Projets novateurs réalisés

Characterization of Naturally-Occurring Neuropathic Pain in Dogs

Clinical experience demonstrates that canine patients commonly suffer from neuropathic pain and little is known to address this issue. Our study aims to investigate different tools for the diagnosis and treatment of neuropathic pain. Forty dogs with naturally-occurring neuropathic pain will be included in a prospective, randomized, masked clinical trial using appropriate inclusion and exclusion criteria. Dogs will be assigned to receive treatment with a drug that is used in humans for neuropathic pain (gabapentin), or gabapentin in combination with an anti-inflammatory drug (meloxicam) in a cross-over design (dogs will receive both treatments during study). QoL, pain scores, client-specific outcome measures, biomarkers of inflammation and QST will be evaluated before and during the study for observation of treatment effect. This study provides insight on the diagnosis of neuropathic pain and may prove the efficacy meloxicam in a new application.

Machine learning in fluid composition quantification

A critical issue in the oil and gas industry is to quantify the composition of fluids flowing back from the hydraulic fracturing process. This quantification is usually carried out by a manual process (frequently via a visual test) to estimate the water and oil produced from a well flow back process. A sample of these onsite tests are sent to laboratories for chemical analysis. This process has been the status quo for decades. This approach is manual, prone to error, and does not lend itself to sophisticated real time analysis. Machine learning techniques have significantly developed in the last decades, and combining with in-depth mathematical basis, it is now capable of producing a revolutionary impact to almost every industrial application. This research project aims at developing a machine learning framework, that can detect the fluid composition based on the sensor data as well as referencing chemical analysis results.

Understanding atmospheric peril risk across re/insurance portfolios

Natural disasters that are associated to the atmosphere (known as atmospheric perils) such as hurricanes, tornadoes and hail, flooding, drought, and wildfire, caused over $100 billion in damage throughout the world in 2015. Insurance companies often cannot afford to be responsible when such catastrophes occur, and so they purchase insurance to protect themselves (called reinsurance) from these large risks. In the case of atmospheric perils, the damage that is caused is spread unevenly throughout the world and strongly influenced by features of the climate system such as the temperature of the ocean. Currently, reinsurance companies do not take advantage of these critical features of atmospheric perils. Our study of how the geographic patterns of atmospheric perils affect our client’s reinsurance investments will enable the partner to better manage its risk, and therefore reduce costs and improve protection to clients.

Development of a simulation model for prediction of performance of an anesthesia circuit using a novel CO2 separation system

Anesthesia is delivered to patients in vapour form, supplied via Mechanical ventilation in closed loop anesthesia circuit. This necessitates the removal of the carbon dioxide (CO2) produced during respiration. Currently, anesthesia circuits use granulate-based CO2 absorbents that react with CO2 to remove it from the gas stream. However, anesthetic vapours also react with the chemical absorber producing toxic bi-products that have been connected to negative patient outcomes. DMF Medical Inc. has developed an early stage prototype device that provides an alternative method for CO2 removal. To finalize device specifications and produce a market ready system, optimization of device performance and material properties is required. In this project, Dr. Hanafi will create a comprehensive and accurate computer model that enables testing a range of mechanical ventilation conditions on different simulated patient conditions to find the optimal design parameters to minimize concentration of CO2 in the system.

Collaborative Service Robot in a Group Home Environment of People with Developmental Disabilities

The vision of this proposed project is to design and build a collaborative service robot that will help people with developmental disabilities (DD) reach their personal goals and achieve greater independence, using existing and new technologies. This will allow the industry partner, JDQ, to advance the type of services provided to people with DD and their caregivers in a group home environment, through its partnership with Developmental Disabilities Association (DDA). This project will support the overall intention of contributing to the creation of better collaborative service robots than exist today, specifically designed to support adults with DD and their families. Ultimately, JDQ and DDA expect the project outcomes to be transferable to health and elder care environments.

Non-intrusive assessment of vigilance in drivers based on eye movement and blinking

Due to lifestyle and work demands, chronic sleep deprivation is now experienced by many people, leading to increased drowsiness and fatigue which can have a negative influence on health, safety and work performance. Drowsiness, in particular, can influence fitness to drive and put people at significant risk. With this in mind and in response to increasing demand from market and public domains, Alcohol Countermeasure Systems (ACS) has launched innovative research into methods and technology for improving driver and vehicle safety. The main objective of this research is to develop non-intrusive techniques for real-time assessment of the state of vigilance of drivers based on behavioural patterns (particularly, eye movements and blinks). In this project, advanced machine learning and signal processing techniques will be used to develop appropriate methodologies for real-time monitoring of drowsiness.

Development of Feed Blocks for Livestock

BNTrading Inc., located in Alberta, is interested in developing a more efficient form (block) of densified feed material compared to small-size cubes and pellets currently available. This new form of densified material is to provide an easier handling, storage, and transportation. The target is firstly to convert the pellet or cube forms to block in trials. If there is a possibility of this transformation to blocks, the work will be conducted for the conversion of these conventional densified feed to feed blocks. Otherwise, the crushed form of feed will be used for production of feed blocks. For this purpose, different types of binding agents (chemical or natural based) will be used in densification process. Parameters of binder concentration, moulding pressure and residence time will be optimized to obtain desired quality attributes. The optimal combinations will be determined based on the quality attributes which are important in the feed industry.

Design, Analysis and Optimization of an Aircraft Seat

Business aircraft seats are typically designed to provide maximum comfort to the occupant, while adhering to strict certification requirements. This tends to result in the designed seat becoming heavy and costly due to conservative tradeoff analysis, and a dependence upon legacy design techniques. With the advent of more powerful computer aided design techniques – it is possible to design a seat that meets both the comfort requirements of the occupant, and strict regulatory requirements. This project leverages advanced computer engineering design tools to optimize comfort and stress analysis to produce a mass and cost efficient design for a business aircraft seat.

Development of In-Situ Characterization tools for Spatial Atomic Layer Deposition System

Atomic Layer Deposition (ALD) is a popular tool for the deposition of thin film materials that are common in solar cells, sensors and display technologies. However, the need for a vacuum environment and slow material growth rates restricts ALD applicability in large scale commercial and industrial materials production. Atmospheric Pressure Spatial Atomic Layer Deposition (AP-SALD or SALD) is a variant of ALD in which thin film materials are deposited without the need of a vacuum environment and with the added benefit of quicker deposition rates. While SALD mitigates the problem of scalability, it also presents a unique opportunity for the learning of material properties as the film is deposited. This in-situ characterization, allows for quantitative tuning of the material property to an optimal value in real time via SALD process parameters. TO BE CONT’D

Advancing Waveform Tomography of Crosswell Data with Applications in the Sulphide Environment

Crosswell seismic tomography is a geophysical survey method in which the propagation of sound waves through the Earth’s crust is used to infer geological structure. An array of acoustic sources and receivers are placed into separate boreholes, and full waveform recordings are made of the response to each source, measured at each receivers, the objective being a “cross-section” of the geology between the two boreholes. Traveltime tomography is used initially to image cross-sectional structure by examining the times at which the receivers first detect seismic waves generated by the sources. Waveform tomography then uses the frequency domain components to improve the model resolution by taking into account the scattering and distortion of seismic waves at different frequencies. Software developed by the University of Western Ontario is used to create these models, which are useful in commercial geophysical exploration. For this project, data from Vale’s Voisey’s Bay property will be examined.

New consensus ranking heuristics to rank big biological data

Ranking biological data is a difficult task for various reasons. Hence several ranking methods have been proposed, but none of them has been deployed on systems currently used by the scientific community. This is why a good solution is, given a set of rankings, to find a consensus ranking that reflect their common points while not putting too large an emphasis on elements that are classified as “good” by only one or a few rankings.

In computer science, we know that this problem is hard to solve when we have to find the consensus of more than 3 rankings and gets even harder when the rankings are getting bigger. Thus, we are interested in designing efficient algorithms to solve the problem and quick heuristics that can give a good quality approximate results on big data. We will also explore mathematical properties of the problem to have a better understanding of its mechanics and to accelerate computational calculations.

KINARM Standard Tests’ Task Scores: towards a validated and effective tool for communicating to clinical communities KINARM measures of brain function

KINARM Labs™ provide robust and objective measures of brain function and dysfunction by the precise measurement of human behavior with robotics. Created by neuroscientists, KINARM Labs allow clinician-scientists to detect and quantify the sensory, motor and cognitive impact of a diverse range neurological impairments caused by stroke, cardiac arrest, TIA, mTBI, concussion, MS or Parkinson’s – all in a short <1h assessment. The MITACS intern will enhance the normative modelling of healthy performance of KINARM Standard Tests – a standardized assessment protocol for identification and quantification of neurological impairments. These contributions will be critical to ensuring that KINARM Labs become an essential tool in the management of patients with brain injuries.