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

A low-power remote IoT device to sense ultrasonic signals for multiple channel system

In the end of this project, the proposed design will be published in two peer-reviewed journals. Also, the measure the data will be saved and analyzed in the UW-STREAM lab. After the analysis, the data converter speed, the channel selection capability and also the power consumption will be summarized and reported. From those data, both the partner and we can make a commercialized strategy. The desired applications and also the way to integrate the proposed design with the current product can be decided. In conclusion, this design can be commercialized into a product which will help the ocean monitoring consumers build up a dynamic monitoring system.

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Faculty Supervisor:

Jean-Francois Bousquet

Student:

Ningcheng Gaoding

Partner:

Springboard Atlantic

Discipline:

Engineering - computer / electrical

Sector:

Professional, scientific and technical services

University:

Dalhousie University

Program:

Accelerate

Optimized Controllers for Second-life Battery Energy Storage Systems

The world’s electricity grids need affordable batteries to store large amounts of energy and allow for increased renewable power sources like wind and solar. Instead of building new batteries from scratch, millions of used batteries from retired electric vehicles can be given a second life on the electricity grids for a lower price and a smaller environmental footprint. This research project will develop a new computer program that can manage large groups of second-life batteries so they work effectively together as a team and even outperform more expensive batteries. Ultimately, this will help make renewable energy more affordable and more sustainable. Partnering with the Lab2Market program will contribute to the development of innovative technologies in Canada.

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Faculty Supervisor:

Lukas Swan

Student:

Chris White

Partner:

Springboard Atlantic

Discipline:

Engineering - mechanical

Sector:

Professional, scientific and technical services

University:

Dalhousie University

Program:

Accelerate

Radiant Energy Spectrum Converter for Enhanced Thermophotovoltaic Systems

Thermo-photovoltaic (TPV) systems are optical heat engines that convert radiant heat to electricity using a photovoltaic cell. TPV is a highly promising technology that can potentially be used to generate electric power from any high-temperature heat source including concentrated solar radiation, industrial waste heat, heat from radioisotope decay, and fuel combustion systems. However, the performance of TPV systems needs to be improved to achieve widespread commercialization. The objective of this project is to develop a novel class of optical cavities to significantly enhance the efficiency of TPVs. The proposed structure will provide for substantial improvements in TPV technology with a wide variety of applications such as solar thermal cogeneration processes, waste heat recuperation systems, auxiliary power conversion devices, fuel-to-electric-power conversion, self-powered devices, and remote power supplies for off-grid applications. These advances bode well for providing alternative energy sources and conversion methods in support of a global transition to cleaner energy.

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Faculty Supervisor:

Paul O'Brien

Student:

Nima Talebzadeh

Partner:

Springboard Atlantic

Discipline:

Engineering - mechanical

Sector:

Professional, scientific and technical services

University:

York University

Program:

Accelerate

Developing a Smart Tool for Enhanced Oil Recovery Screening Based on Artificial Intelligence

Oil production constitutes a significant portion of the world’s demand for sources of energy and raw materials for production of numerous daily-needed items. However, most of the currently producing oilfields are in their production decline phases with much of their oil left unproduced due to technical barriers. Sustained production of these underground resources depends on methods such as Enhanced Oil Recovery (EOR), which involves injection of specific material or energy in oil reservoirs to enhance oil displacement towards producing wells. The first step in making EOR implementation decisions is screening the available EOR technologies and methods. Despite several screening methods proposed over the past five decades, there is no advanced screening method suitable for universal use. The aim of this project is developing Artificial Intelligence (AI)-based EOR screening tools to identify the critical screening parameters as well as to assess and rank the EOR options for any oil reservoir.

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Faculty Supervisor:

Sohrab Zendehboudi

Student:

Seyyed Masoud Seyyedattar Shoushtar

Partner:

Springboard Atlantic

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Memorial University of Newfoundland

Program:

Accelerate

Passive Airborne Sensor Platform

In disaster scenarios involving airborne contaminants, where the dispersal of toxic agents can impact human lives, first responders require fast and accurate dispersal trajectory information. Existing methods that detect the local presence of an agent do not provide insight towards dispersal trajectory, and long range spread is either simulated with sparse reference data or measured long after the dispersion is complete. The lightweight and porous form of the milkweed seed offers natural inspiration for a novel sensor platform. In addition to investigating the market potential for the passive airborne sensor platform, the objective of the project is to quantify the effect of porosity on the response of the sensor platform to rapid changes in wind speed. Understanding how porosity affects the capability of the sensor platform to passively track the flow will assist in scaling the sensor platform design to meet the needs of potential customers.

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Faculty Supervisor:

David Rival

Student:

Joshua Galler

Partner:

Springboard Atlantic

Discipline:

Engineering - mechanical

Sector:

Professional, scientific and technical services

University:

Queen's University

Program:

Accelerate

Show and tell: Testing an alternative simulation-based method for assessing demonstrated soft skills

There is growing recognition that enhanced soft skill acquisition and development are critical for society and its members to adapt to the changes associated with the future of work in Canada, Soft skills in a work context are commonly measured in interviews, but this method has its drawbacks as there is the possibility for interviewer bias and interviewee faking, both of which could skew the assessment of skills in interviews. To circumvent these issues, nugget.ai developed an online simulation-based method for screening soft skills. The nugget.ai app places assessment takers in a simulation that replicates the job context and the work duties that are typically associated with it. nugget.ai differs from common assessment companies such as Hogan and plum.io in that it invites assessment takers to demonstrate their knowledge, skills, and abilities instead of relying on self-report methods. This method allows nugget.ai to assess soft-skills while minimizing the issues associated with typical methods of assessing soft skills such as interviews. With this proposed research, we aim to test the validity and reliability of nugget.ai’s simulation-based soft skill screening method, in hopes of promoting the use of a more valid and reliable hiring tool to organizations.

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Faculty Supervisor:

Peter Hausdorf

Student:

Marian Pitel;Melissa Pike

Partner:

nugget.ai

Discipline:

Psychology

Sector:

Professional, scientific and technical services

University:

University of Guelph

Program:

Developing Digital Brain Assessment Tools for the Home

Attention problems are common with aging and related disorders (like stroke) and are associated with poor recovery and quality of life. Many clinical tests of attention are not based on neurocognitive concepts and are limited to in person visits. The Dalhousie Computerized Attention Battery (DalCAB) is a theory-based, in-depth measure of attention that can be done in person or online. It has been used in-person with young adults, but it still requires more development as a remote test. Sixty healthy adults (30 young, 18-35 yrs, and 30 older, 55-85 yrs) will complete the DalCAB twice, once in the morning and once in the evening. Time of day and age effects on task performance will be analyzed. This research is a first step in the development of a tool for remote assessment that can be made broadly available to many different health care needs and populations.

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Faculty Supervisor:

Derek Fisher;Gail Eskes

Student:

Katelyn McKearney

Partner:

Springboard Atlantic

Discipline:

Other

Sector:

Professional, scientific and technical services

University:

Program:

Accelerate

Investigating Machine Learning Techniques in Performance Improvement for the Next Generation Wireless Networks

The new generation 5G wireless networks will have a huge impact on the society due to the high bandwidth and capacities they provide. The traffic volume is expected to grow significantly and new varieties of applications, e.g., Internet of Things and vehicular networking, are anticipated. As a result, effective management of the new networks will become much more complicated and challenging. Machine learning techniques have made unprecedented progress in recent years, as they are highly efficient for data-driven applications. The proposed project is to investigate machine learning techniques and apply them to 5G networks to effectively facilitate network management.

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Faculty Supervisor:

Chung-Horng Lung;Samuel Ajila

Student:

Calvin Jary;Gazoan Ahmed

Partner:

Ericsson Canada

Discipline:

Engineering - computer / electrical

Sector:

University:

Carleton University

Program:

Accelerate

A constitutive model for the cyclic degradation of clays

The proposed research project will provide SRK a reliable tool to estimate the level of displacements developing in a clayey deposit when subjected to cyclic loading of variable amplitudes, such as earthquake loading conditions. Specifically, this tool is represented by a model which can be used during seismic analysis of geotechnical structures. The model will be developed based on an already existing model which has proved capabilities of capturing many relevant aspects of the clay response under cyclic loading (Seidalinov and Taiebat 2014). However, a preliminary study by the intern demonstrated that the current version of the model has deficiencies in representing the strain accumulation response under various magnitudes of shaking. The observed limitations will be addressed in this project by revising the formulation of the constitutive model. The revised and enhanced model will be validated against a database of element level cyclic shearing of clays.

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Faculty Supervisor:

Mahdi Taiebat

Student:

Francesca Palmieri

Partner:

SRK Consulting (Canada) Inc.

Discipline:

Engineering - civil

Sector:

University:

University of British Columbia

Program:

Accelerate

Fast Awake OSA Screening and Characterization using Anthropometric and Sound Features

Obstructive sleep apnea (OSA) is one of the most common yet underdiagnosed sleep disorders. Undiagnosed OSA, in particular, increases the perioperative morbidity and mortality risks for OSA patients undergoing surgery requiring full anesthesia. OSA screening using the gold-standard Polysomnography (PSG) is expensive and time-consuming. This proposal presents four research projects/points to apply advanced signal processing and machine learning techniques on breathing sounds’ signals for screening OSA disorder during wakefulness. This proposal will enhance the current OSA screening algorithm during wakefulness (AWakeOSA), automate breathing phase detection, investigate the anthropometric effects on acoustic signals, predict OSA characteristics, and enhance/reduce the recording hardware setup. The main outcomes of this work will enhance the performance of the AWakeOSA algorithm as an objective, accurate, reliable and quick OSA screening tool with a high classification power during wakefulness, and providing a more robust, small size, and inexpensive OSA screening setup.

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Faculty Supervisor:

Zahra Kazem-Moussavi

Student:

Ahmed Elwali

Partner:

X-Bioanalysis

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Manitoba

Program:

From pipeline inspection data to insight

To ensure oil and gas pipelines operate safely, instrumented inspections and assessments are completed on a recurring frequency. A common and valuable inspection method is In-line inspection (ILI). This form of inspection uses a measurement device (ILI tool) that is propelled through the pipeline by product flow and the tool identifies and sizes anomalous conditions along the inside and outside walls of the pipeline that could affect the pipes ability to contain the product. Anomalous conditions can include metal loss corrosion, deformations, cracking, weld defects, and other defects on pipe welds. The results of an ILI survey are used in the determination of repair and replacement locations for a pipeline. The purpose of this project is to study the data from multiple historical ILI surveys, subsequent corresponding non-destructive examinations (NDE) reports, and basic pipe attribute and operating data to assess if any trends, patterns, or commonalities can be determined.

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Faculty Supervisor:

ZhangXing John Chen

Student:

Wei Liu;Fuhe Lin

Partner:

Dynamic Risk

Discipline:

Engineering - chemical / biological

Sector:

Professional, scientific and technical services

University:

University of Calgary

Program:

Accelerate

Using drone and remote sensing technology to increase profitability and climate resilience of potato production

Potatoes are among the top five crops worldwide. With the rate of climate change accelerating, the pressures on potato production systems (e.g., heat stress, water stress, pest pressures) will intensify adaptation efforts. Although climate change is already happening, it is often seen as an abstract and distant problem that diverts resources from current production challenges. However, drone and remote sensing technology can reconcile this false dichotomy. They can help growers better respond to crop needs with targeted use of inputs, which improves the bottom line. The same remotely sensed data can also be used to trigger climate change adaptation actions, letting the growers know when to begin proactively adapting to changes in the climate and its impacts. Preparing this research for commercializing can help Canadian potato producers increase profitability and build future climate resilience, giving them a competitive advantage over time.

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Faculty Supervisor:

Adam Fenech

Student:

Stephanie Arnold

Partner:

Springboard Atlantic

Discipline:

Environmental sciences

Sector:

Professional, scientific and technical services

University:

University of Prince Edward Island

Program:

Accelerate

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