Virtial-Gym motion-tracking-based system to exercise regularly and safely at home, guided by the expertise of physical therapists who can remotely monitor their clients’ progress. The innovative exercise grammar that therapists can use to describe personalised exercise regimens for their clients.
The ultimate goal of this project is to produce stronger and lighter gypsum wallboards through more sustainable production procedure. The wallboard production plant is divided in three sections: upstream where the raw gypsum is received, midstream where the raw gypsum is processed to produce Calcium Sulfate Hemihydrate (stucco), and downstream where the final wallboard product is produced. The main focus of this internship will be on the downstream section. We will try to improve the wallboards through controlling their chemical composition and physical structure.
This project aims to provide much needed evidence to community organizations who want to use evaluation findings to better understand how to work with and support vulnerable children and their families in schools. The All in for Youth initiative and its collaborative partners offer integrated, wraparound supports to improve academic outcomes and resiliency of vulnerable children, support family health and stability, get communities involved, and inform policy and systems change.
This project will explore the non-invasive ways to find potential leaks in buried gas distribution pipelines using sound propagation. When there is a sound source at one point of the pipeline, the nature of the sound coming to another point of the pipeline will depend on the properties of the surrounding soil, properties of the pipe and its integrity. We will study the mechanics of sound propagation in a buried pipeline surrounded by soil, using methods of modern mechanics. We will also use similar methods to formulate best practices of data analysis.
Imagine being asked two questions during a job interview: 1) Are you more collaborative or more individual? 2) Would you prefer working from home or working in the office? Now imagine that you feel strongly that you are collaborative, and slightly prefer working from home. An interviewer might look at those two responses and feel they are contradictory. However, if they knew that you were more indecisive about working at home, it would make more sense. Here, we propose to use movement dynamics recoded via mobile apps to provide this more detailed decision information.
Lateral epicondylitis is a common source of lateral elbow pain and causes restrictions in performance during daily activities as the pain increases with wrist and hand movements. It is necessary to explore new treatments that decrease the symptoms of lateral epicondylitis. We aim to investigate the effects of a new non-surgical treatment (the ArmLock Sleeve) on pain, movement, and performance in daily activities in adults diagnosed with lateral epicondylitis. We also want to investigate the acceptance of the ArmLock Sleeve by the study participants.
The goal of this project is to help automate the process of scanning buildings with consumer digital cameras. Currently, fully automated scanning with a commercial camera produces inaccurate scans, while accurate scans require significant manual effort on each individual photograph (of which there are many) of the building to be scanned. We plan to use modern machine learning techniques to reduce the human labor required to create very accurate 3D scans of buildings.
The fundamental challenge when reclaiming oil sands areas is to ensure not only survival, but vigorous growth of the plant material. Finding plants suitable for high salt conditions has offered the opportunity for Alberta-Pacific Forest Industries Inc. to investigate the potential role of using native balsam poplar (Populus balsamifera) as a key reclamation species for the oil sands region.
Recent pipeline projects in Canada and the US have attracted lots of attention due to their importance for our future economy and environment. In the proposed project University of Alberta and Shawcor propose to work together towards developing E-smart pipelines and creating defect free system. We will utilize the vast amount of emerging and cutting-edge technical know-how in wireless technologies and apply that for the benefit of our energy and environmental sectors. Such information provides the opportunity to intelligently develop defect free pipeline.
This project will develop practical workflows, algorithms and programming codes for inferring unknown reservoir properties from distributed temperature and acoustic sensing data. In-situ pressure and flow conditions can be interpreted from downhole fiber signals gathered in real time, which are used to estimate unknown heterogeneous reservoir parameters continuously. Machine learning methods will be incorporated to facilitate the handling of large amount of measured data and computations more efficiently.