Projets novateurs réalisés

Food and Beverage Golf Course Drone Delivery

University of Ottawa students have partnered with the Brookstreet Hotel and Marshes Golf Club to explore the business challenge of improving the golfer experience through the autonomous delivery of food and beverages via drone anywhere on the Marshes Golf Course property. We hope to provide innovation to the industry and enhance the golfer experience through improving the operational and environmental cost of golf course services.

Intelligent Strategies for Optimal Virtual Network Function Placement in 5G Core

The proposed project aims at developing novel algorithms for the 5G Core Network Placement to meet Key Performance Indicators (KPIs). The ultimate goal is to minimize the number of virtual network functions in 5G packet core while meeting the latency requirements. Thus, this project aims to address the trade-off between latency and operational costs in a 5G network, which is expected to cover 82% of the population by 2025. Higher speed, lower latency and greater capacity of 5G networks will enable improved livestreaming and improved augmented reality. These improvements will enable various services such as connected cars, advanced gaming, tactile Internet, remote healthcare and many others. The proposed project will follow a holistic approach anchored around three main research themes: 1) Development of real time and feasible algorithms for user and control plane virtual network function (VNF) placement in the 5G packet core, 2) Optimization and AI-enabled models for VNF placement , and 3) Test bed implementation and verification of the developed algorithms. Achieving each of these schemes will enable meeting the core logical components of efficient and low latency 5G networks.

Insect community metabarcode sequencing for mine reclamation monitoring

Mining is important to the Canadian economy, contributing an estimated $43.9 billion in 2020 while addressing growing demand for minerals. Mine operations disturb natural environments, and Canadian federal and provincial authorities have instituted the Mines Act, and the Health, Safety and Reclamation Code for Mines in British Columbia, providing a regulatory framework for mine closure and reclamation. The intern carrying out this research project will evaluate the utility of DNA metabarcoding techniques as a tool for monitoring invertebrate community response to reclamation techniques with the aim of including this tool in environmental monitoring. Invertebrates are sensitive indicators of environmental change and may provide early metrics to guide reclamation progress. This project represents a new collaboration between researchers at Thompson Rivers University and Chu Cho Environmental, an environmental research and consulting firm wholly owned by Tsay Keh Dene Nation.

Hardware In The Loop Model for Complex Energy Systems

As the global demand for clean energy increases, the next generation power systems design has become more complex than ever. Since renewable electrical power can be generated locally, the power flow is not only from the utility to consumers, but also from any sources (i.e. photovoltaics, internal combustion generator, electric vehicle battery pack, etc.) to consumers, to home batteries, or even back to the utility for redistribution. Among the utility, renewable energy sources, batteries, and consumers, there is a vast amount of power converters that need to work in coordination to manage the power flow. The architectural design of such a complex power system presents a variety of unprecedented challenges. This project proposes a new power converter modeling approach to assist these complex power system design. The proposed converter models can be easily integrated with modern energy system simulation tools to increase the accuracy of the simulation results. Successful outcomes of this project will accelerate the development of the next generation power system.

High-Throughput Linguistic Content Sentiment Analysis

The project has the following objectives:
1. To build a simulation for a generic social model, to represent how effective a toxic text sentiment
detection algorithm/model works within simulation
a. Building a simulation model
b. Testing the performance of the model using a dataset
c. Evaluation of the model
2. Optimizing existing toxic text detection algorithms/model to highly perform within the simulation

Development of AI Solution for intra-operative Optical Coherence Tomography based classification of cancerous lesions

Breast cancer was the most common cancer in 109 countries including Canada (excluding skin cancer) in 2020 (WHO). Early detection is critical in prognosis. The standard-of-care in early-stage breast cancer is breast conserving surgery (BCS), typically followed by radiotherapy and/or chemotherapy and/or endocrine therapy. The gold standard for margin assessment for BCS is histopathology. Results from histopathology analysis typically require 2-4 days, resulting in the need for a second surgery if a margin is positive. Despite decades of progress, reoperation due to tumor at or close to the margin is estimated to occur in over 20% of BCS patients. Our main goal is to improve this clear unmet need. Founded in 2013, Perimeter Medical Imaging AI (https://perimetermed.com/) is a Toronto-based medical device company with U.S. Headquarters in Dallas, Texas. Perimeter. Our Optical Coherence Tomography B-Series device embedded with our ImgAssist AI tool was granted Breakthrough Device Designation by the US FDA, designed to speed up availability of disruptive new technologies. We are currently conducting a randomized, multi-site, pivotal study to test our solution against the current standard of care and assess the impact on re-operation rates. The scope of this project involves 1) improving the current AI model to demonstrate improvements on performance metrics such as: accuracy, sensitivity, specificity, F1 score, AUC and 2) streamline the presentation of findings to the surgeons for quick decision making for next round of product release. Projects provide interns experience in AI development in healthcare with distinct deliverables to improve future product.

Non-thermal Plasma Assisted Photocatalytic Conversion of Low-cost Light Hydrocarbons to Value-added Fuel and Chemicals at Ambient Conditions

Increasing worldwide energy demand has sparked research interests towards the direct transformation of low-cost light hydrocarbons like natural gas into valuable liquid fuels or chemicals. Conventional thermo-catalytic approaches to achieve these reactions require high reaction temperatures (> 673 K), resulting in less desired products formation with severe coke. Non-thermal plasma (NTP) technology offers an attractive alternative since its non-equilibrium characteristics enable thermodynamically unfavorable chemical reactions to readily occur at low temperature and atmospheric pressure. Coupled with specially tailored catalyst, the product selectivity can be better tuned for high valuable commodity formation. We herein propose to organically combine NTP and heterogeneous photocatalysis to effectively valorize light hydrocarbons with limited coke deposition at near room temperature and atmospheric pressure. The outcomes from this proposed one will further ascertain the leading position of Canada in the world’s energy market through more efficient and cleaner utilization of low-cost hydrocarbons abundant in Canada.

Universal Soldier: A deep neural net for unsupervised 3D segmentation of tomographic images of bones

The process of identifying the object in an image, and the object background is accomplished by the human brain instantly. We develop such skills with experience, and often take it for granted. In digital imaging and computer vision, the same operation called image segmentation is a bottleneck of quantitative analysis. In particular, automated image segmentation is difficult in 3D, and especially so when the object is of biological origin. Automated segmentation of 3D datasets would abolish these limitations and increase the precision of quantitative image analysis, with high throughput. A way to mimic the precision and accuracy of human visual perception is to train an artificial neural network on a vast variety of examples. We have accrued a big library of 3D images of bones and skeletons, and will train the Universal Soldier Neural Net to recognize, tag and quantify the image components.

McCol Metals International Market Expansion

At McCol Metals, we have developed an innovative process to recover metal elements and then sell them to the international market. Our innovative recovery process has created a circular economy for Mixed Metal Oxide anodes and provides sustainability to the global Platinum Group Metal markets. We have currently commercialized and secured a 5-year contract locally with Vale and have been able to target global customers who can benefit from the recovery process. Going ahead, we want to focus on growth and development in international markets in the regions of North America, Europe, and South America. This project will help us determine our options for expansion.

AI, IoT, and Cloud-Based Technologies to improve safety in the marine industry

With thousands of accidents annually, often resulting in hundreds of deaths and injuries, billions in economic losses, and devastating environmental pollution, the marine industry remains one of the most dangerous industries nationally and internationally. The work environment on ships is inherently risky and poses numerous challenges to the ship crew. Moreover, the nature of their work requires them to engage in different job tasks and responsibilities that are physically and mentally demanding in an environment full of risks. Human-induced risks on board ships are not well identified, assessed, or mitigated, leaving the ship crew exposed to a higher chance of getting into an accident. Furthermore, the industry relies on basic tools for risk identification and assessment that are tedious, time-consuming, and ineffective. To improve the safety issues in the marine industry, we are proposing a novel approach to risk management. This approach is based on a risk assessment methodology designed for the marine industry to meet its specific characteristics and unique risks through utilizing advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing to assess activities and risky circumstances.

Innovation en méthodologies internes de génie-conseil – Développement durable & Appels d’offres à l’international

Innovation en méthodologies internes de génie-conseil – Développement durable & Appels d’offres à l’international

Le but de ce projet est de définir des méthodes internes pour améliorer la réponse aux appels d’offres de projets à l’international pour l’organisme partenaire, EXP.
Des équipes multidisciplinaires, des outils généralisés et des méthodes fonctionnelles sont nécessaires pour intégrer transversalement le développement durable dans des projets d’envergure, particulièrement dans le milieu du génie-conseil.

Exploring Innovations in the Japanese Robotics Industry

The intern aims to study the Japanese Robotics Industry by looking at several cases such as Ascent Robotics and analyze the advanced products that are produced by the industry. The intern will be studying the market performance, technological proficiency and other such parameters of products such as mobile manipulators that are produced by Japanese startups. Through this, the intern aims to identify strengths and weaknesses of the industry and identify transferrable successful attributes to the Canadian Robotics industry and start up culture. Such a project will allow the partner organization in helping Canadian students and innovators to launch successful technological startups.