Sigma° is a software application for updating topographical maps developed by Synetix for the Quebec government department for natural resources and wildlife (MNRF). The algorithm can be used to update communication channels with remote-detection images and the principle of using topographical maps to guide detection procedures. The initial version of Sigma° was developed and validated for Radarsat-1 and SPOT Panchromatic satellite images with a resolution in the order of 5 m.
This project will develop a mathematical model and computer tools that will minimize transportation and infrastructure costs related to the forest road network, in order to ensure the transport of wood products to receiving plants. The model will consider several decision levels, including the location of transfer yards and the choice of mode of transportation. Constraints will include the storage capacity of terminals and transportation units, and the model will take into account travel distances as well as transportation and handling costs.
Information extraction from unstructured data is a wide and relatively recent domain. For this research project, the focus will be on the information extraction from finance reports and news, more precisely related to the commodities market. This includes Natural Language Processing (NLP), expert systems (such as ontology-based systems) and information fusion as tools for analysing qualitative information in finance and producing investment decisions. NLP is a science studying the automated understanding of natural human languages.
In this project, the intern will use a recently developed interpolation technique based on the Lie group theory to enhance the quality of the classification of Synthetic Apertur Radar images. He will evaluate the effects and discuss the benefits of this interpolation on the complete set of polarimetric features extracted from a fully polarimetric SAR image. The effects on the polarimetric features will impact the performance of target recognition algorithms applied to the detection of man-made ground targets. The recognition process will be achieved by using a neural network.
This internship project aims at creating a system prototype using several surveillance cameras in order to measure customer traffic inside stores. At the moment, most counting methods use sensors that do not differentiate between repeat visits by the same individual and single visits by different individuals. Moreover, these methods cannot always distinguish between potential customers and store employees or security personnel, thus reducing the quality of the counting measurements.
In partnership with Labopharm, a leading company in optimizing the performance of small molecule drugs, a complete pharmacokinetic (PK) study of three new formulations of a commercially available drug will be performed. This study will be conducted in order to choose the most appropriate formulation in terms of bioavailability and absorption rate. Pharmacokinetics parameters of the drug will be estimated using different methods. Mechanisms of drug absorption, distribution, and elimination will also be identified.