Innovative Projects Realized

Linking Desktop and Mobile Devices to Back-end Systems Using HTML5

Recently, more researchers and developers are looking towards HTML5 mobile applications as a solution for targeting multiple devices and platforms. This is because the browser is becoming the default platform and its de facto standards are HTML5 and JavaScript. In the enterprise, compelling HTML5 mobile web applications that promise to offer the same functionality as their native counterparts can be built to enhance users’ experience. This project seeks to explore the current HTML5 frameworks that are in vogue in order to overcome the challenges of browser diversity. Also, the idea behind this project is to build mobile applications that are geared towards solving enterprise needs

The partner organization SAKINA Information Sciences will have the results generated by the research. This research could allow the partner organization to broaden out their offerings of products to different mobile devices and better satisfy their customer’s needs.

Fractal-element based Magnetic Resonance Imaging Coils for Multi-nuclear Imaging

An MRI (Magnetic Resonance Imaging) scanner is a machine that uses magnets and radio waves to take pictures of the inside the human body without needing to use surgery. An MRI scanner is used to look at things like the brain, liver, heart, and other organs in your body. The scanner uses a “coil” placed near the body to take a picture of that area of the body. This research project uses new shapes of coils to better detect the organs hidden inside. The new shapes of coils can also help detect

Machine learning algorithms utilization for impairment screening using balance detection

In this project the human body movement and its balance will be studied, and artificial intelligence-based algorithms will be developed in order to detect the body movement and the posture in real-time. Specific camera technology will be utilized in order to gather required data of the human movement and the environment. The data will then be filtered and analyzed. The cleaned data will then be used in a artificial-intelligence-based system to detect the status of the movement. Finally, the result of the movement detection will be integrated with other indicators in order to detect the imbalances or visible impairments.

Application of Brain 23Na “Big Data” for Future Disease Classification

Sodium is a chemical ion which is essential for a healthy brain. The body naturally regulates its concentration inside and outside of cells through normal metabolism. Disruptions in this intricate balance can be caused by various neurological disorders, such as multiple sclerosis, concussion, or Alzheimer’s disease. In order to provide personalized medical care, a “big-data” approach is required whereby an individual’s brain can be compared to a standardized template or atlas – unfortunately, a sodium atlas, representing normal sodium concentrations, is not currently available. This project’s goal is to use Ceresensa’s expertise in coil design and manufacturing to create an atlas which can be used to further research into sodium’s role into brain illnesses.

Improving 23Na – Magnetic Resonance Spectroscopic Imaging through RF Coil Development

Magnetic Resonance Imaging (MRI) machines use radiowaves and large magnets to safely produce pictures from the insides of the body. The radiowaves are emitted and detected by special antennas that surround the body. Most MRI scanning involves measuring of water in the body. But other atoms, like sodium, can also be seen too. This work described here involves designing new antennas to safely see sodium inside the body. More specifically these antennas will be designed based on novel geometric fractal shapes, which are regularly seen with cell phones. These shapes give a much better signal than traditional designs used currently with MRI.

Productivity Tracking and Analysis of Earthmoving Operations

Monitoring and control earthmoving operations such as highways and dams construction; require the collection of large amounts of data. Collecting this data manually is time-consuming and it lacks accuracy, so there is a necessity for using automated data collection systems in such operations. Most of nowadays available data acquisition systems are costly back boxes, where the user can not use it based on his/her customised needs. The proposed model allows the customization of the data acquisition system using open source and low cost technologies, in which some sensors are selected to acquire data related to the different factors that could influence the productivity of earthmoving operations. The collected data will be transmitted to a server (cloud) instantly using communication protocols like WiFi. This data is usually collected in a raw format where it has no meaning, so should be processed to have meaningful information that helps in efficiently managing the projects. The proposed model process the collected data using cloud computing to allow higher security and flexibility. The results from data proccing could be retrieved using internet browsers by accessing a secure designated webpage using a user name and password. TBC

Techno-Economic Feasibility of Wastewater Heat Recovery for Cold Climates like Canada

Wastewater is an abundant and severely underutilized energy source in North America. Sewers experience predictable flow profiles and nearly constant temperatures between 18 ?C and 20 ?C year-round. When wastewater is used in conjunction with heat pumps, it can serve as an energy source and sink to provide both heating and cooling to buildings. Therefore, there exists the potential to extract significant amounts of thermal energy from the wastewater using heat exchangers, resulting in substantial economic and environmental benefits. The proposed work is to investigate the techno-econo-environmental feasibility of sewage-based hybrid wastewater heat recovery energy systems suitable for North American residential, commercial, and institutional buildings via system modeling, simulation, and optimization of such hybrid systems to be validated with experimental results from a few pilot systems currently being developed.

Pricing and Diversification of Flood Risk in Canada

Flood is the single most important natural hazard in Canada. Since 2015, private (overland) flood insurance coverage is gradually being offered to Canadian homeowners and demand for such protection is on the rise in Canada and elsewhere. For the product to be viable to the insurers and to the customers in the long term, flood insurance needs to be adequately priced and managed with a large and well diversified portfolio. Using a combination of climate and statistical models, the aim of this research project is to analyze pricing and especially spatial diversification of flood risk in Canada. The project will help insurance companies manage flood insurance in the short-term and determine whether such insurance will be viable in the long term as a result of climate change.

Embedding Techniques of Audio Segments Emphasizing Meaningful Semantic Characteristics

This project will develop artificial intelligence techniques to identify characteristics in audio samples that relate to real-world descriptions of sounds. When humans listen to various samples of sounds of a same category, they intuitively affix qualitative words to these sounds. The objective of the research is to find ways to let an AI algorithm find out these characteristics, and rank sounds based on them. For example, given a database of voice sound samples, the algorithm could automatically rank them in order of “kindness” in the voice, or “shyness”. The developed algorithms will help Ubisoft be more efficient and creative when designing the sound aspect of video games.

Development and experimental study of a vibration controller for a robotic system interfaced with machining operations.

Robotic arms are used extensively in machining applications due to a large working envelope and easy access to restricted areas. However, low stiffness of the robot arm caused by joint flexibility introduces additional degrees-of-freedom and vibration modes, which leads to the deterioration of positioning performance. To attenuate these induced motion tracking errors and vibrations, this research presents the development and experimental study of an adaptive control scheme, which combines a state feedback and an adaptive feedforward controller to shape robot dynamics into a desired dynamics that does not produce link oscillations. The proposed method is demonstrated on a six-joint robot manipulator, namely SCOMPI developing at Hydro-Québec’s research institute, performing machining tasks in situ maintenance of hydro turbine runners. Reliability and robustness of the proposed method allow the SCOMPI robot to maximize machining performance and to move SCOMPI technology forward over competitive products.

Net Zero Energy Townhomes: Simulation Comparison between Software Platforms, Calibration Study and Uncertainty Quantification

Designing, building, and verifying highly energy efficient buildings presents complex challenges. Environmental factors such as the local weather, temperature swings, angle of the sun, etc. impose conditions on the building which itself has unique characteristics like its particular construction method, materials used, geometry and orientation, windows of specific properties, and so on. Then, there are active measures taken to make it livable, such as heating and cooling, lighting, using appliances, and such. The design team must take all of the factors and their complex interactions into account for the design, which compels them to use computer simulations. The problem in the industry, however, is that 1) various software tools produce different results, 2) adjusting the computer model to the actual building performance (model calibration) is often done non-systematically, and 3) associated model uncertainties are ignored. This study aims to help address the three issues with a real-life case study.

Supporting Ontario EcoSchools: building capacity in schools and school boards to implement environmental initiatives

Environmental education is essential for young Canadians, yet educators have few resources to meet this rising demand. Ontario EcoSchools is addressing this disconnect by providing an environmental certification program and environmental education resources for Ontario elementary and secondary schools. Ontario EcoSchools certification program motivates students, teachers and staff members to implement initiatives to reduce waste, conserve energy, green the schoolyard and improve environmental literacy. Since 2002, Ontario EcoSchools has seen tremendous growth in the number of schools seeking certification, from just 13 schools in 2003 to 1518 schools in 2011. This project will allow Ontario EcoSchools to critically assess schools certification portfolios and onsite actions, determine the type of required environmental education resources and assist with their development of an online community resource database to help schools seeking certification.