Projets novateurs réalisés

Technology Integration in the Natural Resources Sector

At the request of our community partners, Selkirk Innovates would like to refresh and ensure continuity with internships required to support our ongoing technology integration work in the natural resources sectors critical to the Kootenay environment and economy, specifically in forestry, fisheries, hydrology/ geothermal, wildfire mitigation and Mining/Minerals and with particular focus on applying geomatics and computing expertise to solve partner problems and/or enhance productivity.

In-situ semi-passive phycoremediation of highly contaminated mine water in N and S compounds

The mining industry is constantly seeking to limit its impact on the environment. To this end, new solutions for the removal of dissolved contaminants from mining waters must be developed. These must, in addition to treatment capacity allowing effluent to be discharged into the natural environment, be low in energy and chemical reagent consumption. Many studies have proposed biological or phytoremediation alternatives for mining effluents. These processes may, however, be sensitive to the presence of various inhibitors making them ineffective. Phycoremediation may then prove to be an interesting alternative. Although it has yielded interesting results for wastewater treatment, the use of an algal process is not widespread in the mining industry. Due to the high growth rate and the presence of many different sorption sites on the surface, algae can remove a variety of contaminants. The reagent and energy consumption are therefore much lower than that of conventional active processes. In addition, algae are able to establish symbiotic links with plants and microorganisms, providing them with the nutrients needed for growth or increased resistance to the presence of inhibitors, thus optimizing the treatment process…

Implementation of CLT Connection Mechanical Properties in Alternative Load Path Analysis

The investigation of the behaviour of cross-laminated timber connections under extreme force combinations is crucial for assessment of the safety of tall timber structures under unexpected circumstances, such as an explosion or loss of structural elements due to fire. Previously gathered laboratory data proved that the novel, more cost-, material-, and time-efficient method is a viable way of investigating this connection behaviour. The project will normalize and disseminate this method to accelerate the rate of data acquisition in the field of research. In parallel, design guidance will be developed to process the outcomes to be used in practical engineering design. Finally, both of the above achievements will be put into practice in a proof of concept building analysis.

AI-powered smart glasses for sensing and recognition of human-robot walking environments

The project is to develop an environment recognition system using computer vision and deep learning techniques (i.e., image or video classification of walking environments) and to further develop a functional prototype of an AI-powered glasses. The designed system could achieve high prediction accuracy on real-world visual data and be efficient for onboard real-time inference with embedded devices. The predictions generated by the recognition system can support the development of next-generation environment-adaptive controllers for robotic leg prostheses and exoskeletons, which would turn out to help older adults or individuals with mobility impairments regain mobility and independence through powered locomotor assistance.

Towards accurate 5G assisted management of UAV swarm crowded urban airspace

Low-altitude Unmanned Aerial Vehicle (UAV) flight operations are regulated by safety protocols provided by aviation authorities, particularly in the vicinity of crowded airspace e.g. airports. With a fast-growing market for and penetration of commercial UAVs, the possibility of UAV intrusions within controlled airspace is expected to increase, despite these regulations. At lower altitudes, this increases the risk of UAV collisions with aircrafts, or interference from any other undesirable UAV activity near airports like Vancouver International Airport (YVR), the second busiest airport in Canada. This project will develop a novel system for accurate localization of UAVs utilizing existing 5G network equipment and infrastructure deployed across UBC campus. The proposed research will consider both 3-dimensional (3D) mobility of individual UAVs in crowded urban airspace, and UAV swarm. The project will culminate in an optimal design of 5G based enhanced unmanned aerial system (UAS) traffic management (UTM) system architecture that uses legacy interfaces for detecting and identifying UAVs. This includes development and testing of 5G enabled detection framework for flying UAVs at low-altitude, which requires in-flight authentication and authorization.

Creating an inclusive culture: Building tech workplaces that work

Digital Nova Scotia is a not-for-profit organization dedicated to growing and developing Nova Scotia’s digital technologies industry by facilitating connections, providing professional development and capacity-building opportunities, and promoting the digital sector. Digital Nova Scotia supports the tech sector as it continues to grow, evolve, and thrive. As part of this work, Digital Nova Scotia aims to support greater diversity and inclusion in Nova Scotia’s tech sector. Workplaces with diverse, equitable, and inclusive teams experience increased productivity, higher employee retention rates, and overall superior performance in comparison to competitors.
The two major challenges facing the tech sector in Nova Scotia are:
1. Nova Scotia struggles to identify the cultural change factors needed in the tech sector to recruit, hire, and retain equity deserving groups.
2. Nova Scotia struggles to implement effective cultural change to remove barriers to recruiting, hiring, retaining equity deserving groups.

The research being conducted in this internship will help Digital Nova Scotia identify approaches and techniques being conducted in other jurisdictions and industries. It will also identify an approach for conducting a larger study of factors that contribute to the attraction and retention of diverse technical talent, ideally supported by a Mitacs Accelerate grant. This information will ultimately help cultivate a competitive advantage in our region’s tech sector and help Nova Scotia grow its ICT industries.

Net zero summer internship

TAS has set an ambitious goal of reaching net zero by 2045 with one ground up development and one repositioning asset to be net zero by 2030. This is a unique opportunity to work alongside our project delivery team supporting, innovating, and strategizing pathways for TAS to reach our Net Zero impact goals across the portfolio. The following roles require a mix of innovative, data driven, out-of-the-box thinking supported by the ability to support rigorous technical analysis.

Intern #1: The summer VDA intern will play a key role in working towards developing a costed, embodied-carbon virtual library of building assemblies to help drive low impact development.

Intern #2: The summer NZP intern plays a key role in working towards developing a net zero carbon project strategy – integrating acquisition, product design, procurement, construction and delivery.

Innovation Through Customer Insights and Service Development

We-Watch is a for-profit, social enterprise that has created a mobile platform to report public safety incidents and collect evidence on these incidents from users near the location of the incident. We-Watch has partnered with West Queen West BIA, Downtown Toronto to conduct its first pilot project for their mobile application. During this pilot, the business owners of the BIA will have exclusive access to this mobile platform for 4 months. This project will involve running the pilot launch to uncover insights about the application usage patterns and behavior of the end customers, which will help design the future builds of the application and make improvisations on the identified bottlenecks. The project will also help explore other sales opportunities for the application, by looking at other target audiences such as industrial settings, homeowner associations etc.

Collective community gardening for food security and integration of African immigrants in Edmonton, Alberta

Cultural food insecurity, which is the inability to acquire, afford, and eat the traditional foods of one’s ethnic origin is a form of food insecurity that disproportionately affects new immigrant, especially the Black identifying Canadians. However, research that explores alternative ways to diversity Canadian food systems to accommodate new immigrants and to allow for smooth transition and integration of African immigrants are limited. The current project aims to address this knowledge gap by exploring the barriers and facilitators of use of collective community gardens in providing spaces for production of culturally familiar foods and in supporting the integration of new African immigrants in Edmonton, Alberta. In collaboration with Sinkunia Community Development Organization (SCDO), we will conduct a qualitative and quantitative study to investigate the barriers and facilitators of engagement to community gardening and the most preferred culturally familiar vegetables and fruits. This project will be an initial step towards the diversification of Canadian food system by contributing to creation of knowledge on the most preferred cultural vegetables and fruits.

An Intelligent Method for Bearing Anomaly Detection

Anomaly detection of machinery is a crucial aspect of predictive maintenance. It involves monitoring specific parameters related to the machinery’s condition to identify significant changes that indicate a potential fault. Bearings, which are important components in rotary machinery, are responsible for over 40 percent of rotary machinery failures. Consequently, researchers have been focusing on detecting anomalies in bearings. While there are various methods available for bearing anomaly detection, most of them require high engineering expertise to detect faults. Alternatively, techniques solely based on machine learning models are not always reliable and accurate. Additionally, detecting weak faults accurately has been challenging due to the complexity of bearing vibration signals. As a result, there is a lack of an intelligent method that can automatically and precisely detect faults. In this project, our goal is to develop an intelligent anomaly detection model, and conduct multiple validation tests using various datasets, including three public datasets, one lab dataset generated by the applicants, and industrial data from our partner organization to achieve an accurate, reliable, and robust model.

Monitoring Volume changes and IgA protein in complex biological mixture of vaginal discharge as a real-time ovulation detection

The female body goes through various hormonal changes at each phase of the menstrual cycle every month. These hormonal changes result in detectable biophysical changes, such as skin temperature variations. Moreover, the chances of pregnancy are noticeably higher during the ovulation phase which is a 4-5 days window. Our product, will have sensors inside it to non-invasively monitor these biophysical changes from the surface of the body and determine which phase the user is in with high accuracy. As the result, women can use this personalized information to increase or decrease their chance of pregnancy in a non-hormonal, non-invasive, personalized and accurate, reusable, and easy-to-use manner. This project seeks to prove the feasibility of developing this wearable device and the accuracy of the sensors in taking the parameters.

Analysis of angled nozzle spray profiles and their suitability in site-specific applications.

Spot application of pesticide has economic and environmental benefits. To enable a real-time spraying, the timing of actuation after detecting the pest should be precise to enable hitting the target. One issue in spraying using wide booms is the need to place cameras on the boom itself to avoid possible costly mechanical modifications. As such, the short distance between the sensor (camera) and actuator (nozzle valves) requires nearly instantaneous detection and actuation which is not possible with the current state-of-the-art technology. During the internship, we plan to test and validate new angled nozzle tips that allow the sprayer to spray backwards to gain time that will allow the camera to detect the pest and send a signal to the nozzle valve. Also, we plan to test the spraying pattern while the valve is opening to verify the possibility of including the transitional period of valve opening in the spraying time.