Non-contact sensing for vital signs and human activities utilizing wireless signals has attracted a lot of attention in the last few years. Sensing using Ultra-wide-band or milli-meter wave radios is advantageous in its ability to penetrate garments or walls, operate under different lighting and weather conditions, and better preserve people’s privacy. Despite successful research demonstrations, there remain significant gaps in practical adoption and deployment. Many challenges need to be addressed to handle the presence of multiple subjects, subject diversity and environmental interferences.
This project aims to develop an itinerary demand forecasting model that can handle long-term and short-term forecasting and adjust its parameters under changing situations. General long-term prediction models are relatively precise because the context often remains stationary over time, but can not quickly adapt to unforeseen events, like the global pandemics. It is necessary to develop an adaptive model with multi-horizon perspectives. The model will integrate external data sources to output a plausible range of future booking status.
Under the Lab2Market Program, the intern will develop and test a field notes tool called STRATUM to alleviate stresses caused by documentation in the field.
The goal of the project is to generate realistic human movement in 3D animations. This is important to make movement animations in games and movies appear real. Typically, creating high quality animations is a resource and time consuming process that requires the participation of human actors in motion capture sessions. In this work, we present a data-driven approach that aims to generate novel animations based on a library of past motion capture recordings that can make generating high quality animations low cost and fast by eliminating the need to record human actors.
Petroleum contamination in soil and groundwater caused by the leakage of underground storage tank is one of the most frequently occurred incidents in North America. The cost of remediation can be significantly increased if the contamination was not treated in time or the site is far away from the waste management facility. a mobile soil-flushing and enhanced oxidation (MSFEOP) system is therefore developing for an accessible and affordable options for site remediation.
The main purpose of this project is to develop the methodology to detect and predict driver drowsiness at the early stages using physical and physiological variables. A feasibility test is conducted to evaluate the accuracy and performance of the proposed methodology. The existing databases are leveraged to extract the required data. Signal processing, image processing, AI techniques and decision-making methods are utilized to analyze data for monitoring, detecting, predicting and controlling driver drowsiness.
The project seeks to discover the optimum design and commercialization strategy for newly developed sandwich structures derived from recycled plastic for the civil engineering sector. The sandwich structures are highly sustainable and could potentially consume large amounts of the rapidly produced plastic waste. The final sandwich product would have the potential to be used in various applications such as roof panels and exterior/interior walls of buildings.
What is left after late-life SAGD production is a large amount of valuable energy in the form of heat contained in the reservoirs. Instead of leaving behind the stored energy in a hot reservoir after many years of SAGD operation, considering energy recovery from post-SAGD reservoirs leads to lower carbon emissions by saving energy already injected in the reservoir rather than leaving it to avoid burning more natural gas; saving money for SAGD operators and helping to make operations more sustainable.
Healthcare requires the early and accurate detection of disease indicators, be they small biomolecules or viruses, which is vital for successful treatments, preventative medicine and disease prevention. Improving turnaround times for early and accurate detection will improve patient care, enable the mass screening of large populations during outbreaks and effectively reduce the diagnostic burden. We have developed a small-scale filter detection device to provide high sensitivity, while being inexpensive and portable for diagnostics at the point of care.
As the third documented emergence of an animal-to-human coronavirus during the past two decades (Severe Acute Respiratory Syndrome in 2002, Middle East Respiratory Syndrome in 2012), the current pandemic and near-certainty of future epidemics demands intensified surveillance and proactive screening. Definitive therapy for novel Coronavirus Disease 2019 (COVID-19) is likely at least half a year away. Current standard-of-care diagnostic testing with real-time Reverse Transcription Polymerase Chain Reaction (rRT-PCR) is resource intensive, costly and inaccurate.
