Innovations Realized

Urban Indigenous Housing in BC: Means of and barriers to addressing Indigenous housing need in municipal housing policies and plans

This study aims to understand how municipalities in British Columbia (BC) address urban Indigenous housing need within their Official Community Plans and housing strategies by reviewing a sample of plans and strategies from throughout BC and interviewing municipal planning staff. For AHMA, the Umbrella Organization of Indigenous Housing Providers in BC, this research is necessary to identify policy gaps and influence decision makers accordingly. In addition, municipalities in BC are now eligible to receive funding from the Province to perform a Housing Needs Report in their communities, so this project will help AHMA identify the gaps in Indigenous inclusion for these assessments and intervene to maximize the impact of provincial funding.

Design of Advanced Control Systems for Electron Beam Additive Manufacturing

Additive manufacturing is making tremendous strides in transforming the manufacturing landscape. While there now exist many new technologies for performing additive manufacturing there remain significant challenges in reducing part cost, part rejection rates and overall part quality. A big part of the work that remains to be done is in better understanding the relationship between processing parameters and resulting properties. In this project we will work specifically on advancing the state of the art in process control for a specific form of additive manufacturing, selective electron beam melting (SEBM). Working with our partner CANMORA Tech. we will develop, for the first time in a commercial SEBM system, a closed loop control system that will allow for synchronized control of the electron beam and the mechanical build table. The added flexibility provided by this novel control system will move us towards improved processing strategies, lower costs and reduced waste.

Evaluating the Long-Term Sustainability of Natural Systems in Ontario’s Greenbelt

Ontario’s Greenbelt is composed of nearly 2 million acres of protected land including natural areas that provide ecosystem services to millions of people. While these areas face reduced pressure from land use conversion, they still face a pressures typical of natural systems in peri-urban landscapes including loss of biodiversity, invasive species, impacts from infrastructure projects and a changing climate. In order to determine the extent to which these pressures are shifting natural systems, indicators of system health are needed. This report will use existing natural system spatial data to quantify changes in the natural system across the Greenbelt. Through municipal case studies relying on previously collected high-resolution spatial and field-collected information, a list of recommended indicators will be developed, identifying high priority indicators for collection and reporting. It will also provide recommendations for additional, precise monitoring criteria based on data analysed at both regional and local levels.

Evaluating Tile Drainage/Water Management Effects on Wheat, Canola and Soybeans productivity in Heavy Clay Soils

Tile drainage is becoming popular as a way to control excess moisture in the field to increase productivity. Yet, the economic return on investment (ROI) on installing tile drainage is not known for wheat, canola, and soybeans in Manitoba. This research will allow us to assess the impact of water management through controlled drainage on yield and quality of wheat, canola, and soybeans. Detailed soil moisture measurements along with water table depth at different times will help us model water flow within the rootzone and its impact on crop yield. Data collected in this study will be used to calibrate computer models (HYDRUS, DrainMOD) for this location so that weather data from different years could be modeled to assess the long-term impact of tile drainage. The field has drains placed at 15’, 30’, and 45’ allowing different degrees of drainage. Rotating the three crops through these different spacings will help assess the impact of different drainage intensities. Excess moisture is a big constraint in crop production in Manitoba.

Advanced photogrammetric data capture and data analysis for improved rehabilitation planning and infrastructure spending

This project will evaluate methods of capturing images in underground hydroelectric facilities for the purpose of generating 3D models to be used for inspection. These facilitates are very dark, wet, and large with complicated geometries. Capturing images in these environments suitable for 3D modelling is challenging and, in some cases, not possible with current technology. Methods of analysing the 3D models, as well as automating the analyses, will also be investigated. Current methods of analysis are labour intensive and lack concise metrics to describe condition. The results of this research will help infrastructure owners optimize infrastructure spending and provide the partner organization with new knowledge and tools that will give it a competitive advantage in the industry. This project will also provide the building blocks for future research

Material Formulation and Sheet Extrusion of Thermoplastic-Graphite Composites for Compression Molding of PEMFC Bipolar Plates

Hydrogen is a clean source of energy with zero greenhouse gas emissions. A very efficient method to obtain power from hydrogen is by using fuel cells, which generate electricity via an electrochemical reaction in which oxygen and hydrogen combine to form water with no harmful emissions. Of the major components of fuel cells are Bipolar Plates, which cost about one-third of the total cost of the fuel cells. As a result, reducing the cost of such components, specifically the manufacturing cost, while maintaining or improving their performance is a primary goal for the producers of fuel cells. Polymer-graphite composites has become the low-cost alternative material for the bipolar plates, currently made of thermoset (e.g. epoxy)-graphite composite materials. Such composite materials must have certain characteristics, but most importantly high electrical conductivity, especially in through-plane direction (TPEC). In addition, they should have an adequate flexural strength and fracture toughness to withstand the manufacture and operators’ handling throughout the processing and postprocessing (grooves making) mold-ability. Other desirable characteristics include: i) lost cost (material cost and conversion cost), and ii) low scrap rate (low rate of failure, especially during assembly, and recyclability).

Testing, Integration, and Optimal Control Strategy of Residential Hybrid HVAC System

The Canadian federal government committed to encouraging low carbon alternatives and the growth of clean technology that reduces greenhouse gas (GHG) emissions. It is stated that the new target is to reduce GHG emissions by 80% by 2050, relative to 2005 GHG levels. In order to achieve this goal, one of the government’s strategic plan is to promote systems and technologies that minimize natural gas/fossil fuel usage and increase the use of clean electricity. Although various research groups studied the potentials in energy consumption reduction in residential houses, hybrid integrated energy systems are found to be effective in reducing energy consumption and its associated operating cost and GHG emissions. However, their optimal control methodology is still lacking for cost-effective large-scale deployment and adoption of such hybrid residential HVAC systems in the Canadian residential sector. Therefore, this project will examine the benefits of a state-of-the-art cloud-based Smart Dual Fuel Switching System (SDFSS) of two sets of residential hybrid HVAC system of 1) electric air source heat pump (ASHP) and natural gas furnace (NGF) and 2) ASHP, electric water heater tank, and natural gas instantaneous hot water heater, for simultaneous reduction of energy cost and GHG emission.

Detection for Smart Home Devices’ Environment with Neural Network

As smart home and artificial intelligence technologies are developing rapidly, smart home devices contribute to better living quality and safer spaces. These smart devices are intelligent agents. They receive a variety of signals through sensors placed in ecobee’s thermostats, light switches and other smart devices and controls the heating and cooling, lighting, as well as providing important notifications. In this project, we would like to analyze sensory data and develop various machine learning solutions for characterization of the devices’ environment (e.g. object detection and audio classification). Currently, machine learning and deep learning algorithms have achieved significant improvement in building intelligent agents. We will apply them to assist ecobee’s products to better understand the environment and make better decisions.

Data Science: From Principle to Practice

Data science is an interdisciplinary field that combines statistics, computer science, and domain knowledge. The rise of data science has fundamentally changed how people solve problems in all kinds of industries. To fill the talent gap, SFU professional master’s program (PMP) was launched in 2014. In this Mitacs cluster project, SFU PMP will collaborate with multiple industrial partners to investigate innovative solutions to address various data science challenges in data management, model development, and application & product

Development of targeted degradation of Nuclear Receptor Binding SET Domain Protein 2 (NSD2) by Proteolysis-targeting chimera (PROTAC) for the study of its role in SARS-CoV-2 infections

The recent outbreak of the SARS-CoV-2 associated coronavirus disease, COVID-19, had been declared a global pandemic by the World Health Organization. There is still only a minimal understanding of the virus and an absence of effective targeted therapy for its treatment. Epigenetic regulations in cells control the expression of genes without modifications to the genetic codes itself, and epigenetic-targeted therapy development had been widely proposed as a promising approach to antiviral therapeutics. NSD2 is protein involved in epigenetic control to silence genes and has been reported to be upregulated and known to interact with some key proteins in SARS-CoV-2 infected cells. This project focuses on developing a method to precisely target the degradation of NSD2 via a technique called PROTAC. This could provide a first-in-class method for targeted degradation of NSD2 that could be used to study the protein function and to develop potential therapeutics for COVID-19.

Explore efficiently automated parallel hyperparameter search for optimizing machine learning models over large scale cloud cluster

Machine learning has been applied in various fields and shown promising results in recent years. Researchers have found that tuning machine learning models in a proper way can vastly boost the model performance with respect to the specific AI task. However, tuning machine learning models at scale, especially finding the right hyperparameter values, can be difficult and time-consuming. There is therefore great appeal for automatic approaches that can optimize the hyperparameter of any given model. This project aims to provide an end to end automotive hyperparameter search framework that can help people explore better machine learning models

Towards a lab-on-a-chip-based rapid-screening system for pathogens

In light of the recent outbreak of Covid-19, it is urgent to find a solution for quick and efficient pathogen detection and elimination. Rapid point-of-need diagnostic tests and monitoring devices are urgently required in order to provide testing and care to those infected. Currently, testing is performed at centralized facilities using specialized equipment for molecular-based pathogen detection. Real time-quantitative PCR is the current method for detection, but it has a slow response time due to clinical lab capacity and sample shipping time.

In an effort to rapidly examine cells and microorganisms, we are developing an embedded sensor for lab-on-a-chip platforms with connected micro-tubes and a container for markers and the specimen. The sensor has built-in energy-harvesting and bidirectional communication units to create a contactless platform and analytical support for lab-on-a-chip technology. The proposed sensor attached to a microfluidic capillary carrier or lateral flow-based assay, facilitates rapid analysis and detection of harmful pathogens, drugs or biomolecules. It could also be used as a low-cost point-of-care patient monitoring device.