Projets novateurs réalisés

Radiochemistry for the Pharmacokinetic Evaluation of Novel Bone-Targeting Peptide Hormone Derivatives

This proposed research project is about development of new bone-targeting drug called PTHPEG- BP, this new compound will overcome shortages of current clinical peptide hormone PTH, and show better treatment efficacy and lower price then the latter; several new technologies will be used on research of this PTH, such as micro Positron Emission Tomography (PET/CT), and several of its characteristics will be identified such as structure, bioactivity, and metabolism inside body. After preparation of this compound, its production will be scaled up for commercialization by our partner organization Osteometabolix Pharmaceuticals Inc., which in collaboration with big Pharma, will undertake clinical trials of PTH-PEG-BP in human patients; our ultimate goal is to develop PTH-PEG-BP towards investigational new drug (IND) status.

Development of a Corrosion and Damage Detection Application on 3D Reconstructed Subsea Objects

qualiTEAS’ product, Argus 1.21, is machine learning based computer vision application that can analyze images of offshore structures and detect corrosion-mediated damages. Through this project, qualiTEAS is aimed to develop a next-generation machine vision solution that can detect corrosion impact on a 3D representation of the asset.

Recruitment in cavity-nesting ducks along the lower Saint John River floodplain

Waterfowl are important to many people, organizations and cultures for their beauty, hunting opportunities and environmental importance. While these species are prevalent and well-studied throughout North America, what actually governs the growth and declines are still heavily debated. This work will address this issue by examining some possible population drivers in three waterfowl species (wood ducks, common goldeneyes and hooded mergansers). This work is aimed at helping Ducks Unlimited Canada review and evaluate their nest box program which is used to increase the nesting opportunities of these species. This work will help biologist better implement management strategies to protect the environment and provide abundance needed for hunting opportunities.

Tracking Enabled Hockey Puck Testing

In order to ensure accuracy and efficacy with Drive Hockey’s proprietary tracking enabled hockey pucks, the BCIT intern would perform physical, performance, and signal processing testing. The objective is to produce a set of baseline lab test data for us to compare our tracking enabled puck to an official puck (or possibly two different brands) in order to produce a report that:

a. identifies physical inconsistencies / tolerances of our puck versus regular pucks that we would need to address through R&D
b. identifies conditions where the physical properties cannot be maintained and where/how the internal circuitry would fail to better understand ability to withstand reasonable hockey game play conditions
c. identifies the types of movements and/or conditions by which the signal processing and location identification algorithm accuracy is adversely affected
d. identifies areas and methods to improve tracking enabled puck characteristics in order to increase accuracy and effectiveness of data collection and align performance under reasonable hockey game play conditions to standard hockey pucks

A report that summarizes the lab test results would be beneficial to Drive Hockey Analytics for presenting to hockey leagues, associations, team, and other user groups so to build confidence in the accuracy and effectiveness of the data collection and performance under reasonable

Exploring effective and value-adding measures and/or indicators for business accelerators and incubators in CAIN membership

The efficacy of business accelerators and incubators (BAIs) in advancing innovation is consistently challenged by a variety of players (e.g. media, funders, entrepreneurs, etc.) and it is very difficult to gather defensible evidence beyond anecdotal. CAIN, seen as a representative voice of BAI’s in Canada, is frequently approached to defend the value of BAI’s. BAI’s also are undergoing scrutiny from their funders and need to generate evidence of the impact of their service to Canadian entrepreneurs to generate continued funding support. Very little aggregate data exists on innovation and BAI’s in Canada and the data is typically non-uniform. Furthermore, different BAIs have different business models and may warrant different data capture and reporting, despite many indicators being similar, extending beyond conventional economic metrics. CAIN is embarking on a process to understand what measures and/or indicators are the most effective and desirable to its members and primary stakeholders.

Computational models for understanding the role of STN in encoding different aspects of speech production

In this project I will develop a computational framework for the automatic detection of task-related biomarkers in PD patients during speech production. This framework will help us to predict the timing of electrical stimulation in STN with the purpose of understanding the role of STN in encoding different aspects of speech production. To achieve this goal, I, in collaboration with my supervisors at the University of Toronto and Massachusetts General Hospital, will develop novel inference methods for online identification of state transitions in speech production from neural and behavioral recordings. Specifically, I aim to test and benchmark this novel framework with previously acquired intraoperative data during speech production tasks to integrate neuromodulation technology for assessing the STN rule in speech production. I anticipate that the result of this project will provide new insights for closed-loop neuromodulation for PD patients.

Fate of the ash forming elements during combustion of biomass

In modern kraft pulp mills, the kraft recovery boiler can produce all the steam and electricity needed for the mill and even sell green electricity to the grid. However, most mills still have the fossil CO2 emissions from the combustion of fossil fuels in lime kilns. For mills to eliminate this source of fossil CO2, the fossil fuels must be replaced by biomass derived fuels. Biomass fuels are used in some mills either directly by using powdered fuel burners or indirectly by first gasifying the fuel and then burning the product gas in the lime kiln.
A significant challenge for direct combustion of biomass in lime kilns is the ash forming elements. The ash forming elements in biomass are inorganic elements such as K, Cl, Ca, P, Mg, Mn, Si and Al. If the biomass is burned directly in the kiln using a powered fuel burner, the ash forming elements go into the kiln and will enter both the lime cycle and the larger recovery cycle. Both due to availability and low cost, bark is a desirable fuel to use. However, it has a high ash content when compared to stem wood.

A paper based microfluidics device for E.coli detection from water

This project will result in the development of a simple, reliable, and rapid system for detecting E. coli bacteria in water for public health and environmental monitoring. We will use paper-based microfluidics fabricated by common laser-jet printing techniques. When E. coli is detected by the device, a color change becomes visible and can be quantified to estimate the bacteria count in water. The test will be commercialized for remote water testing applications.

ABGI Canada _ Co-développement de notre nouvelle unité d’affaire: Remettre l’innovation au cœur des pratiques de nos clients

ABGI souhaite développer de nouveaux outils pour aider à la réalisation de projets innovants au Canada. Notre objectif est de donner aux entreprises les moyens de leurs ambitions : les aider à définir leurs axes de croissance et à dégager des ressources financières pour mener à bien ces projets.

Optimization of process equipment and parameters for upgrading bio-oil by catalytic hydrodeoxygenation (HDO)

In recent years, clean and renewable energy sources are urgently needed to partially or completely replace the fossil fuels (e.g., natural gas, petroleum, and coal). Bio-oil derived from fast pyrolysis and hydrothermal liquefaction of lignocellulosic biomass usually contains a high oxygen content (30-50%), leading to a relatively low heating value, high viscosity and poor stability. Catalytic hydro-de-oxygenation (HDO) on the other hand, is one of the most promising ways to upgrade bio-oils. SixRingTM delignification of biomass is a disruptive process that enables the conversion of underutilized lignocellulosic resources into Lignin Hemicellulose Depolymerization Organics (LHDO) and fully delignified cellulose under ambient conditions. In this project, optimization of catalytic HDO will be investigation to develop cost-effective processes for producing high-quality bio-fuels and upgrade SixRingTM bio-oil into commercially viable fuel.

Acrylic Robotics Technical Product Development Internship

Acrylic Robotics is a robotics startup based in Montreal, on a mission to use technology to make fine art accessible to the general public. To make fine art accessible, this project focuses on the development of a physical motion capture system to capture and recreate the artist’s brushstrokes. The intern will assist in the development and commercialization of the technology, leading product and pilot testing ensure that the system meets the needs of the clients and company needs. The

Machine learning based data analysis and anomaly detection for high-voltage bushing health monitoring

POWER HV is a technology company focusing on carbon emission reduction. Through their sensor product, they help power plants increase the efficiency of the grid and improve its stability in challenging weather by tracking every single bushing and predicting a bushing failure. Exploiting the generated massive sensor data can contribute to an automatic and intelligent monitoring system. However, traditional data analysis methods fail to deal with large amounts of data with complex relationships. The emerging machine learning techniques enable us to analyze data efficiently and effectively. This project will apply state-of-the-art machine learning methods on the sensor network data to achieve data analysis and anomaly detection.