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

User Adaptive Systems for Behaviour Change in Health And Wellness

There has been a dramatic increase in digital well-being products in recent years and there is a market saturated with ineffective user experiences and little to no sustainable, desired behavioural change. By assessing and enhancing the effectiveness of a personalized approach to digital well-being app interaction through machine learning and emotion-driven adaptive computing, we can develop a new, intelligent and highly effective digital well-being platform that can support lifestyle wellness behaviour change. We will monitor the user experience using our lifestyle wellness application (the Q-life) to integrate enhanced user experiences for a digital well-being platform (i.e. JackHabbit). This ‘next-generation’ intelligent digital well-being platform will disrupt the currently unstandardized market of digital well-being products and support various health and well-being applications for several user markets.

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Faculty Supervisor:

Jonathan Fowles;Jeffery Zahavich;Rita Orji;Shannon Johnson;David Russell

Student:

Julia Koppernaes;Cristina Forcione;Yasmeen Ibrahim;Ashfaq A Zamil Adib;Oladapo Oyebode;Rose Scoville

Partner:

Habit Forming Technologies

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Program:

Accelerate

Sensor Rank and Selection to Determine the Best Sensor to Increase the Efficiency of a Garbage Disposal Service.

In this modern world one of the biggest problems we face is waste control. As the population grows, so too does the waste being produced by humans. Our company is proposing to add a sensor to commercial garbage bins to detect when bins are full and thus trigger waste collection. This project will aim to identify the best sensor to use in a commercial garbage bin and will propose adding network technology to create a “smart” system. Sensor framework design and selection will be researched by this undergraduate team.

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Faculty Supervisor:

Carlos Bazan

Student:

Derick Daftari;Aneesh Raghupathy;Hossain Al Shahariar Shikhon

Partner:

SmartBin

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Memorial University of Newfoundland

Program:

Strategic Management – Transitioning from the Red Ocean to the Blue Ocean: A Case Study of Blueprint Residential Property Management Company

This research is intended to assess Blueprint Residential Property Management Company’s current organizational structure, business processes, system infrastructures, policies, leadership skills, and employees to determine areas of change and realignment to move the organization to a leading-edge service company. The research is also intended to factor in opportunities that need to be tapped into due to the impact of the global COVID-19 pandemic and recent Bill-20 legislation passed by the Government of Alberta. Finally, we will be supporting the organization when deploying some of the identified systems to be replaced to ensure there is employee adoption of these systems.

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Faculty Supervisor:

Ajnesh Prasad

Student:

Nkem Onyegbula

Partner:

Blueprint Residential Property Management Company

Discipline:

Other

Sector:

Real estate and rental and leasing

University:

Royal Roads University

Program:

Accelerate

Data Analytics to Optimize Drinking Water Quality

Drinking water utilities must maintain water quality in the face of unexpected shocks to the system as well as planned upgrades with unintended consequences. Failure to do so can result in significant threats to public health: the city of Flint, Michigan, for instance, experienced a water quality crisis after making changes to source water chemistry and treatment processes without fully considering the consequences. It is vital, then, that water quality managers synthesize research, practical knowledge, and monitoring data to anticipate and address risks to water quality. This project would see the development of a comprehensive plan to protect drinking water quality, with a focus on regulated contaminants that represent priorities for Halifax Water. Our plan would build resiliency against anticipated process changes and unexpected treatment challenges. Utilities across Canada and beyond make small and large-scale process adjustments continually, and this will become increasingly common given the future demands of climate change and an increasingly stringent regulatory environment. The broader drinking water community will benefit from the outcomes of this research through a deeper understanding of how to plan for system changes that impact drinking water quality.

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Faculty Supervisor:

Graham Gagnon

Student:

Benjamin Trueman

Partner:

Halifax Water

Discipline:

Engineering - civil

Sector:

Energy

University:

Dalhousie University

Program:

COVID-19 and ultraviolet-C disinfection of porous and non-porous surfaces: modeling, validation, and performance of new devices

The COVID-19 pandemic led to an increased demand for disinfection solutions, including ultraviolet C (UVC) light technologies. UVC works by inactivating microorganisms and show a strong potential to break the chain of infection in hospitals and public settings. CleanSlate and the University of Toronto are exploring this potential by characterizing how the virus that causes COVID-19 responds to UVC, and by evaluating the efficacy of new UVC devices for decontamination of high touch and other common surfaces.

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Faculty Supervisor:

Ramin Farnood;Scott Gray-Owen

Student:

John Gibson

Partner:

CleanSlate UV

Discipline:

Engineering - civil

Sector:

University:

University of Toronto

Program:

Mobilizing public sector investments in vaccine R&D to address emerging viral threats

The COVID-19 pandemic reminds us that, first, global pandemics are significant threats to population health and material standard of living and, second, evidently not enough is being done to prevent pandemics. Future pandemics are likely if the system of pandemic preparedness is not improved. The first step to ensure we are ready for the next viral outbreak is to understand how the current system functions: who is funding this system and who is conducting the basic and applied R&D into diagnostics and vaccines? Which viruses are being targeted? How much has been spent on these activities and how are suppliers being incentivized and monitored? What is the apparent effectiveness of the existing system? What are its weaknesses? To date, there has been no systematic assessment of this pandemic preparedness ecosystem. The proposed mixed methods research intends to address this gap. The results will be help policy makers invest funds where they can make the biggest difference and also help Sanofi Pasteur understand the role that they can play to improve the system.

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Faculty Supervisor:

Paul Grootendorst

Student:

Javad Moradpour Taleshi

Partner:

Sanofi Pasteur

Discipline:

Pharmacy / Pharmacology

Sector:

University:

University of Toronto

Program:

Development and Implementation of an Embedded Algorithm for Essential Ocean Variable Monitoring

In 2018 the Global Ocean Observing System (GOOS) approved Ocean Sound as an Essential Ocean Variable (EOV) within the Biology and Ecosystems Panel. This designation recognized that long-term monitoring of sound in the ocean will yield information on ecosystem health, climate change, and the effects of human activity on the environment. The bulk of the current ocean acoustic data collection is performed by archival recorders, supplemented by a small number of real-time gliders, buoys and cabled observatories. All of these systems yield large data sets that must be analyzed to extract information on the sound levels, and the biologic (e.g. marine mammals, fish, crustaceans), geologic (e.g. wind, waves, ice) and man-made sources (e.g ships, sonars & seismic surveys) that contributed to the sound levels. This approach assumes that the data is available for analysis on shore. In contrast, the EOVs that are widely used are derived from either autonomous floats such as the ARGOS series, or from satellite remote sensing. The objective of this project is to define an algorithm that will identify sound sources contributing to the local soundscape based on a small number of easily computed metrics.

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Faculty Supervisor:

Jean-Francois Bousquet;David Barclay

Student:

Afolarin Egbewande

Partner:

JASCO Applied Sciences

Discipline:

Engineering - computer / electrical

Sector:

Professional, scientific and technical services

University:

Dalhousie University

Program:

Advancing Deep Knowledge Integration

The field of artificial intelligence is traditionally divided into two broad paradigms. On one hand there are symbolic, formal, procedural, deterministic, and/or rule-based methods that often rely on a set of atomic elements and rules operating on those elements. Sometimes they are as complex as a comprehensive reasoning system. It is relatively effortless to provide a few manual instructions to these systems, however, these instructions (i.e., rules) are labor-intensive and become unfeasibly time-consuming as the complexity of the system grows beyond a certain point. In addition, symbolic systems do not learn from experience or data. On the other hand, adaptive systems do learn from data a system is exposed to but are not immediately equipped to receive simple instructions from the wealth of stored information, for instance, databases. The process of enabling adaptive systems to incorporate stored facts is called knowledge integration that this project endeavors to improve.

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Faculty Supervisor:

Raj Singh

Student:

Hashem Sadeghiyeh

Partner:

RBC Royal Bank

Discipline:

Psychology

Sector:

Finance, insurance and business

University:

Carleton University

Program:

Tracking the parasympathetic nervous system, emotions, and wellbeing in chronic pain conditions – Part 2

Wearable body sensors are groundbreaking in that they allow for continuous and unobtrusive physiological measurements. The promise is that someday soon, smartphones will monitor our bodily state and thus prevent all kinds of wellbeing implications: acute physiological issues like stroke or heart attack; less acute but still serious illness, such as chronic illnesses caused by destructive behavioural patterns; and everyday psychological experiences such as stress and bad mood. But this is not yet a plug and play matter; simply attaching a heart rate sensor does not fulfill this dream. Physiological data is complex and the scientific validation of continuous real-life physiological measurements is still scarce. There’s a lot to be learned about how different physiological markers can be used to inform us about underlying physiological and psychological processes. The present study is a first step in a program aimed at exploring whether readings from a wearable sensor can be used to improve physical wellbeing by way of improving emotional regulation.

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Faculty Supervisor:

James Enns

Student:

Veronica Dudarev

Partner:

HealthQB Technologies

Discipline:

Psychology

Sector:

Professional, scientific and technical services

University:

University of British Columbia

Program:

Accelerate

Using soil nematodes as indicators of soil health in wet meadow rangelands

Grasslands store carbon, regulate water, recycle nutrients, and conserve biodiversity. In western Canada, seasonally flooded, wet meadow grasslands, also provide habitats for species at risk. Despite their importance to conservation, little is known about the effect of grazing on these ecosystems. This research will help understand how grazing effects plants, soils, and the ecosystem services provided by wet meadow grasslands. Specifically, this project will explore links between grazing, plants, and soil nematodes, small worm-like animals, that recycle carbon and nutrients in soils. Our research will test three questions: 1) Does grazing increase the abundance of grasses, sedges, and rushes in wet meadow grasslands; 2) How do changing plants affect the structure and composition of nematodes in wet meadow soils? 3) Can soil nematodes help monitor changes in carbon and nutrients in wet meadow grasslands? This research will focus on Lake Manitoba Plain Ecoregion and will small differences in soil, vegetation, and moisture to measure the effect that cattle have on plant and soil communities. Findings from this research will help with the sustainable management and conservation of wet meadow rangelands in Canada.

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Faculty Supervisor:

Rafael Otfinowski

Student:

Kristina Elkins

Partner:

The Nature Conservancy of Canada (MB)

Discipline:

Biology

Sector:

Other

University:

University of Winnipeg

Program:

Accelerate

Visualization and Characterization of Novel Coronavirus Structure using Ultrasonic-Atomic Force Microscopy Targeted for anti-COVID-19 Therapeutics

SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, displays a coronashaped layer of spikes which play fundamental role in the infection process. Recent structural data suggest that the spikes possess orientational freedom and the ribonucleoproteins segregate into basketlike structures. How these structural features regulate the dynamic and mechanical behavior of the native virion, however, remain unknown. By imaging and mechanically manipulating individual, native SARS-CoV-2 virions with atomic force microscopy, here we show that their surface displays a dynamic brush owing to the flexibility and rapid motion of the spikes. The virions are highly compliant and able to recover from drastic mechanical perturbations. Their global structure is remarkably temperature resistant, but the virion surface becomes progressively denuded of spikes upon thermal exposure. Thus, both the infectivity and thermal sensitivity of SARS-CoV-2 rely on the dynamics and the mechanics of the virus.

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Faculty Supervisor:

Seonghwan Kim;Raimar Loebenberg;Mohamed Faizal Abdul-Careem

Student:

Zahra Abooali Zadeh

Partner:

Applied Pharmaceutical Innovation

Discipline:

Engineering - mechanical

Sector:

University:

Program:

Accelerate

Linearization of High Peak-to-Average Power Amplifiers for Digital Broadcast Radio Applications

Broadcast radio is changing from an analog medium based on frequency modulation (FM) to a full digital broadcast based on orthogonal frequency division multiplexing (OFDM). The high peak-to-average power ratio of the OFDM waveform requires different power amplifier topologies and a high degree of linearity. The research in this project analyzes current amplifier performance for digital radio broadcasting in the FM band, investigates new linearization techniques and explores new amplifier topologies. Key research aspects are improving linearity to meet spectral emissions requirements and increasing power added efficiency (PAE) for amplifiers broadcasting a hybrid waveform composed of both an FM and OFDM component that is increasing in injected power with the goal of an all digital OFDM waveform.

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Faculty Supervisor:

Jean-Francois Bousquet

Student:

Ibrahim Fatungase

Partner:

Nautel Limited

Discipline:

Engineering - computer / electrical

Sector:

University:

Dalhousie University

Program:

Accelerate

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