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

Determining an effective management strategy for invasive exotic cattails (Typha spp.) in the Fraser River Estuary

Invasive species represent a major threat to global biodiversity, and are projected to increase in impact as globalization promotes the continued introduction of novel species. Proactive research that investigates the ecological, social, and economic threat of novel species prior to or early in their establishment is therefore critical to effective conservation planning. For our research we will be investigating the threat of cattails (Typha spp.) in the Fraser River Estuary (FRE). These species are problematic to wetlands throughout North America, and appear to be relatively recent arrivals to the FRE, as indicated by their limited distribution and lack of historical data. This investigation will include (1) identifying their current distributions using remote sensing, (2) predicting their long-term distributions through modelling, and (3) determining cost-effective removal methods using an eradication experiment. Results will inform conservation decisions and promote an appropriate and effective response to this species in the fragile and globally-significant FRE.

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Faculty Supervisor:

Tara Martin

Student:

Daniel Stewart

Partner:

Ducks Unlimited Canada

Discipline:

Forestry

Sector:

Fisheries and wildlife

University:

University of British Columbia

Program:

Accelerate

Electric mobility in Canada: public discourse in British Columbia, Ontario, and Quebec

The overall objective for this project is to support the research of one master’s student who will help advance the research related to the social discourse of sustainable transportation and climate policy in Canada. In this field, START, in the School of Resource and Environmental Management at SFU, is one of the leading research teams in the country and Navius Research Incorporated is the leading Canadian consulting firm, providing support to governments and other stakeholders in the development and assessment of climate policy. The project focuses on assessing ZEV policy discourse in British Columbia, Ontario, and Quebec by analyzing newspaper coverage and data from a climate policy survey. This research aims to generate a better understanding of the narrative related to the introduction and implementation of zero-emission vehicles and their policy in unique Canadian regions. Comparing results from these three leading provinces will shed light on contrasting policy pathways and regional differences in the socio-politico acceptance of low-carbon technologies.

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Faculty Supervisor:

Jonn Axsen;Shane Gunster

Student:

Audrey Aubertin

Partner:

Navius Research Inc

Discipline:

Environmental sciences

Sector:

University:

Simon Fraser University

Program:

Accelerate

Ecological risk assessment of tire wear particles in water bodies in Interior British Columbia

Microplastic pollution is becoming an emerging environmental issue in developed countries. The tire wearing particles (TWP) have also been categorized as microplastic pollution. TWP size range from a few nanometers to several hundred micrometers and are emitted into the environment. Tire industry is now inclined towards increasing knowledge of TWP origin, mechanism, transmission, and fate in the environment along with their impacts on receiving water bodies. This study aims to identify commonly occurring TWPs and investigate their characteristics, exposure mechanism, and toxicities followed by ERA, to evaluate the impacts on aquatic environment. The project will facilitate in generating a reliable knowledgebase on physicochemical properties, fate and transport, and toxicities of TWP. This project will also increase awareness in identifying the knowledge gaps to perform more comprehensive studies on TWP impacts.

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Faculty Supervisor:

Rehan Sadiq;Kasun Hewage

Student:

Haroon Mian

Partner:

Kal Tire

Discipline:

Engineering - other

Sector:

Consumer goods

University:

Program:

Accelerate

Temperature Prediction using Machine Learning

Synauta is a startup bringing the world’s best Internet of Things solutions to water utilities. Our deep industry knowledge prepares utilities for true connectivity to realize energy savings. We provide cyber security, sensors and software. In this project we will create a temperature prediction algorithm to save energy for water treatment plants. More energy can be saved if operators can plan to make more treated water when temperatures are high and less treated water when temperatures are lower. Over a week, the amount of water produced would be the same, but less energy would be used. To do this, Synauta requires a temperature prediction algorithm that can forecast the temperature of water into the future. This technology will provide customers with clear dollar savings as energy can be a major portion of opex costs.

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Faculty Supervisor:

Qing Zhao

Student:

Saman Dehghanbanadaki

Partner:

Synauta Inc

Discipline:

Engineering - computer / electrical

Sector:

Energy

University:

University of Alberta

Program:

Accelerate

Data Mining and Statistical Analysis of Hydraulic Fracture Performance in the Eagle Ford Formation

As the global supply of oil and gas from conventional reservoirs (i.e., porous rock formations) continues to diminish, it becomes increasingly important to produce these fluids from unconventional (“tight”) reservoirs. Hydraulic fracturing is generally required in order to achieve sufficient production rates from these tight reservoirs. Key questions to be addressed in hydraulic fracture design include the following: How much fluid and proppant (sand) should be injected? How many fractures should be created, and at what spacing? How is the effectiveness of the design affected by the depth, thickness, fluid pressure and temperature of the reservoir? This project will take data from over 1000 wells and use neural network (artificial intelligence) techniques to identify patterns between design parameters, reservoir properties and oil production rates. The outputs of this research will enable the partner organization (Baytex Energy) to design more effective hydraulic fracture treatments, hence increasing oil production and/or reducing well completion costs.

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Faculty Supervisor:

Christopher Hawkes

Student:

Luisa Porras

Partner:

Baytex Energy Corp

Discipline:

Environmental sciences

Sector:

Mining and quarrying

University:

University of Saskatchewan

Program:

Accelerate

Simulation-Enabled Intelligent Decision Support for Planning Precast Concrete Production Operations

Advances in engineering technology and requirements for sustainable development are main drivers for changes and innovations in the current construction industry. The paradigm shift to precast construction moves conventional field construction efforts into the controlled environment of an offsite manufacturing facility. These precast concrete products lend a significant advantage in execution of fast-paced construction projects, making construction schedules better organized, shorter, and less susceptible to environmental factors, while substantially reducing the number of skilled craft workers onsite and improving quality and safety performances. Despite all the advantages of offsite construction, planning and scheduling operations at precast production facilities still present distinctive challenges due to bespoke engineering design, variations in ingredient materials, concurrent execution of multiple projects, and finite limits of skilled trades and space available in a production plant. Thus, a well-formulated production plan for a precast operation plant is vital to deliver made-to-order structural components on site by respective deadlines while keeping production costs within budget limits. This research project is to adapt new planning and scheduling methodologies resulting from latest research and deliver practically feasible solutions with respect to achieving integrated project delivery in a typical precast concrete plant.

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Faculty Supervisor:

Ming Lu

Student:

Md Monjurul Hasan

Partner:

Canadian Precast Prestressed Concrete Institute

Discipline:

Engineering - civil

Sector:

Construction and infrastructure

University:

University of Alberta

Program:

Accelerate

Development of Novel Selective Estrogen Receptor Modulators

Breast cancer is the most common cancer among women worldwide. Estrogen, a sex hormone in women plays a key role in the proliferation of cancer cells especially in post-menopausal women. Selective estrogen receptor modulators (SERMs) like tamoxifen and raloxifene are the most efficient drugs for treatment of breast cancer. These drugs bind to estrogen receptors (ER? or ER? subtypes) in as much as the same manner as estrogen does. However, these drugs are often accompanied by severe side effects. The proposed investigation aims at developing and evaluating new estrogen antagonists. The designed molecules are based on the structure of estrogen itself and are thus expected to show promising bioactivity. We plan to synthesize and bioevaluate a series of rationally designed SERMs, in collaboration of Paraza Pharma Inc. Over the period of 8-months, ~12 rationally designed SERMs will be synthesized, purified and characterized at Acadia University and bioevaluated at Paraza Pharma, Inc.

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Faculty Supervisor:

Amitabh Jha

Student:

Smriti Srivastava

Partner:

Paraza Pharma Inc.

Discipline:

Chemistry

Sector:

Pharmaceuticals

University:

Acadia University

Program:

Accelerate

Private SQL interface for encrypted data

Querying databases without a layer of privacy protection might lead to serious privacy issues. Such issues include access patterns and communication volume patterns. By combining the state-of-the-art privacy standard (differential privacy) and encryption in provides resilience to a host of attacks on remote databases, including data reconstruction attacks. However, there is still research work needed in building a private access system on top of an encrypted database. In this work, we explore the use of a plethora of privacy preserving data publishing (PPDP) techniques with different definitions and guarantees to build a private access system. We aim at exploring two main questions: i) How and if the privacy guarantees deteriorate when a user asks different queries and combines the outputs or when multiple users collude, and ii) How the functionality is affected by each PPDP technique, e.g., can the system still support joins between two anonymized tables.

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Faculty Supervisor:

Yan Liu

Student:

Fatima Zahra Errounda

Partner:

TandemLaunch Technologies Inc.

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Concordia University

Program:

Accelerate

Development of see-through near-eye display using embedded concave micromirror array for augmented reality applications

Near-eye displays (NEDs) are small displays that are positioned closed to the eye, which conveniently places visual information in the line of sight of a user. NEDs need to be compact and lightweight as they are typically worn on the head, taking the form of glasses or goggles. In this research, we design and build a thin and transparent NED. The proposed NED uses a high fill-factor embedded concave micromirror array (ECMMA), and light field principles for virtual image formation. We optimize our NED design using optical simulation software Zemax Opticstudio, and build a prototype using a transparent microdisplay and a custom-fabricated ECMMA.

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Faculty Supervisor:

Boris Stoeber

Student:

Hongbae Sam Park

Partner:

Form Athletica Inc

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

University of British Columbia

Program:

Accelerate

Colonoscopy Video Analysis Framework

Every year in Canada over 1.7 million patients are diagnosed with Ulcerative Colitis (UC), and have to go through colonoscopy procedures multiple times for disease detection and treatment monitoring. Trained clinicians use endoscopy facilities and technologies for colonoscopy procedures and unfortunately, the current error rate in disease detection is up to 20%. This project will build a framework that will analyze colonoscopy video streams in real-time, and offers the outcomes to support clinicians to accurately detect UC and monitor patient’s response to treatments.

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Faculty Supervisor:

David Fleet

Student:

Micha Livne

Partner:

A.I. VALI

Discipline:

Computer science

Sector:

Life sciences

University:

University of Toronto

Program:

Accelerate

Real-time Modeling of Virtual Synchronous Generator Type VSC Converters for Power Supply to Offshore Platforms

The voltage source converter VSC mimicking the behavior of a synchronous machine provides many advantages for grid operation. This “virtual synchronous generator (VSG)” will be implemented as a real-time simulator model on the RTDS simulator and used to investigate several operating scenarios.
The VS G behaves like a synchronous machine, which is one of the most widely used components of the legacy power system, and so it is well understood. The VSG can provide inertia and damping to the network. Particularly when the ac system is weak, frequency oscillations can be minimized with this approach. Also the VSG is versatile- it can function in the grid-connected mode and also islanding mode without a controller structure change. The VSG can also achieve power sharing between different VSC without communication link.
Several operating scenarios will be simulated. First, one single VSC is connect to the grid. And then the simulated system will expand to two or more VSCs to investigate their parallel operation. Both normal steady state operation and abnormal fault conditions will be tested. Besides, the VSC with VSG controller will be investigated as a synchronous compensator to provide reactive power support to the LCC-HVDC station…

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Faculty Supervisor:

Aniruddha (Ani) Gole

Student:

Chen Jiang

Partner:

RTDS Technologies Inc.

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

University of Manitoba

Program:

Accelerate

Accelerated detection and classification for surveillance applications

Object detection and classification for surveillance applications via deep neural networks have attracted a lot of interests in computer vision (CV) communities. Accurate and fast CV algorithms can alleviate intensive manual labour and reduce human errors due to fatigue and distraction. In detection problem, the aim is to determine bounding boxes which contain interested objects and classify the category of the detected object. Thus, the detection problem can be formulated as a regression problem to localize multiple objects within a frame. Due to very limited computational budgets on the edge devices, server-side solutions like YOLO and R-CNN are not suitable for embedded devices or high-throughput applications that scale to thousands of cameras. It is challenging to achieve real-time object detection performance while maintaining high accuracy. In this research proposal, we focus on reducing computation latency by developing models with a smaller number of trainable parameters to accelerate object detection and classification. First, we take the advantage of the depth separable convolution layer which has less model complexity. Second, we consider hierarchical processing units to localize multiple objects in one-time forward pass of the neural network.

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Faculty Supervisor:

Rong Zheng

Student:

Keivan Nalaie

Partner:

Caliber Communications

Discipline:

Computer science

Sector:

Information and communications technologies

University:

McMaster University

Program:

Accelerate

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