With more and more buildings being controlled by automation systems, one would expect their energy performance to be optimised. This is not the case however. Buildings can still go out of tune, and building operators can become overwhelmed by the alarms sounding from the automation systems, not knowing how to prioritize them. SES consulting is well poised to provide a human in the loop performance analysis service, leveraging their expert knowledge and the data from the building automation system.
The proposed research aims to target large-scale consumer-generated data to analyze, visualize, and make predictions out of. The data will be collected from the consumers to make assessments on their lifestyles, and will come in forms such as heart-rate variance, that is, being temporal data. Researchers with visual analytics background will apply new visualization techniques on the data in order to grasp the insights and improve the model to interpret the data. The research problem is to relate measures of stress, recovery and mindful activities to the data obtained.
Frequent usage patterns generated can provide valuable information for several applications such as platform restructuring and recommendation. In this project, we aim to compare different practical methods, and to investigate the effect of user identity and user intention information on them. To that end, a technique and a framework need to be developed, in which frequent patterns are composed of more refined analysis result instead of simple frequent sequences of basic operations over all users behavior.
I am to import ten years worth of amassed historical data on news events, price movement of equities and public sentiment metrics to Microsoft Azure platform for study and analysis through the latest Data Mining techniques with an Economics point of view to uncover the hidden correlation and casualty between events and price movement of global markets in multiple timeframes (three hours, daily, weekly, monthly and yearly).
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.