Bigmotion Inc. was created to develop wearable health monitoring sensors and service the at-home care segment of the elder care market. This project involves studying of existing literature and development of novel solutions for
power management and energy harvesting for the product including tracking and fall detection systems using hybridpower.
The energy-hungry telecomm industry is in need of power supplies with ever-increasing efficiencies to conserve energy and reduce carbon footprint. In collaboration with the industry partner, the proposed research project aims at developing a power factor correction (PFC) system, an essential component in a telecomm power supply, for achieving efficiency of 99% or above. The project will make use of emerging power semiconductors with superior characteristics to build a PFC circuit using one of the most promising circuit structures.
The Resident Assessment Instrument Minimum Data Set (RAI-MDS) is used by health authorities for collecting information about individuals in continuing care facilities. Collected quarterly, RAI-MDS records contain more than 500 data elements, including cognition, psychosocial well-being, health conditions, communication, physical function, and activity patterns. Because of this it has great potential for providing an incomparable quantitative view on the lives of the oldest and most vulnerable Canadians.
Atmospheric acid emissions are increasing in north coastal British Columbia from increased metallurgical smelting, marine fossil fuel transport, and development of liquefied natural gas. Acid deposition can cause episodic acidification of streams when acidic compounds are flushed into streams after snowmelt and precipitation events over hours to weeks. Many salmon-bearing coastal streams are likely sensitive to episodic acidification, but these events are poorly quantified in western Canada.
In this project, we will develop solid-state hydrogen storage materials for the potential applications of fuel cell electric vehicles. Based on the most cutting-edge achievements in related fields, two categories of two-dimensional layered nanomaterials are proposed. Their hydrogen storage capabilities will be elaborated by in-depth characterization of material structure and hydrogen storage properties.
Oscillatory neuronal activity can be quantified to help diagnose states of health and disease in the brain. These activities change on a fast time scale of milliseconds, which can only be captured by direct measurement of the brains electromagnetic activity. This is accomplished utilizing MEG and EEG technology, which can measure non-invasively these fast changes on the scalp surface. Moreover, using MEG, these signals can be observed within the brain volume through a localization process.
The objective of the proposed research is to investigate novel solid-state materials that have potential for hydrogen storage applications in fuel cell electric vehicles. Of interest are materials that can store hydrogen at ambient conditions and low pressures, have high gravimetric and volumetric hydrogen capacities, and can be safely packed into a hydrogen storage tank for automotive use. The research will focus on assessing the feasibility of threedimensional structures consisting of two-dimensional layered nanomaterials such as graphene as viable media to store hydrogen.
Helicopter and snowcat skiing in the backcountry involves different hazards such as avalanches, tree wells or helicopter incidents, which can result in serious injuries or even death. While the risk associated with avalanche involvements is well understood, no systematic analyses have been conducted on the other risks.
Over 350,000 Canadians live in long-term care facilities where the rate of falls is up to 3 times higher than among individuals living in the community. Wearable sensor technology holds great promise to accurately monitor an individuals fall risk based on the activity profile and to detect dangerous fall events. With the understanding that hip protectors are commonly used in this population the objective of this project is to investigate the feasibility of a sensor-based hip protector to accurately detect falls and monitor daily activities.
Recent advances in applications of deep learning in natural language processing has provided potential opportunities in building robust information retrieval and conversational models that require far less hand-crafted features for understanding the intent of queries and ultimately building question-answering systems. In particular, there has been several advances in factoid question-answering systems and some recent attempts to moving beyond factoid questions.