor:

Hector Budman;Luis Ricardez Sandoval;Qinqin Zhu

Student:

Mohammad Aghaee Foroushani;Abhishek Mishra

Partner:

Sanofi Pasteur

Discipline:

Engineering - chemical / biological

Sector:

University:

University of Waterloo

Program:

The role of historical Indigenous burning patterns in reducing risk to mountain communities

For thousands of years before European arrival, the Indigenous people of the Rocky Mountains regularly used low-intensity surface fires to keep forests clear of debris and fuel to mitigate the risk of high-intensity wildfire. The proposed project will investigate the historical extent of landscape management by Indigenous burning methods and explore the incorporation of Indigenous burning practices into modern forest management programs to cope with recent extreme wildfire seasons. The project will analyze historical photos taken from early mountain surveys in the late 19th century to quantify the extent of the landscape that was regularly burned before fire exclusion policies were passed in the early 20th century. Utilizing image analysis software and other data from tree ring analysis and traditional knowledge, traditional forest management through Indigenous burning can be quantified in a way that is useful to modern forest management.

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Faculty Supervisor:

Eric Higgs

Student:

Maya Frederickson

Partner:

Foothills Research Institute

Discipline:

Environmental sciences

Sector:

University:

University of Victoria

Program:

Accelerate

Accelerating database storage engines using Processing In Memory

A notorious phenomenon limiting general-purpose computing today is memory wall. Memory – the hardware used to store the data- is located relatively far from the central processing unit (CPU), so applications spend a lot of time waiting on data to travel from memory to the CPU. New memory hardware, such as the one addressed in this project, aims to address this problem at a fundamental level by adding processing units to the memory itself. This way, the data can be processed right where it lives, instead of being shuffled to and from the CPU. This idea is called Processing In Memory (PIM). The proposed project will experiment with novel PIM hardware with the goal of understanding how to adopt the software to make the best use of it.

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Faculty Supervisor:

Alexandra Fedorova

Student:

Joel Nider

Partner:

UPMEM S.A.S.

Discipline:

Engineering - computer / electrical

Sector:

Manufacturing

University:

University of British Columbia

Program:

Accelerate

Molecular diagnostics for temperature stress and pesticide stress on queens

Honey bee colony health and productivity is intrinsically linked to the quality of the queen. Unfortunately, queen quality is compromised by stressors such as extreme temperatures and pesticide exposure. When queens are heat- and cold-shocked, the viability of their stored sperm drastically decreases, causing colonies to dwindle, produce less honey, and ultimately fail. Pesticide exposure has similar effects. But we currently don’t have diagnostic tools for identifying root causes of queen failure, so beekeepers are often simply left guessing or wondering why. Some queen tests exist, such as measuring sperm viability from dissected spermathecae, but they have limited practical utility because they still do not identify the underlying cause of viability losses. This project aims to develop molecular diagnostic tests for heat-stress, cold-stress, and pesticide-stress events for queens as a further step towards evidence-based colony management

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Faculty Supervisor:

Leonard Foster

Student:

Abigail Chapman;Mopelola (Lola) Akinlaja

Partner:

Canadian Honey Council

Discipline:

Biochemistry / Molecular biology

Sector:

Agriculture

University:

University of British Columbia

Program:

Accelerate

Daily Question Assessment Methodology: The Development and Validation of Industrial-Organizational Constructs and Methodologies for Business Intelligence Application

Many well-constructed, validated survey instruments in the field of industrial/organizational psychology are lengthy and complex to score and interpret. This is a significant issue as the intent of developing survey instruments is for their translation into industry practice. Short form survey instruments are becoming an increasingly common alternative for collecting data (Fisher, Matthews, & Gibbons, 2015). The objective of this research project is to create efficient survey instruments and methodologies. This involves developing and validating several short-form survey instruments and assessing the psychometric properties of the new one-question-a-day methodology. The proposed methodology involves two major steps: (1) collecting multiple data samples to test full, short and single-item versions of survey instruments and (2) testing the psychometric properties of the short-form instruments and one-question-a-day data collection methodology.

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Faculty Supervisor:

Mark Fleming

Student:

Dylan Powell George Smibert;Brianna Cregan

Partner:

Data Simple Ltd.

Discipline:

Psychology

Sector:

Professional, scientific and technical services

University:

Saint Mary's University

Program:

Development of Deep Learning Models for Amylose Estimation in Cereal Grains with Near-Infrared Spectroscopy

NIRS is a popular secondary analytical method that is being used for non-destructive quantification of compounds and mixtures in the agriculture and agri-food sector. The study aims to estimate the starch content (amylose and amylopectin) in rice samples with NIRS. A dataset is being established by obtaining NIRS spectra (400 to 2500 nm, 0.5 nm resolution) on over 400 milled and ground rice samples. Iodine-binding and spectrophotometric techniques will be used for acquiring the ground-truth. Upon analysis, this study would report the methodologies and evaluation metrics comparing the conventional (PLS and PCA) algorithms with deep learning (ANN and CNN) algorithms. Moreover, If the deep learning models outperforms conventional models, a Python-based data analysis pipeline will be developed for the end-users

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Faculty Supervisor:

Young Chang

Student:

Prabahar Ravichandran

Partner:

Cerasoidus Analytica Inc

Discipline:

Engineering - mechanical

Sector:

Professional, scientific and technical services

University:

Dalhousie University

Program:

Deep Neural Networks for applications in public safety

Deep Neural networks have revolutionized machine learning and in particular computer vision. The revolution was achieved by a combination of big data, graphical processing units and advances in numerical optimization. In this work we propose to extend and develop machine learning techniques, focusing on deep learning methods for public health and safety applications. We will use and extend deep learning methodology to deal with 3D seismic and electromagnetic data for signals that are emitted for public safety

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Faculty Supervisor:

Eldad Haber

Student:

Jingrong Lin;Keegan Lensink;Tue Boesen

Partner:

Xtract AI

Discipline:

Geography / Geology / Earth science

Sector:

Professional, scientific and technical services

University:

University of British Columbia

Program:

Accelerate

Combating loneliness at the neighbourhood level: Developing a Community Cohesion Index

With an increasing concern toward social isolation and loneliness issues in society, there is growing interest in social initiatives aiming to enhance social connectedness within communities. To make such efforts sustainable, the next step would be to evaluate their effectiveness to develop evidence-based practices. The goal of this project is to build a reliable assessment tool, named Community Cohesion Index (CCI), that measures the social cohesion between and among neighbors. To achieve this, this project will collect multi-source data tapping into different aspects of community cohesion (i.e., social connectedness, belonging, ownership, and safety) within different neighbourhoods in the Lower Mainland. We will then use the CCI to test the effectiveness of the “Hi Neighbour” initiative that the partner organization developed to promote connectedness in neighborhoods. Furthermore, we will develop a set of guidelines to assist the public use of CCI. We expect the CCI will provide a holistic assessment of the effectiveness of social policy and intervention aimed at better-connected neighborhoods.

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Faculty Supervisor:

Frances Chen

Student:

Yeeun Lee

Partner:

United Way

Discipline:

Psychology

Sector:

Other services (except public administration)

University:

University of British Columbia

Program:

Accelerate

Development of an optical sports monitoring sensor and method for non-invasive evaluation of muscle metabolic fitness and function in elite athletes

Currently, elite athletes and their coaches rely on the subjective self-awareness and measurement of muscle force, heart rate, and the amount of blood lactic acid level to monitor body fitness and athletic performance. These methods, however, are not ideal and always reliable due to subjective errors, technological limitations and individual confounding factors that limit the validity and accuracy of the measures. Furthermore, current methods are not able to monitor muscle function and fitness during exercise and recovery periods. This study aims to approach this challenge by establishing a new method and developing a wearable sensor for non-invasive monitoring of muscle performance and fitness during exercise. The ultimate goal of this project is to help elite Canadian athletes to enhance their 2022 and 2024 Olympic and Paralympic podium performances by using this new sports monitoring technology

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Faculty Supervisor:

Babak Shadgan

Student:

Leili Ghazi-Zadeh;Majid Shokoufi

Partner:

Own the Podium

Discipline:

Other

Sector:

University:

University of British Columbia

Program:

Elevate

Mapping Social Media Use in Kyrgyzstan: A Pilot Study

The research analyzes the structures of social networks, the varying uses of social media and information diffusion among social networking sites’ users in Kyrgyzstan as a pilot study and possibly expand in a subsequent project to Central Asia. We will study social media consequences on politics, the rise of populism, democracy, equality, participation, diversity, deliberation, privacy, surveillance, community building, informal social networks, public sphere and everyday life. The information heterogeneity of social media content in Kyrgyzstan will also be analyzed in this research. Using visualizations of complex networks and network matrices, this research will reveal the structure of the network, calling out cliques, clusters, communities, and key participants. Through mapping attribute data and network metric scores this research will also visualize network matrices. While in Kyrgyzstan social media has grown exponentially and has growing significance as a medium where social and political attitudes are shaped, and is the platform where civil activism and volunteering is developed, there is virtually no analysis of content and audiences.

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Faculty Supervisor:

Ahmed Al-Rawi;Peter Chow-White

Student:

Takhmina Inoyatova

Partner:

Aga Khan Foundation Canada

Discipline:

Journalism / Media studies and communication

Sector:

Other services (except public administration)

University:

Simon Fraser University

Program:

Accelerate

The Fluid Dynamics of Flow Control Devices

As increasing international competition and environmental pressures, Canadian oil sands producers must develop new technologies to more competitively deliver their product to market that have lower greenhouse gas (GHG) emissions. Flow Control Devices (FCDs) are one such technology. These devices are placed in the injection and production wells and enable more efficient access to the reservoir. The result is improved economics and thermal efficiency which directly is tied to GHG emissions. At this point, the design of FCDs is hit and miss with some working well and others showing poor performance. Thus, there is a gap for optimal design of these devices. The objective of this project is to optimally design FCDs using detailed computational fluid dynamics (CFD) modelling. This involves fundamental fluid dynamics, multiphase (emulsion) and gas flow, potential heat transfer, and advanced design so that flow regime can be used in and of itself to optimally control the flow into and out of the reservoir. We will use CFD together with observations from existing experiments for validation as well as adaptive design to optimize the performance of the devices.

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Faculty Supervisor:

Ian Donald Gates

Student:

Giuseppe Antonio Rosi

Partner:

RGL Reservoir Management Inc.

Discipline:

Engineering - chemical / biological

Sector:

University:

University of Calgary

Program:

Accelerate

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