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

Design and Control of a Reconfigurable Packaging System

DCZS Intelligent Systems Inc. focuses on providing solutions for industrial automation as well as on facilitating the need of research facilitates and universities. DCZS is currently developing a Box Packaging Automation System (BPAS), targeting the growing needs in packaging/assembling processes in industry. Currently, BPAS is designed based on certain box packaging style and limited size range, and is not versatile for packing boxes with different creasing pattern. To meet seasonal demand from different end customers, a rapid change of the folding tools is required. Therefore, the design and control of a reconfigurable packaging system is proposed. This project concerns the development a reconfigurable packaging line as well as its adaptive control scheme to overcome the aforementioned challenging. As such, it is expected to result in both a technological advancement for enhancing DCZS’s R&D capability as well as an increase of their competitiveness in the global marketplace.

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Faculty Supervisor:

Chris Zhang

Student:

Yu Cao

Partner:

Discipline:

Engineering - mechanical

Sector:

Manufacturing

University:

University of Saskatchewan

Program:

Elevate

En Route to 5G: Long-term Evolution (LTE) Enhancements for the Internet of Things (IoT)

Cellular wireless communication has reached a level of coverage and reliability that it is considered a commodity. However, the dramatic increase in Internet traffic to and from wireless devices poses significant challenges for network operators. While the current growth of traffic is mostly due to consumers communicating more frequently and larger amounts of data over the wireless infrastructure, much of the future growth is predicted to come from non-human operated devices or so-called machine-to-machine (M2M) communication. M2M devices have mostly small amounts of data to communicate, but they will appear in massive numbers. This development is part of the vision of the “Internet of Things” that foresees Internet connectivity for almost everything we use in everyday life. The proposed project is a continuation of collaborative research between UBC and Sierra Wireless, a leader in the M2M space, with the objective to advance cellular wireless communication to support the IoT paradigm.

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Faculty Supervisor:

Lutz Lampe

Student:

Ali Cirik

Partner:

Sierra Wireless

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Program:

Elevate

Diversity and structure of coastal eelgrass communities and their importance for maintaining juvenile Pacific salmon

On the coast of British Columbia, both eelgrass meadows and Pacific salmon species are declining, yet eelgrass community dynamics and reliance of juvenile salmon on these communities are poorly understood. We will assemble the first large-scale dataset from monitoring efforts of coastal BC organizations in order to assess eelgrass community diversity and structure across environmental and human disturbance gradients (including boating, fishing, and non-native species). The final outcome of this research will be an index of eelgrass ecosystem health for all monitored meadows based on their ability to provide ecosystem services including provision of habitat for juveniles of salmon and other commercially-important fishes. In addition, the assembled dataset will enable an assessment of monitoring gaps. This project supports the Pacific Salmon Foundation’s (PSF) mission to guide management of Pacific salmon and their ecosystems, and contributes to research sanctioned by PSF’s ongoing Salish Sea Marine Survival Project.

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Faculty Supervisor:

Julia Baum

Student:

Josephine Iacarella

Partner:

Pacific Salmon Foundation

Discipline:

Biology

Sector:

Natural resources

University:

University of Victoria

Program:

Elevate

Harnessing imaging spectroscopy for multivariate rock sorting in the mine environment

The proposed research focuses on imaging spectroscopy of geological materials encountered at mineral deposits. Imaging spectroscopy (also known as hyperspectral imaging) in the geosciences traditionally utilizes airborne or spaceborne platforms but ground-based studies at outcrop and smaller scales are becoming more common. This technique collects reflectance data as images, and allows quick analysis of specific mineralogical properties that are visually undetectable (e.g., phyllosilicate mineralogy). We will investigate mineralogical and geochemical variability and spectral characteristics of various ore and non-ore lithologies from three mineral deposits. Results will improve the understanding of spectroscopy from these ore deposits and will also provide information exploitable by the mining industry. In particular, this research will directly guide how to integrate imaging spectroscopy into multivariate rock sorting methodologies developed by MineSense Technologies. Mineralogical information available through imaging spectroscopy complements the company’s existing sensor suite, and can potentially improve decision making for ore acceptance or waste rejection.

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Faculty Supervisor:

Lee Groat

Student:

David Turner

Partner:

MineSense Technologies

Discipline:

Geography / Geology / Earth science

Sector:

Mining and quarrying

University:

Program:

Elevate

Versatile applications of a safe and efficient peptide in gene/drug delivery

Gene therapy is one of the most attractive new therapeutic strategies in the treatment of multiple diseases. However, to apply gene therapy in clinic, an efficient and safe delivery system must be developed to transport these therapeutic reagents to target organs. The existing gene carriers suffer from either high cytotoxicity or immunogenicity problems, which will cause severe side effects when used in human. The new peptide based delivery system we developed demonstrated better performance and lower toxicity than the commercialized product on market. Preliminary animal experiments also proved the great therapeutic potential of this novel peptide. Now we propose to further confirm the efficiency of this peptide on various cells from different tissues, as well as to extend the application to deliver other therapeutic molecules. The partner organization will benefit from the potential of commercializing a product with bright market prospect and promising therapeutic applications.

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Faculty Supervisor:

Pu Chen

Student:

Wen Xu

Partner:

Positec

Discipline:

Engineering - chemical / biological

Sector:

Chemicals

University:

University of Waterloo

Program:

Elevate

Neural and autonomic correlates of post-traumatic stress disorder during processing of trauma-related stimuli

Post-traumatic stress disorder (PTSD) emerges after the exposure to an event that elicits horror or helplessness, including threat of injury or death to one’s self or another person. Community-based studies have evaluated its occurrence with a lifetime prevalence of 9.2% in the Canadian population. This research project aims to develop innovative, neural-substrate based, and novel theoretical paradigms for understanding psychological trauma and its clinical outcomes, including problems in emotion regulation, self-awareness, social emotional and self-referential processing. Cuttingedge neuroimaging analyses will be utilized to compare the response of individuals with and without PTSD, with the ultimate goal of significantly improving treatment of PTSD. The implementation of advanced neuroimaging methodologies and the translation of evidence-based outcomes into clinical knowledge will benefit the partner organization as well as the health services providing treatment of PTSD. Dissemination of the results will also constitute an indirect benefit for all the parties involved.

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Faculty Supervisor:

Ruth Lanius

Student:

Daniela Rabellino

Partner:

Homewood Health Centre

Discipline:

Psychology

Sector:

Service industry

University:

Western University

Program:

Accelerate

Rechargeable Hybrid Aqueous Gel Batteries for Start-Stop Applications in Automobile Vehicles

Currently, all types of vehicles utilize a 12 volt lead-acid battery for start-stop, controls, comfort features, redundancy, and safety features. We aim to replace it by introducing a new rechargeable hybrid aqueous battery, which is lead-free and possesses more than twofold higher energy storage capacity. There are requirements to further improve the rate capability and to reduce water-loss of this battery. In this proposed research, we will use nanotechnology to re-design the cathode materials and electrode structures to improve the rate capability. We will design and fabricate new gel electrolytes which protect the water component from evaporation and thus enhance the battery’s service life. The collective advances attribute to this new battery and make it practical for use as the power source for the electrical system in vehicles. The research knowledge will contribute to the scale-up projects in POSITEC Canada Ltd., a company based in Toronto.

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Faculty Supervisor:

Pu Chen

Student:

Tuan Hoang

Partner:

Positec

Discipline:

Engineering - chemical / biological

Sector:

Chemicals

University:

University of Waterloo

Program:

Elevate

Validation of the Performance of an Embedded Sensor for Monitoring the In-situ Strength of Concrete at Low Temperatures

This project is designed to verify the performance of a wireless Smart-Rock™ sensor that is embedded in concrete during construction for non-destructive quality control at low temperatures. Various laboratory experiments will be conducted using the smart sensor at Queen’s University on different concrete mixtures at low temperatures. The data from these sensors are analyzed using a proprietary algorithm to estimate the compressive strength of concrete in real-time. The results will be validated against those obtained by direct compression test on cylindrical specimens. The successful completion of this project will provide useful data for the validation of the Smart-Rock™ sensor for quality control of concrete. These data will also be used to modify algorithms for estimating the strength gain of concrete at low temperatures. This will position the industrial partner (i.e. Giatec Scientific Inc.) as a leading-edge company in the area of smart concrete testing technologies.

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Faculty Supervisor:

Mark Green

Student:

Farid Moradi-Marani

Partner:

Giatec Scientific Inc.

Discipline:

Engineering - civil

Sector:

Construction and infrastructure

University:

Queen's University

Program:

Elevate

Optimizing Fibre Flow in the Forest Products Industry

FPInnovations has developed FPInterface, a software platform in which forest operations are simulated in order to estimate fibre supply costs, including harvesting and transportation. It currently has 7 sub-modules including MaxTour and FPAlloc. The goal of this project is to develop and validate optimization techniques in order to implement new sub-modules to FPInterface that will allow for more accurate scenario valuations. TruckScheduler will optimize the truck fleet’s schedule based on supply, demand, and drivers’ preferred working hour constraints, to minimize unproductive deadheading and queuing at loaders by maximizing the use of backhaul tours and synchronizing the vehicle routes in time. RoadOpt will minimizing harvesting and road construction and upgrading costs to provide mills with a continuous supply of wood products. BioPathways will calculate the socio-economic benefits of all potential bio-products manufacturing facilities in order to better use the harvested wood fibre in various forms.

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Faculty Supervisor:

Louis-Martin Rousseau

Student:

James Gregory Rix

Partner:

FPInnovations

Discipline:

Mathematics

Sector:

Forestry

University:

Program:

Elevate

Selective Laser Melting Process Simulation of a Nickel-Based Superalloy Gas Turbine Component

Selective laser meting (SLM) is a promising additive manufacturing process that can be effectively utilized to manufacture structural components with complex geometries. Instead of removing the material, SLM adds the material selectively layer after layer using high power laser beam to form near net shape parts. Due to high cost associated with experimental development of the technology, the need for an accurate model to simulate the process is inevitable. Considering this, the main objective of this research project is to develop a high fidelity finite element model to simulate the SLM process on a gas turbine component such as fuel burner or high pressure nuzzle guide vane in order to highlight the influence of each processing parameter on the quality of the part. The finite element model will be validated by the experimental analysis that has been envisaged to be conducted at Siemens facilities.

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Faculty Supervisor:

Ramin Sedaghati2

Student:

Marjan Molavi-Zarandi

Partner:

Siemens Canada

Discipline:

Engineering - mechanical

Sector:

Medical devices

University:

Concordia University

Program:

Elevate

Statistical Analysis and Modeling of Vaccine Manufacturing Process Applied to Cell-Culture Based Adenovirus Vaccine

This project intends to research vaccine manufacturing process from a broader perspective and apply Quality by Design (QbD) concepts to develop a robust novel process for commercial vaccine production. To implement QbD principles, the impact of several process parameters (including cell density, pH, temperature, and harvest time) on the Critical Quality Attributes (CQAs) of the vaccine product (including yield, potency, and purity) will be revealed using statistical process analytical technologies such as Profiling, Principal Component Analysis, and Monte Carlo simulations. Statistical models will be developed, accounting for potential co-dependencies among the CQAs. The method will be established from data on cell-culture based Adenovirus as a vector for antigen delivery, generated in collaboration with other researchers. Subsequently, experiments will be designed and statistical analysis and modeling will be completed based on the new results. The methodology enables the industrial partner to establish QbD concepts for a range of vaccine manufacturing processes.

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Faculty Supervisor:

Amine Kamen

Student:

Bahareh Golfar

Partner:

PnuVax SL Biopharmaceuticals Inc.

Discipline:

Engineering - chemical / biological

Sector:

Pharmaceuticals

University:

McGill University

Program:

Elevate

Signals of Opportunity-based Positioning Techniques for Challenging GNSS Environments Year Two

The availability and accuracy of GPS in some environments, such as indoors, is limited. Positioning utilizing radio frequency (RF) signals intended for communication, broadcasting, or networking is an attractive alternative or complement to GPS in such environments. The proposed research will develop novel methods to use RF signals of opportunity (SoO), such as WiFi signals and cellular telephone signals, for accurate and robust positioning in GPS challenged environments. This necessitates an investigation of the effects of RF signal characteristics on positioning performance. Detailed mathematical models will be developed to compensate for the negative impacts of such characteristics on positioning performance. The sponsoring company (RxNetworks Inc.) is deeply involved in utilizing SoO for positioning. This research will directly contribute to algorithmic improvements required by the company to enhance the accuracy and robustness of its SoO based positioning products. The development of such algorithms is of utmost importance to the company.

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Faculty Supervisor:

Richard Klukas

Student:

Mahsa Shafiee

Partner:

Rx Networks Inc.

Discipline:

Engineering

Sector:

Information and communications technologies

University:

Program:

Elevate

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