Recent technological advancements have led to widespread usage of interactive and collaborative systems in educational settings. In particular, SMART Technologies interactive products are being widely used at schools and universities across the country. Although this is a valuable achievement, it also presents new challenges to evolve these systems to new user needs. For example, one of the main challenges of the teachers using SMART systems in classrooms is the need to go back in time and review missed meeting or class material.
This project involves researching and integrating constraints into existing route and schedule optimization software. The partner organization (Western Heritage) has developed software that calculates the shortest route and optimal daily schedule for home care workers visiting patients in their homes. These schedules must consider constraints for each worker and patient, such as: smoker, pet in the house, lifting requirements, gender requirements, etc. as well as constraints for multiple starting points for each worker.
NightShift optimizes customers' bandwidth usage when using streaming services. It does this by taking advantage of off-peak bandwidth availability to cache digital media content
addressing both problems of limited bandwidth (which impacts real-time streaming) and bandwidth quotas (which can result in bandwidth throttling or overage charges). The goal of
this research is to explore interfaces that allow end-user control of local storage and download while still preserving digital rights security and the platform (e.g. Netflix) experience
of viewing content. In particular, we will explore:
The primary functions of the Risk Alive Analytics tool are to predict risk and time to unsafe days and predict the occurrence of hazardous events (incidents with the potential to cause injury to personnel, damage to the environment, or financial loss) at given processing facility(s) and in addition develop a Risk Profile of a facility under study, and benchmark it to other facilities and similar equipment and processes. ACM has gathered a large amount of process hazards and risk analyses (for example, Hazard and Operability Studies) and data on Oil & Gas facilities and pipeline operations.
Enhancement of technology and computer science has helped researchers in multiple fields and industries, from health care to automotive industry. Smoking is one of the habits that could harm humans dramatically. Lung cancer, heart attack is just some of the diseases that come with smoking. A large number of people strive to quit smoking each year by various methods, but not all of them are successful. In this research, we try to study what are the reasons that tempt people who quit smoking to smoke again.
The main objective of this project is using deep learning algorithm to enhance the current state of the art tooth wear monitoring system used in mining shovels. Unlike the current approach, the proposed deep learning method operates by building a model from input images in order to make data-driven predictions. We use deep learning approach to identify the pixels that belong to the teeth-line in each video frame taken by camera located on the mining device.
Merging different sub-companies into TELUS caused some of customer records to be repeated through the merged data-set. Algorithms are needed to determine the duplicate records. Currently a deterministic algorithm is being used in TELUS. In this project, we will investigate if machine learning can help to detect duplicates. Solving this problem has several parts. We have to preprocess the data and select some features from the TELUS records that help us in our model. A probabilistic model should be selected, implemented and tuned.
In this project, we will establish biomarkers that objectively reflect the severity of injury, measure its progression, and predict neurologic outcome after acute spinal cord injury (SCI). This will be accomplished by comprehensively analyzing blood and spinal fluid samples from acute SCI patients. In addition, we will conduct a parallel experimental study in a large animal model of SCI with a similar analysis of blood and spinal fluid samples.
This research aims at improving the accuracy of a 3D-vision tracking system. The physical set-up consists of a tool to be tracked, such as a drill, with one or more planar patterns attached to it and a set of cameras. This set consists of one to four camera clusters, where each cluster has one or more cameras. The current tracking system consists of several modules, including one for the calibration of the cameras (intrinsic and extrinsic), and another one for the calculation of the 3D coordinates of an unknown physical point, the tip of the tool.
The objective of this project is to develop methodologies for automatically generating responses in a natural language to converse with humans. Responses directly generated from the question-answer database are inflexible and cannot meet users' needs. On one hand, the responses should take into account the previous utterances that can keep a conversation more active. On the other hand, the responses should be appropriate for the emotions conveyed in a conversation that can make a conversion more user-friendly.