Innovative Projects Realized

Sensor for the detection of space debris, communication signals and moon dust

This research project will develop new methods for detecting space debris, communication signals, and moon dust equipped on spacecraft and satellites to be launched in the future. One of the major concerns with equipment orbiting Earth is the possible collision with space debris. Also, a lunar outpost equipped with the Canadarm3 is
planned for completion by 2024 as part of the lunar Gateway. Spacecraft travelling to the lunar outpost and to the surface of the moon will be heavily affected by abrasive moon dust, resulting in the need to detect its presence. The intern will develop a prototype for sensing space debris and communication signals and will propose innovative techniques for sensing moon dust to assist Magnestar in moving towards their goal of building sense and respond technology for the aerospace industry.

Deep Learning with Big Data for Innovation Acceleration

Xlscout has developed/launched many products like Automated Novelty Checker, Techscaper, Company Explorer, NLP enriched Landscaper, etc. The primary objective of this project is to continuously add new functionalities to the existing novelty checker module of Xlscout and also develop new innovative products/features that help in automating research & development. This project is important to XLScout as it will provide critical components for enabling semantic patent search. Integration of intellectual property (IP) and R&D is important for active and continuous innovation as well as for risk reduction; however, there is no good way to connect IP and R&D departments. XLScout platform will provide a service to engage IP and R&D departments. The platform will assist to help XLScout customers to search patents and other related databases more smartly, taking semantics into consideration. This will also keep consumers updated about competitors through alerts. 

Electrochemical reduction of carbonate and bicarbonate to ethylene

Climate change is one of the most urgent and pressing issues facing society. In order to meet aggressive greenhouse gas emission targets, the removal of carbon dioxide from the atmosphere (known as Direct Air Capture, DAC) is required. However, once captured, the CO2 must be purified from the capture solution before it can be used or transported. This purification step is energy intensive. An attractive alternative is to directly use the captured CO2, thereby avoiding the energy-intensive purification step. In this project, the Dinh group at Queen’s University will collaborate with CERT Systems Inc, a carbontech startup based in Toronto. Together, they will develop technology for the efficient conversion of CO2 captured from the atmosphere into valuable chemical products.

Mining Governance Programs

With the overall goal of reducing the risk of costly and dangerous mining tailings failures, Enviro Integration Strategies (EIS) consistently identifies and evaluates risks, supports compliance with global management standards, and the production of transparent performance disclosures, helping mining executives make safer decisions. To meet an overwhelming demand for consulting services to develop ESG risk management guidance, waste and water management governance programs, we are building a consultancy to develop governance and management programs to improve performance and oversight, reducing risks during operations. We also see a rising interest in, and can support, the exploration of mining circularity opportunities. Building capacity in our next generation of leaders is the foundation of this internship, and we hope to create relationships with these new graduates for more permanent roles in the future.

Machine Learning Techniques for Speech Enhancement in Audio Conferencing Systems

The core value of Nureva is to provide reliable and easy-to-use audio-conferencing products that offer a good user experience and maximize productivity. A very important factor of that is to output clean audio which oftentimes in its raw form is contaminated with environmental noise, reverberation, and echo. These problems have been researched using traditional signal processing methods for a long time. While these can be quite effective in many situations, they do have limitations with non-stationary noises that vary with time. The success of Machine Learning (ML) in various fields also unlocks new possibilities in noise suppression and speech enhancement in that manual feature engineering is reduced or eliminated. That allows ML models to generalize to situations it has not encountered in training. This research aims to investigate the merits of various ML techniques and how they can be applied with Nureva products to ensure better audio performance.

Effects of sow grouping practices on reproductive performance and piglet development in relation to prenatal stress

After being bred, sows are commonly housed in individual stalls, which restricts their movement and impacts their well-being. For this reason, Canadian farmers are transitioning to housing sows in groups where sows have social interaction and greater movement. Groups can be formed right after breeding, with either a constant group (static: the same sows remain together until farrowing) or with smaller groups of sows being periodically removed and replaced (dynamic). While group housing allows sows greater movement and ability to socialize it can also compromise welfare by increasing aggression and stress, particularly in subordinate sows, if not properly managed. Stress on the sow can also impact the development of piglets in utero, potentially affecting the quantity and quality of pigs produced and piglets’ behaviour, immune system and physiology later in life. This study will compare the stress that sows experience in static and dynamic groups, and study the effects their piglets. The results will be used to assist pig producers in implementing better sow grouping practices that can decrease stress and improve production outcomes.

Monark Leadership Application: Early Product Development and Innovation

Monark is a mobile-first, on-demand, self-led leadership development platform that combines psychology-based learning with a community of practice, empowering leaders to own their growth anywhere, anytime. By integrating timely educational content with learnings and experiences from a network of growth-oriented leaders, Monark prepares high-performers for the challenges of tomorrow. Our holistic training model engages users in personalized, evidence-based development by utilizing 360-degree assessment, microlearning, on-the-job practice, behavioural nudges, real peer feedback, and the first-hand knowledge that lives outside of textbooks, all with the goal of driving measurable and lasting behaviour change. This project will help Monark address the current challenges of the iteration and expansion of the product in its early form, by focusing on the collection, analysis and interpretation of early data trends and habits from our pilot users.

Statistical Analysis of Prostate Brachytherapy Treatment Data

Using data obtained over a period of 15 years from Brachytherapy procedures used on patients to treat prostate cancer, statistical analysis will be carried out to confirm the hypothesis that Brachtherapy treatments offer the best outcomes at the lowest cost in comparison to other treatment modalities. The first stage of the project will be to review the scope and objectives of the study with the stakeholders and finalize the methodology for this analysis. It is suggested that this analysis start initially with logistic regression, which would be used for estimating the probability of being in remission after a certain period of time following treatment. We then propose to continue with a more detailed analysis using transition models. Such models are used to analyze the data where a strong dependence is suspected between successive observations on a patient. These models distinguish themselves from survival models by allowing for transitions into and out of a set of states defined by the response variable instead of focusing on the time for a certain event to occur. TOBECONT’D

New Works XR (eXtended Reality)

New Works XR advances creative digital literacy of Canadian dance artists. It is a multi-faceted project supporting dance makers and companies working with or in early stages of exploring creation involving XR technologies. Designed by artists and XR technicians the project provides catered support: mentoring and consulting, test projects, workshops, virtual community building, peer-to-peer learning, and access to equipment, partnership, and information resources.New Works XR prioritizes affordable barrier free access to new digital creation tools for advancement of the artform and career development of Canadian dancers.

Mathematical model for electric arc welding process selection based on static and dynamic characteristics

Welding plays an important role in fabrication and assembly manufacturing. Selecting the appropriate process of electric arc welding is a task assigned to the experience of those who manufacture. The materials selection for applications is as important as the selection of the manufacturing process. The consequences of not perceiving this scope cause affectation in the performance or in the life of the element. This work proposes the study of the microstructures formed in materials affected by welding processes and the changes in their mechanical properties that contribute to the performance of the fabricated elements. It is expected to identify numerically these changes and thus formulate mathematically the influence on the expected performance. It constitutes an element of analysis for the selection of the adequate electric arc welding process.

Analyse granulométrique et teneur en matière résiduelle par technique d’analyse d’images

La proportion de déchets dans les sols contaminés a une grande influence sur leur gestion. Les méthodes standards pour déterminer ces proportions sont visuelles et subjectives. L’émergence récente des réseaux de neurones convolutifs (RNC) a révolutionné l’analyse automatique des images dans différents domaines. Ces réseaux de neurones peuvent recevoir des données complexes, comme des images, pour les classer et les interpréter. On rencontre encore très peu d’applications en géotechnique et en géoenvironnement. Dans ce projet, nous proposons de créer une base de données de photographies de sols contaminés et d’entrainer un réseau de neurones convolutifs pour prédire la taille des particules et le pourcentage de déchets dans les sols contaminés à partir de photographies. Cet outil permettra d’améliorer les méthodes de caractérisation des sols contaminés et la réhabilitation des sites contaminés.

Market Research Analyst: Total Addressable Market for Consumer Goods Made from Captured Carbon Emissions

Oco is a consumer division of Carbon Upcycling Technologies, a Canadian carbon utilization company and winner of the XFactor Award in the Carbon XPRIZE. Oco uses Carbon Upcycling’s technology to offer our partners some of the easiest to integrate material decarbonization solutions on the market. Oco offers low carbon material solutions to consumer products brands and helps them take the first steps toward reaching their ambitious, world-saving climate goals.

Oco has already partnered with 25+ companies and developed 300+ CO2-embedded materials, but Oco must apply market research to further their collaborations and development. The challenge faced by the company is a limited understanding of the plans consumer goods companies have to reach their net-zero targets. This project will help Oco address this challenge by analyzing the current market for consumer products and other companies’ net-zero carbon emission goals. Furthermore, this will help Oco strengthen its knowledge of recent innovations and possible future partnerships to grow as a company to further decarbonization.