Projets novateurs réalisés

Portrait de l’écosystème startup du Québec

Le nombre de jeunes entreprises ainsi que les structures qui les accompagnent se sont multipliées ces dernières années au Québec. Plus que jamais, tous les acteurs (entrepreneurs, structures d’accompagnement, décideurs publics, etc.) ont besoin de parler un langage commun, d’avoir une vue d’ensemble et d’accéder à des statistiques et des analyses pour prendre des décisions éclairées. Le projet du Portrait de l’écosystème startup du Québec vise à combler ce besoin par la combinaison d’outils structurants panquébécois et d’analyses thématiques multi régions qui permettront d’approfondir la compréhension de cet écosystème au moyen de données probantes à jour et de la recherche appliquée. L’approche adoptée pour la création et le renouvellement de ces connaissances à mesure que se transforme l’écosystème, sera celle de la co-construction, c’est-à-dire, en collaboration avec les différents acteurs et univers qui gravitent autour des startups incluant : les chercheurs, les structures d’accompagnement, les acteurs de développement économique, les entrepreneurs, les décideurs, etc.).

Vibration Analysis: Fault detection, classification, prediction

Vibration analysis is probably the most widely used technique to perform health monitoring of mechanical machinery. Specifically, we are interested in monitoring ‘Vibrating screens’, machines that are for example used by the mining industry to sort aggregate by size. Over the last 10 years the research group of Dr. v. Mohrenschildt has developed hardware, software and theory to accomplish this. The goal is to further the understanding of feature extraction and classification to perform effective predictive maintenance. Several activities contribute to this overall initiative: Data Mining: perform feature extraction on the data sets we recorded data sets to obtain machine health information. These topics are very timely as recent successes in machine learning sparked significant interest in industry and academia. Develop methods to perform additional machine measurement: impact based modal analysis: determine the resonance frequencies of a machine and bearing analysis again with the goal to determine bearing health.

Étude de la faisabilité des huiles électrostatiques pour la protection contre les rouilles automobiles

Antirouille Métropolitain est une compagnie québécoise fondée en 1976. Depuis ses débuts, elle prend l’innovation très aux sérieux. Œuvrant dans un marché très compétitif où elle est le chef de file, elle doit sans cesse chercher des moyens de s’améliorer. La méthodologie d’opération préconisé exige des aires de travails spacieux et une quantité minimale de clients afin d’assurer la rentabilité, ce qui limite son expansion potentiel. De plus, les normes de CSST obligent de plus en plus d’ajout de matériel qui servent à diminuer les particules en suspensions dans l’air. Ces normes augmentent les coûts d’opération et complique l’utilisation de l’espace de travail. Plusieurs recherches internes ont été effectuées et des contacts avec des fournisseurs d’équipement ont été faits. Cependant ce sont uniquement des fournisseurs œuvrant dans le domaine de l’application de peinture industrielle et le transfert de connaissance ne s’appliquait pas au domaine de l’antirouille. La volonté des compagnies telles Graco inc (graco.com), Spraying System Co (spray.com) de faire affaire avec Antirouille Métropolitain était très basse, dû principalement à l’ampleur de la potentialité financière basse du marché Québécois.

Evaluation of a test system to measure safe force thresholds on delicate tissue

Able Innovations is creating novel technology that eliminates the need for human involvement in transfers of individuals with restricted mobility replacing existing sling transfers systems that require significant physical labour by clinical staff that frequently results in staff injury and missed work. Sling systems are also undignified for the patient and can injure the patient with delicate skin because of the forces applied. Able Innovations has been collaborating with Carleton University to develop a “skin” force sensor that can measure the normal and shear forces applied by lift systems. This project will apply the sensor to evaluate forces in existing lift systems and the novel Able Innovation systems. The goal of the work is to specifically evaluate sheer forces in existing lift systems that have had limited study. The long term goal is assessment of the Able Innovation lift will ensure that the system operates within a safe range.

Optimizations of AC/DC Converters for Powering 5G Network

The telecommunication industry is rapidly evolving towards providing faster services to accommodate the global surge in network demand. The next-generation (5G) network aims to increase the current network speed by more than twentyfold. This transition brings enormous benefits for the economy and society. A main challenge faced by network providers, however, is that the existing infrastructure cannot fulfill the new requirements of the 5G network. For example, the future 5G small cells need to be more powerful than the conventional 4G network architecture. Thus, further research and development are required in powering future 5G network. The research and development at Alpha Technologies Ltd. in Burnaby is currently focussed on developing new two-stage power converter for powering 5G small cells with power 3KW. The work involves layout of power converters optimized for electrical and thermal performance. In addition, the converter will be evaluated in terms of its EMI performance.

Probiotics Counteract Gut Microbial Dysbiosis and Long-Term Neural degeneration in Male and Female CD1

Changes in the intestinal microbial composition (dysbiosis) during development have an impact on the progression of neuro-inflammatory and neuronal degenerative disorders. Probiotics have been shown to reduce these effects. However, the current literature lacks research on intestinal dysbiosis during the maturation of the intestine and the brain and does not recognize the sexual dimorphism of the neuro-inflammatory and degenerative neural pathways either. The objective of our research is to determine the mechanisms of neuro-inflammation induced by dysbiosis during development and its relationships with neurodegeneration. Bacterial dysbiosis will be induced via antibiotics in mice. Subsequently, the mice will receive probiotics. Then, an acute immune response will be induced by injection of lipopolysaccharide or saline solution. The microbial composition as well as the immune responses and neuro-inflammation will be assessed. Then, microglial activation will be monitored over the long term, as well as neural oxidation, necrosis and cognitive measures. This research will expand our knowledge of the mechanisms underlying the development of neurodegenerative diseases.

Transcritical CO2 Pulverization

Crushing and grinding rock is the largest consumer of energy at a mining operation. Breaking rock by expansion within is a lower energy process than crushing or impact from outside, since rock tensile strength is significantly lower than rock compressive strength. This research intends to explore and develop a novel method for rock comminution using a new form of explosive pulverization or shattering based on transcritical CO2 cycling rather than traditional compressive approaches. The intent of this project is to both reduce comminution energy as well as reduce equipment wear. Both result in reduced energy consumption with the associated social and environmental benefits that come from reducing energy usage.

Employing Raman Spectroscopy for monitoring a vaccine manufacturing process

Vaccine antigen production is a process that entails numerous variables. In order to have a consistent and robust process, monitoring of process parameters and controlling output variables within a certain range is the best practice. To accomplish this objective, analytical tools are used, on-line, off-line, at line. Real time monitoring of the processes is advantageous as operating parameters can always be adjusted to keep the process in check. For the sake of efficiency, it would be reasonable to determine those parameters that are correlated to the productivity obtained at the end of the process. On-line measurements would facilitate calculating and defining these parameters. There are non-invasive tools that can collect continuous measurements without interfering with the integrity of the process. Raman probes and NIR probes are examples to this type of approach. In this study the intern will be tasked to use these tools to identify any patterns which can be linked to the final productivity. The instrument will be tested in all unit operation steps to map the evolution of measured metabolites/substrates upstream and downstream of the process. Mathematical modelling, such as multivariate analysis, Principal Component Analysis, Partial least Squares will be used in synthesizing and interpreting the data.

Using document embedding for code completion

Dans les recherches récentes en génie logiciel et plus particulièrement en compréhension du logiciel, une grande partie des approches utilisent des techniques issues de la communauté du traitement automatique des langues naturelles. L’utilisation de ces techniques de traitement des langues au logiciel s’est avérée être efficace pour capturer des informations textuelles et sémantiques dans des programmes informatiques. Des travaux précédents ont montré que l’utilisation de ces informations sémantiques extraites automatiquement sur de larges ensembles de données ont des impacts bénéfiques dans la production d’outils d’aide au développement logiciel.
L’objectif de ce projet de recherche est d’appliquer des techniques de traitement des langues et d’apprentissage automatique au logiciel afin d’automatiser des tâches du développement logiciel (ex: suggérer au programmeur le code qu’il devrait écrire étant donné ce qu’il a écrit précédemment)

Optimization of Auxiliary Electrical Operation of a Handy-Size Bulk Carrier

Environmental regulations that aim to reduce greenhouse gas (GHG) emissions from new ships will become effective by 2025 [1]. A lot of solutions are evaluated by the industry to reduce their emissions including shore power, a process that aims to reduce the ships emissions in ports by turning ship engines off and supplying the ship by the energy available on shore. A lot of issues reside within this solution: the high-power demand on the microgrid during loading and unloading process, the lack of standardization for the electrical connection, the capital expenditure (CAPEX) needed to implement a shore power system and more. Therefore, the project is about detailing the energy demand of a handy-size FEDNAV [2] bulk carrier multi-sources multi-generators of 35,000 Dead Weight Tonnage (DWT) with one to four deck cranes while at the port, understanding and mathematically modelling the different energy sources that can interact with the ship, providing a microgrid solution for the port and producing a technical-economic study on the viability of the best solution.

Operationalizing quality evaluation for heterogeneous legacy systems

Most large companies use software systems that were developed over decades. They comprise

systems that are in different programing languages and must all communicate with one another to solve

bUSiness problems. As these systems become increasingly large and complex, there is a need to develop

tools and techniques to ensure their quality. Current state-of-the-art techniques deal with systems individually,

yet many quality Issues arise due to the interactions between different systems. The goal of this project is to

develop techniques to analyse these complex heterogeneous systems and evaluate their quality. The two

first interns will to provide ways to recover the interactions between systems. The second two will find and

categorize good and bad patterns corresponding to these interactions. The final pair will assess the impact of

these patterns on quality.

Exploring and modeling human cognition through deep learning

Some of us recall our dreams very often, while other hardly ever remember them. The neuroscience of sleep and dream research is a thriving field with still many unanswered questions. Differences in brain network dynamics between individuals with high versus low dream recall rates, are still poorly understood. In this internship, we will address this question using state-of-the-art machine learning tools. In particular, we will frame it as a classification problem where we apply deep convolutional neural networks (CNN) to sleep EEG recordings in order to predict whether subjects belong to a high or low dream recall group (HDR and LDR resp.). In addition, to explore the neural properties that the AI approach used for successful discrimination, we will test several techniques for the visualization of the feature space learned by the network. This project lies at the intersection between AI and Neuroscience and carries the potential to advance our understanding of dream-related cognitive processes y combining EEG and data-driven machine learning tools.