Widespread decline in social capital is well-documented and has been attributed to a range of underlying root causes, from policies regarding the physical design of our neighbourhoods, increasing geographic mobility, shifting away from more localized economies, the complexity of increasing cultural diversity in many areas, to societal beliefs and norms related to individualism. We know these issues touch many of us, however, where you live makes a difference. Levels of social connection are lower in multi-unit buildings than in single detached houses.
Video games are highly interactive experience. With modern approaches to game design it becomes impossible to anticipate all the situations a player can ended up in. Building up on research regarding generative music systems at the Metacreation Lab for Creative AI, PhD student Cale Plutt will research, design and implement a generative music system tailored to Inscape VR game. In particular, the music generation will respond to affective state of the games in terms of their valence, arousal and tension.
The SmartRING strander was developed in the mid-nineties to address the need in the Oriented Strand Board (OSB) industry for a higher capacity machine over the SmartDISC strander. Since being introduced to the market, the ring strander has not had any significant improvements and/or upgrades. Although, it is deemed the preferred machine by OSB customers over our one competitor (i.e.
The Embedding Project is a public-benefit research project that relies on strong social science research methods to bring together thoughtful sustainability intrapreneurs from across industries and around the world, and harnesses their collective knowledge to develop rigorous and practical guidance that benefits everyone. This internship will offer an MBA student the opportunity to gain experience in both practice and research, while learning from leaders in the field.
Avcorp Industries provides the world’s leading aircraft manufacturers with supply chain solutions and repair support. Yield optimization, predictive maintenance, and equipment calibration are needs that are widespread throughout the manufacturing industry. The root cause of failures in product testing is often difficult to determine particularly when the failure signals are sparse relative to the available background data. Compounding the problem, the process must meet a variety of specifications for multiple customers simultaneously.
Recent advances in using machine learning for object recognition and image manipulation have resulted in a new and emerging market for mobile applications that use machine learning for creating a variety of new artistic expressions. This research will develop a framework for performing machine-learning-based photo and video manipulation on mobile devices with the goal of integrating it with the Generate Toolkit. This proposal follows previous MITACS internships between the same partners and further extends our objectives.
In this project, a comprehensive testing station for impairment screening will be implemented. The station includes an eye testing goggle, movement detectors, biophysiological measurement sensors, and an integration algorithm to integrate the result of measurement to diagnose the status and type of impairment. The hardware technology resides at the industry partner while this project is focused on implementation of data gathering and data storage platforms, feature extraction and selection algorithms and machine learning algorithms to quantify levels of impairment.
Tendencies towards workaholism have been linked to poor health and increased risk for diabetes and other chronic condition. A health improvement program that is interwoven within the workplace and leverages the ubiquitous use of smartphones has good potential of benefiting the workforce. The aim of this research project is to evaluate Transform, a digital health program created by Blue Mesa Health. The program is designed to prevent diabetes by helping people adopt healthier lifestyles.
Assessing dangerous avalanche conditions requires a reliable stream of weather and snowpack data, which can be difficult and expensive to collect in many remote areas of Canada. Snowpack conditions can be simulated in these areas by coupling weather forecast models with physical snowpack models, however, this method has had limited adoption by avalanche forecasters.
We are in the process of creating and growing a team of researchers expert in the field of machine learning and data-mining. Ultimately, our aim is to create solutions to eliminate the need to manually define personalization strategies. We are working with more than 1000 retail locations across North America and collecting large-scale datasets of customer behaviour. Through a data-sharing/consulting partnership we plan to perform research on the design of recommender systems and predictive models customized for the datasets available to retailers.