Innovative Projects Realized

Research and test of the full waveform inversion method

Full waveform inversion (FWI) is an inversion technique that uses least squares theory to compute a velocity model of the Earth that minimizes the difference between an acquired shot and a synthetic shot. The technique proved to be of hard usage in industry and the goal of the project is to research for solutions that allow the application on real seismic data. The gradient (direction of the model update) will be computed with the PSPI migration and the scale factor (for proper update) will be computed by least squares. The final implement is to apply it on elastic waves (real data). The partner will have the opportunity to have a FWI experienced worker in the group and will be able to
research a new processing product to be part of a selected group of companies that offer such kind of seismic inversion.

Efficient Prototyping of Linear Actuators Through Numerical Modeling

Within the gaming and aviation industries, there is a demand for simulators that imitate flight. Peripheral flight yokes are often used. The industry desires yokes that are both affordable and realistic. Recently, Iris Dynamics Ltd. has developed a new flight yoke that offers superior performance with respect to its competitors at a relatively low price. Their unique design is based on the use of a linear actuator, wherein the force on a magnetic shaft centered within current carrying coils is used to create the feeling of an actual flight yoke; their competitors simulate the feeling of flight through mechanical means. Using a single linear actuator is a novel approach to peripheral yoke design and scaling this technology is an active area of inquiry. The objective of the internships is to provide Iris Dynamics Ltd. with a numerical simulation tool that will allow for the optimal design of new flight yokes of various sizes, these designs being compatible with force, input power, and heat management specifications.

Investigation of Driven FRP Reinforced Concrete Piles for Bridges, Tunnels, Marine, Lateral Support Applications

Reinforced concrete (RC) structures with steel reinforcement have a limited service life due to corrosion especially for these used in harsh marine and/or aggressive environments or which existed under water ground table. RC vertical structural members as columns and piles transmit axial compressive loads with or without moments are of critical importance for the safety of structures. These members usually exposed to shear and lateral forces. Using alternative noncorrosive reinforcing materials as fiber-reinforced-polymer (FRP) bars has been the focus of many studies in recent years to attack the problem from its root cause. This project intended to investigate the shear strength behavior of FRP RC piles under lateral loads with/without axial loading. The outcomes of this project will be important for examining the validity of the Canadian Highway Bridge Design Code (CAN/CSA S6-06) design provisions for bridge deep foundation and water front and marine driven piles with FRP reinforcements.

Optimization Techniques for Automatic Assessment of Manually Drawn Diagrams in Educational Problem Sets

In STEM education, many problem sets require a student to answer a question with a drawing. For example, kinematic physics or statics/dynamics engineering problems often require the student to construct a free-body diagram as a necessary step in the solution. However, when such subjects are taught through online education (such as though a MOOC), most automatic assessment is only done through the constraints of multi-choice answers. The objective of this research project is to create an automatic assessment framework that can grade diagrams drawn and submitted by students through an online web interface. Our approach is a backend server-side module that decomposes the drawing into a set of shape primitives that are then automatically labelled and matched through an optimization approach to a set of solution primitives. Our approach must handle not only multiple correct solutions, but also select an appropriate grade based on a distance metric to the true solution.

Effects of Pre-Print Sterilization on the Material Properties of 3D Printed Products

With 3D printing comes the capacity to create custom plastic components needed in orthopaedic implants between surgeries, as short-term and possibly long-term measures. This customization and flexibility comes at the cost of facing new challenges: how can we sterilize and clean a 3D printed product that cannot withstand high temperatures, and what effects do low temperature surface sterilization have on the plastics being printed? This research will focus on determining the outcomes of current sterilization methods on raw materials and assess the current capacity to achieve the goals of custom plastic orthopaedic implants. The planned experiment will identify changes in material strength, physical dimensions and chemical signatures of three plastics identified by the industrial partner as key to their future products

An Economic Analysis of Improving the Grain Export Capacity at the West Coast Terminals

It was evident in the 2013/14 crop year that when limited export capacity is rationed by price, large export basis reduces Saskatchewan prices at a substantial cost to grain producers and the provincial economy. Given the increasing long term yield trend, the projected 10 million tonne increase in crop production under the Saskatchewan Plan for Growth, and the continued growth in Asian grain markets, West Coast export capacity is likely to be an issue for decades to come. The goal of this study is to quantify the future economic impact of alternative policy options to increase the capacity of West Coast export terminals. The analysis will inform all investors in the Western Canadian grain industry and will be of particular importance to policy makers and organizations such as Saskatchewan Wheat Development Commission that represent Western grain producers.

Wi-Fi Based Activity Recognition

Sensing technologies require the deployment and maintenance of complex and large infrastructures. This research proposal is focused on people’s activity recognition technologies though existing WiFi infrastructures. The information gathered by this technology can be applied to different industries like home automation, security, etc. In the future, this technology will powered applications in the home automation industry as the one described next. Mary comes home and leaves her cellphone on the couch. As the system recognizes her, no alarm is activated. Given the time of the day and her habits, the platform understands that she wants to prepare dinner. Cooking is a defined profile for her so the kitchen starts to react based on her presence and activity profile (turning on lights, changing temperature, turning on cooking music). If Mary wants to sleep, the system gathers the necessary information again and changes the whole environment. This project is bringing a new technology to the partner organization and the Canadian Industry.

Development of a laser backscattering Raman gauge for online wood pulp process control

Comparative study of approximate and rigorous approaches to lightning discharge through power system towers

The project performs comparative study of simplified and rigorous approaches to analysis of lightning strike discharge through complex 3D structures of the power line towers and their grounding systems. Simplified models of the commercial tools such as PSCAD and TFlash will be compared to the new rigorous electromagnetic model (EM) based on full-wave algorithms analysis. The rigorous 3D EM modeling (developed at the University of Manitoba) is based on the novel fast direct method of moments solution of the integral equations of electromagnetics which account for the presence of the stratified media of the soil. The rigorous modelling results will be used to identify deficiencies in simplified models at the high frequencies (above 100kHz) where the full-wave effects become important. Such effects are commonly distorted in simple models, yielding an overall high error in predicting the performance of the lightning strike protection systems, including the grounding electrodes buried in the soil.

Recurrent Neural Networks for Video Similarity Metrics and Video Captioning.

The objective of this project is to develop a system that provides video recommendations to the user based on the content of the video they are currently watching. Existing methods generally create their recommendations based on the title or tags of the video or on the viewing patterns of other users who have watched the same video. However, text based methods are only accurate if the titles and text are correctly spelled, use similar colloquial language, and accurately reflect the important content of the video, which are factors that are not guaranteed. Furthermore, it is often difficult to infer meaningful viewing patterns and determine similar user groups when attempting to create video recommendations based on the viewing behavior patterns of similar users, particularly if the user base is small. Hence, a more accurate system for video recommendations could be created through an artificial intelligence method that is able to autonomously infer what the important content of a video is. The method would then use this “important content” to find other videos that contain similar “important content” in order to create video recommendations for the user as well as describing this “important content” in a natural language sentence.

User Demographic and Interest Prediction

Addictive Mobility is the leading Big-Data online advertising company in Canada. It uses Real-Time Bidding (RTB) platform for online display advertising in mobile devices. In this domain, a number of companies compete with each other to display a specific Ad to a customer within a specific time. Thus, in such a competitive system, the goal is to develop and optimize such a system which can not only minimize the cost over the campaign period, but also send targeted ads to maximize the Return on Investment such as number of clicks or purchases. Methodologies such as Data Visualization, Data Mining and Machine Learning are needed to handle the massive amount of data sent by Ad Exchanges and to make a better sense out of it. Also, targeting the right audience is important as that can lead to better experience for the users as well as a higher value generation for the company both in terms of revenue and reputation. It can also lead to increase in user interaction which can prove a valuable asset to the company’s business. Enhancing this and developing such a system is at the core of company’s business and important for its reputation and returns.

Prototyping for Manufacturing Point-of-Care Instruments

Remote health settings require affordable blood tests to provide care. ChipCare aims to reduce the cost of these tests by producing a user-friendly device the size of a credit-card reader. Disposable cartridges that go into the device will bring down costs. One aim of this project is to improve the design of the reader in a 3D modelling software. The newer version of the reader can then accept multiple sized cartridges. This will expand the detection scope by allowing multiple diseases to be detected. The other aim of the project is to develop a manufacturing method that will allow the above cartridges to be produced quickly. This involves using a
manufacturing tool called the Hot Embosser. The tool allows the cartridge design to be imprinted onto any plastic material. The turnaround time for the cartridges will be drastically reduced which will shorten the development time. The attainment of these two aims will allow ChipCare Corp to eventually mass-produce inexpensive medical testing kits.