Heat exchangers, used in building heating, ventilation and air conditioning (HVAC) systems to transfer heat from hot to cold fluids, are designed to operate under ideal conditions. However, in practice operating conditions may vary with ambient temperature or humidity. HVAC system efficiency can be improved significantly if fluid flow rates are adjusted in response to such changes. Armstrong Fluid Technology is a Canadian firm that has developed control systems to adjust the flow through building heat exchangers to maximize their efficiency.
SOTI Inc. is a Canadian company providing control and management for mobile devices. Insight Agent is a product made by SOTI to help collect various battery specifications such as battery level, voltage, current and other metrics from mobile devices. This research project aims to use a machine learning and neural networks framework to predict the state-of-health for batteries in mobile devices. The intern will use the metrics collected by SOTI Insight Agent to derive formulas to calculate the key performance indicators (KPIs) of the battery system.
Urinary tract infections caused by indwelling catheters (CAUTIs) employed for the treatment of urinary flow are very common. Almost 100 million of these devices are sold on an annual basis with around 25% of these being marketed in the USA. In addition to the cost of catheters and their insertion, hospital treatment of CAUITs runs into the hundreds of millions of dollars every year.
The wide adoption and development of wireless sensing technologies for the monitoring and autonomous identification of financial activities have affected financial institutions in the past decade. However, wider utilization of RFID technologies in the banking sector has introduced challenges regarding the security and privacy of sensitive financial data. The proposed innovations and technological developments will revolutionize the banking sector by increasing efficiency, decreasing cost and provide secure and privacy sensitive financial transactions.
Assessment of surgical data from an operating room is a complex process that may require significant resources such as expert input and advanced technology. Automation brings a considerable opportunity to greatly reducing these significant resource requirements - e.g., using computer vision software to detect clinically relevant actions during surgery. However, those detections should be interpretable, or more actionable in order to be audited or reviewed.
Given the current global environmental crisis, developing sustainable solutions to enhance or replace our current agricultural practices is critical: the agricultural sector exerts important environmental pressure through its aggressive land, water and pesticide usage combined with the ever increasing demand on food supply. Mitigating this problem requires developing more sustainable and efficient agricultural techniques.
The multiple sclerosis (MS) clinic at St. Michaelâs Hospital (SMH) is among the largest in the world. While considerable data is collected from the MS clinic in both structured and unstructured form, the ability to glean this information to assess quality of care and conduct advanced analytics such as predictive modeling is limited. In this project, a quality improvement dashboard will be developed based on automation of clinical information extraction process.
SS&C processes more than 80% of financial scanned and faxed documents in the US and requires large amount of manual labor in order to map information from a document into another form. Advances in neural networks applied to computer vision have produced text detection and recognition that nears human performance.
Delphiaâs business model revolves around generating insights for investing firms that allow them to make better trading decisions. It has been shown that detecting when Twitter users post about recent or future purchases has the potential to increase the accuracy of company sales forecasts, which in turn can inform stock trading strategies. This internship project aims to develop automated means to detect and quantify purchase related posts on Twitter.
On time discovery of problems and constant monitoring of construction sites have great economical benefit. It requires the capability of highly efficient and accurate object detection and segmentation algorithms that can work with coarsely labelled training samples. The project is aimed to develop new learning-based object detection and segmentation algorithms for problem detection and mapping of construction sites with high accuracy and efficiency. This project will improve operation efficiency for construction related projects.