The proposed project seeks to develop a transit terminal assessment tool that can be used to provide safer and more efficient service to passengers. The objective is to provide the industrial partner with a specifically designed and calibrated analysis tool that can be used estimate the impact of passenger movements around the terminal platforms on the frequency of subway/bus arrivals/departures at terminals. The partner organization is a consultant with long-time expertise in public transportation analysis area.

Using a multiphysics approach, the intern will construct a model for the topological optimization of two distinct classes of cantilever-type sensors: one often encountered in the general industrial application and the other one customized for the specific needs of a biosensor. Altair Hyperworks will be the main software tool to be used for this particular type of optimization and the aforementioned type of sensors will be modeled and their behavior simulated in complex multiphysics conditions.

Janro Imaging Laboratory (JIL) will work with researchers at Concordia and Emily Carr universities to develop 3D motion-tracking based interface tools for drawing and content creation in spatial drawing applications. As a basis for applied design research, researchers will use an alpha version of the “Optical Wand,” a product currently in development at JIL for use with SANDDE, the animator works in the stereoscopic environment itself and can therefore make changes intuitively and rapidly to the spatial composition of their work in 3D and in real time.

Text mining is the process of automatically extracting knowledge from unstructured, natural language documents. It aims to support users in dealing with large amount of textual information. Examples for specific text mining tasks are entity detection, summarization, and opinion mining. Due to the complexity and ambiguity of natural language, this analysis is broken down into individual processing steps, which are based on the techniques from the fields of machine learning, natural language processing, and semantic computing.

Accurate forecasting is of crucial importance in managing insurance risks and ensuring a solvent and profitable operation. In recent years the property/Casualty insurance industry has adopted generalized linear models (GLMs) to improve the fit and prediction accuracy of their insurance portfolio models. Yet, the interdependence between the different insurance covers included in packaged products, such as car insurance, need to be explained in the GLM in order to include them in the predictive process.

Word segmentation in handwritten document is a difficult task because inter-word-spacing (i.e. the space between parts of the same word) is sometimes wider than the intra-word-spacing (i.e. the space between two consecutive words). Many different approaches to segmenting words have been proposed so far. However these segmentation approaches usually use some parameters that are manually tuned; meaning that they do not take into account the properties of the document in order to automatically calibrate the parameters.